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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "## Install requirements\n",
        "\n",
        "!pip install transformers datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "flfjx2VBWeBf",
        "outputId": "d5707506-5e00-4544-906d-891785eab359"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting transformers\n",
            "  Downloading transformers-4.31.0-py3-none-any.whl (7.4 MB)\n",
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            "\u001b[?25hCollecting datasets\n",
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            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.2)\n",
            "Collecting huggingface-hub<1.0,>=0.14.1 (from transformers)\n",
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            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
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            "\u001b[?25hCollecting safetensors>=0.3.1 (from transformers)\n",
            "  Downloading safetensors-0.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
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            "\u001b[?25hCollecting multiprocess (from datasets)\n",
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            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.14.1->transformers) (4.7.1)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (1.26.16)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2023.7.22)\n",
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            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2022.7.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
            "Installing collected packages: tokenizers, safetensors, xxhash, dill, multiprocess, huggingface-hub, transformers, datasets\n",
            "Successfully installed datasets-2.14.3 dill-0.3.7 huggingface-hub-0.16.4 multiprocess-0.70.15 safetensors-0.3.1 tokenizers-0.13.3 transformers-4.31.0 xxhash-3.3.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Load SQuADv1.1 dataset\n",
        "\n",
        "from datasets import load_dataset\n",
        "squad = load_dataset(\"squad\")"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
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          "metadata": {}
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            }
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          "metadata": {}
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        {
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            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "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": "5b7d0c94bc454362baa32d67c6fa32ac"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "50b925ad50524fa08c0dfcba10a88aa8"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Dataset Structure\n",
        "\n",
        "squad"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ldqb7vSFZjtl",
        "outputId": "99f39b54-a4d3-4012-db62-4c4e859d22f1"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'title', 'context', 'question', 'answers'],\n",
              "        num_rows: 87599\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['id', 'title', 'context', 'question', 'answers'],\n",
              "        num_rows: 10570\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Sample from dataset\n",
        "\n",
        "squad[\"train\"][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e93Krxnu6xTM",
        "outputId": "9fd014ac-4f20-4a23-9c02-87890cbec3c9"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'id': '56cbd8c66d243a140015ed85',\n",
              " 'title': 'Frédéric_Chopin',\n",
              " 'context': \"In his native Poland, in France, where he composed most of his works, and beyond, Chopin's music, his status as one of music's earliest superstars, his association (if only indirect) with political insurrection, his love life and his early death have made him, in the public consciousness, a leading symbol of the Romantic era. His works remain popular, and he has been the subject of numerous films and biographies of varying degrees of historical accuracy.\",\n",
              " 'question': \"What was the degree of Frédéric's association with political insurrection?\",\n",
              " 'answers': {'text': ['indirect'], 'answer_start': [173]}}"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Before we can feed these texts to our model, we need to preprocess them. We use the tokenizer to\n",
        "## tokenize the inputs (including converting the tokens to their corresponding IDs in the pretrained vocabulary)\n",
        "## and put it in a format the model expects, as well as generate the other inputs that model requires.\n",
        "\n",
        "from transformers import AutoTokenizer\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"distilbert-base-uncased\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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        },
        "id": "zWMwXf5w60DK",
        "outputId": "e757bbe4-4929-4cd1-e48b-ddb65c70aad2"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
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            ],
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              "model_id": "16002214b0a6489aae375b5555cad9eb"
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    },
    {
      "cell_type": "code",
      "source": [
        "## Preprocessing\n",
        "\n",
        "def prepare_train_features(examples):\n",
        "    # Tokenize our examples with truncation and padding, but keep the overflows using a stride. This results\n",
        "    # in one example possible giving several features when a context is long, each of those features having a\n",
        "    # context that overlaps a bit the context of the previous feature.\n",
        "    tokenized_examples = tokenizer(\n",
        "        examples[\"question\"],\n",
        "        examples[\"context\"],\n",
        "        truncation=\"only_second\",\n",
