ELECTRA_Marian_DJANGO.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AhmedSSoliman/Leveraging-Pretrained-Language-Models-for-Code-Generation/blob/master/ELECTRA_Marian_DJANGO.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "97Av32IAvNOU",
        "outputId": "591d3833-e62e-4587-bcd9-aabd96d7bf04"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ExFzbWxoC-oN",
        "outputId": "9da3ac67-0db1-4139-859b-f158e8ea8398"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "drive.mount(\"/content/drive\", force_remount=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v8IdHyYYzK2i"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RgWA_Vg_ZXrQ"
      },
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3hulJwaYT8Jd"
      },
      "source": [
        "# **Install dependencies**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dIVCID_z9pMw"
      },
      "outputs": [],
      "source": [
        "%%capture\n",
        "!pip install tensorflow\n",
        "!pip install keras\n",
        "!pip install netron\n",
        "!pip install nltk\n",
        "\n",
        "!pip install git+https://github.com/huggingface/transformers\n",
        "!pip install datasets\n",
        "!pip install nlp\n",
        "!pip install rouge-score\n",
        "#!pip install sacrebleu\n",
        "!pip install git-python\n",
        "!pip install sentencepiece\n",
        "#!pip install torchdata\n",
        "#!pip install transformers\n",
        "!pip install sacrebleu==1.5.1\n",
        "!pip install tree-sitter\n",
        "!pip install evaluate"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6e9_D0csUCTl"
      },
      "source": [
        "# **Import Libraries**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "m4IWUUZYvcBH"
      },
      "outputs": [],
      "source": [
        "import transformers\n",
        "import datasets\n",
        "from datasets import load_dataset, load_metric\n",
        "import logging\n",
        "from transformers import BertTokenizer, GPT2Tokenizer, GPT2TokenizerFast, EncoderDecoderModel, Trainer, TrainingArguments, BertTokenizerFast\n",
        "from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments\n",
        "from transformers import AutoModelForSeq2SeqLM, DataCollatorForSeq2Seq, Seq2SeqTrainingArguments, Seq2SeqTrainer\n",
        "\n",
        "logging.basicConfig(level=logging.INFO)\n",
        "\n",
        "import types\n",
        "import argparse\n",
        "import logging\n",
        "from functools import partial\n",
        "import json\n",
        "\n",
        "import torch\n",
        "from torch import optim\n",
        "from torch.utils.data import DataLoader\n",
        "from tqdm import tqdm\n",
        "from transformers import (\n",
        "    BertGenerationConfig,\n",
        "    BertGenerationEncoder,\n",
        "    BertTokenizer,\n",
        "    EncoderDecoderModel,\n",
        "    EncoderDecoderConfig,\n",
        "    GPT2LMHeadModel,\n",
        "    GPT2TokenizerFast,\n",
        "    Trainer,\n",
        "    TrainingArguments,\n",
        "    get_cosine_schedule_with_warmup,\n",
        ")\n",
        "\n",
        "import sacrebleu\n",
        "import random\n",
        "import numpy as np\n",
        "#from tensorboardX import SummaryWriter\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fhYGg8K_TzYl"
      },
      "outputs": [],
      "source": [
        "device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2YE9oRS-YmTp",
        "outputId": "8c124cbc-f2e4-4a42-f72f-95d0aad109ea"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/models/CodeGeneration/New/DJANGO/ELECTRA-Marian\n"
          ]
        }
      ],
      "source": [
        "%cd /content/drive/MyDrive/models/CodeGeneration/New/DJANGO/ELECTRA-Marian"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cy34HZRCduwK"
      },
      "source": [
        "# **Load Data**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281,
          "referenced_widgets": [
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        },
        "id": "yGq0hRAvsR83",
        "outputId": "f0c3401b-b005-4564-b50c-0859bb2616a4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Using custom data configuration AhmedSSoliman--DJANGO-3a814141e88829f9\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset csv/AhmedSSoliman--DJANGO to /root/.cache/huggingface/datasets/AhmedSSoliman___csv/AhmedSSoliman--DJANGO-3a814141e88829f9/0.0.0/652c3096f041ee27b04d2232d41f10547a8fecda3e284a79a0ec4053c916ef7a...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b13f183791a14ef9aa89d0d620b0a1b9"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/1.91M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c6599dcdfb0c498e9fb1f724f821e2ff"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/226k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3dce795aa22748a9a5fc549ca8ff59fb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading data:   0%|          | 0.00/117k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2dce8b309ee7488ca10fd98c21e4da69"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Extracting data files:   0%|          | 0/3 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "517b2c43b275499bb9c2fd4548e841f7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0 tables [00:00, ? tables/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6aaa283b57854d5b94473f6177ad56f4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "0 tables [00:00, ? tables/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6c3e419eca69424a98e75cc156e5d260"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "0 tables [00:00, ? tables/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0efde1c1dda84b31a51b134e504e0e08"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/AhmedSSoliman___csv/AhmedSSoliman--DJANGO-3a814141e88829f9/0.0.0/652c3096f041ee27b04d2232d41f10547a8fecda3e284a79a0ec4053c916ef7a. Subsequent calls will reuse this data.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f12e81dd8d964b008c9c1eae160cd94b"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "raw_dataset = load_dataset(\"AhmedSSoliman/DJANGO\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yNL3RItYwOSl"
      },
      "outputs": [],
      "source": [
        "train_dataset= raw_dataset[\"train\"]\n",
        "val_dataset = raw_dataset[\"validation\"]\n",
        "test_dataset = raw_dataset[\"test\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6gWJvHZ-RbMP",
        "outputId": "2b78be60-e69d-4edc-bc96-27fc6e032afe"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['Unnamed: 0', 'nl', 'code'],\n",
              "        num_rows: 16000\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['Unnamed: 0', 'nl', 'code'],\n",
              "        num_rows: 1805\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['Unnamed: 0', 'nl', 'code'],\n",
              "        num_rows: 1000\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "raw_dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-8qaEXWphXWk"
      },
      "source": [
        "# **Tokenizer**"
      ]
    },
    {
      "cell_type": "code",
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            "0dacaa407c804e86ad473d42f7f17206",
            "6294387ddfa048229610eec53a001ae5",
            "4052c543f60248f4bbee8cca2b67f337",
            "da314c06e71f4657b174ee8c6fd043f9"
          ]
        },
        "id": "M2OT3nMFU4Rc",
        "outputId": "0138d233-2b02-4897-d1ef-ae9e27de9065"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e47322dc1f6448ac9519817cfd9856fd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "00c18d58d2804390bdad4788c3d8ea0f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/232k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d48ba11a75804fc6bbe13ad2ef392524"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/466k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "59ccdbc055f84fb9b7f32b3cc5df733c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PreTrainedTokenizerFast(name_or_path='google/electra-small-discriminator', vocab_size=30522, model_max_len=512, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]'})"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"google/electra-small-discriminator\", use_fast = True)\n",
        "tokenizer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "07QX2HqSERRj"
      },
      "source": [
        "# **Model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "2d929e57040547e68232f921929ee5da",
            "d19e68d0be5c4022a2c6866286dcd79e",
            "2f28f8f5451c418f9ebdf0c236a07ed4",
            "d644b33409034c5fa972e4fb5c83a995",
            "44350c3cd932429c8ee25703377a3de8",
            "a015ca39f9584f698dc204cee7d502c7",
            "43d91ca5116944e38e1879ae8b22545f",
            "cea0ef4a9aab47d381415fa990d6732a",
            "71f9ca4de98f49ea82495081a812b6fd",
            "eccdcebf71394ee0a97e8b087dad126c",
            "5c36a20bab3d4739bdc20bb6d64ca72b",
            "24ce9062622444de87223389b07a4935",
            "132bad30199642e3a4306834b59d46ef",
            "9ffca14971214f29abb895a94427a664",
            "19535ca74e134b399c57d2e383efa5e5",
            "bc5bba2687684abfa412f2742e0b80ca",
            "ea5542abeab44cd4a394caabb0370f6a",
            "83013a7e69c544e8abc6e84ebb6ca986",
            "aa8a9232d677416aa2014d7473f7bc4b",
            "55e3b99ed4e443c5a8934d615451b242",
            "8bd32d49f5a24371be158d6cab409572",
            "441d6f26522e4416ab7d374c0a0b946f",
            "d2b65ee4cdb84671a0b181dc4f25544b",
            "5ad3fc13c976427a83f773b4239856ab",
            "3126f76a46ed42ddb9211ae5487d53a4",
            "9ea696daf95c4fd5b06c25168b7046db",
            "73a829a1596f448aa64cc6460bf1d175",
            "da7285840b62497982738ae126effd34",
            "6d75ca4f33274d09a602d22fee72f761",
            "7e70f319a45944f39e2b705f43801e7c",
            "6e8cb24463534878aa89c493215429c5",
            "85acf3bcbd764f0a879af9f65b8a9def",
            "5f5e4fdb79fe4d3f91c5584fac59a17c",
            "43ecf2a406474211b8e0c14375a2bf93",
            "0cffcc1468cf47169ff6a05150d8f23b",
            "66548ad8f9c0463196c5788cb37fdcd2",
            "acf6c1785643464ca3d01340794c5cb5",
            "5608ff954b6749f284088102dbeba2f1",
            "7bd132c853674138aefbdd95051eb1ac",
            "dd71c79fa09e46f0a184a004f8ca4332",
            "6630ca74fbbb4ddaa6679e781676108f",
            "4194db0ee7554b498caf63813b4c4c32",
            "caddebcc1a4744b7b19b1a4a5f4dfb77",
            "4fc4474c9d1247328f9bbfee3c625fe0"
          ]
        },
        "id": "0PaI2Odffj0F",
        "outputId": "97f8cdc0-f448-4039-a9c8-c25d0e842629"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2d929e57040547e68232f921929ee5da",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/665 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "24ce9062622444de87223389b07a4935",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/54.2M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at google/electra-small-discriminator were not used when initializing ElectraModel: ['discriminator_predictions.dense.weight', 'discriminator_predictions.dense.bias', 'discriminator_predictions.dense_prediction.weight', 'discriminator_predictions.dense_prediction.bias']\n",
            "- This IS expected if you are initializing ElectraModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing ElectraModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d2b65ee4cdb84671a0b181dc4f25544b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.38k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "43ecf2a406474211b8e0c14375a2bf93",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/316M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Some weights of the model checkpoint at Helsinki-NLP/opus-mt-en-nl were not used when initializing MarianForCausalLM: ['model.encoder.layers.5.fc2.bias', 'model.encoder.layers.0.self_attn.q_proj.bias', 'model.encoder.layers.2.fc2.bias', 'model.encoder.layers.0.self_attn_layer_norm.bias', 'model.encoder.layers.5.self_attn.v_proj.weight', 'model.shared.weight', 'model.encoder.layers.1.self_attn.v_proj.weight', 'model.encoder.layers.0.self_attn.k_proj.weight', 'model.encoder.layers.3.self_attn.q_proj.weight', 'model.encoder.layers.1.self_attn.q_proj.weight', 'model.encoder.layers.3.fc2.weight', 'model.encoder.layers.2.self_attn.q_proj.bias', 'model.encoder.layers.0.self_attn.v_proj.weight', 'model.encoder.layers.4.self_attn.k_proj.weight', 'model.encoder.layers.4.self_attn.v_proj.bias', 'model.encoder.layers.4.fc2.weight', 'model.encoder.layers.4.final_layer_norm.weight', 'model.encoder.layers.5.self_attn.out_proj.weight', 'model.encoder.layers.4.fc1.bias', 'model.encoder.layers.5.fc1.weight', 'model.encoder.layers.5.self_attn.k_proj.weight', 'model.encoder.layers.2.fc2.weight', 'model.encoder.layers.5.self_attn_layer_norm.weight', 'model.encoder.layers.5.fc2.weight', 'model.encoder.layers.0.fc2.weight', 'model.encoder.layers.4.self_attn.k_proj.bias', 'model.encoder.layers.1.final_layer_norm.bias', 'model.encoder.layers.0.fc1.weight', 'model.encoder.layers.3.self_attn.k_proj.weight', 'model.encoder.layers.3.self_attn.q_proj.bias', 'model.encoder.layers.2.self_attn_layer_norm.weight', 'model.encoder.embed_positions.weight', 'model.encoder.layers.3.self_attn.v_proj.weight', 'model.encoder.layers.3.self_attn.k_proj.bias', 'model.encoder.layers.0.self_attn_layer_norm.weight', 'model.encoder.layers.4.self_attn.v_proj.weight', 'model.encoder.layers.0.fc1.bias', 'model.encoder.layers.0.final_layer_norm.bias', 'model.encoder.layers.4.final_layer_norm.bias', 'model.encoder.layers.5.self_attn.k_proj.bias', 'model.encoder.layers.4.fc2.bias', 'model.encoder.layers.1.self_attn.k_proj.bias', 'model.encoder.layers.3.fc2.bias', 'model.encoder.layers.3.final_layer_norm.weight', 'model.encoder.layers.1.self_attn_layer_norm.weight', 'model.encoder.layers.4.self_attn_layer_norm.bias', 'model.encoder.layers.5.self_attn_layer_norm.bias', 'model.encoder.layers.2.final_layer_norm.bias', 'model.encoder.layers.0.self_attn.k_proj.bias', 'model.encoder.layers.1.self_attn.v_proj.bias', 'model.encoder.layers.5.fc1.bias', 'model.encoder.layers.2.fc1.weight', 'model.encoder.layers.0.self_attn.q_proj.weight', 'model.encoder.layers.0.fc2.bias', 'model.encoder.layers.3.final_layer_norm.bias', 'model.encoder.layers.0.final_layer_norm.weight', 'model.encoder.layers.1.fc1.weight', 'model.encoder.layers.2.self_attn.v_proj.weight', 'model.encoder.layers.0.self_attn.v_proj.bias', 'model.encoder.layers.1.self_attn.k_proj.weight', 'model.encoder.layers.3.self_attn.out_proj.bias', 'model.encoder.layers.5.final_layer_norm.weight', 'model.encoder.layers.1.self_attn.out_proj.bias', 'model.encoder.layers.5.self_attn.q_proj.bias', 'model.encoder.layers.2.self_attn.v_proj.bias', 'model.encoder.layers.4.self_attn.out_proj.bias', 'model.encoder.layers.1.fc1.bias', 'final_logits_bias', 'model.encoder.layers.1.fc2.bias', 'model.encoder.layers.2.self_attn.k_proj.weight', 'model.encoder.layers.3.self_attn_layer_norm.bias', 'model.encoder.layers.4.fc1.weight', 'model.encoder.layers.2.fc1.bias', 'model.encoder.layers.2.self_attn.out_proj.bias', 'model.encoder.layers.1.final_layer_norm.weight', 'model.encoder.layers.5.self_attn.out_proj.bias', 'model.encoder.layers.4.self_attn.q_proj.weight', 'model.encoder.layers.5.final_layer_norm.bias', 'model.encoder.layers.2.final_layer_norm.weight', 'model.encoder.layers.2.self_attn.out_proj.weight', 'model.encoder.layers.4.self_attn.out_proj.weight', 'model.encoder.layers.1.self_attn.out_proj.weight', 'model.encoder.layers.5.self_attn.v_proj.bias', 'model.encoder.layers.1.fc2.weight', 'model.encoder.layers.3.fc1.bias', 'model.encoder.layers.4.self_attn.q_proj.bias', 'model.encoder.layers.3.self_attn_layer_norm.weight', 'model.encoder.layers.0.self_attn.out_proj.weight', 'model.encoder.embed_tokens.weight', 'model.encoder.layers.4.self_attn_layer_norm.weight', 'model.encoder.layers.2.self_attn.k_proj.bias', 'model.encoder.layers.5.self_attn.q_proj.weight', 'model.encoder.layers.0.self_attn.out_proj.bias', 'model.encoder.layers.1.self_attn_layer_norm.bias', 'model.encoder.layers.1.self_attn.q_proj.bias', 'model.encoder.layers.3.self_attn.out_proj.weight', 'model.encoder.layers.3.self_attn.v_proj.bias', 'model.encoder.layers.3.fc1.weight', 'model.encoder.layers.2.self_attn.q_proj.weight', 'model.encoder.layers.2.self_attn_layer_norm.bias']\n",
            "- This IS expected if you are initializing MarianForCausalLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing MarianForCausalLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of MarianForCausalLM were not initialized from the model checkpoint at Helsinki-NLP/opus-mt-en-nl and are newly initialized: ['lm_head.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "EncoderDecoderModel(\n",
              "  (encoder): ElectraModel(\n",
              "    (embeddings): ElectraEmbeddings(\n",
              "      (word_embeddings): Embedding(30522, 128, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 128)\n",
              "      (token_type_embeddings): Embedding(2, 128)\n",
              "      (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (embeddings_project): Linear(in_features=128, out_features=256, bias=True)\n",
