BERT2BERT_for_CNN_Dailymail.ipynb

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  "cells": [
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      "source": [
        "<a href=\"https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/BERT2BERT_for_CNN_Dailymail.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      },
      "source": [
        "## **Warm-starting BERT2BERT for CNN/Dailymail**\n",
        "\n",
        "***Note***: This notebook only uses a few training, validation, and test data samples for demonstration purposes. To fine-tune an encoder-decoder model on the full training data, the user should change the training and data preprocessing parameters accordingly as highlighted by the comments.\n"
      ]
    },
    {
      "cell_type": "markdown",
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      },
      "source": [
        "### **Data Preprocessing**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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      },
      "source": [
        "%%capture\n",
        "!pip install datasets==1.0.2\n",
        "!pip install transformers\n",
        "\n",
        "import datasets\n",
        "import transformers"
      ],
      "execution_count": 6,
      "outputs": []
    },
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        }
      },
      "source": [
        "from transformers import BertTokenizerFast\n",
        "\n",
        "tokenizer = BertTokenizerFast.from_pretrained(\"bert-base-uncased\")\n",
        "tokenizer.bos_token = tokenizer.cls_token\n",
        "tokenizer.eos_token = tokenizer.sep_token\n",
        "\n",
        "train_data = datasets.load_dataset(\"cnn_dailymail\", \"3.0.0\", split=\"train\")\n",
        "val_data = datasets.load_dataset(\"cnn_dailymail\", \"3.0.0\", split=\"validation[:10%]\")"
      ],
      "execution_count": 5,
      "outputs": [
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        },
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          "output_type": "stream",
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            "\n"
          ],
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        },
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            "\n",
            "Downloading and preparing dataset cnn_dailymail/3.0.0 (download: 558.32 MiB, generated: 1.28 GiB, post-processed: Unknown size, total: 1.82 GiB) to /root/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602...\n"
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            "\n"
          ],
          "name": "stdout"
        },
        {
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        },
        {
          "output_type": "stream",
          "text": [
            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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        },
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          "output_type": "stream",
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            "\r"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
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          },
          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\rDataset cnn_dailymail downloaded and prepared to /root/.cache/huggingface/datasets/cnn_dailymail/3.0.0/3.0.0/0128610a44e10f25b4af6689441c72af86205282d26399642f7db38fa7535602. Subsequent calls will reuse this data.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yoN2q0hZUbXN",
        "outputId": "0a954042-bebc-4d1e-a78c-806672ebf6ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117,
          "referenced_widgets": [
            "a17c3dfa12364f0089bbeeb84802be70",
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            "557901ee2556427098e5e3086445e67f",
            "e994714f2c6441c49694b7e82d4133b9",
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            "ec57b9616f66493b8f0de030c572c086",
            "b1d575cc621044f9958fbe64aba1109a",
            "832abe53dede4cd4880c44f6400b72b5",
            "899d4953b0cd4e5fbaeff3b0880519ed",
            "f7a8a4c3ec8942a9995f4e83b2706a4a",
            "ba2cfbbaafee4edc9740b4b903b8e94d",
            "e507b550c4d34882a7bde9e4750f80a3",
            "d04589c1c4d74894bff5a6f184414920",
            "a5a646dd8c514d89841bc12ad72cc8bb"
          ]
        }
      },
      "source": [
        "batch_size=4  # change to 16 for full training\n",
        "encoder_max_length=512\n",
        "decoder_max_length=128\n",
        "\n",
        "def process_data_to_model_inputs(batch):\n",
        "  # tokenize the inputs and labels\n",
        "  inputs = tokenizer(batch[\"article\"], padding=\"max_length\", truncation=True, max_length=encoder_max_length)\n",
        "  outputs = tokenizer(batch[\"highlights\"], padding=\"max_length\", truncation=True, max_length=decoder_max_length)\n",
        "\n",
        "  batch[\"input_ids\"] = inputs.input_ids\n",
        "  batch[\"attention_mask\"] = inputs.attention_mask\n",
        "  batch[\"decoder_input_ids\"] = outputs.input_ids\n",