        "        max_length=384,\n",
        "        stride=128,\n",
        "        return_overflowing_tokens=True,\n",
        "        return_offsets_mapping=True,\n",
        "        padding=\"max_length\",\n",
        "    )\n",
        "\n",
        "    # Since one example might give us several features if it has a long context, we need a map from a feature to\n",
        "    # its corresponding example. This key gives us just that.\n",
        "    sample_mapping = tokenized_examples.pop(\"overflow_to_sample_mapping\")\n",
        "    # The offset mappings will give us a map from token to character position in the original context. This will\n",
        "    # help us compute the start_positions and end_positions.\n",
        "    offset_mapping = tokenized_examples.pop(\"offset_mapping\")\n",
        "\n",
        "    # Let's label those examples!\n",
        "    tokenized_examples[\"start_positions\"] = []\n",
        "    tokenized_examples[\"end_positions\"] = []\n",
        "\n",
        "    for i, offsets in enumerate(offset_mapping):\n",
        "        # We will label impossible answers with the index of the CLS token.\n",
        "        input_ids = tokenized_examples[\"input_ids\"][i]\n",
        "        cls_index = input_ids.index(tokenizer.cls_token_id)\n",
        "\n",
        "        # Grab the sequence corresponding to that example (to know what is the context and what is the question).\n",
        "        sequence_ids = tokenized_examples.sequence_ids(i)\n",
        "\n",
        "        # One example can give several spans, this is the index of the example containing this span of text.\n",
        "        sample_index = sample_mapping[i]\n",
        "        answers = examples[\"answers\"][sample_index]\n",
        "        # If no answers are given, set the cls_index as answer.\n",
        "        if len(answers[\"answer_start\"]) == 0:\n",
        "            tokenized_examples[\"start_positions\"].append(cls_index)\n",
        "            tokenized_examples[\"end_positions\"].append(cls_index)\n",
        "        else:\n",
        "            # Start/end character index of the answer in the text.\n",
        "            start_char = answers[\"answer_start\"][0]\n",
        "            end_char = start_char + len(answers[\"text\"][0])\n",
        "\n",
        "            # Start token index of the current span in the text.\n",
        "            token_start_index = 0\n",
        "            while sequence_ids[token_start_index] != 1:\n",
        "                token_start_index += 1\n",
        "\n",
        "            # End token index of the current span in the text.\n",
        "            token_end_index = len(input_ids) - 1\n",
        "            while sequence_ids[token_end_index] != 1:\n",
        "                token_end_index -= 1\n",
        "\n",
        "            # Detect if the answer is out of the span (in which case this feature is labeled with the CLS index).\n",
        "            if not (\n",
        "                offsets[token_start_index][0] <= start_char\n",
        "                and offsets[token_end_index][1] >= end_char\n",
        "            ):\n",
        "                tokenized_examples[\"start_positions\"].append(cls_index)\n",
        "                tokenized_examples[\"end_positions\"].append(cls_index)\n",
        "            else:\n",
        "                # Otherwise move the token_start_index and token_end_index to the two ends of the answer.\n",
        "                # Note: we could go after the last offset if the answer is the last word (edge case).\n",
        "                while (\n",
        "                    token_start_index < len(offsets)\n",
        "                    and offsets[token_start_index][0] <= start_char\n",
        "                ):\n",
        "                    token_start_index += 1\n",
        "                tokenized_examples[\"start_positions\"].append(token_start_index - 1)\n",
        "                while offsets[token_end_index][1] >= end_char:\n",
        "                    token_end_index -= 1\n",
        "                tokenized_examples[\"end_positions\"].append(token_end_index + 1)\n",
        "\n",
        "    return tokenized_examples"
      ],
      "metadata": {
        "id": "ngGDygU66-Uh"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Apply the preprocessing on the dataset\n",
        "\n",
        "tokenized_squad = squad.map(prepare_train_features, batched=True, remove_columns=squad[\"train\"].column_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
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        },
        "id": "0w7oWprL7TXQ",
        "outputId": "ef56c30f-7898-44e1-dbd7-4a6be21289cb"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/87599 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b534db2db0384a8899ad43c2cbcc9004"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "9d71b9aae5db40eabccfe595eab24aa5"
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          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Load the pretrained model\n",
        "\n",
        "from transformers import TFAutoModelForQuestionAnswering\n",
        "model = TFAutoModelForQuestionAnswering.from_pretrained(\"distilbert-base-uncased\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 156,
          "referenced_widgets": [
            "51c090b6641a48108cf6be0fead780b3",
            "2b5c3c59740e403a8b0af46ef4b012ef",
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          ]
        },
        "id": "X0KjoIvg77_7",
        "outputId": "34c5d7a0-914f-4414-9015-f81bdc38c717"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading model.safetensors:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "51c090b6641a48108cf6be0fead780b3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the PyTorch model were not used when initializing the TF 2.0 model TFDistilBertForQuestionAnswering: ['vocab_transform.weight', 'vocab_transform.bias', 'vocab_layer_norm.weight', 'vocab_projector.bias', 'vocab_layer_norm.bias']\n",