              "    (encoder): ElectraEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (1): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (2): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (3): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (4): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (5): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (6): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (7): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (8): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (9): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (10): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (11): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (decoder): MarianForCausalLM(\n",
              "    (model): MarianDecoderWrapper(\n",
              "      (decoder): MarianDecoder(\n",
              "        (embed_tokens): Embedding(67028, 512, padding_idx=67027)\n",
              "        (embed_positions): MarianSinusoidalPositionalEmbedding(512, 512)\n",
              "        (layers): ModuleList(\n",
              "          (0): MarianDecoderLayer(\n",
              "            (self_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (activation_fn): SiLUActivation()\n",
              "            (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (encoder_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "            (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "          (1): MarianDecoderLayer(\n",
              "            (self_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (activation_fn): SiLUActivation()\n",
              "            (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (encoder_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "            (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "          (2): MarianDecoderLayer(\n",
              "            (self_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (activation_fn): SiLUActivation()\n",
              "            (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (encoder_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "            (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "          (3): MarianDecoderLayer(\n",
              "            (self_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (activation_fn): SiLUActivation()\n",
              "            (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (encoder_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "            (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "          (4): MarianDecoderLayer(\n",
              "            (self_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (activation_fn): SiLUActivation()\n",
              "            (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (encoder_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "            (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "          (5): MarianDecoderLayer(\n",
              "            (self_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (activation_fn): SiLUActivation()\n",
              "            (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (encoder_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "            (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (lm_head): Linear(in_features=512, out_features=67028, bias=False)\n",
              "  )\n",
              "  (enc_to_dec_proj): Linear(in_features=256, out_features=512, bias=True)\n",
              ")"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model = EncoderDecoderModel.from_encoder_decoder_pretrained(\"google/electra-small-discriminator\", \"Helsinki-NLP/opus-mt-en-nl\")\n",
        "model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b8o5qdiagbh2",
        "outputId": "2cc81a71-dd5b-4b07-e54d-490eb368cac5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "EncoderDecoderConfig {\n",
              "  \"_commit_hash\": null,\n",
              "  \"decoder\": {\n",
              "    \"_name_or_path\": \"Helsinki-NLP/opus-mt-en-nl\",\n",
              "    \"_num_labels\": 3,\n",
              "    \"activation_dropout\": 0.0,\n",
              "    \"activation_function\": \"swish\",\n",
              "    \"add_bias_logits\": false,\n",
              "    \"add_cross_attention\": true,\n",
              "    \"add_final_layer_norm\": false,\n",
              "    \"architectures\": [\n",
              "      \"MarianMTModel\"\n",
              "    ],\n",
              "    \"attention_dropout\": 0.0,\n",
              "    \"bad_words_ids\": [\n",
              "      [\n",
              "        67027\n",
              "      ]\n",
              "    ],\n",
              "    \"bos_token_id\": 0,\n",
              "    \"chunk_size_feed_forward\": 0,\n",
              "    \"classif_dropout\": 0.0,\n",
              "    \"classifier_dropout\": 0.0,\n",
              "    \"cross_attention_hidden_size\": null,\n",
              "    \"d_model\": 512,\n",
              "    \"decoder_attention_heads\": 8,\n",
              "    \"decoder_ffn_dim\": 2048,\n",
              "    \"decoder_layerdrop\": 0.0,\n",
              "    \"decoder_layers\": 6,\n",
              "    \"decoder_start_token_id\": 67027,\n",
              "    \"decoder_vocab_size\": 67028,\n",
              "    \"diversity_penalty\": 0.0,\n",
              "    \"do_sample\": false,\n",
              "    \"dropout\": 0.1,\n",
              "    \"early_stopping\": false,\n",
              "    \"encoder_attention_heads\": 8,\n",
              "    \"encoder_ffn_dim\": 2048,\n",
              "    \"encoder_layerdrop\": 0.0,\n",
              "    \"encoder_layers\": 6,\n",
              "    \"encoder_no_repeat_ngram_size\": 0,\n",
              "    \"eos_token_id\": 0,\n",
              "    \"exponential_decay_length_penalty\": null,\n",
              "    \"finetuning_task\": null,\n",
              "    \"forced_bos_token_id\": null,\n",
              "    \"forced_eos_token_id\": 0,\n",
              "    \"id2label\": {\n",
              "      \"0\": \"LABEL_0\",\n",
              "      \"1\": \"LABEL_1\",\n",
              "      \"2\": \"LABEL_2\"\n",
              "    },\n",
              "    \"init_std\": 0.02,\n",
              "    \"is_decoder\": true,\n",
              "    \"is_encoder_decoder\": false,\n",
              "    \"label2id\": {\n",
              "      \"LABEL_0\": 0,\n",
              "      \"LABEL_1\": 1,\n",
              "      \"LABEL_2\": 2\n",
              "    },\n",
              "    \"length_penalty\": 1.0,\n",
              "    \"max_length\": 512,\n",
              "    \"max_position_embeddings\": 512,\n",
              "    \"min_length\": 0,\n",
              "    \"model_type\": \"marian\",\n",
              "    \"no_repeat_ngram_size\": 0,\n",
              "    \"normalize_before\": false,\n",
              "    \"normalize_embedding\": false,\n",
              "    \"num_beam_groups\": 1,\n",
              "    \"num_beams\": 4,\n",
              "    \"num_hidden_layers\": 6,\n",
              "    \"num_return_sequences\": 1,\n",
              "    \"output_attentions\": false,\n",
              "    \"output_hidden_states\": false,\n",
              "    \"output_scores\": false,\n",
              "    \"pad_token_id\": 67027,\n",
              "    \"prefix\": null,\n",
              "    \"problem_type\": null,\n",
              "    \"pruned_heads\": {},\n",
              "    \"remove_invalid_values\": false,\n",
              "    \"repetition_penalty\": 1.0,\n",
              "    \"return_dict\": true,\n",
              "    \"return_dict_in_generate\": false,\n",
              "    \"scale_embedding\": true,\n",
              "    \"sep_token_id\": null,\n",
              "    \"share_encoder_decoder_embeddings\": true,\n",
              "    \"static_position_embeddings\": true,\n",
              "    \"task_specific_params\": null,\n",
              "    \"temperature\": 1.0,\n",
              "    \"tf_legacy_loss\": false,\n",
              "    \"tie_encoder_decoder\": false,\n",
              "    \"tie_word_embeddings\": true,\n",
              "    \"tokenizer_class\": null,\n",
              "    \"top_k\": 50,\n",
              "    \"top_p\": 1.0,\n",
              "    \"torch_dtype\": null,\n",
              "    \"torchscript\": false,\n",
              "    \"transformers_version\": \"4.23.0.dev0\",\n",
              "    \"typical_p\": 1.0,\n",
              "    \"use_bfloat16\": false,\n",
              "    \"use_cache\": true,\n",
              "    \"vocab_size\": 67028\n",
              "  },\n",
              "  \"encoder\": {\n",
              "    \"_name_or_path\": \"google/electra-small-discriminator\",\n",
              "    \"add_cross_attention\": false,\n",
              "    \"architectures\": [\n",
              "      \"ElectraForPreTraining\"\n",
              "    ],\n",
              "    \"attention_probs_dropout_prob\": 0.1,\n",
              "    \"bad_words_ids\": null,\n",
              "    \"bos_token_id\": null,\n",
              "    \"chunk_size_feed_forward\": 0,\n",
              "    \"classifier_dropout\": null,\n",
              "    \"cross_attention_hidden_size\": null,\n",
              "    \"decoder_start_token_id\": null,\n",
              "    \"diversity_penalty\": 0.0,\n",
              "    \"do_sample\": false,\n",
              "    \"early_stopping\": false,\n",
              "    \"embedding_size\": 128,\n",
              "    \"encoder_no_repeat_ngram_size\": 0,\n",
              "    \"eos_token_id\": null,\n",
              "    \"exponential_decay_length_penalty\": null,\n",
              "    \"finetuning_task\": null,\n",
              "    \"forced_bos_token_id\": null,\n",
              "    \"forced_eos_token_id\": null,\n",
              "    \"hidden_act\": \"gelu\",\n",
              "    \"hidden_dropout_prob\": 0.1,\n",
              "    \"hidden_size\": 256,\n",
              "    \"id2label\": {\n",
              "      \"0\": \"LABEL_0\",\n",
              "      \"1\": \"LABEL_1\"\n",
              "    },\n",
              "    \"initializer_range\": 0.02,\n",
              "    \"intermediate_size\": 1024,\n",
              "    \"is_decoder\": false,\n",
              "    \"is_encoder_decoder\": false,\n",
              "    \"label2id\": {\n",
              "      \"LABEL_0\": 0,\n",
              "      \"LABEL_1\": 1\n",
              "    },\n",
              "    \"layer_norm_eps\": 1e-12,\n",
              "    \"length_penalty\": 1.0,\n",
              "    \"max_length\": 20,\n",
              "    \"max_position_embeddings\": 512,\n",
              "    \"min_length\": 0,\n",
              "    \"model_type\": \"electra\",\n",
              "    \"no_repeat_ngram_size\": 0,\n",
              "    \"num_attention_heads\": 4,\n",
              "    \"num_beam_groups\": 1,\n",
              "    \"num_beams\": 1,\n",
              "    \"num_hidden_layers\": 12,\n",
              "    \"num_return_sequences\": 1,\n",
              "    \"output_attentions\": false,\n",
              "    \"output_hidden_states\": false,\n",
              "    \"output_scores\": false,\n",
              "    \"pad_token_id\": 0,\n",
              "    \"position_embedding_type\": \"absolute\",\n",
              "    \"prefix\": null,\n",
              "    \"problem_type\": null,\n",
              "    \"pruned_heads\": {},\n",
              "    \"remove_invalid_values\": false,\n",
              "    \"repetition_penalty\": 1.0,\n",
              "    \"return_dict\": true,\n",
              "    \"return_dict_in_generate\": false,\n",
              "    \"sep_token_id\": null,\n",
              "    \"summary_activation\": \"gelu\",\n",
              "    \"summary_last_dropout\": 0.1,\n",
              "    \"summary_type\": \"first\",\n",
              "    \"summary_use_proj\": true,\n",
              "    \"task_specific_params\": null,\n",
              "    \"temperature\": 1.0,\n",
              "    \"tf_legacy_loss\": false,\n",
              "    \"tie_encoder_decoder\": false,\n",
              "    \"tie_word_embeddings\": true,\n",
              "    \"tokenizer_class\": null,\n",
              "    \"top_k\": 50,\n",
              "    \"top_p\": 1.0,\n",
              "    \"torch_dtype\": null,\n",
              "    \"torchscript\": false,\n",
              "    \"transformers_version\": \"4.23.0.dev0\",\n",
              "    \"type_vocab_size\": 2,\n",
              "    \"typical_p\": 1.0,\n",
              "    \"use_bfloat16\": false,\n",
              "    \"use_cache\": true,\n",
              "    \"vocab_size\": 30522\n",
              "  },\n",
              "  \"is_encoder_decoder\": true,\n",
              "  \"model_type\": \"encoder-decoder\",\n",
              "  \"transformers_version\": null\n",
              "}"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.config"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ml1eZC5FIPWw"
      },
      "outputs": [],
      "source": [
        "model.base_model.config.encoder.num_hidden_layers = 6"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QHm4VdrLZVev"
      },
      "outputs": [],
      "source": [
        "model.tie_weights = True"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EilK2ZWBVn2-"
      },
      "outputs": [],
      "source": [
        "model.base_model.config.decoder_start_token_id = model.base_model.config.decoder.bos_token_id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DzGm_QPXHvI4"
      },
      "outputs": [],
      "source": [
        "model.base_model.config.bos_token_id = tokenizer.bos_token_id\n",
        "model.base_model.config.eos_token_id = tokenizer.eos_token_id\n",
        "model.base_model.config.pad_token_id = tokenizer.pad_token_id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "V0rvsHv7HF6T"
      },
      "outputs": [],
      "source": [
        "model.config.max_length =512"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1GR3W0pOs7Na"
      },
      "outputs": [],
      "source": [
        "model.save_pretrained(\"ELECTRA-Marian-base-model\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hvxtV52E3V3D",
        "outputId": "5b88c9ad-55c7-472d-9479-523aabb70ab2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "EncoderDecoderModel(\n",
              "  (encoder): ElectraModel(\n",
              "    (embeddings): ElectraEmbeddings(\n",
              "      (word_embeddings): Embedding(30522, 128, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 128)\n",
              "      (token_type_embeddings): Embedding(2, 128)\n",
              "      (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (embeddings_project): Linear(in_features=128, out_features=256, bias=True)\n",
              "    (encoder): ElectraEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (1): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (2): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (3): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (4): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (5): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (decoder): MarianForCausalLM(\n",
              "    (model): MarianDecoderWrapper(\n",
              "      (decoder): MarianDecoder(\n",
              "        (embed_tokens): Embedding(67028, 512, padding_idx=67027)\n",
              "        (embed_positions): MarianSinusoidalPositionalEmbedding(512, 512)\n",
              "        (layers): ModuleList(\n",
              "          (0): MarianDecoderLayer(\n",
              "            (self_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (activation_fn): SiLUActivation()\n",
              "            (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (encoder_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "            (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "          (1): MarianDecoderLayer(\n",
              "            (self_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (activation_fn): SiLUActivation()\n",
              "            (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (encoder_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "            (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "          (2): MarianDecoderLayer(\n",
              "            (self_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (activation_fn): SiLUActivation()\n",
              "            (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (encoder_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "            (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "          (3): MarianDecoderLayer(\n",
              "            (self_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (activation_fn): SiLUActivation()\n",
              "            (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (encoder_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "            (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "          (4): MarianDecoderLayer(\n",
              "            (self_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (activation_fn): SiLUActivation()\n",
