DistilRoBERTa_Marian_CoNaLa.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/DistilRoBERTa_Marian_CoNaLa.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": {
        "id": "97Av32IAvNOU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f03700a6-331f-40a9-8c1a-752689d06968"
      },
      "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",
      "source": [
        "drive.mount(\"/content/drive\", force_remount=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ExFzbWxoC-oN",
        "outputId": "f794aadb-7868-4eaa-f586-6c707a2f6822"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v8IdHyYYzK2i"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "torch.cuda.empty_cache()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "RgWA_Vg_ZXrQ"
      }
    },
    {
      "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",
      "source": [
        "%cd /content/drive/MyDrive/models/CodeGeneration/New/CoNaLa/distilRoBERTa-Marian"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2YE9oRS-YmTp",
        "outputId": "086baadb-d8c6-41d6-ea75-7cec436b836b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/models/CodeGeneration/New/CoNaLa/distilRoBERTa-Marian\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cy34HZRCduwK"
      },
      "source": [
        "# **Load Data**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_dataset\n",
        "\n",
        "raw_dataset = load_dataset(\"AhmedSSoliman/CoNaLa-Large\")"
      ],
      "metadata": {
        "id": "yGq0hRAvsR83",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316,
          "referenced_widgets": [
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        },
        "outputId": "00a08fa0-3700-4ac9-e6cb-948fbd4d5dbd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/huggingface_hub/utils/_deprecation.py:97: FutureWarning: Deprecated argument(s) used in 'dataset_info': token. Will not be supported from version '0.12'.\n",
            "  warnings.warn(message, FutureWarning)\n",
            "WARNING:datasets.builder:Using custom data configuration AhmedSSoliman--CoNaLa-Large-87e6f6b36beed736\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset csv/AhmedSSoliman--CoNaLa-Large to /root/.cache/huggingface/datasets/AhmedSSoliman___csv/AhmedSSoliman--CoNaLa-Large-87e6f6b36beed736/0.0.0/652c3096f041ee27b04d2232d41f10547a8fecda3e284a79a0ec4053c916ef7a...\n"
          ]
        },
        {
          "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": "70087a92632c4ae9b9f59b88533d82fe"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "fe272f166f7242299fa73d9b64f4b35d"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "542f48062fb547f2b3f42dd89b8cf9b1"
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        {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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              "version_minor": 0,
              "model_id": "e2ab46ce158e4c26a0c93da0c4fe906b"
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          },
          "metadata": {}
        },
        {
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              "0 tables [00:00, ? tables/s]"
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              "version_minor": 0,
              "model_id": "8d7a3ddf38bc40d0973401713a599b72"
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        {
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset csv downloaded and prepared to /root/.cache/huggingface/datasets/AhmedSSoliman___csv/AhmedSSoliman--CoNaLa-Large-87e6f6b36beed736/0.0.0/652c3096f041ee27b04d2232d41f10547a8fecda3e284a79a0ec4053c916ef7a. Subsequent calls will reuse this data.\n"
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      ]
    },
    {
      "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",
      "source": [
        "raw_dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6gWJvHZ-RbMP",
        "outputId": "f7e4b6d8-2c10-40ad-d514-a38617c214b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['intent', 'snippet'],\n",
              "        num_rows: 24687\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['intent', 'snippet'],\n",
              "        num_rows: 500\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['intent', 'snippet'],\n",
              "        num_rows: 1237\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-8qaEXWphXWk"
      },
      "source": [
        "# **Tokenizer**"
      ]
    },
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            "c34d14456b764bb595bf3cc473145fbb",
            "a3a600f9b14749f79c0e24e45e0a975a"
          ]
        },
        "id": "M2OT3nMFU4Rc",
        "outputId": "79ae40a8-830c-4813-e317-86a5228061fd"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "afbb670313f94adc88e062d1649a7e46"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "df5895abecfb4e4787e19150954a90f1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5cdcedc88fe94aa5aef3a921cb8a78fd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d9ae0bf46856410299b7908277a3b10e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PreTrainedTokenizerFast(name_or_path='distilroberta-base', vocab_size=50265, model_max_len=512, is_fast=True, padding_side='right', truncation_side='right', special_tokens={'bos_token': '<s>', 'eos_token': '</s>', 'unk_token': '<unk>', 'sep_token': '</s>', 'pad_token': '<pad>', 'cls_token': '<s>', 'mask_token': AddedToken(\"<mask>\", rstrip=False, lstrip=True, single_word=False, normalized=False)})"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "from transformers import AutoTokenizer\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"distilroberta-base\", use_fast = True)\n",
        "tokenizer\n",
        "# \"Helsinki-NLP/opus-mt-en-nl\"\n",
        "# \"distilroberta-base\"\n",
        "##### \"studio-ousia/luke-base\"\n",
        "#\"AhmedSSoliman/MarianCG_NL-to-Code\"\n",
        "#\"distilbert-base-uncased\"\n",
        "#studio-ousia/mluke-base-lite\n",
        "# \"google/electra-small-discriminator\"\n",
        "#\"bert-base-uncased\"\n",
        "#\"microsoft/deberta-v3-xsmall\"\n",
        "#\"microsoft/deberta-v3-base\"\n",
        "# \"xlnet-base-cased\"\n",
        "#\"google/fnet-base\"\n",
        "#\"google/bigbird-roberta-base\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "07QX2HqSERRj"
      },
      "source": [
        "# **Model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P5BLbilUN5G4"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = EncoderDecoderModel.from_encoder_decoder_pretrained(\"distilroberta-base\", \"Helsinki-NLP/opus-mt-en-nl\")\n",
        "model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "6218183b09b2438582cf19b6159540f0",
            "ee4d1332b0224f15a11ecf46dc01fae4",
            "40cd1c9748e84099986da70089fcaa7c",
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            "b2c98cf198744bd4ba50d95bdaf3fffc",
            "f124610259944e398417e548baec500f",
            "3ef762161251403297bd558cffe8abe5",
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            "c1fee1f1bc434f2b812080274c93fc4d",
            "2e04160d83c742149d533f954b1a048e",
            "5b8fd080f1544961be198d0ba8472728",
            "3cbadea45bfd4d47a6ff358e603bf129",
            "896e370404f24aafbf77f538bd58181c",
            "824c8f09c2c54b6796e6126584458b7d",
            "7664106ac9604295a8a35b99e50fb4fc",
            "03eb88d0247f497bb1e2cef5ff64b9a5",
            "7f9f819bc95241109d4f2cf4c7e6a237",
            "cd7706971f5b4fdca4b633f75ef99882",
            "3d491088730d42cebd8a1d5d16bfb4fb",
            "b84c3bb8743d42dfb50d840fdddfc87d",
            "5ef299298e324601b57a70bd845691d1",
            "5a9f592dbd72444ebcb46c1ceec74bb9",
            "c43f420239da4ee19ddc74a9d8275af2",
            "d4a0db1f6f8b4378a3431af0e9eacb2d",
            "abba640ab10d41b6a3029652df541261",
            "b628aacbdb204210940c0f6cd95b2b16",
            "00133a8146d647dcbcef30c1acdb8841",
            "9e8b26acfde24746890e6657cfc70790",
            "ac5eb1a99deb4f2abe4f8fb0e80d970a",
            "bbe50e087118455d9eb6b5e6774a2d0c",
            "926daf967cfc426fabf6a1ec2557d3d6"
          ]
        },
        "id": "0PaI2Odffj0F",
        "outputId": "5cbef14d-eea0-4aeb-a192-0d5232bff089"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/331M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6218183b09b2438582cf19b6159540f0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at distilroberta-base were not used when initializing RobertaModel: ['lm_head.bias', 'lm_head.layer_norm.bias', 'lm_head.dense.weight', 'lm_head.decoder.weight', 'lm_head.layer_norm.weight', 'lm_head.dense.bias']\n",
            "- This IS expected if you are initializing RobertaModel 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 RobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading:   0%|          | 0.00/1.38k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2e04160d83c742149d533f954b1a048e"
            }
          },