        "  batch[\"decoder_attention_mask\"] = outputs.attention_mask\n",
        "  batch[\"labels\"] = outputs.input_ids.copy()\n",
        "\n",
        "  # because BERT automatically shifts the labels, the labels correspond exactly to `decoder_input_ids`. \n",
        "  # We have to make sure that the PAD token is ignored\n",
        "  batch[\"labels\"] = [[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch[\"labels\"]]\n",
        "\n",
        "  return batch\n",
        "\n",
        "# only use 32 training examples for notebook - DELETE LINE FOR FULL TRAINING\n",
        "train_data = train_data.select(range(32))\n",
        "\n",
        "train_data = train_data.map(\n",
        "    process_data_to_model_inputs, \n",
        "    batched=True, \n",
        "    batch_size=batch_size, \n",
        "    remove_columns=[\"article\", \"highlights\", \"id\"]\n",
        ")\n",
        "train_data.set_format(\n",
        "    type=\"torch\", columns=[\"input_ids\", \"attention_mask\", \"decoder_input_ids\", \"decoder_attention_mask\", \"labels\"],\n",
        ")\n",
        "\n",
        "\n",
        "# only use 16 training examples for notebook - DELETE LINE FOR FULL TRAINING\n",
        "val_data = val_data.select(range(16))\n",
        "\n",
        "val_data = val_data.map(\n",
        "    process_data_to_model_inputs, \n",
        "    batched=True, \n",
        "    batch_size=batch_size, \n",
        "    remove_columns=[\"article\", \"highlights\", \"id\"]\n",
        ")\n",
        "val_data.set_format(\n",
        "    type=\"torch\", columns=[\"input_ids\", \"attention_mask\", \"decoder_input_ids\", \"decoder_attention_mask\", \"labels\"],\n",
        ")"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a17c3dfa12364f0089bbeeb84802be70",
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              "version_major": 2
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              "HBox(children=(FloatProgress(value=0.0, max=8.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b1d575cc621044f9958fbe64aba1109a",
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              "version_major": 2
            },
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              "HBox(children=(FloatProgress(value=0.0, max=4.0), HTML(value='')))"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aEjb026cNC38"
      },
      "source": [
        "### **Warm-starting the Encoder-Decoder Model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tS0UndNoQh8t",
        "outputId": "745e278d-5cbb-486d-bf73-1f3449458013",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 117,
          "referenced_widgets": [
            "73046f1e2e264e8c844a4f8b71658ca9",
            "83324ff551ce46dfb91a9d0124ac907e",
            "12839b60d5854e768d74802bf2b4c31b",
            "3d93773486ae4eaabfa1cb446730678b",
            "f99a670198c74b47ae6009ebb9dc6ddb",
            "ad90c49a97f44cf29ce291dbc1928e23",
            "46bb0baea1734bbb9afc5017d322f751",
            "db0f190e8bb1420aaadcb7cce92ef17a",
            "dd82e45c339240c2ba59f282f8137264",
            "19c8a174d8cd4bd1bbff56b32e7fc582",
            "36c5f79c12e949ff95a978d4b5107acb",
            "f6d67ad967964b239ff6275bc88db088",
            "130cb2e62508429ba1ee7e809a01748c",
            "0d97ed88772b431aa72ff820156e0c75",
            "c75d96f3a0584da79b1496448fee91cc",
            "bdcb5c57206448d383b3702fad22f078"
          ]
        }
      },
      "source": [
        "from transformers import EncoderDecoderModel\n",
        "\n",
        "bert2bert = EncoderDecoderModel.from_encoder_decoder_pretrained(\"bert-base-uncased\", \"bert-base-uncased\")"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "73046f1e2e264e8c844a4f8b71658ca9",
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=433.0, style=ProgressStyle(description_…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dd82e45c339240c2ba59f282f8137264",
              "version_minor": 0,
              "version_major": 2
            },
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              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=440473133.0, style=ProgressStyle(descri…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JD2jv3GkyjR-"
      },
      "source": [
        "# set special tokens\n",
        "bert2bert.config.decoder_start_token_id = tokenizer.bos_token_id\n",
        "bert2bert.config.eos_token_id = tokenizer.eos_token_id\n",
        "bert2bert.config.pad_token_id = tokenizer.pad_token_id\n",
        "\n",
        "# sensible parameters for beam search\n",
        "bert2bert.config.vocab_size = bert2bert.config.decoder.vocab_size\n",
        "bert2bert.config.max_length = 142\n",
        "bert2bert.config.min_length = 56\n",
        "bert2bert.config.no_repeat_ngram_size = 3\n",
        "bert2bert.config.early_stopping = True\n",
        "bert2bert.config.length_penalty = 2.0\n",
        "bert2bert.config.num_beams = 4"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u98CLZiTkgzv"
      },
      "source": [