              "            (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (encoder_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "            (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "          (5): MarianDecoderLayer(\n",
              "            (self_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (activation_fn): SiLUActivation()\n",
              "            (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (encoder_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "            (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (lm_head): Linear(in_features=512, out_features=67028, bias=False)\n",
              "  )\n",
              "  (enc_to_dec_proj): Linear(in_features=256, out_features=512, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "from transformers import AutoModelForSeq2SeqLM\n",
        "#model = AutoModelForSeq2SeqLM.from_pretrained(\"./ELECTRA-Marian-base-model\")\n",
        "model=AutoModelForSeq2SeqLM.from_pretrained(\"./ELECTRA-marian-training\")\n",
        "model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kOgQ6XU5oGTP",
        "outputId": "52e20040-934e-4ce4-ff8a-2f7c3c86d90f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.base_model.config.decoder_start_token_id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NWYFhLfWIU2X",
        "outputId": "aa9a1e56-6971-4ce6-c74c-5e5626ffb441"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.base_model.config.pad_token_id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OIG2yWiKIoNz",
        "outputId": "8553c6f2-7458-49b0-9fc4-c128fc0f4003"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer.pad_token_id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lZchqrwtkDSw",
        "outputId": "abd3ffa0-9744-427c-bc5c-45e02f8984d2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "EncoderDecoderConfig {\n",
              "  \"_commit_hash\": null,\n",
              "  \"_name_or_path\": \"./ELECTRA-Marian-base-model\",\n",
              "  \"architectures\": [\n",
              "    \"EncoderDecoderModel\"\n",
              "  ],\n",
              "  \"decoder\": {\n",
              "    \"_name_or_path\": \"Helsinki-NLP/opus-mt-en-nl\",\n",
              "    \"_num_labels\": 3,\n",
              "    \"activation_dropout\": 0.0,\n",
              "    \"activation_function\": \"swish\",\n",
              "    \"add_bias_logits\": false,\n",
              "    \"add_cross_attention\": true,\n",
              "    \"add_final_layer_norm\": false,\n",
              "    \"architectures\": [\n",
              "      \"MarianMTModel\"\n",
              "    ],\n",
              "    \"attention_dropout\": 0.0,\n",
              "    \"bad_words_ids\": [\n",
              "      [\n",
              "        67027\n",
              "      ]\n",
              "    ],\n",
              "    \"bos_token_id\": 0,\n",
              "    \"chunk_size_feed_forward\": 0,\n",
              "    \"classif_dropout\": 0.0,\n",
              "    \"classifier_dropout\": 0.0,\n",
              "    \"cross_attention_hidden_size\": null,\n",
              "    \"d_model\": 512,\n",
              "    \"decoder_attention_heads\": 8,\n",
              "    \"decoder_ffn_dim\": 2048,\n",
              "    \"decoder_layerdrop\": 0.0,\n",
              "    \"decoder_layers\": 6,\n",
              "    \"decoder_start_token_id\": 67027,\n",
              "    \"decoder_vocab_size\": 67028,\n",
              "    \"diversity_penalty\": 0.0,\n",
              "    \"do_sample\": false,\n",
              "    \"dropout\": 0.1,\n",
              "    \"early_stopping\": false,\n",
              "    \"encoder_attention_heads\": 8,\n",
              "    \"encoder_ffn_dim\": 2048,\n",
              "    \"encoder_layerdrop\": 0.0,\n",
              "    \"encoder_layers\": 6,\n",
              "    \"encoder_no_repeat_ngram_size\": 0,\n",
              "    \"eos_token_id\": 0,\n",
              "    \"exponential_decay_length_penalty\": null,\n",
              "    \"finetuning_task\": null,\n",
              "    \"forced_bos_token_id\": null,\n",
              "    \"forced_eos_token_id\": 0,\n",
              "    \"id2label\": {\n",
              "      \"0\": \"LABEL_0\",\n",
              "      \"1\": \"LABEL_1\",\n",
              "      \"2\": \"LABEL_2\"\n",
              "    },\n",
              "    \"init_std\": 0.02,\n",
              "    \"is_decoder\": true,\n",
              "    \"is_encoder_decoder\": false,\n",
              "    \"label2id\": {\n",
              "      \"LABEL_0\": 0,\n",
              "      \"LABEL_1\": 1,\n",
              "      \"LABEL_2\": 2\n",
              "    },\n",
              "    \"length_penalty\": 1.0,\n",
              "    \"max_length\": 512,\n",
              "    \"max_position_embeddings\": 512,\n",
              "    \"min_length\": 0,\n",
              "    \"model_type\": \"marian\",\n",
              "    \"no_repeat_ngram_size\": 0,\n",
              "    \"normalize_before\": false,\n",
              "    \"normalize_embedding\": false,\n",
              "    \"num_beam_groups\": 1,\n",
              "    \"num_beams\": 4,\n",
              "    \"num_hidden_layers\": 6,\n",
              "    \"num_return_sequences\": 1,\n",
              "    \"output_attentions\": false,\n",
              "    \"output_hidden_states\": false,\n",
              "    \"output_scores\": false,\n",
              "    \"pad_token_id\": 67027,\n",
              "    \"prefix\": null,\n",
              "    \"problem_type\": null,\n",
              "    \"pruned_heads\": {},\n",
              "    \"remove_invalid_values\": false,\n",
              "    \"repetition_penalty\": 1.0,\n",
              "    \"return_dict\": true,\n",
              "    \"return_dict_in_generate\": false,\n",
              "    \"scale_embedding\": true,\n",
              "    \"sep_token_id\": null,\n",
              "    \"share_encoder_decoder_embeddings\": true,\n",
              "    \"static_position_embeddings\": true,\n",
              "    \"task_specific_params\": null,\n",
              "    \"temperature\": 1.0,\n",
              "    \"tf_legacy_loss\": false,\n",
              "    \"tie_encoder_decoder\": false,\n",
              "    \"tie_word_embeddings\": true,\n",
              "    \"tokenizer_class\": null,\n",
              "    \"top_k\": 50,\n",
              "    \"top_p\": 1.0,\n",
              "    \"torch_dtype\": null,\n",
              "    \"torchscript\": false,\n",
              "    \"transformers_version\": \"4.23.0.dev0\",\n",
              "    \"typical_p\": 1.0,\n",
              "    \"use_bfloat16\": false,\n",
              "    \"use_cache\": true,\n",
              "    \"vocab_size\": 67028\n",
              "  },\n",
              "  \"decoder_start_token_id\": 0,\n",
              "  \"encoder\": {\n",
              "    \"_name_or_path\": \"google/electra-small-discriminator\",\n",
              "    \"add_cross_attention\": false,\n",
              "    \"architectures\": [\n",
              "      \"ElectraForPreTraining\"\n",
              "    ],\n",
              "    \"attention_probs_dropout_prob\": 0.1,\n",
              "    \"bad_words_ids\": null,\n",
              "    \"bos_token_id\": null,\n",
              "    \"chunk_size_feed_forward\": 0,\n",
              "    \"classifier_dropout\": null,\n",
              "    \"cross_attention_hidden_size\": null,\n",
              "    \"decoder_start_token_id\": null,\n",
              "    \"diversity_penalty\": 0.0,\n",
              "    \"do_sample\": false,\n",
              "    \"early_stopping\": false,\n",
              "    \"embedding_size\": 128,\n",
              "    \"encoder_no_repeat_ngram_size\": 0,\n",
              "    \"eos_token_id\": null,\n",
              "    \"exponential_decay_length_penalty\": null,\n",
              "    \"finetuning_task\": null,\n",
              "    \"forced_bos_token_id\": null,\n",
              "    \"forced_eos_token_id\": null,\n",
              "    \"hidden_act\": \"gelu\",\n",
              "    \"hidden_dropout_prob\": 0.1,\n",
              "    \"hidden_size\": 256,\n",
              "    \"id2label\": {\n",
              "      \"0\": \"LABEL_0\",\n",
              "      \"1\": \"LABEL_1\"\n",
              "    },\n",
              "    \"initializer_range\": 0.02,\n",
              "    \"intermediate_size\": 1024,\n",
              "    \"is_decoder\": false,\n",
              "    \"is_encoder_decoder\": false,\n",
              "    \"label2id\": {\n",
              "      \"LABEL_0\": 0,\n",
              "      \"LABEL_1\": 1\n",
              "    },\n",
              "    \"layer_norm_eps\": 1e-12,\n",
              "    \"length_penalty\": 1.0,\n",
              "    \"max_length\": 20,\n",
              "    \"max_position_embeddings\": 512,\n",
              "    \"min_length\": 0,\n",
              "    \"model_type\": \"electra\",\n",
              "    \"no_repeat_ngram_size\": 0,\n",
              "    \"num_attention_heads\": 4,\n",
              "    \"num_beam_groups\": 1,\n",
              "    \"num_beams\": 1,\n",
              "    \"num_hidden_layers\": 6,\n",
              "    \"num_return_sequences\": 1,\n",
              "    \"output_attentions\": false,\n",
              "    \"output_hidden_states\": false,\n",
              "    \"output_scores\": false,\n",
              "    \"pad_token_id\": 0,\n",
              "    \"position_embedding_type\": \"absolute\",\n",
              "    \"prefix\": null,\n",
              "    \"problem_type\": null,\n",
              "    \"pruned_heads\": {},\n",
              "    \"remove_invalid_values\": false,\n",
              "    \"repetition_penalty\": 1.0,\n",
              "    \"return_dict\": true,\n",
              "    \"return_dict_in_generate\": false,\n",
              "    \"sep_token_id\": null,\n",
              "    \"summary_activation\": \"gelu\",\n",
              "    \"summary_last_dropout\": 0.1,\n",
              "    \"summary_type\": \"first\",\n",
              "    \"summary_use_proj\": true,\n",
              "    \"task_specific_params\": null,\n",
              "    \"temperature\": 1.0,\n",
              "    \"tf_legacy_loss\": false,\n",
              "    \"tie_encoder_decoder\": false,\n",
              "    \"tie_word_embeddings\": true,\n",
              "    \"tokenizer_class\": null,\n",
              "    \"top_k\": 50,\n",
              "    \"top_p\": 1.0,\n",
              "    \"torch_dtype\": null,\n",
              "    \"torchscript\": false,\n",
              "    \"transformers_version\": \"4.23.0.dev0\",\n",
              "    \"type_vocab_size\": 2,\n",
              "    \"typical_p\": 1.0,\n",
              "    \"use_bfloat16\": false,\n",
              "    \"use_cache\": true,\n",
              "    \"vocab_size\": 30522\n",
              "  },\n",
              "  \"is_encoder_decoder\": true,\n",
              "  \"max_length\": 512,\n",
              "  \"model_type\": \"encoder-decoder\",\n",
              "  \"pad_token_id\": 0,\n",
              "  \"torch_dtype\": \"float32\",\n",
              "  \"transformers_version\": null\n",
              "}"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "model.config"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NJNVStnrjr8_"
      },
      "source": [
        "# **Evaluator**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Iq7Zu-kXTY3",
        "outputId": "da466272-a362-49d1-cd57-b0149dfc860b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/models/CodeGeneration/New/DJANGO/ELECTRA-Marian/Utils\n"
          ]
        }
      ],
      "source": [
        "%cd Utils"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200,
          "referenced_widgets": [
            "ba6c6fc6f4be47f1847dad4030980e5b",
            "d008ccd85a984bf3b2ea4b1d637cc100",
            "09e6a26fcd26472dad51c7d95a7e43a7",
            "7864893582804fcf9df9669d5d97a512",
            "0fa951a4d8034cbe8b34da002f1ce13b",
            "a86dfeb7435a462ea6f901e14c794094",
            "dc990eb7b64e45e3b0143ed09882ac58",
            "537ce4f7baaa4ea38c788c0207296bef",
            "72d69f96dd07462c93a02483df2beb57",
            "0431fd152b6b491493759d043fc964f5",
            "badf017875a14e1aac2891ba8715322f",
            "7d5b1ebfbad144b5b439232df0f46d8f",
            "badc41b76e194f369ba520ff4c65750a",
            "3076efaff3f7459ca29f5b053a24eea4",
            "2bb7aec8ec0e4c4b9d5a0d495ddfa2af",
            "fc5bc72b51b54451934fb0ed8bfc3e30",
            "c75c4412489647cf862050b220cb7516",
            "3c58be2dd8a64c7da0d78b6b699ad245",
            "3475cc33a2fa4b4da0587e1388d13838",
            "bb9fd9c170dc4a3e89246665924ba0ec",
            "956d3cbb6c0d4ee7b50b7137e369fadd",
            "d210d35a9cdd4fae97928bdec0266e5e",
            "5ac03900ab4f4cb1bdb7c3e0c0c2bf36",
            "38cc593d324e4cf18768852a7c380f75",
            "5923f3b6544e4e5b810720b204d26caa",
            "e7f756c4676d4cceb6227ddf22af07dc",
            "9fbf1cfeb009406d9b6e7511cb08531e",
            "09b40f62b0c844a2bf37509945c17662",
            "440e333d28b4452bb6818d7a2b92df0b",
            "aed90745b83449729ee8df19c495fdc6",
            "fe752991173841e6970f728f96de657e",
            "4f9dad1f61744bed9eee0e1c54fdc5c3",
            "636e1544ab684db99309eaabb42592c8",
            "ec77a831b71040a6b83b505562e775b5",
            "df5d153c2f77411ebefc3e6ba1b22d67",
            "addeab2085244b3f988401ffb5c2f57f",
            "83d3c665f50e41e3b4ed5013acbab181",
            "3c3adc2764594d3f845459df85444e52",
            "ef04738479264d8d97da3170a472457e",
            "b1be09113baf48da88a034bee8c9ac8b",
            "1cc98f362664490ba322657cade14541",
            "2cd72448578d4bc9874e6f40ee400f85",
            "c94799f00787451d92877a30c7ffd761",
            "3d186b21202a42cca915cb1baa9c43f5"
          ]
        },
        "id": "ygEx8goOgfIy",
        "outputId": "8d87468f-66d1-4613-b35c-b1c8f5615a50"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/content/drive/MyDrive/models/CodeGeneration/New/DJANGO/ELECTRA-Marian/Utils/evaluator.py:32: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
            "  self.sacre_bleu: Metric = load_metric('sacrebleu')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.85k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ba6c6fc6f4be47f1847dad4030980e5b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.48k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7d5b1ebfbad144b5b439232df0f46d8f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading extra modules:   0%|          | 0.00/1.55k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5ac03900ab4f4cb1bdb7c3e0c0c2bf36"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.16k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ec77a831b71040a6b83b505562e775b5"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from google.colab import files\n",
        "import evaluator\n",
        "evaluator = evaluator.CodeGenerationEvaluator(tokenizer, device,smooth_bleu=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WtUzOY89cpQw",
        "outputId": "c71e2e62-42a4-43bc-ff17-60d4c3c5eaf4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/models/CodeGeneration/New/DJANGO/ELECTRA-Marian\n"
          ]
        }
      ],
      "source": [
        "%cd .."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tYvdujyfro1i"
      },
      "source": [
        "\n",
        "\n",
        "# **Prepare the data.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4ORxNxB7bINN"
      },
      "outputs": [],
      "source": [
        "encoder_length = 32\n",
        "decoder_length = 32\n",
        "batch_size = 1\n",
        "\n",
        "\n",
        "# map data correctly\n",
        "def map_to_encoder_decoder_inputs(batch):\n",
        "    inputs = tokenizer(batch[\"nl\"], padding=\"max_length\", truncation=True, max_length=encoder_length)\n",
        "    outputs = tokenizer(batch[\"code\"], padding=\"max_length\", truncation=True, max_length=decoder_length)\n",
        "    batch[\"input_ids\"] = inputs.input_ids\n",
        "    batch[\"attention_mask\"] = inputs.attention_mask\n",
        "    batch[\"decoder_input_ids\"] = outputs.input_ids\n",
        "    batch[\"labels\"] = outputs.input_ids.copy()\n",
        "    batch[\"decoder_attention_mask\"] = outputs.attention_mask\n",
        "\n",
        "    \"\"\"\n",
        "    # complicated list comprehension here because pad_token_id alone is not good enough to know whether label should be excluded or not\n",
        "    batch[\"labels\"] = [\n",
        "        [-100 if mask == 0 else token for mask, token in mask_and_tokens] for mask_and_tokens in [zip(masks, labels) for masks, labels in zip(batch[\"decoder_attention_mask\"], batch[\"labels\"])]\n",
        "    ]\n",
        "    \"\"\"\n",
        "    assert all([len(x) == encoder_length for x in inputs.input_ids])\n",
        "    assert all([len(x) == decoder_length for x in outputs.input_ids])\n",
        "\n",
        "    return batch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "2d7f6a2c058d48e9ada47bd6c1f0da7f",
            "d5b4e24208d244418acae69197bab183",
            "d79d8800263f4cdb978f6734d44e7fca",
            "8ec728e9b2684f7fb94c2041ef9c8b1a",