          "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": "5ef299298e324601b57a70bd845691d1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at Helsinki-NLP/opus-mt-en-nl were not used when initializing MarianForCausalLM: ['model.encoder.layers.2.self_attn_layer_norm.weight', 'model.encoder.layers.3.self_attn.k_proj.weight', 'model.encoder.layers.2.self_attn.k_proj.weight', 'model.encoder.layers.2.fc2.weight', 'model.encoder.layers.5.fc2.weight', 'model.encoder.layers.4.final_layer_norm.bias', 'model.encoder.layers.1.self_attn.k_proj.bias', 'model.encoder.layers.0.self_attn.out_proj.bias', 'model.encoder.layers.4.fc1.bias', 'model.encoder.layers.0.self_attn.v_proj.bias', 'model.encoder.layers.2.self_attn.v_proj.bias', 'model.encoder.layers.2.self_attn.k_proj.bias', 'model.encoder.layers.5.self_attn.v_proj.bias', 'model.encoder.layers.1.self_attn.v_proj.bias', 'model.encoder.layers.3.fc1.bias', 'model.encoder.layers.1.fc2.weight', 'model.encoder.embed_positions.weight', 'model.encoder.layers.5.self_attn.out_proj.weight', 'model.encoder.layers.5.fc2.bias', 'model.encoder.layers.4.self_attn.out_proj.weight', 'model.encoder.layers.2.final_layer_norm.bias', 'model.encoder.layers.5.fc1.weight', 'model.encoder.layers.4.self_attn_layer_norm.weight', 'model.encoder.layers.1.fc2.bias', 'model.encoder.layers.5.self_attn.out_proj.bias', 'model.encoder.layers.1.self_attn_layer_norm.bias', 'model.encoder.layers.3.self_attn.v_proj.weight', 'model.encoder.layers.3.self_attn_layer_norm.bias', 'model.encoder.layers.4.self_attn.q_proj.weight', 'model.encoder.layers.4.self_attn.v_proj.bias', 'model.encoder.layers.1.final_layer_norm.bias', 'model.encoder.layers.4.self_attn.k_proj.bias', 'model.encoder.layers.4.self_attn_layer_norm.bias', 'model.encoder.layers.3.fc2.weight', 'model.encoder.layers.4.self_attn.v_proj.weight', 'model.encoder.layers.0.self_attn.k_proj.bias', 'model.encoder.layers.0.final_layer_norm.bias', 'model.encoder.layers.1.self_attn.out_proj.bias', 'model.encoder.layers.0.final_layer_norm.weight', 'model.encoder.layers.1.final_layer_norm.weight', 'model.encoder.layers.2.final_layer_norm.weight', 'model.encoder.layers.0.self_attn_layer_norm.bias', 'model.encoder.layers.1.self_attn_layer_norm.weight', 'model.encoder.layers.2.fc1.weight', 'model.encoder.layers.2.self_attn.q_proj.bias', 'model.encoder.layers.3.self_attn.q_proj.weight', 'model.encoder.layers.4.self_attn.k_proj.weight', 'model.encoder.layers.5.self_attn_layer_norm.weight', 'model.encoder.layers.2.self_attn.out_proj.bias', 'model.encoder.layers.0.self_attn.q_proj.bias', 'model.encoder.layers.5.self_attn.v_proj.weight', 'model.encoder.layers.0.self_attn.k_proj.weight', 'model.encoder.layers.3.fc2.bias', 'model.encoder.layers.3.self_attn.out_proj.weight', 'model.encoder.layers.1.self_attn.out_proj.weight', 'model.encoder.layers.0.self_attn.out_proj.weight', 'model.encoder.layers.3.final_layer_norm.bias', 'model.encoder.layers.3.self_attn.k_proj.bias', 'model.encoder.layers.5.self_attn.k_proj.weight', 'model.encoder.layers.5.self_attn.q_proj.weight', 'model.shared.weight', 'model.encoder.layers.3.self_attn.out_proj.bias', 'model.encoder.layers.5.final_layer_norm.weight', 'model.encoder.layers.1.fc1.weight', 'model.encoder.layers.2.self_attn.q_proj.weight', 'model.encoder.layers.3.fc1.weight', 'model.encoder.layers.4.final_layer_norm.weight', 'model.encoder.layers.5.self_attn.q_proj.bias', 'model.encoder.layers.1.self_attn.q_proj.weight', 'model.encoder.layers.0.fc2.bias', 'model.encoder.layers.1.fc1.bias', 'model.encoder.layers.0.fc2.weight', 'model.encoder.layers.2.self_attn_layer_norm.bias', 'model.encoder.layers.5.final_layer_norm.bias', 'model.encoder.layers.3.self_attn_layer_norm.weight', 'model.encoder.layers.4.fc2.bias', 'model.encoder.layers.0.self_attn.q_proj.weight', 'model.encoder.layers.0.self_attn.v_proj.weight', 'model.encoder.layers.2.self_attn.v_proj.weight', 'model.encoder.layers.5.self_attn_layer_norm.bias', 'model.encoder.layers.1.self_attn.k_proj.weight', 'model.encoder.embed_tokens.weight', 'model.encoder.layers.4.fc2.weight', 'model.encoder.layers.5.self_attn.k_proj.bias', 'model.encoder.layers.0.self_attn_layer_norm.weight', 'model.encoder.layers.0.fc1.weight', 'model.encoder.layers.4.self_attn.q_proj.bias', 'model.encoder.layers.1.self_attn.q_proj.bias', 'model.encoder.layers.5.fc1.bias', 'model.encoder.layers.2.self_attn.out_proj.weight', 'model.encoder.layers.3.self_attn.q_proj.bias', 'model.encoder.layers.4.self_attn.out_proj.bias', 'final_logits_bias', 'model.encoder.layers.4.fc1.weight', 'model.encoder.layers.2.fc1.bias', 'model.encoder.layers.2.fc2.bias', 'model.encoder.layers.3.self_attn.v_proj.bias', 'model.encoder.layers.3.final_layer_norm.weight', 'model.encoder.layers.1.self_attn.v_proj.weight', 'model.encoder.layers.0.fc1.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"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "EncoderDecoderModel(\n",
              "  (encoder): RobertaModel(\n",
              "    (embeddings): RobertaEmbeddings(\n",
              "      (word_embeddings): Embedding(50265, 768, padding_idx=1)\n",
              "      (position_embeddings): Embedding(514, 768, padding_idx=1)\n",
              "      (token_type_embeddings): Embedding(1, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): RobertaEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (1): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (2): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (3): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (4): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (5): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): RobertaPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\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=768, out_features=512, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.config"
      ],
      "metadata": {
        "id": "b8o5qdiagbh2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ba9abf71-d5bd-4d93-bda3-34552aa03cf5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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\": \"4.22.0.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\": \"distilroberta-base\",\n",
              "    \"add_cross_attention\": false,\n",
              "    \"architectures\": [\n",
              "      \"RobertaForMaskedLM\"\n",
              "    ],\n",
              "    \"attention_probs_dropout_prob\": 0.1,\n",
              "    \"bad_words_ids\": null,\n",
              "    \"bos_token_id\": 0,\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",
              "    \"encoder_no_repeat_ngram_size\": 0,\n",
              "    \"eos_token_id\": 2,\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\": 768,\n",
              "    \"id2label\": {\n",
              "      \"0\": \"LABEL_0\",\n",
              "      \"1\": \"LABEL_1\"\n",
              "    },\n",
              "    \"initializer_range\": 0.02,\n",
              "    \"intermediate_size\": 3072,\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-05,\n",
              "    \"length_penalty\": 1.0,\n",
              "    \"max_length\": 20,\n",
              "    \"max_position_embeddings\": 514,\n",
              "    \"min_length\": 0,\n",
              "    \"model_type\": \"roberta\",\n",
              "    \"no_repeat_ngram_size\": 0,\n",
              "    \"num_attention_heads\": 12,\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\": 1,\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",
              "    \"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\": \"4.22.0.dev0\",\n",
              "    \"type_vocab_size\": 1,\n",
              "    \"typical_p\": 1.0,\n",
              "    \"use_bfloat16\": false,\n",
              "    \"use_cache\": true,\n",
              "    \"vocab_size\": 50265\n",
              "  },\n",
              "  \"is_encoder_decoder\": true,\n",
              "  \"model_type\": \"encoder-decoder\",\n",
              "  \"transformers_version\": null\n",
              "}"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.base_model.config.encoder.num_hidden_layers = 6"
      ],
      "metadata": {
        "id": "ml1eZC5FIPWw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "533a8cd1-28ef-4fb9-b421-72c89848f1a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.tie_weights = True"
      ],
      "metadata": {
        "id": "QHm4VdrLZVev"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.base_model.config.decoder_start_token_id = model.base_model.config.decoder.bos_token_id"
      ],
      "metadata": {
        "id": "EilK2ZWBVn2-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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"
      ],
      "metadata": {
        "id": "DzGm_QPXHvI4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.config.max_length =512"
      ],
      "metadata": {
        "id": "V0rvsHv7HF6T"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1GR3W0pOs7Na"
      },
      "outputs": [],
      "source": [
        "model.save_pretrained(\"distilRoBERTa-Marian-base-model\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hvxtV52E3V3D",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a77ebd3-7812-4f3b-fbd2-49be669d2866"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "EncoderDecoderModel(\n",
              "  (encoder): RobertaModel(\n",
              "    (embeddings): RobertaEmbeddings(\n",
              "      (word_embeddings): Embedding(50265, 768, padding_idx=1)\n",
              "      (position_embeddings): Embedding(514, 768, padding_idx=1)\n",
              "      (token_type_embeddings): Embedding(1, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): RobertaEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (1): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (2): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (3): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (4): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (5): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): RobertaPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\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=768, out_features=512, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "from transformers import AutoModelForSeq2SeqLM\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(\"./distilRoBERTa-Marian-base-model\")\n",
        "#model=AutoModelForSeq2SeqLM.from_pretrained(\"./distilroberta-marian-training\")\n",
        "model"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.base_model.config.decoder_start_token_id"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kOgQ6XU5oGTP",
        "outputId": "6a6e0c31-cbdf-4c29-ad17-d90d35f01f36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.base_model.config.pad_token_id"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NWYFhLfWIU2X",
        "outputId": "cad1515c-949d-4094-b2ee-92409a44bece"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.pad_token_id"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OIG2yWiKIoNz",
        "outputId": "02d4fc13-0feb-4a94-f97b-d0fbfb521c2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.config"
      ],
      "metadata": {