            "- This IS expected if you are initializing TFDistilBertForQuestionAnswering from a PyTorch model trained on another task or with another architecture (e.g. initializing a TFBertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing TFDistilBertForQuestionAnswering from a PyTorch model that you expect to be exactly identical (e.g. initializing a TFBertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights or buffers of the TF 2.0 model TFDistilBertForQuestionAnswering were not initialized from the PyTorch model and are newly initialized: ['qa_outputs.weight', 'qa_outputs.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Prepare training and validations sets\n",
        "\n",
        "train_set = model.prepare_tf_dataset(\n",
        "    tokenized_squad[\"train\"],\n",
        "    shuffle=True,\n",
        "    batch_size=32,\n",
        ")\n",
        "\n",
        "validation_set = model.prepare_tf_dataset(\n",
        "    tokenized_squad[\"validation\"],\n",
        "    shuffle=False,\n",
        "    batch_size=32,\n",
        ")"
      ],
      "metadata": {
        "id": "cFBjDzbh7-xe"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "learning_rate = 2e-5\n",
        "num_train_epochs = 2"
      ],
      "metadata": {
        "id": "IV0iFNiHdf3C"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Create Optimizer\n",
        "\n",
        "from transformers import create_optimizer\n",
        "\n",
        "total_train_steps = len(train_set) * num_train_epochs\n",
        "\n",
        "optimizer, schedule = create_optimizer(\n",
        "    init_lr=learning_rate, num_warmup_steps=0, num_train_steps=total_train_steps\n",
        ")"
      ],
      "metadata": {
        "id": "c-GhoP_Odbr9"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Compile the model\n",
        "\n",
        "import tensorflow as tf\n",
        "\n",
        "model.compile(optimizer=optimizer, jit_compile=True, metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "id": "aBzblgTX8csZ"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Training\n",
        "\n",
        "model.fit(train_set, validation_data=validation_set, epochs=num_train_epochs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hv6JHUBX8eQh",
        "outputId": "0a8ef003-eb4c-4a1e-fcb3-a7a21fc42ac5"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/2\n",
            "2766/2766 [==============================] - 3437s 1s/step - loss: 1.5155 - end_logits_accuracy: 0.6016 - start_logits_accuracy: 0.5648 - val_loss: 1.2068 - val_end_logits_accuracy: 0.6768 - val_start_logits_accuracy: 0.6414\n",
            "Epoch 2/2\n",
            "2766/2766 [==============================] - 3374s 1s/step - loss: 1.0571 - end_logits_accuracy: 0.7089 - start_logits_accuracy: 0.6699 - val_loss: 1.1485 - val_end_logits_accuracy: 0.6886 - val_start_logits_accuracy: 0.6558\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x79d72cea5fc0>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Save Model and Tokenizer\n",
        "!mkdir saved_model\n",
        "tokenizer.save_pretrained('saved_model')\n",
        "model.save_pretrained('saved_model')"
      ],
      "metadata": {
        "id": "ZOHzViP08tnK"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "## Inference using transformers pipeline\n",
        "\n",
        "question = \"How many programming languages does BLOOM support?\"\n",
        "context = \"BLOOM has 176 billion parameters and can generate text in 46 languages natural languages and 13 programming languages.\"\n",
        "\n",
        "from transformers import pipeline\n",
        "\n",
        "question_answerer = pipeline(\"question-answering\", model='saved_model', tokenizer='saved_model')\n",
        "question_answerer(question=question, context=context)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rDCE3OQm_6p1",
        "outputId": "23626476-7426-42a7-c65d-416da482f1bf"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some layers from the model checkpoint at saved_model were not used when initializing TFDistilBertForQuestionAnswering: ['dropout_19']\n",
            "- This IS expected if you are initializing TFDistilBertForQuestionAnswering 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 TFDistilBertForQuestionAnswering from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some layers of TFDistilBertForQuestionAnswering were not initialized from the model checkpoint at saved_model and are newly initialized: ['dropout_39']\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": [
              "{'score': 0.7422705292701721, 'start': 93, 'end': 95, 'answer': '13'}"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!zip -r out.zip saved_model"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pEnnvjm98L0S",
        "outputId": "f828d085-d6ea-493a-835c-58bbe53b07d7"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  adding: saved_model/ (stored 0%)\n",
            "  adding: saved_model/tokenizer.json (deflated 71%)\n",
            "  adding: saved_model/tf_model.h5 (deflated 8%)\n",
            "  adding: saved_model/special_tokens_map.json (deflated 42%)\n",
            "  adding: saved_model/tokenizer_config.json (deflated 43%)\n",
            "  adding: saved_model/vocab.txt (deflated 53%)\n",
            "  adding: saved_model/config.json (deflated 43%)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!du -h out.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F7Qly7se8ujn",
        "outputId": "1d482ff6-f3bc-4c6b-9a71-2ee4293961aa"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "234M\tout.zip\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mv out.zip qa.zip"
      ],
      "metadata": {
        "id": "fSKPNwSc9Px8"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wvJT32AW9Dl2",
        "outputId": "209f59e6-8b14-4a22-fbb6-54ecbcf6fe04"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cp qa.zip /content/drive/MyDrive"
      ],
      "metadata": {
        "id": "Esq579jC9H2C"
      },
      "execution_count": 44,
      "outputs": []
    }
  ]
}