            "278d8354459e49cebceaf2acb2442163",
            "316857e4333a43b58dbf07360203cd92",
            "7c461923ef284813b6125336a760d54a",
            "ef8d5b2d065a4ea5822dcdfb1195a0c4",
            "167a8853005f4b0cae4cbceafd63e49e",
            "1ffd5518db094bf5bf8c5e4abe863a57",
            "d401a59a2f0c4abab8864a8c41a646bf"
          ]
        },
        "id": "5p_GH0x5a-hY",
        "outputId": "00be2c83-1056-40e3-fcc7-bec5953b7eb0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/2000 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2d7f6a2c058d48e9ada47bd6c1f0da7f"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# make train dataset ready\n",
        "train_data = train_dataset.map(\n",
        "    map_to_encoder_decoder_inputs, batched=True, batch_size=8, remove_columns=['nl', 'code'],\n",
        ")\n",
        "train_data.set_format(\n",
        "    type=\"torch\", columns=[\"input_ids\", \"attention_mask\", \"decoder_input_ids\", \"decoder_attention_mask\", \"labels\"],\n",
        ")\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "f0fa094e93ed4332873900f2c0720f55",
            "ea121508657e42108dfd3935b219ef66",
            "a1596942e4d74527ad61d419ef3011fe",
            "a0b031c0d7db459daefefe526ed501aa",
            "e90495ea16024c9cb88e25940c5d2e62",
            "86324efefc4f4dc1aad92350d6d5de76",
            "e1421232474b44cd9acba73a11d48718",
            "53a05052f93946f584b02d099ef0b9f8",
            "4580b8b6a9974d98acb1ea6b55a5e373",
            "00d183713e494c73acc271d2025721fd",
            "36e4a0e4e9cc4fd383f0d135fb51aad6"
          ]
        },
        "id": "zUWfkeRqoCdW",
        "outputId": "1aa50d5d-9e1b-4b34-dbc0-d7068e8342e0"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/250 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f0fa094e93ed4332873900f2c0720f55"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# same for validation dataset\n",
        "val_data = val_dataset.map(\n",
        "    map_to_encoder_decoder_inputs, batched=True, batch_size=4, remove_columns=['nl', 'code'],\n",
        ")\n",
        "val_data.set_format(\n",
        "    type=\"torch\", columns=[\"input_ids\", \"attention_mask\", \"decoder_input_ids\", \"decoder_attention_mask\", \"labels\"],\n",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tHv_U6xgz8Y0"
      },
      "outputs": [],
      "source": [
        "data_collator = DataCollatorForSeq2Seq(tokenizer=tokenizer,\n",
        "                                       max_length=512,padding=True, ####new\n",
        "                                       model = model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yhetWPjLkQ5P"
      },
      "source": [
        "# **Training**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qF6rhycZPr2m",
        "outputId": "3bd09a58-9304-4677-86ca-1904b6af2ccf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using cuda_amp half precision backend\n"
          ]
        }
      ],
      "source": [
        "# set training arguments - these params are not really tuned, feel free to change\n",
        "training_args = Seq2SeqTrainingArguments(\n",
        "    output_dir=\"./ELECTRA-marian-training1\",\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy = \"epoch\",\n",
        "    per_device_train_batch_size=8,\n",
        "    per_device_eval_batch_size=4,\n",
        "    predict_with_generate=True,\n",
        "    num_train_epochs=10,\n",
        "    do_train=True,\n",
        "    do_eval=True,\n",
        "    fp16=True,\n",
        "    overwrite_output_dir=True,\n",
        "    learning_rate = 1e-5,\n",
        "    weight_decay=0.01,\n",
        "    warmup_ratio = 0.05,\n",
        "    seed = 1995,\n",
        "    save_total_limit = 2,\n",
        "    load_best_model_at_end = True,\n",
        "    #push_to_hub=True,\n",
        ")\n",
        "#\n",
        "\n",
        "# instantiate trainer\n",
        "trainer = Seq2SeqTrainer(\n",
        "    model=model,\n",
        "    tokenizer=tokenizer,\n",
        "    args=training_args,\n",
        "    compute_metrics=evaluator,\n",
        "    data_collator = data_collator,\n",
        "    train_dataset=train_data,\n",
        "    eval_dataset =val_data,\n",
        "\n",
        ")\n",
        "#    save_total_limit=3,\n",
        "#"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "PaDkt0QWtKM6",
        "outputId": "0c7c8b91-f38d-4e43-9c9c-25bcaa951524"
      },
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The following columns in the training set don't have a corresponding argument in `EncoderDecoderModel.forward` and have been ignored: Unnamed: 0. If Unnamed: 0 are not expected by `EncoderDecoderModel.forward`,  you can safely ignore this message.\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n",
            "***** Running training *****\n",
            "  Num examples = 16000\n",
            "  Num Epochs = 10\n",
            "  Instantaneous batch size per device = 8\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 8\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 20000\n",
            "You're using a ElectraTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2319: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
            "  \"`max_length` is ignored when `padding`=`True` and there is no truncation strategy. \"\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/encoder_decoder/modeling_encoder_decoder.py:533: FutureWarning: Version v4.12.0 introduces a better way to train encoder-decoder models by computing the loss inside the encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if fine-tuning a model trained with versions anterior to 4.12.0. The decoder_input_ids are now created based on the labels, no need to pass them yourself anymore.\n",
            "  warnings.warn(DEPRECATION_WARNING, FutureWarning)\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='10001' max='20000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [10001/20000 55:01 < 55:01, 3.03 it/s, Epoch 5/10]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Bleu</th>\n",
              "      <th>Sacrebleu</th>\n",
              "      <th>Bleu-unigram-precision</th>\n",
              "      <th>Bleu-bigram-precision</th>\n",
              "      <th>Bleu-trigram-precision</th>\n",
              "      <th>Rouge-2</th>\n",
              "      <th>Rouge-l</th>\n",
              "      <th>Sacre-unigram-precision</th>\n",
              "      <th>Sacre-bigram-precision</th>\n",
              "      <th>Sacre-trigram-precision</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.276800</td>\n",
              "      <td>0.326564</td>\n",
              "      <td>57.129997</td>\n",
              "      <td>56.243272</td>\n",
              "      <td>69.550147</td>\n",
              "      <td>60.867085</td>\n",
              "      <td>52.997963</td>\n",
              "      <td>67.631827</td>\n",
              "      <td>84.795241</td>\n",
              "      <td>67.982106</td>\n",
              "      <td>59.367946</td>\n",
              "      <td>52.628582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.254700</td>\n",
              "      <td>0.309880</td>\n",
              "      <td>63.708308</td>\n",
              "      <td>63.056128</td>\n",
              "      <td>76.105616</td>\n",
              "      <td>67.310603</td>\n",
              "      <td>59.514289</td>\n",
              "      <td>68.872176</td>\n",
              "      <td>85.556110</td>\n",
              "      <td>74.869474</td>\n",
              "      <td>66.050324</td>\n",
              "      <td>59.367115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.229600</td>\n",
              "      <td>0.297664</td>\n",
              "      <td>64.798095</td>\n",
              "      <td>63.967569</td>\n",
              "      <td>76.756713</td>\n",
              "      <td>68.195148</td>\n",
              "      <td>60.965638</td>\n",
              "      <td>69.300396</td>\n",
              "      <td>85.788301</td>\n",
              "      <td>75.532377</td>\n",
              "      <td>67.025226</td>\n",
              "      <td>60.430446</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.200100</td>\n",
              "      <td>0.289870</td>\n",
              "      <td>71.960637</td>\n",
              "      <td>71.360042</td>\n",
              "      <td>83.481429</td>\n",
              "      <td>75.063390</td>\n",
              "      <td>68.158292</td>\n",
              "      <td>71.300249</td>\n",
              "      <td>87.393370</td>\n",
              "      <td>82.603720</td>\n",
              "      <td>74.106484</td>\n",
              "      <td>67.830688</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>\n",
              "    <div>\n",
              "      \n",
              "      <progress value='155' max='250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [155/250 06:27 < 03:58, 0.40 it/s]\n",
              "    </div>\n",
              "    "
            ],
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              "<IPython.core.display.HTML object>"
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        {
          "metadata": {
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          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The following columns in the evaluation set don't have a corresponding argument in `EncoderDecoderModel.forward` and have been ignored: Unnamed: 0. If Unnamed: 0 are not expected by `EncoderDecoderModel.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1000\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to ./ELECTRA-marian-training1/checkpoint-2000\n",
            "Configuration saved in ./ELECTRA-marian-training1/checkpoint-2000/config.json\n",
            "Model weights saved in ./ELECTRA-marian-training1/checkpoint-2000/pytorch_model.bin\n",
            "tokenizer config file saved in ./ELECTRA-marian-training1/checkpoint-2000/tokenizer_config.json\n",
            "Special tokens file saved in ./ELECTRA-marian-training1/checkpoint-2000/special_tokens_map.json\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2319: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
            "  \"`max_length` is ignored when `padding`=`True` and there is no truncation strategy. \"\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/encoder_decoder/modeling_encoder_decoder.py:533: FutureWarning: Version v4.12.0 introduces a better way to train encoder-decoder models by computing the loss inside the encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if fine-tuning a model trained with versions anterior to 4.12.0. The decoder_input_ids are now created based on the labels, no need to pass them yourself anymore.\n",
            "  warnings.warn(DEPRECATION_WARNING, FutureWarning)\n",
            "The following columns in the evaluation set don't have a corresponding argument in `EncoderDecoderModel.forward` and have been ignored: Unnamed: 0. If Unnamed: 0 are not expected by `EncoderDecoderModel.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1000\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to ./ELECTRA-marian-training1/checkpoint-4000\n",
            "Configuration saved in ./ELECTRA-marian-training1/checkpoint-4000/config.json\n",
            "Model weights saved in ./ELECTRA-marian-training1/checkpoint-4000/pytorch_model.bin\n",
            "tokenizer config file saved in ./ELECTRA-marian-training1/checkpoint-4000/tokenizer_config.json\n",
            "Special tokens file saved in ./ELECTRA-marian-training1/checkpoint-4000/special_tokens_map.json\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2319: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
            "  \"`max_length` is ignored when `padding`=`True` and there is no truncation strategy. \"\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/encoder_decoder/modeling_encoder_decoder.py:533: FutureWarning: Version v4.12.0 introduces a better way to train encoder-decoder models by computing the loss inside the encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if fine-tuning a model trained with versions anterior to 4.12.0. The decoder_input_ids are now created based on the labels, no need to pass them yourself anymore.\n",
            "  warnings.warn(DEPRECATION_WARNING, FutureWarning)\n",
            "The following columns in the evaluation set don't have a corresponding argument in `EncoderDecoderModel.forward` and have been ignored: Unnamed: 0. If Unnamed: 0 are not expected by `EncoderDecoderModel.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1000\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to ./ELECTRA-marian-training1/checkpoint-6000\n",
            "Configuration saved in ./ELECTRA-marian-training1/checkpoint-6000/config.json\n",
            "Model weights saved in ./ELECTRA-marian-training1/checkpoint-6000/pytorch_model.bin\n",
            "tokenizer config file saved in ./ELECTRA-marian-training1/checkpoint-6000/tokenizer_config.json\n",
            "Special tokens file saved in ./ELECTRA-marian-training1/checkpoint-6000/special_tokens_map.json\n",
            "Deleting older checkpoint [ELECTRA-marian-training1/checkpoint-2000] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2319: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
            "  \"`max_length` is ignored when `padding`=`True` and there is no truncation strategy. \"\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/encoder_decoder/modeling_encoder_decoder.py:533: FutureWarning: Version v4.12.0 introduces a better way to train encoder-decoder models by computing the loss inside the encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if fine-tuning a model trained with versions anterior to 4.12.0. The decoder_input_ids are now created based on the labels, no need to pass them yourself anymore.\n",
            "  warnings.warn(DEPRECATION_WARNING, FutureWarning)\n",
            "The following columns in the evaluation set don't have a corresponding argument in `EncoderDecoderModel.forward` and have been ignored: Unnamed: 0. If Unnamed: 0 are not expected by `EncoderDecoderModel.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1000\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to ./ELECTRA-marian-training1/checkpoint-8000\n",
            "Configuration saved in ./ELECTRA-marian-training1/checkpoint-8000/config.json\n",
            "Model weights saved in ./ELECTRA-marian-training1/checkpoint-8000/pytorch_model.bin\n",
            "tokenizer config file saved in ./ELECTRA-marian-training1/checkpoint-8000/tokenizer_config.json\n",
            "Special tokens file saved in ./ELECTRA-marian-training1/checkpoint-8000/special_tokens_map.json\n",
            "Deleting older checkpoint [ELECTRA-marian-training1/checkpoint-4000] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2319: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
            "  \"`max_length` is ignored when `padding`=`True` and there is no truncation strategy. \"\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/encoder_decoder/modeling_encoder_decoder.py:533: FutureWarning: Version v4.12.0 introduces a better way to train encoder-decoder models by computing the loss inside the encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if fine-tuning a model trained with versions anterior to 4.12.0. The decoder_input_ids are now created based on the labels, no need to pass them yourself anymore.\n",
            "  warnings.warn(DEPRECATION_WARNING, FutureWarning)\n",
            "The following columns in the evaluation set don't have a corresponding argument in `EncoderDecoderModel.forward` and have been ignored: Unnamed: 0. If Unnamed: 0 are not expected by `EncoderDecoderModel.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1000\n",
            "  Batch size = 4\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='20000' max='20000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [20000/20000 2:10:44, Epoch 10/10]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Bleu</th>\n",
              "      <th>Sacrebleu</th>\n",
              "      <th>Bleu-unigram-precision</th>\n",
              "      <th>Bleu-bigram-precision</th>\n",
              "      <th>Bleu-trigram-precision</th>\n",
              "      <th>Rouge-2</th>\n",
              "      <th>Rouge-l</th>\n",
              "      <th>Sacre-unigram-precision</th>\n",
              "      <th>Sacre-bigram-precision</th>\n",
              "      <th>Sacre-trigram-precision</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.276800</td>\n",
              "      <td>0.326564</td>\n",
              "      <td>57.129997</td>\n",
              "      <td>56.243272</td>\n",
              "      <td>69.550147</td>\n",
              "      <td>60.867085</td>\n",
              "      <td>52.997963</td>\n",
              "      <td>67.631827</td>\n",
              "      <td>84.795241</td>\n",
              "      <td>67.982106</td>\n",
              "      <td>59.367946</td>\n",
              "      <td>52.628582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.254700</td>\n",
              "      <td>0.309880</td>\n",
              "      <td>63.708308</td>\n",
              "      <td>63.056128</td>\n",
              "      <td>76.105616</td>\n",