        "id": "lZchqrwtkDSw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "99120e41-bf1f-46c6-8b3c-4e1ed7c2adde"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "EncoderDecoderConfig {\n",
              "  \"_commit_hash\": null,\n",
              "  \"_name_or_path\": \"./distilRoBERTa-Marian--base-model\",\n",
              "  \"architectures\": [\n",
              "    \"EncoderDecoderModel\"\n",
              "  ],\n",
              "  \"bos_token_id\": 0,\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\": \"4.22.0.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\": \"distilroberta-base\",\n",
              "    \"add_cross_attention\": false,\n",
              "    \"architectures\": [\n",
              "      \"RobertaForMaskedLM\"\n",
              "    ],\n",
              "    \"attention_probs_dropout_prob\": 0.1,\n",
              "    \"bad_words_ids\": null,\n",
              "    \"bos_token_id\": 0,\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",
              "    \"encoder_no_repeat_ngram_size\": 0,\n",
              "    \"eos_token_id\": 2,\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\": 768,\n",
              "    \"id2label\": {\n",
              "      \"0\": \"LABEL_0\",\n",
              "      \"1\": \"LABEL_1\"\n",
              "    },\n",
              "    \"initializer_range\": 0.02,\n",
              "    \"intermediate_size\": 3072,\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-05,\n",
              "    \"length_penalty\": 1.0,\n",
              "    \"max_length\": 20,\n",
              "    \"max_position_embeddings\": 514,\n",
              "    \"min_length\": 0,\n",
              "    \"model_type\": \"roberta\",\n",
              "    \"no_repeat_ngram_size\": 0,\n",
              "    \"num_attention_heads\": 12,\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\": 1,\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",
              "    \"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\": \"4.22.0.dev0\",\n",
              "    \"type_vocab_size\": 1,\n",
              "    \"typical_p\": 1.0,\n",
              "    \"use_bfloat16\": false,\n",
              "    \"use_cache\": true,\n",
              "    \"vocab_size\": 50265\n",
              "  },\n",
              "  \"eos_token_id\": 2,\n",
              "  \"is_encoder_decoder\": true,\n",
              "  \"max_length\": 512,\n",
              "  \"model_type\": \"encoder-decoder\",\n",
              "  \"pad_token_id\": 1,\n",
              "  \"torch_dtype\": \"float32\",\n",
              "  \"transformers_version\": null\n",
              "}"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NJNVStnrjr8_"
      },
      "source": [
        "# **Evaluator**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd Utils"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5Iq7Zu-kXTY3",
        "outputId": "162f0dc0-6c58-4b74-cb76-32662c6fd8c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/models/CodeGeneration/New/CoNaLa/distilRoBERTa-Marian/Utils\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200,
          "referenced_widgets": [
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            "5f14452abf3d41b3b18d5057f01b9699",
            "8cd8569a248f4e67baa2f184da917827"
          ]
        },
        "id": "ygEx8goOgfIy",
        "outputId": "dc52612d-fde4-4024-8dbd-245d2e85cf24"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/content/drive/MyDrive/models/CodeGeneration/New/CoNaLa/distilRoBERTa-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": "2bd2a65faed843dda4347da0d769f721"
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          "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": "9bacdb5b20e5468a8fbdd494db009cd8"
            }
          },
          "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": "47dca852e8da4855ac1360effdfe8378"
            }
          },
          "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": "2b6b9967213543c3975b1b1d8d278d3f"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from google.colab import files\n",
        "import evaluator\n",
        "evaluator = evaluator.CodeGenerationEvaluator(tokenizer, device,smooth_bleu=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd .."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WtUzOY89cpQw",
        "outputId": "21a310bf-e587-4600-873b-9ba462e884a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/models/CodeGeneration/New/CoNaLa/distilRoBERTa-Marian\n"
          ]
        }
      ]
    },
    {
      "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[\"intent\"], padding=\"max_length\", truncation=True, max_length=encoder_length)\n",
        "    outputs = tokenizer(batch[\"snippet\"], 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": [
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            "b6f98b7f89c24be08a3b05fb925d0e8a"
          ]
        },
        "id": "5p_GH0x5a-hY",
        "outputId": "83955fa9-4a8e-4cbf-a6b1-b08fa18dfa1d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/24687 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6c0628edd49f4874aa271befa35c8f88"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# make train dataset ready\n",
        "train_data = train_dataset.map(\n",
        "    map_to_encoder_decoder_inputs, batched=True, batch_size=1, remove_columns=['intent', 'snippet'],\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": [
            "3eb27091bcfb43deb68cd1976025218a",
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            "575b73f7785246db82edaaacd65a7d28"
          ]
        },
        "id": "zUWfkeRqoCdW",
        "outputId": "988144d4-5fef-46b6-fed2-4078af450d92"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/1237 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3eb27091bcfb43deb68cd1976025218a"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# same for validation dataset\n",
        "val_data = val_dataset.map(\n",
        "    map_to_encoder_decoder_inputs, batched=True, batch_size=1, remove_columns=['intent', 'snippet'],\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": "cc005ab8-962c-4717-a274-795a452fdf0f"
      },
      "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=\"./distilroberta-marian-training\",\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy = \"epoch\",\n",
        "    per_device_train_batch_size=1,\n",
        "    per_device_eval_batch_size=1,\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",
      "source": [],
      "metadata": {
        "id": "KQN90KlnlE3n"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# distilRoBERTa-Marian-Training\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "EAnDeZoOH6Ne",
        "outputId": "e8086074-2d2d-446f-ee1b-6103096b0e61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:309: 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 = 24687\n",
            "  Num Epochs = 15\n",
            "  Instantaneous batch size per device = 2\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 2\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 185160\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2279: 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:530: 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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='127397' max='185160' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [127397/185160 4:29:00 < 2:01:58, 7.89 it/s, Epoch 10.32/15]\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>2.007100</td>\n",
              "      <td>1.917734</td>\n",
              "      <td>6.089487</td>\n",
              "      <td>4.947906</td>\n",
              "      <td>24.776739</td>\n",
              "      <td>9.272330</td>\n",
              "      <td>3.407614</td>\n",
              "      <td>6.651839</td>\n",
              "      <td>17.882538</td>\n",
              "      <td>20.454028</td>\n",
              "      <td>6.890851</td>\n",
              "      <td>2.871239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>1.504700</td>\n",
              "      <td>1.486641</td>\n",
              "      <td>11.866654</td>\n",
              "      <td>10.589252</td>\n",
              "      <td>37.520175</td>\n",
              "      <td>16.021111</td>\n",
              "      <td>7.349348</td>\n",
              "      <td>8.715316</td>\n",
              "      <td>20.726921</td>\n",
              "      <td>34.049287</td>\n",
              "      <td>13.227372</td>\n",
              "      <td>6.690539</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>1.321200</td>\n",
              "      <td>1.275110</td>\n",
              "      <td>15.074518</td>\n",
              "      <td>14.028332</td>\n",
              "      <td>40.000827</td>\n",
              "      <td>19.263777</td>\n",
              "      <td>10.106824</td>\n",
              "      <td>11.965103</td>\n",
              "      <td>25.095416</td>\n",
              "      <td>37.141518</td>\n",
              "      <td>16.711651</td>\n",
              "      <td>9.660363</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>1.136100</td>\n",
              "      <td>1.132923</td>\n",
              "      <td>19.793712</td>\n",
              "      <td>18.158199</td>\n",
              "      <td>48.722325</td>\n",
              "      <td>26.138195</td>\n",
              "      <td>14.990064</td>\n",
              "      <td>14.271862</td>\n",
              "      <td>28.416362</td>\n",
              "      <td>45.755581</td>\n",
              "      <td>22.930507</td>\n",
              "      <td>14.304247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.951900</td>\n",
              "      <td>1.030566</td>\n",
              "      <td>23.376455</td>\n",
              "      <td>21.980032</td>\n",
              "      <td>49.490420</td>\n",
              "      <td>28.248560</td>\n",
              "      <td>17.680967</td>\n",
              "      <td>17.497598</td>\n",
              "      <td>31.315467</td>\n",
              "      <td>46.764278</td>\n",
              "      <td>25.448386</td>\n",
              "      <td>17.202018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.845400</td>\n",