              "      <td>67.310603</td>\n",
              "      <td>59.514289</td>\n",
              "      <td>68.872176</td>\n",
              "      <td>85.556110</td>\n",
              "      <td>74.869474</td>\n",
              "      <td>66.050324</td>\n",
              "      <td>59.367115</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.229600</td>\n",
              "      <td>0.297664</td>\n",
              "      <td>64.798095</td>\n",
              "      <td>63.967569</td>\n",
              "      <td>76.756713</td>\n",
              "      <td>68.195148</td>\n",
              "      <td>60.965638</td>\n",
              "      <td>69.300396</td>\n",
              "      <td>85.788301</td>\n",
              "      <td>75.532377</td>\n",
              "      <td>67.025226</td>\n",
              "      <td>60.430446</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.200100</td>\n",
              "      <td>0.289870</td>\n",
              "      <td>71.960637</td>\n",
              "      <td>71.360042</td>\n",
              "      <td>83.481429</td>\n",
              "      <td>75.063390</td>\n",
              "      <td>68.158292</td>\n",
              "      <td>71.300249</td>\n",
              "      <td>87.393370</td>\n",
              "      <td>82.603720</td>\n",
              "      <td>74.106484</td>\n",
              "      <td>67.830688</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.202400</td>\n",
              "      <td>0.288515</td>\n",
              "      <td>66.408262</td>\n",
              "      <td>65.905713</td>\n",
              "      <td>78.252151</td>\n",
              "      <td>69.878749</td>\n",
              "      <td>62.338572</td>\n",
              "      <td>70.570793</td>\n",
              "      <td>86.737653</td>\n",
              "      <td>77.082219</td>\n",
              "      <td>68.748776</td>\n",
              "      <td>62.378168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.187200</td>\n",
              "      <td>0.282070</td>\n",
              "      <td>68.610940</td>\n",
              "      <td>68.179235</td>\n",
              "      <td>80.267302</td>\n",
              "      <td>71.917744</td>\n",
              "      <td>64.646258</td>\n",
              "      <td>70.819503</td>\n",
              "      <td>86.887933</td>\n",
              "      <td>79.222081</td>\n",
              "      <td>70.916015</td>\n",
              "      <td>64.703267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.174400</td>\n",
              "      <td>0.282296</td>\n",
              "      <td>69.710374</td>\n",
              "      <td>69.336497</td>\n",
              "      <td>80.830973</td>\n",
              "      <td>73.011550</td>\n",
              "      <td>65.735857</td>\n",
              "      <td>72.234126</td>\n",
              "      <td>87.997952</td>\n",
              "      <td>79.781371</td>\n",
              "      <td>71.930002</td>\n",
              "      <td>65.944307</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.172300</td>\n",
              "      <td>0.275766</td>\n",
              "      <td>70.735417</td>\n",
              "      <td>70.418702</td>\n",
              "      <td>81.844980</td>\n",
              "      <td>73.846592</td>\n",
              "      <td>66.862047</td>\n",
              "      <td>72.086200</td>\n",
              "      <td>87.927455</td>\n",
              "      <td>80.884265</td>\n",
              "      <td>72.946959</td>\n",
              "      <td>67.049852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.166900</td>\n",
              "      <td>0.274685</td>\n",
              "      <td>70.487838</td>\n",
              "      <td>70.225852</td>\n",
              "      <td>81.454891</td>\n",
              "      <td>73.695673</td>\n",
              "      <td>66.590126</td>\n",
              "      <td>72.013763</td>\n",
              "      <td>87.879404</td>\n",
              "      <td>80.490514</td>\n",
              "      <td>72.728200</td>\n",
              "      <td>66.900850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.157500</td>\n",
              "      <td>0.274240</td>\n",
              "      <td>70.323272</td>\n",
              "      <td>70.171477</td>\n",
              "      <td>81.493647</td>\n",
              "      <td>73.602044</td>\n",
              "      <td>66.357284</td>\n",
              "      <td>71.644532</td>\n",
              "      <td>87.609261</td>\n",
              "      <td>80.532939</td>\n",
              "      <td>72.726346</td>\n",
              "      <td>66.821477</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to ./ELECTRA-marian-training1/checkpoint-10000\n",
            "Configuration saved in ./ELECTRA-marian-training1/checkpoint-10000/config.json\n",
            "Model weights saved in ./ELECTRA-marian-training1/checkpoint-10000/pytorch_model.bin\n",
            "tokenizer config file saved in ./ELECTRA-marian-training1/checkpoint-10000/tokenizer_config.json\n",
            "Special tokens file saved in ./ELECTRA-marian-training1/checkpoint-10000/special_tokens_map.json\n",
            "Deleting older checkpoint [ELECTRA-marian-training1/checkpoint-6000] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2319: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
            "  \"`max_length` is ignored when `padding`=`True` and there is no truncation strategy. \"\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/encoder_decoder/modeling_encoder_decoder.py:533: FutureWarning: Version v4.12.0 introduces a better way to train encoder-decoder models by computing the loss inside the encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if fine-tuning a model trained with versions anterior to 4.12.0. The decoder_input_ids are now created based on the labels, no need to pass them yourself anymore.\n",
            "  warnings.warn(DEPRECATION_WARNING, FutureWarning)\n",
            "The following columns in the evaluation set don't have a corresponding argument in `EncoderDecoderModel.forward` and have been ignored: Unnamed: 0. If Unnamed: 0 are not expected by `EncoderDecoderModel.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1000\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to ./ELECTRA-marian-training1/checkpoint-12000\n",
            "Configuration saved in ./ELECTRA-marian-training1/checkpoint-12000/config.json\n",
            "Model weights saved in ./ELECTRA-marian-training1/checkpoint-12000/pytorch_model.bin\n",
            "tokenizer config file saved in ./ELECTRA-marian-training1/checkpoint-12000/tokenizer_config.json\n",
            "Special tokens file saved in ./ELECTRA-marian-training1/checkpoint-12000/special_tokens_map.json\n",
            "Deleting older checkpoint [ELECTRA-marian-training1/checkpoint-8000] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2319: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
            "  \"`max_length` is ignored when `padding`=`True` and there is no truncation strategy. \"\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/encoder_decoder/modeling_encoder_decoder.py:533: FutureWarning: Version v4.12.0 introduces a better way to train encoder-decoder models by computing the loss inside the encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if fine-tuning a model trained with versions anterior to 4.12.0. The decoder_input_ids are now created based on the labels, no need to pass them yourself anymore.\n",
            "  warnings.warn(DEPRECATION_WARNING, FutureWarning)\n",
            "The following columns in the evaluation set don't have a corresponding argument in `EncoderDecoderModel.forward` and have been ignored: Unnamed: 0. If Unnamed: 0 are not expected by `EncoderDecoderModel.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1000\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to ./ELECTRA-marian-training1/checkpoint-14000\n",
            "Configuration saved in ./ELECTRA-marian-training1/checkpoint-14000/config.json\n",
            "Model weights saved in ./ELECTRA-marian-training1/checkpoint-14000/pytorch_model.bin\n",
            "tokenizer config file saved in ./ELECTRA-marian-training1/checkpoint-14000/tokenizer_config.json\n",
            "Special tokens file saved in ./ELECTRA-marian-training1/checkpoint-14000/special_tokens_map.json\n",
            "Deleting older checkpoint [ELECTRA-marian-training1/checkpoint-10000] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2319: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
            "  \"`max_length` is ignored when `padding`=`True` and there is no truncation strategy. \"\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/encoder_decoder/modeling_encoder_decoder.py:533: FutureWarning: Version v4.12.0 introduces a better way to train encoder-decoder models by computing the loss inside the encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if fine-tuning a model trained with versions anterior to 4.12.0. The decoder_input_ids are now created based on the labels, no need to pass them yourself anymore.\n",
            "  warnings.warn(DEPRECATION_WARNING, FutureWarning)\n",
            "The following columns in the evaluation set don't have a corresponding argument in `EncoderDecoderModel.forward` and have been ignored: Unnamed: 0. If Unnamed: 0 are not expected by `EncoderDecoderModel.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1000\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to ./ELECTRA-marian-training1/checkpoint-16000\n",
            "Configuration saved in ./ELECTRA-marian-training1/checkpoint-16000/config.json\n",
            "Model weights saved in ./ELECTRA-marian-training1/checkpoint-16000/pytorch_model.bin\n",
            "tokenizer config file saved in ./ELECTRA-marian-training1/checkpoint-16000/tokenizer_config.json\n",
            "Special tokens file saved in ./ELECTRA-marian-training1/checkpoint-16000/special_tokens_map.json\n",
            "Deleting older checkpoint [ELECTRA-marian-training1/checkpoint-12000] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2319: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
            "  \"`max_length` is ignored when `padding`=`True` and there is no truncation strategy. \"\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/encoder_decoder/modeling_encoder_decoder.py:533: FutureWarning: Version v4.12.0 introduces a better way to train encoder-decoder models by computing the loss inside the encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if fine-tuning a model trained with versions anterior to 4.12.0. The decoder_input_ids are now created based on the labels, no need to pass them yourself anymore.\n",
            "  warnings.warn(DEPRECATION_WARNING, FutureWarning)\n",
            "The following columns in the evaluation set don't have a corresponding argument in `EncoderDecoderModel.forward` and have been ignored: Unnamed: 0. If Unnamed: 0 are not expected by `EncoderDecoderModel.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1000\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to ./ELECTRA-marian-training1/checkpoint-18000\n",
            "Configuration saved in ./ELECTRA-marian-training1/checkpoint-18000/config.json\n",
            "Model weights saved in ./ELECTRA-marian-training1/checkpoint-18000/pytorch_model.bin\n",
            "tokenizer config file saved in ./ELECTRA-marian-training1/checkpoint-18000/tokenizer_config.json\n",
            "Special tokens file saved in ./ELECTRA-marian-training1/checkpoint-18000/special_tokens_map.json\n",
            "Deleting older checkpoint [ELECTRA-marian-training1/checkpoint-14000] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2319: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
            "  \"`max_length` is ignored when `padding`=`True` and there is no truncation strategy. \"\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/encoder_decoder/modeling_encoder_decoder.py:533: FutureWarning: Version v4.12.0 introduces a better way to train encoder-decoder models by computing the loss inside the encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if fine-tuning a model trained with versions anterior to 4.12.0. The decoder_input_ids are now created based on the labels, no need to pass them yourself anymore.\n",
            "  warnings.warn(DEPRECATION_WARNING, FutureWarning)\n",
            "The following columns in the evaluation set don't have a corresponding argument in `EncoderDecoderModel.forward` and have been ignored: Unnamed: 0. If Unnamed: 0 are not expected by `EncoderDecoderModel.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1000\n",
            "  Batch size = 4\n",
            "Saving model checkpoint to ./ELECTRA-marian-training1/checkpoint-20000\n",
            "Configuration saved in ./ELECTRA-marian-training1/checkpoint-20000/config.json\n",
            "Model weights saved in ./ELECTRA-marian-training1/checkpoint-20000/pytorch_model.bin\n",
            "tokenizer config file saved in ./ELECTRA-marian-training1/checkpoint-20000/tokenizer_config.json\n",
            "Special tokens file saved in ./ELECTRA-marian-training1/checkpoint-20000/special_tokens_map.json\n",
            "Deleting older checkpoint [ELECTRA-marian-training1/checkpoint-16000] due to args.save_total_limit\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n",
            "Loading best model from ./ELECTRA-marian-training1/checkpoint-20000 (score: 0.27424004673957825).\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=20000, training_loss=0.2068538417816162, metrics={'train_runtime': 7846.7767, 'train_samples_per_second': 20.391, 'train_steps_per_second': 2.549, 'total_flos': 925520363520000.0, 'train_loss': 0.2068538417816162, 'epoch': 10.0})"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "# ELECTRA-Marian-Training-1\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "WA3z0NRHfUEO",
        "outputId": "7fada54d-108e-47fc-a0e9-d6e24c4ff10f"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The following columns in the training set don't have a corresponding argument in `EncoderDecoderModel.forward` and have been ignored: Unnamed: 0. If Unnamed: 0 are not expected by `EncoderDecoderModel.forward`,  you can safely ignore this message.\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:310: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  FutureWarning,\n",
            "***** Running training *****\n",
            "  Num examples = 16000\n",
            "  Num Epochs = 10\n",
            "  Instantaneous batch size per device = 1\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 1\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 160000\n",
            "You're using a ElectraTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2319: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
            "  \"`max_length` is ignored when `padding`=`True` and there is no truncation strategy. \"\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/encoder_decoder/modeling_encoder_decoder.py:533: FutureWarning: Version v4.12.0 introduces a better way to train encoder-decoder models by computing the loss inside the encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if fine-tuning a model trained with versions anterior to 4.12.0. The decoder_input_ids are now created based on the labels, no need to pass them yourself anymore.\n",
            "  warnings.warn(DEPRECATION_WARNING, FutureWarning)\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='144001' max='160000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [144001/160000 12:42:36 < 1:24:43, 3.15 it/s, Epoch 9/10]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Bleu</th>\n",
              "      <th>Sacrebleu</th>\n",
              "      <th>Bleu-unigram-precision</th>\n",
              "      <th>Bleu-bigram-precision</th>\n",
              "      <th>Bleu-trigram-precision</th>\n",
              "      <th>Rouge-2</th>\n",
              "      <th>Rouge-l</th>\n",
              "      <th>Sacre-unigram-precision</th>\n",
              "      <th>Sacre-bigram-precision</th>\n",
              "      <th>Sacre-trigram-precision</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.376300</td>\n",
              "      <td>1.239425</td>\n",
              "      <td>2.203191</td>\n",
              "      <td>1.906826</td>\n",
              "      <td>8.395680</td>\n",
              "      <td>3.562467</td>\n",
              "      <td>1.413251</td>\n",
              "      <td>9.560291</td>\n",
              "      <td>37.718242</td>\n",
              "      <td>7.532761</td>\n",
              "      <td>2.886806</td>\n",
              "      <td>1.217413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.779000</td>\n",
              "      <td>0.757898</td>\n",
              "      <td>12.312874</td>\n",
              "      <td>11.350797</td>\n",
              "      <td>22.744566</td>\n",
              "      <td>15.094053</td>\n",
              "      <td>9.863434</td>\n",
              "      <td>36.264078</td>\n",
              "      <td>63.170344</td>\n",
              "      <td>21.169270</td>\n",
              "      <td>13.638625</td>\n",
              "      <td>9.155042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.611200</td>\n",
              "      <td>0.562777</td>\n",
              "      <td>16.239822</td>\n",
              "      <td>15.290831</td>\n",
              "      <td>25.796314</td>\n",
              "      <td>18.910498</td>\n",
              "      <td>13.651567</td>\n",
              "      <td>48.616289</td>\n",
              "      <td>71.354147</td>\n",
              "      <td>24.182517</td>\n",
              "      <td>17.498952</td>\n",
              "      <td>13.043478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.487500</td>\n",
              "      <td>0.470647</td>\n",
              "      <td>27.887886</td>\n",
              "      <td>26.594496</td>\n",
              "      <td>39.815365</td>\n",
              "      <td>31.258082</td>\n",
              "      <td>24.390734</td>\n",
              "      <td>56.295989</td>\n",
              "      <td>77.229506</td>\n",
              "      <td>37.935923</td>\n",
              "      <td>29.456403</td>\n",
              "      <td>23.501199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.437300</td>\n",
              "      <td>0.424375</td>\n",
              "      <td>34.909961</td>\n",
              "      <td>33.579115</td>\n",
              "      <td>46.974018</td>\n",
              "      <td>38.492417</td>\n",
              "      <td>31.192498</td>\n",
              "      <td>61.034227</td>\n",
              "      <td>80.372459</td>\n",
              "      <td>44.977182</td>\n",
              "      <td>36.550881</td>\n",
              "      <td>30.344913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.371200</td>\n",
              "      <td>0.390502</td>\n",
              "      <td>43.710626</td>\n",
              "      <td>42.223256</td>\n",
              "      <td>56.020453</td>\n",
              "      <td>47.416373</td>\n",
              "      <td>39.873373</td>\n",
              "      <td>65.040797</td>\n",
              "      <td>83.227432</td>\n",
              "      <td>54.053700</td>\n",
              "      <td>45.367905</td>\n",
              "      <td>38.788563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.309700</td>\n",
              "      <td>0.367391</td>\n",
              "      <td>57.833668</td>\n",
              "      <td>56.799111</td>\n",
              "      <td>71.301542</td>\n",
              "      <td>61.667236</td>\n",
              "      <td>53.467562</td>\n",
              "      <td>65.848466</td>\n",
              "      <td>83.961561</td>\n",
              "      <td>69.766857</td>\n",
              "      <td>60.097034</td>\n",
              "      <td>52.855994</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.314500</td>\n",
              "      <td>0.352238</td>\n",
              "      <td>38.903482</td>\n",
              "      <td>37.579054</td>\n",
              "      <td>49.907779</td>\n",
              "      <td>42.153264</td>\n",
              "      <td>35.359183</td>\n",
              "      <td>66.642574</td>\n",
              "      <td>84.131158</td>\n",
              "      <td>47.977369</td>\n",
              "      <td>40.293853</td>\n",
              "      <td>34.514176</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>\n",
              "    <div>\n",
              "      \n",
              "      <progress value='232' max='1000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [ 232/1000 14:04 < 46:47, 0.27 it/s]\n",
              "    </div>\n",
              "    "
            ],
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              "<IPython.core.display.HTML object>"
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The following columns in the evaluation set don't have a corresponding argument in `EncoderDecoderModel.forward` and have been ignored: Unnamed: 0. If Unnamed: 0 are not expected by `EncoderDecoderModel.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1000\n",
            "  Batch size = 1\n",
            "Saving model checkpoint to ./ELECTRA-marian-training/checkpoint-16000\n",
            "Configuration saved in ./ELECTRA-marian-training/checkpoint-16000/config.json\n",
            "Model weights saved in ./ELECTRA-marian-training/checkpoint-16000/pytorch_model.bin\n",
            "tokenizer config file saved in ./ELECTRA-marian-training/checkpoint-16000/tokenizer_config.json\n",
            "Special tokens file saved in ./ELECTRA-marian-training/checkpoint-16000/special_tokens_map.json\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2319: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
            "  \"`max_length` is ignored when `padding`=`True` and there is no truncation strategy. \"\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/encoder_decoder/modeling_encoder_decoder.py:533: FutureWarning: Version v4.12.0 introduces a better way to train encoder-decoder models by computing the loss inside the encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if fine-tuning a model trained with versions anterior to 4.12.0. The decoder_input_ids are now created based on the labels, no need to pass them yourself anymore.\n",
            "  warnings.warn(DEPRECATION_WARNING, FutureWarning)\n",
            "The following columns in the evaluation set don't have a corresponding argument in `EncoderDecoderModel.forward` and have been ignored: Unnamed: 0. If Unnamed: 0 are not expected by `EncoderDecoderModel.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1000\n",
            "  Batch size = 1\n",
            "Saving model checkpoint to ./ELECTRA-marian-training/checkpoint-32000\n",
            "Configuration saved in ./ELECTRA-marian-training/checkpoint-32000/config.json\n",
            "Model weights saved in ./ELECTRA-marian-training/checkpoint-32000/pytorch_model.bin\n",
            "tokenizer config file saved in ./ELECTRA-marian-training/checkpoint-32000/tokenizer_config.json\n",
            "Special tokens file saved in ./ELECTRA-marian-training/checkpoint-32000/special_tokens_map.json\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2319: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
            "  \"`max_length` is ignored when `padding`=`True` and there is no truncation strategy. \"\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/encoder_decoder/modeling_encoder_decoder.py:533: FutureWarning: Version v4.12.0 introduces a better way to train encoder-decoder models by computing the loss inside the encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if fine-tuning a model trained with versions anterior to 4.12.0. The decoder_input_ids are now created based on the labels, no need to pass them yourself anymore.\n",
            "  warnings.warn(DEPRECATION_WARNING, FutureWarning)\n",
            "The following columns in the evaluation set don't have a corresponding argument in `EncoderDecoderModel.forward` and have been ignored: Unnamed: 0. If Unnamed: 0 are not expected by `EncoderDecoderModel.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1000\n",
            "  Batch size = 1\n",
            "Saving model checkpoint to ./ELECTRA-marian-training/checkpoint-48000\n",
            "Configuration saved in ./ELECTRA-marian-training/checkpoint-48000/config.json\n",
            "Model weights saved in ./ELECTRA-marian-training/checkpoint-48000/pytorch_model.bin\n",
            "tokenizer config file saved in ./ELECTRA-marian-training/checkpoint-48000/tokenizer_config.json\n",
            "Special tokens file saved in ./ELECTRA-marian-training/checkpoint-48000/special_tokens_map.json\n",
            "Deleting older checkpoint [ELECTRA-marian-training/checkpoint-16000] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2319: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
            "  \"`max_length` is ignored when `padding`=`True` and there is no truncation strategy. \"\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/encoder_decoder/modeling_encoder_decoder.py:533: FutureWarning: Version v4.12.0 introduces a better way to train encoder-decoder models by computing the loss inside the encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if fine-tuning a model trained with versions anterior to 4.12.0. The decoder_input_ids are now created based on the labels, no need to pass them yourself anymore.\n",
            "  warnings.warn(DEPRECATION_WARNING, FutureWarning)\n",
            "The following columns in the evaluation set don't have a corresponding argument in `EncoderDecoderModel.forward` and have been ignored: Unnamed: 0. If Unnamed: 0 are not expected by `EncoderDecoderModel.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1000\n",
            "  Batch size = 1\n",
            "Saving model checkpoint to ./ELECTRA-marian-training/checkpoint-64000\n",
            "Configuration saved in ./ELECTRA-marian-training/checkpoint-64000/config.json\n",
            "Model weights saved in ./ELECTRA-marian-training/checkpoint-64000/pytorch_model.bin\n",
            "tokenizer config file saved in ./ELECTRA-marian-training/checkpoint-64000/tokenizer_config.json\n",
            "Special tokens file saved in ./ELECTRA-marian-training/checkpoint-64000/special_tokens_map.json\n",
            "Deleting older checkpoint [ELECTRA-marian-training/checkpoint-32000] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2319: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
            "  \"`max_length` is ignored when `padding`=`True` and there is no truncation strategy. \"\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/encoder_decoder/modeling_encoder_decoder.py:533: FutureWarning: Version v4.12.0 introduces a better way to train encoder-decoder models by computing the loss inside the encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if fine-tuning a model trained with versions anterior to 4.12.0. The decoder_input_ids are now created based on the labels, no need to pass them yourself anymore.\n",
            "  warnings.warn(DEPRECATION_WARNING, FutureWarning)\n",
            "The following columns in the evaluation set don't have a corresponding argument in `EncoderDecoderModel.forward` and have been ignored: Unnamed: 0. If Unnamed: 0 are not expected by `EncoderDecoderModel.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1000\n",
            "  Batch size = 1\n",
            "Saving model checkpoint to ./ELECTRA-marian-training/checkpoint-80000\n",
            "Configuration saved in ./ELECTRA-marian-training/checkpoint-80000/config.json\n",
            "Model weights saved in ./ELECTRA-marian-training/checkpoint-80000/pytorch_model.bin\n",
            "tokenizer config file saved in ./ELECTRA-marian-training/checkpoint-80000/tokenizer_config.json\n",
            "Special tokens file saved in ./ELECTRA-marian-training/checkpoint-80000/special_tokens_map.json\n",
            "Deleting older checkpoint [ELECTRA-marian-training/checkpoint-48000] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2319: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
            "  \"`max_length` is ignored when `padding`=`True` and there is no truncation strategy. \"\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/encoder_decoder/modeling_encoder_decoder.py:533: FutureWarning: Version v4.12.0 introduces a better way to train encoder-decoder models by computing the loss inside the encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if fine-tuning a model trained with versions anterior to 4.12.0. The decoder_input_ids are now created based on the labels, no need to pass them yourself anymore.\n",
            "  warnings.warn(DEPRECATION_WARNING, FutureWarning)\n",
            "The following columns in the evaluation set don't have a corresponding argument in `EncoderDecoderModel.forward` and have been ignored: Unnamed: 0. If Unnamed: 0 are not expected by `EncoderDecoderModel.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1000\n",
            "  Batch size = 1\n",
            "Saving model checkpoint to ./ELECTRA-marian-training/checkpoint-96000\n",
            "Configuration saved in ./ELECTRA-marian-training/checkpoint-96000/config.json\n",
            "Model weights saved in ./ELECTRA-marian-training/checkpoint-96000/pytorch_model.bin\n",
            "tokenizer config file saved in ./ELECTRA-marian-training/checkpoint-96000/tokenizer_config.json\n",
            "Special tokens file saved in ./ELECTRA-marian-training/checkpoint-96000/special_tokens_map.json\n",
            "Deleting older checkpoint [ELECTRA-marian-training/checkpoint-64000] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2319: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
            "  \"`max_length` is ignored when `padding`=`True` and there is no truncation strategy. \"\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/encoder_decoder/modeling_encoder_decoder.py:533: FutureWarning: Version v4.12.0 introduces a better way to train encoder-decoder models by computing the loss inside the encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if fine-tuning a model trained with versions anterior to 4.12.0. The decoder_input_ids are now created based on the labels, no need to pass them yourself anymore.\n",
            "  warnings.warn(DEPRECATION_WARNING, FutureWarning)\n",
            "The following columns in the evaluation set don't have a corresponding argument in `EncoderDecoderModel.forward` and have been ignored: Unnamed: 0. If Unnamed: 0 are not expected by `EncoderDecoderModel.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1000\n",
            "  Batch size = 1\n",
            "Saving model checkpoint to ./ELECTRA-marian-training/checkpoint-112000\n",
            "Configuration saved in ./ELECTRA-marian-training/checkpoint-112000/config.json\n",
            "Model weights saved in ./ELECTRA-marian-training/checkpoint-112000/pytorch_model.bin\n",
            "tokenizer config file saved in ./ELECTRA-marian-training/checkpoint-112000/tokenizer_config.json\n",
            "Special tokens file saved in ./ELECTRA-marian-training/checkpoint-112000/special_tokens_map.json\n",
            "Deleting older checkpoint [ELECTRA-marian-training/checkpoint-80000] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2319: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
            "  \"`max_length` is ignored when `padding`=`True` and there is no truncation strategy. \"\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/encoder_decoder/modeling_encoder_decoder.py:533: FutureWarning: Version v4.12.0 introduces a better way to train encoder-decoder models by computing the loss inside the encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if fine-tuning a model trained with versions anterior to 4.12.0. The decoder_input_ids are now created based on the labels, no need to pass them yourself anymore.\n",
            "  warnings.warn(DEPRECATION_WARNING, FutureWarning)\n",
            "The following columns in the evaluation set don't have a corresponding argument in `EncoderDecoderModel.forward` and have been ignored: Unnamed: 0. If Unnamed: 0 are not expected by `EncoderDecoderModel.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1000\n",
            "  Batch size = 1\n",
            "Saving model checkpoint to ./ELECTRA-marian-training/checkpoint-128000\n",
            "Configuration saved in ./ELECTRA-marian-training/checkpoint-128000/config.json\n",
            "Model weights saved in ./ELECTRA-marian-training/checkpoint-128000/pytorch_model.bin\n",
            "tokenizer config file saved in ./ELECTRA-marian-training/checkpoint-128000/tokenizer_config.json\n",
            "Special tokens file saved in ./ELECTRA-marian-training/checkpoint-128000/special_tokens_map.json\n",
            "Deleting older checkpoint [ELECTRA-marian-training/checkpoint-96000] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2319: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
            "  \"`max_length` is ignored when `padding`=`True` and there is no truncation strategy. \"\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/encoder_decoder/modeling_encoder_decoder.py:533: FutureWarning: Version v4.12.0 introduces a better way to train encoder-decoder models by computing the loss inside the encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if fine-tuning a model trained with versions anterior to 4.12.0. The decoder_input_ids are now created based on the labels, no need to pass them yourself anymore.\n",
            "  warnings.warn(DEPRECATION_WARNING, FutureWarning)\n",
            "The following columns in the evaluation set don't have a corresponding argument in `EncoderDecoderModel.forward` and have been ignored: Unnamed: 0. If Unnamed: 0 are not expected by `EncoderDecoderModel.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1000\n",
            "  Batch size = 1\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='160000' max='160000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [160000/160000 15:15:22, Epoch 10/10]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Bleu</th>\n",
              "      <th>Sacrebleu</th>\n",
              "      <th>Bleu-unigram-precision</th>\n",
              "      <th>Bleu-bigram-precision</th>\n",
              "      <th>Bleu-trigram-precision</th>\n",
              "      <th>Rouge-2</th>\n",