              "      <td>0.947849</td>\n",
              "      <td>27.993576</td>\n",
              "      <td>26.622312</td>\n",
              "      <td>53.086809</td>\n",
              "      <td>32.443404</td>\n",
              "      <td>21.948124</td>\n",
              "      <td>21.575308</td>\n",
              "      <td>35.581860</td>\n",
              "      <td>50.590707</td>\n",
              "      <td>29.903856</td>\n",
              "      <td>21.540573</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.777100</td>\n",
              "      <td>0.879183</td>\n",
              "      <td>30.281223</td>\n",
              "      <td>29.087174</td>\n",
              "      <td>54.857349</td>\n",
              "      <td>34.821898</td>\n",
              "      <td>24.208547</td>\n",
              "      <td>24.095648</td>\n",
              "      <td>38.536785</td>\n",
              "      <td>52.397085</td>\n",
              "      <td>32.387986</td>\n",
              "      <td>23.808525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.662500</td>\n",
              "      <td>0.830730</td>\n",
              "      <td>33.166844</td>\n",
              "      <td>32.041068</td>\n",
              "      <td>56.926781</td>\n",
              "      <td>37.672650</td>\n",
              "      <td>27.392283</td>\n",
              "      <td>27.057785</td>\n",
              "      <td>40.873964</td>\n",
              "      <td>54.404121</td>\n",
              "      <td>35.207523</td>\n",
              "      <td>26.836173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.646000</td>\n",
              "      <td>0.791341</td>\n",
              "      <td>36.115456</td>\n",
              "      <td>34.859708</td>\n",
              "      <td>59.368594</td>\n",
              "      <td>40.619871</td>\n",
              "      <td>30.465140</td>\n",
              "      <td>29.274882</td>\n",
              "      <td>42.885528</td>\n",
              "      <td>57.334087</td>\n",
              "      <td>38.535462</td>\n",
              "      <td>30.140860</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.581800</td>\n",
              "      <td>0.757475</td>\n",
              "      <td>38.893631</td>\n",
              "      <td>37.687065</td>\n",
              "      <td>61.092768</td>\n",
              "      <td>42.971445</td>\n",
              "      <td>33.099058</td>\n",
              "      <td>32.234142</td>\n",
              "      <td>45.450288</td>\n",
              "      <td>59.065883</td>\n",
              "      <td>40.960634</td>\n",
              "      <td>32.751345</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 1237\n",
            "  Batch size = 1\n",
            "INFO:seq_to_seq:Got BLEU of 6.09 and SacreBLEU of 4.95\n",
            "Saving model checkpoint to ./distilRoBERTa-Marian-Training/checkpoint-12344\n",
            "Configuration saved in ./distilRoBERTa-Marian-Training/checkpoint-12344/config.json\n",
            "Model weights saved in ./distilRoBERTa-Marian-Training/checkpoint-12344/pytorch_model.bin\n",
            "tokenizer config file saved in ./distilRoBERTa-Marian-Training/checkpoint-12344/tokenizer_config.json\n",
            "Special tokens file saved in ./distilRoBERTa-Marian-Training/checkpoint-12344/special_tokens_map.json\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2279: 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:530: 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",
            "***** Running Evaluation *****\n",
            "  Num examples = 1237\n",
            "  Batch size = 1\n",
            "INFO:seq_to_seq:Got BLEU of 11.87 and SacreBLEU of 10.59\n",
            "Saving model checkpoint to ./distilRoBERTa-Marian-Training/checkpoint-24688\n",
            "Configuration saved in ./distilRoBERTa-Marian-Training/checkpoint-24688/config.json\n",
            "Model weights saved in ./distilRoBERTa-Marian-Training/checkpoint-24688/pytorch_model.bin\n",
            "tokenizer config file saved in ./distilRoBERTa-Marian-Training/checkpoint-24688/tokenizer_config.json\n",
            "Special tokens file saved in ./distilRoBERTa-Marian-Training/checkpoint-24688/special_tokens_map.json\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2279: 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:530: 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",
            "***** Running Evaluation *****\n",
            "  Num examples = 1237\n",
            "  Batch size = 1\n",
            "INFO:seq_to_seq:Got BLEU of 15.07 and SacreBLEU of 14.03\n",
            "Saving model checkpoint to ./distilRoBERTa-Marian-Training/checkpoint-37032\n",
            "Configuration saved in ./distilRoBERTa-Marian-Training/checkpoint-37032/config.json\n",
            "Model weights saved in ./distilRoBERTa-Marian-Training/checkpoint-37032/pytorch_model.bin\n",
            "tokenizer config file saved in ./distilRoBERTa-Marian-Training/checkpoint-37032/tokenizer_config.json\n",
            "Special tokens file saved in ./distilRoBERTa-Marian-Training/checkpoint-37032/special_tokens_map.json\n",
            "Deleting older checkpoint [distilRoBERTa-Marian-Training/checkpoint-12344] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2279: 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:530: 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",
            "***** Running Evaluation *****\n",
            "  Num examples = 1237\n",
            "  Batch size = 1\n",
            "INFO:seq_to_seq:Got BLEU of 19.79 and SacreBLEU of 18.16\n",
            "Saving model checkpoint to ./distilRoBERTa-Marian-Training/checkpoint-49376\n",
            "Configuration saved in ./distilRoBERTa-Marian-Training/checkpoint-49376/config.json\n",
            "Model weights saved in ./distilRoBERTa-Marian-Training/checkpoint-49376/pytorch_model.bin\n",
            "tokenizer config file saved in ./distilRoBERTa-Marian-Training/checkpoint-49376/tokenizer_config.json\n",
            "Special tokens file saved in ./distilRoBERTa-Marian-Training/checkpoint-49376/special_tokens_map.json\n",
            "Deleting older checkpoint [distilRoBERTa-Marian-Training/checkpoint-24688] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2279: 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:530: 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",
            "***** Running Evaluation *****\n",
            "  Num examples = 1237\n",
            "  Batch size = 1\n",
            "INFO:seq_to_seq:Got BLEU of 23.38 and SacreBLEU of 21.98\n",
            "Saving model checkpoint to ./distilRoBERTa-Marian-Training/checkpoint-61720\n",
            "Configuration saved in ./distilRoBERTa-Marian-Training/checkpoint-61720/config.json\n",
            "Model weights saved in ./distilRoBERTa-Marian-Training/checkpoint-61720/pytorch_model.bin\n",
            "tokenizer config file saved in ./distilRoBERTa-Marian-Training/checkpoint-61720/tokenizer_config.json\n",
            "Special tokens file saved in ./distilRoBERTa-Marian-Training/checkpoint-61720/special_tokens_map.json\n",
            "Deleting older checkpoint [distilRoBERTa-Marian-Training/checkpoint-37032] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2279: 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:530: 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",
            "***** Running Evaluation *****\n",
            "  Num examples = 1237\n",
            "  Batch size = 1\n",
            "INFO:seq_to_seq:Got BLEU of 27.99 and SacreBLEU of 26.62\n",
            "Saving model checkpoint to ./distilRoBERTa-Marian-Training/checkpoint-74064\n",
            "Configuration saved in ./distilRoBERTa-Marian-Training/checkpoint-74064/config.json\n",
            "Model weights saved in ./distilRoBERTa-Marian-Training/checkpoint-74064/pytorch_model.bin\n",
            "tokenizer config file saved in ./distilRoBERTa-Marian-Training/checkpoint-74064/tokenizer_config.json\n",
            "Special tokens file saved in ./distilRoBERTa-Marian-Training/checkpoint-74064/special_tokens_map.json\n",
            "Deleting older checkpoint [distilRoBERTa-Marian-Training/checkpoint-49376] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2279: 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:530: 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",
            "***** Running Evaluation *****\n",
            "  Num examples = 1237\n",
            "  Batch size = 1\n",
            "INFO:seq_to_seq:Got BLEU of 30.28 and SacreBLEU of 29.09\n",
            "Saving model checkpoint to ./distilRoBERTa-Marian-Training/checkpoint-86408\n",
            "Configuration saved in ./distilRoBERTa-Marian-Training/checkpoint-86408/config.json\n",
            "Model weights saved in ./distilRoBERTa-Marian-Training/checkpoint-86408/pytorch_model.bin\n",
            "tokenizer config file saved in ./distilRoBERTa-Marian-Training/checkpoint-86408/tokenizer_config.json\n",
            "Special tokens file saved in ./distilRoBERTa-Marian-Training/checkpoint-86408/special_tokens_map.json\n",
            "Deleting older checkpoint [distilRoBERTa-Marian-Training/checkpoint-61720] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2279: 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:530: 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",
            "***** Running Evaluation *****\n",
            "  Num examples = 1237\n",
            "  Batch size = 1\n",
            "INFO:seq_to_seq:Got BLEU of 33.17 and SacreBLEU of 32.04\n",
            "Saving model checkpoint to ./distilRoBERTa-Marian-Training/checkpoint-98752\n",
            "Configuration saved in ./distilRoBERTa-Marian-Training/checkpoint-98752/config.json\n",
            "Model weights saved in ./distilRoBERTa-Marian-Training/checkpoint-98752/pytorch_model.bin\n",
            "tokenizer config file saved in ./distilRoBERTa-Marian-Training/checkpoint-98752/tokenizer_config.json\n",
            "Special tokens file saved in ./distilRoBERTa-Marian-Training/checkpoint-98752/special_tokens_map.json\n",