              "      <th>Rouge-l</th>\n",
              "      <th>Sacre-unigram-precision</th>\n",
              "      <th>Sacre-bigram-precision</th>\n",
              "      <th>Sacre-trigram-precision</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>1.376300</td>\n",
              "      <td>1.239425</td>\n",
              "      <td>2.203191</td>\n",
              "      <td>1.906826</td>\n",
              "      <td>8.395680</td>\n",
              "      <td>3.562467</td>\n",
              "      <td>1.413251</td>\n",
              "      <td>9.560291</td>\n",
              "      <td>37.718242</td>\n",
              "      <td>7.532761</td>\n",
              "      <td>2.886806</td>\n",
              "      <td>1.217413</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.779000</td>\n",
              "      <td>0.757898</td>\n",
              "      <td>12.312874</td>\n",
              "      <td>11.350797</td>\n",
              "      <td>22.744566</td>\n",
              "      <td>15.094053</td>\n",
              "      <td>9.863434</td>\n",
              "      <td>36.264078</td>\n",
              "      <td>63.170344</td>\n",
              "      <td>21.169270</td>\n",
              "      <td>13.638625</td>\n",
              "      <td>9.155042</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.611200</td>\n",
              "      <td>0.562777</td>\n",
              "      <td>16.239822</td>\n",
              "      <td>15.290831</td>\n",
              "      <td>25.796314</td>\n",
              "      <td>18.910498</td>\n",
              "      <td>13.651567</td>\n",
              "      <td>48.616289</td>\n",
              "      <td>71.354147</td>\n",
              "      <td>24.182517</td>\n",
              "      <td>17.498952</td>\n",
              "      <td>13.043478</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.487500</td>\n",
              "      <td>0.470647</td>\n",
              "      <td>27.887886</td>\n",
              "      <td>26.594496</td>\n",
              "      <td>39.815365</td>\n",
              "      <td>31.258082</td>\n",
              "      <td>24.390734</td>\n",
              "      <td>56.295989</td>\n",
              "      <td>77.229506</td>\n",
              "      <td>37.935923</td>\n",
              "      <td>29.456403</td>\n",
              "      <td>23.501199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.437300</td>\n",
              "      <td>0.424375</td>\n",
              "      <td>34.909961</td>\n",
              "      <td>33.579115</td>\n",
              "      <td>46.974018</td>\n",
              "      <td>38.492417</td>\n",
              "      <td>31.192498</td>\n",
              "      <td>61.034227</td>\n",
              "      <td>80.372459</td>\n",
              "      <td>44.977182</td>\n",
              "      <td>36.550881</td>\n",
              "      <td>30.344913</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.371200</td>\n",
              "      <td>0.390502</td>\n",
              "      <td>43.710626</td>\n",
              "      <td>42.223256</td>\n",
              "      <td>56.020453</td>\n",
              "      <td>47.416373</td>\n",
              "      <td>39.873373</td>\n",
              "      <td>65.040797</td>\n",
              "      <td>83.227432</td>\n",
              "      <td>54.053700</td>\n",
              "      <td>45.367905</td>\n",
              "      <td>38.788563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.309700</td>\n",
              "      <td>0.367391</td>\n",
              "      <td>57.833668</td>\n",
              "      <td>56.799111</td>\n",
              "      <td>71.301542</td>\n",
              "      <td>61.667236</td>\n",
              "      <td>53.467562</td>\n",
              "      <td>65.848466</td>\n",
              "      <td>83.961561</td>\n",
              "      <td>69.766857</td>\n",
              "      <td>60.097034</td>\n",
              "      <td>52.855994</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.314500</td>\n",
              "      <td>0.352238</td>\n",
              "      <td>38.903482</td>\n",
              "      <td>37.579054</td>\n",
              "      <td>49.907779</td>\n",
              "      <td>42.153264</td>\n",
              "      <td>35.359183</td>\n",
              "      <td>66.642574</td>\n",
              "      <td>84.131158</td>\n",
              "      <td>47.977369</td>\n",
              "      <td>40.293853</td>\n",
              "      <td>34.514176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.261300</td>\n",
              "      <td>0.346803</td>\n",
              "      <td>48.068095</td>\n",
              "      <td>46.746668</td>\n",
              "      <td>59.706362</td>\n",
              "      <td>51.602094</td>\n",
              "      <td>44.151368</td>\n",
              "      <td>67.939793</td>\n",
              "      <td>85.036337</td>\n",
              "      <td>57.803587</td>\n",
              "      <td>49.730647</td>\n",
              "      <td>43.377615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.271800</td>\n",
              "      <td>0.343194</td>\n",
              "      <td>53.955464</td>\n",
              "      <td>52.821465</td>\n",
              "      <td>66.020533</td>\n",
              "      <td>57.537319</td>\n",
              "      <td>49.861913</td>\n",
              "      <td>68.263831</td>\n",
              "      <td>85.212553</td>\n",
              "      <td>64.334017</td>\n",
              "      <td>55.801332</td>\n",
              "      <td>49.253197</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Saving model checkpoint to ./ELECTRA-marian-training/checkpoint-144000\n",
            "Configuration saved in ./ELECTRA-marian-training/checkpoint-144000/config.json\n",
            "Model weights saved in ./ELECTRA-marian-training/checkpoint-144000/pytorch_model.bin\n",
            "tokenizer config file saved in ./ELECTRA-marian-training/checkpoint-144000/tokenizer_config.json\n",
            "Special tokens file saved in ./ELECTRA-marian-training/checkpoint-144000/special_tokens_map.json\n",
            "Deleting older checkpoint [ELECTRA-marian-training/checkpoint-112000] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2319: UserWarning: `max_length` is ignored when `padding`=`True` and there is no truncation strategy. To pad to max length, use `padding='max_length'`.\n",
            "  \"`max_length` is ignored when `padding`=`True` and there is no truncation strategy. \"\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/models/encoder_decoder/modeling_encoder_decoder.py:533: FutureWarning: Version v4.12.0 introduces a better way to train encoder-decoder models by computing the loss inside the encoder-decoder framework rather than in the decoder itself. You may observe training discrepancies if fine-tuning a model trained with versions anterior to 4.12.0. The decoder_input_ids are now created based on the labels, no need to pass them yourself anymore.\n",
            "  warnings.warn(DEPRECATION_WARNING, FutureWarning)\n",
            "The following columns in the evaluation set don't have a corresponding argument in `EncoderDecoderModel.forward` and have been ignored: Unnamed: 0. If Unnamed: 0 are not expected by `EncoderDecoderModel.forward`,  you can safely ignore this message.\n",
            "***** Running Evaluation *****\n",
            "  Num examples = 1000\n",
            "  Batch size = 1\n",
            "Saving model checkpoint to ./ELECTRA-marian-training/checkpoint-160000\n",
            "Configuration saved in ./ELECTRA-marian-training/checkpoint-160000/config.json\n",
            "Model weights saved in ./ELECTRA-marian-training/checkpoint-160000/pytorch_model.bin\n",
            "tokenizer config file saved in ./ELECTRA-marian-training/checkpoint-160000/tokenizer_config.json\n",
            "Special tokens file saved in ./ELECTRA-marian-training/checkpoint-160000/special_tokens_map.json\n",
            "Deleting older checkpoint [ELECTRA-marian-training/checkpoint-128000] due to args.save_total_limit\n",
            "\n",
            "\n",
            "Training completed. Do not forget to share your model on huggingface.co/models =)\n",
            "\n",
            "\n",
            "Loading best model from ./ELECTRA-marian-training/checkpoint-160000 (score: 0.34319353103637695).\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "TrainOutput(global_step=160000, training_loss=0.6986664794921875, metrics={'train_runtime': 54923.2659, 'train_samples_per_second': 2.913, 'train_steps_per_second': 2.913, 'total_flos': 925520363520000.0, 'train_loss': 0.6986664794921875, 'epoch': 10.0})"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# ELECTRA-Marian-Training\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3sctuvauTLQg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "947f6dc9-e0fe-48fa-9ab9-3b88f9820979"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Saving model checkpoint to ./ELECTRA-marian-training1\n",
            "Configuration saved in ./ELECTRA-marian-training1/config.json\n",
            "Model weights saved in ./ELECTRA-marian-training1/pytorch_model.bin\n",
            "tokenizer config file saved in ./ELECTRA-marian-training1/tokenizer_config.json\n",
            "Special tokens file saved in ./ELECTRA-marian-training1/special_tokens_map.json\n"
          ]
        }
      ],
      "source": [
        "trainer.save_model()\n",
        "trainer.save_state()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JjzDA4UJVRc3"
      },
      "source": [
        "# **Visualize**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IhQfjGo_6Y2X"
      },
      "outputs": [],
      "source": [
        "%load_ext tensorboard"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IYgohOde7ECZ"
      },
      "source": [
        "### **distilRoBERTa-marian**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 821
        },
        "id": "gOUtDLCsd7_b",
        "outputId": "be88df14-ea53-4f20-cdef-64f0dbe9b91b"
      },
      "outputs": [
        {
          "data": {
            "application/javascript": [
              "\n",
              "        (async () => {\n",
              "            const url = new URL(await google.colab.kernel.proxyPort(6007, {'cache': true}));\n",
              "            url.searchParams.set('tensorboardColab', 'true');\n",
              "            const iframe = document.createElement('iframe');\n",
              "            iframe.src = url;\n",
              "            iframe.setAttribute('width', '100%');\n",
              "            iframe.setAttribute('height', '800');\n",
              "            iframe.setAttribute('frameborder', 0);\n",
              "            document.body.appendChild(iframe);\n",
              "        })();\n",
              "    "
            ],
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "tensorboard --logdir ./distilroberta-Marian-Training/runs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y5aiwU7SvGOo"
      },
      "source": [
        "# **Inference**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zUeup-K_kDZT"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n",
        "new_model = AutoModelForSeq2SeqLM.from_pretrained(\"./ELECTRA-marian-training1/\")\n",
        "new_model.to(\"cuda\")\n",
        "\n",
        "new_tokenizer = AutoTokenizer.from_pretrained(\"./ELECTRA-marian-training1/\", use_fast=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yth1WAaS37V1",
        "outputId": "99470658-ce67-4c3c-ded0-50f0c43d1da4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "EncoderDecoderModel(\n",
              "  (encoder): ElectraModel(\n",
              "    (embeddings): ElectraEmbeddings(\n",
              "      (word_embeddings): Embedding(30522, 128, padding_idx=0)\n",
              "      (position_embeddings): Embedding(512, 128)\n",
              "      (token_type_embeddings): Embedding(2, 128)\n",
              "      (LayerNorm): LayerNorm((128,), eps=1e-12, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (embeddings_project): Linear(in_features=128, out_features=256, bias=True)\n",
              "    (encoder): ElectraEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (1): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (2): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (3): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (4): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (5): ElectraLayer(\n",
              "          (attention): ElectraAttention(\n",
              "            (self): ElectraSelfAttention(\n",
              "              (query): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (key): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (value): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): ElectraSelfOutput(\n",
              "              (dense): Linear(in_features=256, out_features=256, bias=True)\n",
              "              (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): ElectraIntermediate(\n",
              "            (dense): Linear(in_features=256, out_features=1024, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): ElectraOutput(\n",
              "            (dense): Linear(in_features=1024, out_features=256, bias=True)\n",
              "            (LayerNorm): LayerNorm((256,), eps=1e-12, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (decoder): MarianForCausalLM(\n",
              "    (model): MarianDecoderWrapper(\n",
              "      (decoder): MarianDecoder(\n",
              "        (embed_tokens): Embedding(67028, 512, padding_idx=67027)\n",
              "        (embed_positions): MarianSinusoidalPositionalEmbedding(512, 512)\n",
              "        (layers): ModuleList(\n",
              "          (0): MarianDecoderLayer(\n",
              "            (self_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (activation_fn): SiLUActivation()\n",
              "            (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (encoder_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "            (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "          (1): MarianDecoderLayer(\n",
              "            (self_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (activation_fn): SiLUActivation()\n",
              "            (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (encoder_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "            (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "          (2): MarianDecoderLayer(\n",
              "            (self_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (activation_fn): SiLUActivation()\n",
              "            (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (encoder_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "            (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "          (3): MarianDecoderLayer(\n",
              "            (self_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (activation_fn): SiLUActivation()\n",
              "            (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (encoder_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "            (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "          (4): MarianDecoderLayer(\n",
              "            (self_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (activation_fn): SiLUActivation()\n",
              "            (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (encoder_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "            (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "          (5): MarianDecoderLayer(\n",
              "            (self_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (activation_fn): SiLUActivation()\n",
              "            (self_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (encoder_attn): MarianAttention(\n",
              "              (k_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (v_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (q_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "              (out_proj): Linear(in_features=512, out_features=512, bias=True)\n",
              "            )\n",
              "            (encoder_attn_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "            (fc1): Linear(in_features=512, out_features=2048, bias=True)\n",
              "            (fc2): Linear(in_features=2048, out_features=512, bias=True)\n",
              "            (final_layer_norm): LayerNorm((512,), eps=1e-05, elementwise_affine=True)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (lm_head): Linear(in_features=512, out_features=67028, bias=False)\n",
              "  )\n",
              "  (enc_to_dec_proj): Linear(in_features=256, out_features=512, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "new_model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ujXTqkBhRHgh",
        "outputId": "f8f19efe-cbf1-463a-d4c1-ee1643351d12"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "512"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "new_model.config.max_length\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "H6ny8t34jDoE"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1E101Hm6jEck"