            "Deleting older checkpoint [distilRoBERTa-Marian-Training/checkpoint-74064] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2279: 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:530: 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",
            "***** Running Evaluation *****\n",
            "  Num examples = 1237\n",
            "  Batch size = 1\n",
            "INFO:seq_to_seq:Got BLEU of 36.12 and SacreBLEU of 34.86\n",
            "Saving model checkpoint to ./distilRoBERTa-Marian-Training/checkpoint-111096\n",
            "Configuration saved in ./distilRoBERTa-Marian-Training/checkpoint-111096/config.json\n",
            "Model weights saved in ./distilRoBERTa-Marian-Training/checkpoint-111096/pytorch_model.bin\n",
            "tokenizer config file saved in ./distilRoBERTa-Marian-Training/checkpoint-111096/tokenizer_config.json\n",
            "Special tokens file saved in ./distilRoBERTa-Marian-Training/checkpoint-111096/special_tokens_map.json\n",
            "Deleting older checkpoint [distilRoBERTa-Marian-Training/checkpoint-86408] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2279: 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:530: 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",
            "***** Running Evaluation *****\n",
            "  Num examples = 1237\n",
            "  Batch size = 1\n",
            "INFO:seq_to_seq:Got BLEU of 38.89 and SacreBLEU of 37.69\n",
            "Saving model checkpoint to ./distilRoBERTa-Marian-Training/checkpoint-123440\n",
            "Configuration saved in ./distilRoBERTa-Marian-Training/checkpoint-123440/config.json\n",
            "Model weights saved in ./distilRoBERTa-Marian-Training/checkpoint-123440/pytorch_model.bin\n",
            "tokenizer config file saved in ./distilRoBERTa-Marian-Training/checkpoint-123440/tokenizer_config.json\n",
            "Special tokens file saved in ./distilRoBERTa-Marian-Training/checkpoint-123440/special_tokens_map.json\n",
            "Deleting older checkpoint [distilRoBERTa-Marian-Training/checkpoint-98752] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2279: 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:530: 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"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-39-96d1118ab1ef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# distilRoBERTa-Marian-Training\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m   1416\u001b[0m                         \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1417\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1418\u001b[0;31m                     \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1420\u001b[0m                 if (\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtraining_step\u001b[0;34m(self, model, inputs)\u001b[0m\n\u001b[1;32m   2015\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2016\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_grad_scaling\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2017\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscaler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2018\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muse_apex\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2019\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mamp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscale_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moptimizer\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mscaled_loss\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    305\u001b[0m                 \u001b[0mcreate_graph\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m                 inputs=inputs)\n\u001b[0;32m--> 307\u001b[0;31m         \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    308\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mregister_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhook\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    154\u001b[0m     Variable._execution_engine.run_backward(\n\u001b[1;32m    155\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 156\u001b[0;31m         allow_unreachable=True, accumulate_grad=True)  # allow_unreachable flag\n\u001b[0m\u001b[1;32m    157\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    158\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "SuNZUkDelXnB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# distilRoBERTa-Marian-Training-1\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "WA3z0NRHfUEO",
        "outputId": "6d29fc9e-fd12-4c7f-c766-c9676256b711"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/optimization.py:309: 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 = 24687\n",
            "  Num Epochs = 15\n",
            "  Instantaneous batch size per device = 2\n",
            "  Total train batch size (w. parallel, distributed & accumulation) = 2\n",
            "  Gradient Accumulation steps = 1\n",
            "  Total optimization steps = 185160\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2279: 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:530: 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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='185160' max='185160' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [185160/185160 4:41:35, Epoch 15/15]\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>0.560500</td>\n",
              "      <td>0.759882</td>\n",
              "      <td>39.178874</td>\n",
              "      <td>37.892805</td>\n",
              "      <td>61.227260</td>\n",
              "      <td>43.190643</td>\n",
              "      <td>33.442038</td>\n",
              "      <td>32.557061</td>\n",
              "      <td>45.860162</td>\n",
              "      <td>59.189391</td>\n",
              "      <td>41.062491</td>\n",
              "      <td>33.035950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.479200</td>\n",
              "      <td>0.709103</td>\n",
              "      <td>40.242731</td>\n",
              "      <td>39.046538</td>\n",
              "      <td>64.244560</td>\n",
              "      <td>46.362072</td>\n",
              "      <td>36.633022</td>\n",
              "      <td>34.676962</td>\n",
              "      <td>47.621424</td>\n",
              "      <td>62.238689</td>\n",
              "      <td>44.213991</td>\n",
              "      <td>36.260054</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.469000</td>\n",
              "      <td>0.674257</td>\n",
              "      <td>44.352946</td>\n",
              "      <td>43.257314</td>\n",
              "      <td>65.919806</td>\n",
              "      <td>49.455068</td>\n",
              "      <td>40.382078</td>\n",
              "      <td>39.453178</td>\n",
              "      <td>51.453299</td>\n",
              "      <td>64.303993</td>\n",
              "      <td>47.745712</td>\n",
              "      <td>40.346548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.389800</td>\n",
              "      <td>0.638110</td>\n",
              "      <td>46.646363</td>\n",
              "      <td>45.663731</td>\n",
              "      <td>66.183112</td>\n",
              "      <td>50.555985</td>\n",
              "      <td>41.806777</td>\n",
              "      <td>40.526679</td>\n",
              "      <td>52.712707</td>\n",
              "      <td>64.262985</td>\n",
              "      <td>48.686237</td>\n",
              "      <td>41.503942</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>5</td>\n",
              "      <td>0.318400</td>\n",
              "      <td>0.614325</td>\n",
              "      <td>47.481972</td>\n",
              "      <td>46.357521</td>\n",
              "      <td>68.421300</td>\n",
              "      <td>52.719478</td>\n",
              "      <td>44.174680</td>\n",
              "      <td>42.339843</td>\n",
              "      <td>54.173485</td>\n",
              "      <td>66.719953</td>\n",
              "      <td>50.994538</td>\n",
              "      <td>43.951569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>6</td>\n",
              "      <td>0.291500</td>\n",
              "      <td>0.585153</td>\n",
              "      <td>51.234245</td>\n",
              "      <td>50.220265</td>\n",
              "      <td>69.955899</td>\n",
              "      <td>55.199454</td>\n",
              "      <td>47.170105</td>\n",
              "      <td>45.228799</td>\n",
              "      <td>56.957199</td>\n",
              "      <td>68.419808</td>\n",
              "      <td>53.742590</td>\n",
              "      <td>46.933419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>7</td>\n",
              "      <td>0.267700</td>\n",
              "      <td>0.562906</td>\n",
              "      <td>52.902339</td>\n",
              "      <td>52.081580</td>\n",
              "      <td>70.002689</td>\n",
              "      <td>55.739027</td>\n",
              "      <td>47.645932</td>\n",
              "      <td>47.268497</td>\n",
              "      <td>58.821163</td>\n",
              "      <td>68.476614</td>\n",
              "      <td>54.184915</td>\n",
              "      <td>47.325636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>8</td>\n",
              "      <td>0.227100</td>\n",
              "      <td>0.542791</td>\n",
              "      <td>54.248125</td>\n",
              "      <td>53.411113</td>\n",
              "      <td>72.236539</td>\n",
              "      <td>58.307715</td>\n",
              "      <td>50.545285</td>\n",
              "      <td>48.675929</td>\n",
              "      <td>59.886669</td>\n",
              "      <td>70.587405</td>\n",
              "      <td>56.599530</td>\n",
              "      <td>50.181121</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>9</td>\n",
              "      <td>0.220700</td>\n",
              "      <td>0.530568</td>\n",
              "      <td>55.284456</td>\n",
              "      <td>54.387091</td>\n",
              "      <td>72.951008</td>\n",
              "      <td>59.356440</td>\n",
              "      <td>51.561772</td>\n",
              "      <td>49.785676</td>\n",
              "      <td>60.690415</td>\n",
              "      <td>71.525680</td>\n",
              "      <td>57.928511</td>\n",
              "      <td>51.453768</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>10</td>\n",
              "      <td>0.198700</td>\n",
              "      <td>0.515304</td>\n",
              "      <td>56.708361</td>\n",
              "      <td>55.837988</td>\n",
              "      <td>74.559778</td>\n",
              "      <td>61.357715</td>\n",
              "      <td>53.650495</td>\n",
              "      <td>51.599450</td>\n",
              "      <td>62.424221</td>\n",
              "      <td>73.212075</td>\n",
              "      <td>59.926136</td>\n",
              "      <td>53.464652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>11</td>\n",
              "      <td>0.207200</td>\n",
              "      <td>0.503921</td>\n",
              "      <td>58.028912</td>\n",
              "      <td>57.195424</td>\n",
              "      <td>74.231904</td>\n",
              "      <td>61.366478</td>\n",
              "      <td>53.997546</td>\n",
              "      <td>52.254440</td>\n",
              "      <td>63.106436</td>\n",
              "      <td>72.881277</td>\n",
              "      <td>60.004938</td>\n",
              "      <td>53.681498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>12</td>\n",
              "      <td>0.195800</td>\n",
              "      <td>0.495879</td>\n",
              "      <td>57.862468</td>\n",
              "      <td>57.169010</td>\n",
              "      <td>74.015317</td>\n",
              "      <td>61.423257</td>\n",
              "      <td>54.167095</td>\n",
              "      <td>52.511843</td>\n",
              "      <td>62.956816</td>\n",
              "      <td>72.603826</td>\n",
              "      <td>60.020799</td>\n",
              "      <td>53.935429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>13</td>\n",
              "      <td>0.176800</td>\n",
              "      <td>0.489294</td>\n",
              "      <td>59.138901</td>\n",
              "      <td>58.254094</td>\n",
              "      <td>75.302586</td>\n",
              "      <td>62.979344</td>\n",
              "      <td>55.869273</td>\n",
              "      <td>53.496065</td>\n",
              "      <td>63.849336</td>\n",
              "      <td>73.998588</td>\n",
              "      <td>61.560376</td>\n",
              "      <td>55.569762</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>14</td>\n",
              "      <td>0.157100</td>\n",
              "      <td>0.484331</td>\n",
              "      <td>58.771248</td>\n",
              "      <td>57.962860</td>\n",
              "      <td>74.597471</td>\n",
              "      <td>62.075281</td>\n",
              "      <td>55.017425</td>\n",
              "      <td>53.531043</td>\n",
              "      <td>63.841770</td>\n",
              "      <td>73.237403</td>\n",
              "      <td>60.641399</td>\n",
              "      <td>54.631579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>15</td>\n",
              "      <td>0.164100</td>\n",
              "      <td>0.481315</td>\n",
              "      <td>58.696414</td>\n",
              "      <td>57.921365</td>\n",
              "      <td>74.973640</td>\n",
              "      <td>62.446540</td>\n",
              "      <td>55.390661</td>\n",
              "      <td>53.763988</td>\n",
              "      <td>63.938110</td>\n",
              "      <td>73.599173</td>\n",
              "      <td>61.050165</td>\n",
              "      <td>55.109741</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "***** Running Evaluation *****\n",
            "  Num examples = 1237\n",
            "  Batch size = 1\n",
            "INFO:seq_to_seq:Got BLEU of 39.18 and SacreBLEU of 37.89\n",
            "Saving model checkpoint to ./distilRoBERTa-Marian-Training-1/checkpoint-12344\n",
            "Configuration saved in ./distilRoBERTa-Marian-Training-1/checkpoint-12344/config.json\n",
            "Model weights saved in ./distilRoBERTa-Marian-Training-1/checkpoint-12344/pytorch_model.bin\n",
            "tokenizer config file saved in ./distilRoBERTa-Marian-Training-1/checkpoint-12344/tokenizer_config.json\n",
            "Special tokens file saved in ./distilRoBERTa-Marian-Training-1/checkpoint-12344/special_tokens_map.json\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2279: 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:530: 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",
            "***** Running Evaluation *****\n",
            "  Num examples = 1237\n",
            "  Batch size = 1\n",
            "INFO:seq_to_seq:Got BLEU of 40.24 and SacreBLEU of 39.05\n",
            "Saving model checkpoint to ./distilRoBERTa-Marian-Training-1/checkpoint-24688\n",
            "Configuration saved in ./distilRoBERTa-Marian-Training-1/checkpoint-24688/config.json\n",
            "Model weights saved in ./distilRoBERTa-Marian-Training-1/checkpoint-24688/pytorch_model.bin\n",
            "tokenizer config file saved in ./distilRoBERTa-Marian-Training-1/checkpoint-24688/tokenizer_config.json\n",
            "Special tokens file saved in ./distilRoBERTa-Marian-Training-1/checkpoint-24688/special_tokens_map.json\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2279: 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:530: 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",
            "***** Running Evaluation *****\n",
            "  Num examples = 1237\n",
            "  Batch size = 1\n",
            "INFO:seq_to_seq:Got BLEU of 44.35 and SacreBLEU of 43.26\n",
            "Saving model checkpoint to ./distilRoBERTa-Marian-Training-1/checkpoint-37032\n",
            "Configuration saved in ./distilRoBERTa-Marian-Training-1/checkpoint-37032/config.json\n",
            "Model weights saved in ./distilRoBERTa-Marian-Training-1/checkpoint-37032/pytorch_model.bin\n",
            "tokenizer config file saved in ./distilRoBERTa-Marian-Training-1/checkpoint-37032/tokenizer_config.json\n",
            "Special tokens file saved in ./distilRoBERTa-Marian-Training-1/checkpoint-37032/special_tokens_map.json\n",
            "Deleting older checkpoint [distilRoBERTa-Marian-Training-1/checkpoint-12344] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2279: 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:530: 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",
            "***** Running Evaluation *****\n",
            "  Num examples = 1237\n",
            "  Batch size = 1\n",
            "INFO:seq_to_seq:Got BLEU of 46.65 and SacreBLEU of 45.66\n",
            "Saving model checkpoint to ./distilRoBERTa-Marian-Training-1/checkpoint-49376\n",
            "Configuration saved in ./distilRoBERTa-Marian-Training-1/checkpoint-49376/config.json\n",
            "Model weights saved in ./distilRoBERTa-Marian-Training-1/checkpoint-49376/pytorch_model.bin\n",
            "tokenizer config file saved in ./distilRoBERTa-Marian-Training-1/checkpoint-49376/tokenizer_config.json\n",
            "Special tokens file saved in ./distilRoBERTa-Marian-Training-1/checkpoint-49376/special_tokens_map.json\n",
            "Deleting older checkpoint [distilRoBERTa-Marian-Training-1/checkpoint-24688] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2279: 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:530: 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",
            "***** Running Evaluation *****\n",
            "  Num examples = 1237\n",
            "  Batch size = 1\n",
            "INFO:seq_to_seq:Got BLEU of 47.48 and SacreBLEU of 46.36\n",
            "Saving model checkpoint to ./distilRoBERTa-Marian-Training-1/checkpoint-61720\n",
            "Configuration saved in ./distilRoBERTa-Marian-Training-1/checkpoint-61720/config.json\n",
            "Model weights saved in ./distilRoBERTa-Marian-Training-1/checkpoint-61720/pytorch_model.bin\n",
            "tokenizer config file saved in ./distilRoBERTa-Marian-Training-1/checkpoint-61720/tokenizer_config.json\n",
            "Special tokens file saved in ./distilRoBERTa-Marian-Training-1/checkpoint-61720/special_tokens_map.json\n",
            "Deleting older checkpoint [distilRoBERTa-Marian-Training-1/checkpoint-37032] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2279: 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:530: 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",
            "***** Running Evaluation *****\n",
            "  Num examples = 1237\n",
            "  Batch size = 1\n",
            "INFO:seq_to_seq:Got BLEU of 51.23 and SacreBLEU of 50.22\n",
            "Saving model checkpoint to ./distilRoBERTa-Marian-Training-1/checkpoint-74064\n",
            "Configuration saved in ./distilRoBERTa-Marian-Training-1/checkpoint-74064/config.json\n",
            "Model weights saved in ./distilRoBERTa-Marian-Training-1/checkpoint-74064/pytorch_model.bin\n",
            "tokenizer config file saved in ./distilRoBERTa-Marian-Training-1/checkpoint-74064/tokenizer_config.json\n",
            "Special tokens file saved in ./distilRoBERTa-Marian-Training-1/checkpoint-74064/special_tokens_map.json\n",
            "Deleting older checkpoint [distilRoBERTa-Marian-Training-1/checkpoint-49376] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2279: 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:530: 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",
            "***** Running Evaluation *****\n",
            "  Num examples = 1237\n",
            "  Batch size = 1\n",
            "INFO:seq_to_seq:Got BLEU of 52.90 and SacreBLEU of 52.08\n",
            "Saving model checkpoint to ./distilRoBERTa-Marian-Training-1/checkpoint-86408\n",
            "Configuration saved in ./distilRoBERTa-Marian-Training-1/checkpoint-86408/config.json\n",
            "Model weights saved in ./distilRoBERTa-Marian-Training-1/checkpoint-86408/pytorch_model.bin\n",