      },
      "source": [
        "# **Evaluator**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F8myiUJ-lGmO",
        "outputId": "e528b2bf-6648-487e-90a1-cd6557ea6560"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/models/CodeGeneration/New/DJANGO/ELECTRA-Marian/Utils\n"
          ]
        }
      ],
      "source": [
        "%cd Utils"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jxOpBKhWdfla",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200,
          "referenced_widgets": [
            "c6e4cbd435374cfeb4711a2f846900ce",
            "83f5ba2a2ebf464d9a7ef9b73ae1f726",
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            "31298c1c533b46e7897391abe1dd5ef9",
            "5a7a14f683544ba29fe08e31e45e9220",
            "c66d1015513d44329bc15fb94183cf9f",
            "0ed4a1f83747432b800a13d16a360892",
            "c017a6699b2e47fc830c433b635727b4",
            "dc1dee81590b440894e3791f0b50876b",
            "8e70eb11a949485290c91c4bfb8a310c",
            "d81525793c304269af39b97f1d6ee6a6",
            "7eeeef113c5a4a2aab8b7a5fd0cf35bc",
            "bcc034f294da4db78a979429dff3f4c4",
            "a938882c3468465c8c8dcb48c9aff3fa",
            "b6c631b062d848db8f9513ad8b7eb994",
            "6ef19fe8dcaa44d8bb3f9bc5082987b1",
            "d62a39d8133544c7b1d6872a416e9045",
            "63b912bd54244daeb0ddb9a6afa759fb",
            "b808be9db2904f12b8e2efef10fcb19f",
            "d8720ba725984123a8e2fa8bfea9a3b7",
            "4a1984ea84f846518e505b7e35ec41a9",
            "a236c2b0f4d349eb947ebd55170b4774",
            "70203113915b48608fb52cb21f30c449",
            "f5bff8a9afbd41b081033853c3bef955",
            "a2ff43d279c54284b3ac5dffd208025c",
            "e06aa151294d48a99e82067e25df51ce",
            "62452838e2b84574949b87216305a0f0",
            "c7451767b74140e499d188a8fabe0dc9",
            "c03c7be1691f4bafa551e53ff82f7f82",
            "6c0c8bfde7044328a722de20f34d42c3",
            "b217cf6565cb47158e16ed2759277007",
            "467ec70e435441f7b6a94c3b586cedf6",
            "27494a02ed9942cda2b15f9c723b99a2",
            "38af7cc3ea114d06ba9e7716b68971b3",
            "71fd85b00a4f48a4bfc504e16082ff27",
            "019340df0a19476cade4d19110d28212",
            "e6ae3227e8f6484291cc61e7b310dc68",
            "e0c3f8f1cda74a0fb5a009d7f972669e",
            "28fe71a2e3574aec90dff5bd02ec710e",
            "3a554cd24d44471ca15eacddad11ade8",
            "314e2e560ea34e0e9b9ead80d233b997",
            "742ef8d7395c41bda00c8b170a476029",
            "b686ae0179964e7fafdeee162f486cb1",
            "2386420339854853a30724847f65be71"
          ]
        },
        "outputId": "624ecc3d-a80d-4642-dec0-c15895da5f3a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/content/drive/MyDrive/models/CodeGeneration/New/DJANGO/ELECTRA-Marian/Utils/evaluator.py:32: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
            "  self.sacre_bleu: Metric = load_metric('sacrebleu')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.85k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c6e4cbd435374cfeb4711a2f846900ce"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.48k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7eeeef113c5a4a2aab8b7a5fd0cf35bc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading extra modules:   0%|          | 0.00/1.55k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "70203113915b48608fb52cb21f30c449"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.16k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "38af7cc3ea114d06ba9e7716b68971b3"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from google.colab import files\n",
        "import evaluator\n",
        "evaluator = evaluator.CodeGenerationEvaluator(new_tokenizer, device,smooth_bleu=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yr54jYmVjEcn"
      },
      "outputs": [],
      "source": [
        "batch_size = 1\n",
        "\n",
        "# map data correctly\n",
        "def generate_new_code(batch):\n",
        "    inputs = new_tokenizer(batch[\"nl\"], padding=\"max_length\", truncation=True, max_length=512, return_tensors=\"pt\")\n",
        "\n",
        "    input_ids = inputs.input_ids.to(\"cuda\")\n",
        "    attention_mask = inputs.attention_mask.to(\"cuda\")\n",
        "\n",
        "    outputs = new_model.generate(input_ids, attention_mask=attention_mask)\n",
        "\n",
        "    # all special tokens including will be removed\n",
        "    output_str = new_tokenizer.batch_decode(outputs, skip_special_tokens=True)\n",
        "\n",
        "    batch[\"pred_code\"] = output_str\n",
        "\n",
        "    return batch\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "664c3454033c44fba7e73e8b8766650c",
            "fa11ddd3b5824f2ba6bc3df2a18790e9",
            "d53fe86eed6e49449adab4c73ae0252b",
            "4183983434d64181aebba13c00185b88",
            "b57fb7bac0744805a1b7790e12f83c1a",
            "0fa36684ba1e4f67992a09735acf5f32",
            "bda140f12a1544dab8885bfe30922ea7",
            "c7187b2ba31e4fffbd0d75394378c00d",
            "499d023441d646e8a166fa80259e9cf5",
            "5f18cbf5beec44e38af26535972ed786",
            "90237d6e6a7241eb87a99a79e91fcb40"
          ]
        },
        "id": "aW-fQq9xd_Ei",
        "outputId": "31200d4d-cccc-46e4-a654-05c3a642e9d2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1805 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "664c3454033c44fba7e73e8b8766650c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/generation_utils.py:1261: UserWarning: Neither `max_length` nor `max_new_tokens` has been set, `max_length` will default to 512 (`self.config.max_length`). Controlling `max_length` via the config is deprecated and `max_length` will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
            "  UserWarning,\n"
          ]
        }
      ],
      "source": [
        "results = test_dataset.map(generate_new_code, batched=True, batch_size=batch_size)\n",
        "\n",
        "#results = test_dataset.map(generate_new_code, batched=True, batch_size=batch_size, remove_columns=[\"intent\"])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bg9jBzHg-hdB",
        "outputId": "c51ea466-31be-4dce-939b-61eefbdb1f3c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/models/CodeGeneration/New/DJANGO/ELECTRA-Marian\n"
          ]
        }
      ],
      "source": [
        "%cd .."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kZPghyh7wo9Q"
      },
      "outputs": [],
      "source": [
        "import re\n",
        "import string\n",
        "\n",
        "import datasets\n",
        "import numpy as np\n",
        "\n",
        "import evaluate\n",
        "from evaluate import load"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "a846f4572ff44abcb662e0522325899c",
            "cf4fde6d79c445d5a762bec7e862eeff",
            "d587614319e54b98aadc703bdf601159",
            "b7083078b1f043448391b9e8a86ca8b9",
            "ce494f4c3da84a4b8bb69b8a2ce89cc0",
            "90a3260829f5465997c8172ee00a7748",
            "0bf43bc361fc4770a815d18dc23fa28c",
            "3c9ebd8f516447f4bb09b926936b89dc",
            "1d187e9702f34d449ba77913d363c793",
            "87338a21d1ca4fd4b77f0c2090cd0ce2",
            "6258f7b4fe114e4fa2b2da6adc31a1eb"
          ]
        },
        "id": "bZxA3QjuwT7v",
        "outputId": "b6a0d666-3391-4c4d-af69-5bfea9606df4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/5.67k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a846f4572ff44abcb662e0522325899c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\nfor i in range(len(results)):\\n  refs = results[\"code\"][i]\\n  preds = results[\"pred_code\"][i]\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "\n",
        "exact_match = evaluate.load(\"exact_match\")\n",
        "\n",
        "#refs = [\"The cat sat on the mat.\", \"Theaters are great.\", \"It's like comparing oranges and apples.\"]\n",
        "#preds = [\"The cat sat on the mat?\", \"Theaters are great.\", \"It's like comparing apples and oranges.\"]\n",
        "\n",
        "\"\"\"\n",
        "for i in range(len(results)):\n",
        "  refs = results[\"code\"][i]\n",
        "  preds = results[\"pred_code\"][i]\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rsJnOenL1B-6"
      },
      "outputs": [],
      "source": [
        "refs = results[\"code\"]\n",
        "preds = results[\"pred_code\"]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "exact_match_results = exact_match.compute(references=refs, predictions=preds, regexes_to_ignore=[\"▁\", \" \"], ignore_case=True, ignore_punctuation=True, ignore_numbers=True)\n",
        "print(round(exact_match_results[\"exact_match\"], 5)*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lx8JKN7_28d4",
        "outputId": "2b35184b-cbb0-4b2d-b1ed-b5b3cc13e002"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "65.319\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gatvWetJlgJL"
      },
      "outputs": [],
      "source": [
        "with open(\"./references.txt\", 'w') as f:\n",
        "  for i in range(len(results)):\n",
        "    nl = results[\"nl\"][i]\n",
        "    ref = results[\"code\"][i]\n",
        "    pred = results[\"pred_code\"][i]\n",
        "\n",
        "    if pred is not None and pred != \"\":\n",
        "      if ref is not None and ref != \"\":\n",
        "        f.write(ref + '\\n')\n",
        "    else:\n",
        "      continue\n",
        "\n",
        "\n",
        "with open(\"./predictions.txt\", 'w') as f:\n",
        "  for i in range(len(results)):\n",
        "    nl = results[\"nl\"][i]\n",
        "    ref = results[\"code\"][i]\n",
        "    pred = results[\"pred_code\"][i]\n",
        "\n",
        "    if pred is not None and pred != \"\":\n",
        "      if ref is not None and ref != \"\":\n",
        "        f.write(pred + '\\n')\n",
        "    else:\n",
        "      continue\n",
        ""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RRbbazVujEcw"
      },
      "outputs": [],
      "source": [
        "# Ok Evaluator\n",
        "bleu_score =0\n",
        "rouge_score = 0\n",
        "sacre_bleu = 0\n",
        "\n",
        "for i in range(len(results)):\n",
        "  ref = results[\"code\"][i]\n",
        "  pred = results[\"pred_code\"][i]\n",
        "  if pred is not None and pred != \"\":\n",
        "      if ref is not None and ref != \"\":\n",
        "        #bleu_metric = metric.compute(predictions=[pred], references=[[ref]])\n",
        "        #bleu_score  += bleu_metric[\"score\"]\n",
        "\n",
        "        bleu_metric = evaluator.evaluate([pred], [ref])\n",
        "        #bleu_metric = evaluator.evaluateSingle(pred, ref)\n",
        "\n",
        "        #bleu_score += bleu_metric['BLEU-Unigram-Precision']\n",
        "        bleu_score += bleu_metric['BLEU']\n",
        "        rouge_score += bleu_metric['ROUGE-L']\n",
        "        sacre_bleu += bleu_metric['SacreBLEU']\n",
        "        #print(bleu_metric['SacreBLEU'])\n",
        "  else:\n",
        "    continue"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4y1LccDPRJc-"
      },
      "source": [
        "# **Performance Metrics**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AAjUc9xleXTI"
      },
      "source": [
        "**BLEU Score**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pfKvIRDGjEcx",
        "outputId": "a8fca176-a66c-4163-8df0-5b7955ead13b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bleu Score: 53.01968844718701 (scale 0-100)\n"
          ]
        }
      ],
      "source": [
        "# print(\"Bleu Score: \",bleu_score/len(results))\n",
        "print('Bleu Score: {} (scale 0-100)'.format(bleu_score/len(results)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WApdMa6AeZnS"
      },
      "source": [
        "**SacreBLEU**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F6ExrnImjEcy",
        "outputId": "e0d69859-3a11-4574-e655-62e9549bf59b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sacre Bleu: 58.15526577711313 (scale 0-100)\n"
          ]
        }
      ],
      "source": [
        "# print(\"Bleu Score: \",bleu_score/len(results))\n",
        "print('Sacre Bleu: {} (scale 0-100)'.format(sacre_bleu/len(results)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4-EepZCaecsi"
      },
      "source": [
        "**Rouge Score**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "U1ErsIXejEcz",
        "outputId": "7b895da7-77ee-4d5e-a6a5-b1d182ed3ac4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ROUGE Score: 83.90547445301046 (scale 0-100)\n"
          ]
        }
      ],
      "source": [
        "# print(\"Bleu Score: \",bleu_score/len(results))\n",
        "print('ROUGE Score: {} (scale 0-100)'.format(rouge_score/len(results)))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xJ4YMtTtOtmW"
      },
      "source": [
        "**CodeBLEU**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yhJT_NsiyiZt"
      },
      "outputs": [],
      "source": [
        "from google.colab import files"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tC6ETJ0u2KRL"
      },
      "outputs": [],
      "source": [
        "import calc_code_bleu\n",
        "ngram_match_score, weighted_ngram_match_score, syntax_match_score, dataflow_match_score, code_bleu_score = calc_code_bleu.calc_code_bleu(\"references.txt\",\"predictions.txt\",\"python\", \"keywords\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7Oi6CvK_2GZl"
      },
      "outputs": [],
      "source": [
        "print(\"ngram_match_score: \", ngram_match_score*100)\n",
        "print(\"weighted_ngram_match_score: \", weighted_ngram_match_score*100)\n",
        "print(\"syntax_match_score: \", syntax_match_score*100)\n",
        "print(\"dataflow_match_score: \", dataflow_match_score*100)\n",
        "print(\"code_bleu_score: \", code_bleu_score*100)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "veX-I4ZP-Xjv"
      },
      "source": [
        "# **Save the  results**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tAIlTgKwyjS6",
        "outputId": "dd61db00-7635-41e9-f43d-ef2d1859fba7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/models/CodeGeneration/MarianCG-CoNaLa-Large\n"
          ]
        }
      ],
      "source": [
        "#%cd .."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2wHa-z0Fy5z3"
      },
      "outputs": [],
      "source": [
        "bleu_score = 0\n",
        "with open(\"./ELECTRA-marian-training/Output.txt\", 'w') as f:\n",
        "  for i in range(len(results)):\n",
        "    nl = results[\"nl\"][i]\n",
        "    ref = results[\"code\"][i]\n",
        "    pred = results[\"pred_code\"][i]\n",
        "\n",
        "    if pred is not None and pred != \"\":\n",
        "      if ref is not None and ref != \"\":\n",
        "\n",
        "        bleu_metric = evaluator.evaluate([pred], [ref])\n",
        "        f.write( \"NL: \" + nl + '\\n')\n",
        "        f.write( \"ref: \" + ref + '\\n')\n",
        "        f.write( \"Output: \" + pred + '\\n')\n",
        "        f.write(\"bleu_metric: \" + str(bleu_metric[\"BLEU\"]) + '\\n')\n",
        "        f.write(10*\"-\" + '\\n')\n",
        "        bleu_score  += bleu_metric[\"BLEU\"]\n",
        "\n",
        "    else:\n",
        "      continue\n",
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tCTiVafqjEc5"
      },
      "source": [
        "# **Test**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NcbWMi7ejEc5"
      },
      "outputs": [],
      "source": [
        "# map data correctly\n",
        "def generate_code(NL):\n",
        "    inputs = new_tokenizer([NL], padding=\"max_length\", truncation=True, max_length=512, return_tensors=\"pt\")\n",
        "\n",
        "    input_ids = inputs.input_ids.to(\"cuda\")\n",
        "    attention_mask = inputs.attention_mask.to(\"cuda\")\n",
        "\n",
        "    outputs = new_model.generate(input_ids, attention_mask=attention_mask)\n",
        "\n",
        "    # all special tokens including will be removed\n",
        "    output_code = new_tokenizer.batch_decode(outputs, skip_special_tokens=True)\n",
        "\n",
        "    return output_code"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "0Ymh6pfPjEc6"
      },
      "outputs": [],
      "source": [
        "\n",
        "#NL = \"decode a hex string to utf-8.\"\n",
        "NL = \"remove first and last lines of string `s`\"\n",
        "output_code = generate_code(NL)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mMHqDePjjEc7"
      },
      "outputs": [],
      "source": [
        "output_code"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "wIP7fiupjEc7"
      },
      "outputs": [],
      "source": [
        "natural_language = \"How I can get rid of None values in dictionary?\"\n",
        "gen_code = generate_code(natural_language)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QhjxswQrjEc8"
      },
      "outputs": [],
      "source": [
        "gen_code"
      ]
    },
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      "execution_count": null,
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        "id": "o_QuAYeQjDkF"
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