            "tokenizer config file saved in ./distilRoBERTa-Marian-Training-1/checkpoint-86408/tokenizer_config.json\n",
            "Special tokens file saved in ./distilRoBERTa-Marian-Training-1/checkpoint-86408/special_tokens_map.json\n",
            "Deleting older checkpoint [distilRoBERTa-Marian-Training-1/checkpoint-61720] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2279: 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:530: 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",
            "***** Running Evaluation *****\n",
            "  Num examples = 1237\n",
            "  Batch size = 1\n",
            "INFO:seq_to_seq:Got BLEU of 54.25 and SacreBLEU of 53.41\n",
            "Saving model checkpoint to ./distilRoBERTa-Marian-Training-1/checkpoint-98752\n",
            "Configuration saved in ./distilRoBERTa-Marian-Training-1/checkpoint-98752/config.json\n",
            "Model weights saved in ./distilRoBERTa-Marian-Training-1/checkpoint-98752/pytorch_model.bin\n",
            "tokenizer config file saved in ./distilRoBERTa-Marian-Training-1/checkpoint-98752/tokenizer_config.json\n",
            "Special tokens file saved in ./distilRoBERTa-Marian-Training-1/checkpoint-98752/special_tokens_map.json\n",
            "Deleting older checkpoint [distilRoBERTa-Marian-Training-1/checkpoint-74064] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2279: 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:530: 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",
            "***** Running Evaluation *****\n",
            "  Num examples = 1237\n",
            "  Batch size = 1\n",
            "INFO:seq_to_seq:Got BLEU of 55.28 and SacreBLEU of 54.39\n",
            "Saving model checkpoint to ./distilRoBERTa-Marian-Training-1/checkpoint-111096\n",
            "Configuration saved in ./distilRoBERTa-Marian-Training-1/checkpoint-111096/config.json\n",
            "Model weights saved in ./distilRoBERTa-Marian-Training-1/checkpoint-111096/pytorch_model.bin\n",
            "tokenizer config file saved in ./distilRoBERTa-Marian-Training-1/checkpoint-111096/tokenizer_config.json\n",
            "Special tokens file saved in ./distilRoBERTa-Marian-Training-1/checkpoint-111096/special_tokens_map.json\n",
            "Deleting older checkpoint [distilRoBERTa-Marian-Training-1/checkpoint-86408] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2279: 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:530: 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",
            "***** Running Evaluation *****\n",
            "  Num examples = 1237\n",
            "  Batch size = 1\n",
            "INFO:seq_to_seq:Got BLEU of 56.71 and SacreBLEU of 55.84\n",
            "Saving model checkpoint to ./distilRoBERTa-Marian-Training-1/checkpoint-123440\n",
            "Configuration saved in ./distilRoBERTa-Marian-Training-1/checkpoint-123440/config.json\n",
            "Model weights saved in ./distilRoBERTa-Marian-Training-1/checkpoint-123440/pytorch_model.bin\n",
            "tokenizer config file saved in ./distilRoBERTa-Marian-Training-1/checkpoint-123440/tokenizer_config.json\n",
            "Special tokens file saved in ./distilRoBERTa-Marian-Training-1/checkpoint-123440/special_tokens_map.json\n",
            "Deleting older checkpoint [distilRoBERTa-Marian-Training-1/checkpoint-98752] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2279: 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:530: 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",
            "***** Running Evaluation *****\n",
            "  Num examples = 1237\n",
            "  Batch size = 1\n",
            "INFO:seq_to_seq:Got BLEU of 58.03 and SacreBLEU of 57.20\n",
            "Saving model checkpoint to ./distilRoBERTa-Marian-Training-1/checkpoint-135784\n",
            "Configuration saved in ./distilRoBERTa-Marian-Training-1/checkpoint-135784/config.json\n",
            "Model weights saved in ./distilRoBERTa-Marian-Training-1/checkpoint-135784/pytorch_model.bin\n",
            "tokenizer config file saved in ./distilRoBERTa-Marian-Training-1/checkpoint-135784/tokenizer_config.json\n",
            "Special tokens file saved in ./distilRoBERTa-Marian-Training-1/checkpoint-135784/special_tokens_map.json\n",
            "Deleting older checkpoint [distilRoBERTa-Marian-Training-1/checkpoint-111096] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2279: 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:530: 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",
            "***** Running Evaluation *****\n",
            "  Num examples = 1237\n",
            "  Batch size = 1\n",
            "INFO:seq_to_seq:Got BLEU of 57.86 and SacreBLEU of 57.17\n",
            "Saving model checkpoint to ./distilRoBERTa-Marian-Training-1/checkpoint-148128\n",
            "Configuration saved in ./distilRoBERTa-Marian-Training-1/checkpoint-148128/config.json\n",
            "Model weights saved in ./distilRoBERTa-Marian-Training-1/checkpoint-148128/pytorch_model.bin\n",
            "tokenizer config file saved in ./distilRoBERTa-Marian-Training-1/checkpoint-148128/tokenizer_config.json\n",
            "Special tokens file saved in ./distilRoBERTa-Marian-Training-1/checkpoint-148128/special_tokens_map.json\n",
            "Deleting older checkpoint [distilRoBERTa-Marian-Training-1/checkpoint-123440] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2279: 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:530: 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",
            "***** Running Evaluation *****\n",
            "  Num examples = 1237\n",
            "  Batch size = 1\n",
            "INFO:seq_to_seq:Got BLEU of 59.14 and SacreBLEU of 58.25\n",
            "Saving model checkpoint to ./distilRoBERTa-Marian-Training-1/checkpoint-160472\n",
            "Configuration saved in ./distilRoBERTa-Marian-Training-1/checkpoint-160472/config.json\n",
            "Model weights saved in ./distilRoBERTa-Marian-Training-1/checkpoint-160472/pytorch_model.bin\n",
            "tokenizer config file saved in ./distilRoBERTa-Marian-Training-1/checkpoint-160472/tokenizer_config.json\n",
            "Special tokens file saved in ./distilRoBERTa-Marian-Training-1/checkpoint-160472/special_tokens_map.json\n",
            "Deleting older checkpoint [distilRoBERTa-Marian-Training-1/checkpoint-135784] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2279: 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:530: 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",
            "***** Running Evaluation *****\n",
            "  Num examples = 1237\n",
            "  Batch size = 1\n",
            "INFO:seq_to_seq:Got BLEU of 58.77 and SacreBLEU of 57.96\n",
            "Saving model checkpoint to ./distilRoBERTa-Marian-Training-1/checkpoint-172816\n",
            "Configuration saved in ./distilRoBERTa-Marian-Training-1/checkpoint-172816/config.json\n",
            "Model weights saved in ./distilRoBERTa-Marian-Training-1/checkpoint-172816/pytorch_model.bin\n",
            "tokenizer config file saved in ./distilRoBERTa-Marian-Training-1/checkpoint-172816/tokenizer_config.json\n",
            "Special tokens file saved in ./distilRoBERTa-Marian-Training-1/checkpoint-172816/special_tokens_map.json\n",
            "Deleting older checkpoint [distilRoBERTa-Marian-Training-1/checkpoint-148128] due to args.save_total_limit\n",
            "/usr/local/lib/python3.7/dist-packages/transformers/tokenization_utils_base.py:2279: 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:530: 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",
            "***** Running Evaluation *****\n",
            "  Num examples = 1237\n",
            "  Batch size = 1\n",
            "INFO:seq_to_seq:Got BLEU of 58.70 and SacreBLEU of 57.92\n",
            "Saving model checkpoint to ./distilRoBERTa-Marian-Training-1/checkpoint-185160\n",
            "Configuration saved in ./distilRoBERTa-Marian-Training-1/checkpoint-185160/config.json\n",
            "Model weights saved in ./distilRoBERTa-Marian-Training-1/checkpoint-185160/pytorch_model.bin\n",
            "tokenizer config file saved in ./distilRoBERTa-Marian-Training-1/checkpoint-185160/tokenizer_config.json\n",
            "Special tokens file saved in ./distilRoBERTa-Marian-Training-1/checkpoint-185160/special_tokens_map.json\n",
            "Deleting older checkpoint [distilRoBERTa-Marian-Training-1/checkpoint-160472] 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 ./distilRoBERTa-Marian-Training-1/checkpoint-185160 (score: 0.4813149571418762).\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=185160, training_loss=0.2904757134933455, metrics={'train_runtime': 16896.1398, 'train_samples_per_second': 21.917, 'train_steps_per_second': 10.959, 'total_flos': 4887121626316800.0, 'train_loss': 0.2904757134933455, 'epoch': 15.0})"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.save_model()\n",
        "trainer.save_state()"
      ],
      "metadata": {
        "id": "3sctuvauTLQg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Visualize**"
      ],
      "metadata": {
        "id": "JjzDA4UJVRc3"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IhQfjGo_6Y2X"
      },
      "outputs": [],
      "source": [
        "%load_ext tensorboard"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **distilRoBERTa-marian**"
      ],
      "metadata": {
        "id": "IYgohOde7ECZ"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gOUtDLCsd7_b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 821
        },
        "outputId": "be88df14-ea53-4f20-cdef-64f0dbe9b91b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "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",
              "    "
            ]
          },
          "metadata": {}
        }
      ],
      "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(\"./distilroberta-marian-training1/\")\n",
        "new_model.to(\"cuda\")\n",
        "\n",
        "new_tokenizer = AutoTokenizer.from_pretrained(\"./distilroberta-marian-training1/\", use_fast=True)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yth1WAaS37V1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ef3697ef-52f4-42c3-ef7c-38e2e9fcb909"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "EncoderDecoderModel(\n",
              "  (encoder): RobertaModel(\n",
              "    (embeddings): RobertaEmbeddings(\n",
              "      (word_embeddings): Embedding(50265, 768, padding_idx=1)\n",
              "      (position_embeddings): Embedding(514, 768, padding_idx=1)\n",
              "      (token_type_embeddings): Embedding(1, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.1, inplace=False)\n",
              "    )\n",
              "    (encoder): RobertaEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (1): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (2): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (3): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (4): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "        (5): RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.1, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.1, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (pooler): RobertaPooler(\n",
              "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "      (activation): Tanh()\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=768, out_features=512, bias=True)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "new_model"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_model.config.max_length\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ujXTqkBhRHgh",
        "outputId": "526f3756-e5a5-4636-cedb-872d50a184e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "512"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1E101Hm6jEck"
      },
      "source": [
        "# **Evaluator**"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd Utils"
      ],
      "metadata": {
        "id": "F8myiUJ-lGmO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ab1f3f40-a9bb-4857-8ffb-fb591cf73467"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/models/CodeGeneration/New/CoNaLa/distilRoBERTa-Marian/Utils\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jxOpBKhWdfla"
      },
      "outputs": [],
      "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[\"intent\"], 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",
      "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\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104,
          "referenced_widgets": [
            "6e2e3aeee1fd4cc299948ec6f7016716",
            "c5159e5dccb84b108e7d326d214bfefd",
            "43c374130c26419ca315bd190db2eb53",
            "666f6d7c984b43a88d71429e694e6449",
            "38f09b6ad76749b4b1c9df9bc6e24664",
            "cd4851d524d74f47a29e3d3ae5fea3da",
            "18b86037139f400eb7d37ae34b4c2858",
            "73d06aa046214965bef1cf0dbc92ebd8",
            "7d5ed8eb833e46b3ad413f15b04e4580",
            "421f68678af0498ea583afc7c81e1eca",
            "cee0b4b9160f448ca21014cfbc7af0a2"
          ]
        },
        "id": "aW-fQq9xd_Ei",
        "outputId": "4078e842-998a-4a13-b7a5-f7bd0ff5fb5d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/500 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6e2e3aeee1fd4cc299948ec6f7016716"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/transformers/generation_utils.py:1268: 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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd .."
      ],
      "metadata": {
        "id": "bg9jBzHg-hdB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "24bfb78a-1dca-4cd5-aa65-bf40c54b3ec1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/models/CodeGeneration/New/CoNaLa/distilRoBERTa-Marian\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "import string\n",
        "\n",
        "import datasets\n",
        "import numpy as np\n",
        "\n",
        "import evaluate\n",
        "from evaluate import load"
      ],
      "metadata": {
        "id": "kZPghyh7wo9Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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",
        "\"\"\""
      ],
      "metadata": {
        "id": "bZxA3QjuwT7v",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "104d9b764c7d421884cfdc191e24a198",
            "fbf409a91c3a4f74b18d54f02ddc7633",
            "cee165cd197246fab23e50b91b3c8f7b",
            "595c9e3a25874f0a8769c52ae01dde0f",
            "0751da2303df45f083b4b6c2204677f4",
            "10e84778c1804d8eabee0f978153b887",
            "c3e86ce398d14c9ba84a896e9927b249",
            "abf014f00e414cdabf29b85da30c8d21",
            "795adf90028b45948cba189acbe1e735",
            "94f3d44c03db42a5b11062da94192879",
            "71bb83e36f3e497f82289d755c92f0ea"
          ]
        },
        "outputId": "7c75a033-8a49-46c6-e2cc-7337df50656e"
      },
      "execution_count": null,
      "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": "104d9b764c7d421884cfdc191e24a198"
            }
          },
          "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": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "refs = results[\"snippet\"]\n",
        "preds = results[\"pred_code\"]"
      ],
      "metadata": {
        "id": "rsJnOenL1B-6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "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": "yyQJP8ki0hoD",
        "outputId": "a085df6a-afa5-4330-efa9-d1e38c438508"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "13.8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "with open(\"./references.txt\", 'w') as f:\n",
        "  for i in range(len(results)):\n",
        "    nl = results[\"intent\"][i]\n",
        "    ref = results[\"snippet\"][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[\"intent\"][i]\n",
        "    ref = results[\"snippet\"][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",
        ""
      ],
      "metadata": {
        "id": "gatvWetJlgJL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "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[\"snippet\"][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",
      "source": [
        "# **Performance Metrics**"
      ],
      "metadata": {
        "id": "4y1LccDPRJc-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**BLEU Score**"
      ],
      "metadata": {
        "id": "AAjUc9xleXTI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# distilRoBERTa-Marian\n",
        "# print(\"Bleu Score: \",bleu_score/len(results))\n",
        "print('Bleu Score: {} (scale 0-100)'.format(bleu_score/len(results)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X-W_ViyiGgZT",
        "outputId": "f48de7cd-59da-431c-a034-b81126c327bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Bleu Score: 35.736454995041484 (scale 0-100)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**SacreBLEU**"
      ],
      "metadata": {
        "id": "WApdMa6AeZnS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print('Sacre Bleu: {} (scale 0-100)'.format(sacre_bleu/len(results)))"
      ],
      "metadata": {
        "id": "OIRriaZdnysh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "77e45027-b089-43b0-df17-534b60e417e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Sacre Bleu: 31.30251113133285 (scale 0-100)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Rouge Score**"
      ],
      "metadata": {
        "id": "4-EepZCaecsi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#distilRoBERTa-Marian\n",
        "# print(\"Bleu Score: \",bleu_score/len(results))\n",
        "print('ROUGE Score: {} (scale 0-100)'.format(rouge_score/len(results)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rkXVcBK2GgMY",
        "outputId": "ad7aa634-8faa-42c3-96de-4420f2be669b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ROUGE Score: 44.25465925857973 (scale 0-100)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**CodeBLEU**"
      ],
      "metadata": {
        "id": "xJ4YMtTtOtmW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files"
      ],
      "metadata": {
        "id": "yhJT_NsiyiZt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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\")"
      ],
      "metadata": {
        "id": "tC6ETJ0u2KRL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "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)"
      ],
      "metadata": {
        "id": "7Oi6CvK_2GZl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Save the  results**"
      ],
      "metadata": {
        "id": "veX-I4ZP-Xjv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## %cd .."
      ],
      "metadata": {
        "id": "tAIlTgKwyjS6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dd61db00-7635-41e9-f43d-ef2d1859fba7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/drive/MyDrive/models/CodeGeneration/MarianCG-CoNaLa-Large\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pwd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "NS4mH-3KVZug",
        "outputId": "ed56fea5-bd71-47af-8e2e-d5d43ef97b70"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/drive/MyDrive/models/CodeGeneration/New/CoNaLa/distilRoBERTa-Marian'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2wHa-z0Fy5z3"
      },
      "outputs": [],
      "source": [
        "bleu_score = 0\n",
        "with open(\"./Output.txt\", 'w') as f:\n",
        "  for i in range(len(results)):\n",
        "    nl = results[\"intent\"][i]\n",
        "    ref = results[\"snippet\"][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",
      "source": [
        "# **Test**"
      ],
      "metadata": {
        "id": "tCTiVafqjEc5"
      }
    },
    {
      "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"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "o_QuAYeQjDkF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "tGxnFOE2jDgB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bhfGalXdjDb-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gS48lsC8jDWQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kfBCBRRVjDPL"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "machine_shape": "hm",
      "provenance": [],
      "include_colab_link": true
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