        "### **Fine-Tuning Warm-Started Encoder-Decoder Models**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-gYzA-w96wCt"
      },
      "source": [
        "The `Seq2SeqTrainer` that can be found under [examples/seq2seq/seq2seq_trainer.py](https://github.com/huggingface/transformers/blob/master/examples/seq2seq/seq2seq_trainer.py) will be used to fine-tune a warm-started encoder-decoder model.\n",
        "\n",
        "Let's download the `Seq2SeqTrainer` code and import the module along with `TrainingArguments`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pyiwaF0noA5c"
      },
      "source": [
        "%%capture\n",
        "!rm seq2seq_trainer.py\n",
        "!wget https://raw.githubusercontent.com/huggingface/transformers/master/examples/seq2seq/seq2seq_trainer.py\n",
        "\n",
        "!pip install git-python==1.0.3\n",
        "!pip install sacrebleu==1.4.12\n",
        "!pip install rouge_score\n",
        "\n",
        "from seq2seq_trainer import Seq2SeqTrainer\n",
        "from transformers import TrainingArguments\n",
        "from dataclasses import dataclass, field\n",
        "from typing import Optional"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5nmQRT3XuHHz"
      },
      "source": [
        "We need to add some additional parameters to make `TrainingArguments` compatible with the `Seq2SeqTrainer`. Let's just copy the `dataclass` arguments as defined in [this file](https://github.com/patrickvonplaten/transformers/blob/make_seq2seq_trainer_self_contained/examples/seq2seq/finetune_trainer.py)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-zkkd66rtsnA"
      },
      "source": [
        "@dataclass\n",
        "class Seq2SeqTrainingArguments(TrainingArguments):\n",
        "    label_smoothing: Optional[float] = field(\n",
        "        default=0.0, metadata={\"help\": \"The label smoothing epsilon to apply (if not zero).\"}\n",
        "    )\n",
        "    sortish_sampler: bool = field(default=False, metadata={\"help\": \"Whether to SortishSamler or not.\"})\n",
        "    predict_with_generate: bool = field(\n",
        "        default=False, metadata={\"help\": \"Whether to use generate to calculate generative metrics (ROUGE, BLEU).\"}\n",
        "    )\n",
        "    adafactor: bool = field(default=False, metadata={\"help\": \"whether to use adafactor\"})\n",
        "    encoder_layerdrop: Optional[float] = field(\n",
        "        default=None, metadata={\"help\": \"Encoder layer dropout probability. Goes into model.config.\"}\n",
        "    )\n",
        "    decoder_layerdrop: Optional[float] = field(\n",
        "        default=None, metadata={\"help\": \"Decoder layer dropout probability. Goes into model.config.\"}\n",
        "    )\n",
        "    dropout: Optional[float] = field(default=None, metadata={\"help\": \"Dropout probability. Goes into model.config.\"})\n",
        "    attention_dropout: Optional[float] = field(\n",
        "        default=None, metadata={\"help\": \"Attention dropout probability. Goes into model.config.\"}\n",
        "    )\n",
        "    lr_scheduler: Optional[str] = field(\n",
        "        default=\"linear\", metadata={\"help\": f\"Which lr scheduler to use.\"}\n",
        "    )"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dPUAgo7pxH24"
      },
      "source": [
        "Also, we need to define a function to correctly compute the ROUGE score during validation. ROUGE is a much better metric to track during training than only language modeling loss."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "68IHmFYLx09W",
        "outputId": "9ed45c24-eaea-4aac-dc88-d2820f7450de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 67,
          "referenced_widgets": [
            "2cd3d553b6b84c079b36e4f21abb837f",
            "a0f82133e05f476d8b7306a991a2a720",
            "e60f74c273764eefa08e32375da92dcb",
            "12a2a2e00e1a44cea17e1c9e21bb1c34",
            "ae1f09968f76406caee36d7d0639a4f2",
            "37b1b12827f94d5f8379ff03f30bac7e",
            "afa0aa69d9b04d2ba8c977fa77fb8196",
            "1661a489cdd342c3a9a4793eec128e78"
          ]
        }
      },
      "source": [
        "# load rouge for validation\n",
        "rouge = datasets.load_metric(\"rouge\")\n",
        "\n",
        "def compute_metrics(pred):\n",
        "    labels_ids = pred.label_ids\n",
        "    pred_ids = pred.predictions\n",
        "\n",
        "    # all unnecessary tokens are removed\n",
        "    pred_str = tokenizer.batch_decode(pred_ids, skip_special_tokens=True)\n",
        "    labels_ids[labels_ids == -100] = tokenizer.pad_token_id\n",
        "    label_str = tokenizer.batch_decode(labels_ids, skip_special_tokens=True)\n",
        "\n",
        "    rouge_output = rouge.compute(predictions=pred_str, references=label_str, rouge_types=[\"rouge2\"])[\"rouge2\"].mid\n",
        "\n",
        "    return {\n",
        "        \"rouge2_precision\": round(rouge_output.precision, 4),\n",
        "        \"rouge2_recall\": round(rouge_output.recall, 4),\n",
        "        \"rouge2_fmeasure\": round(rouge_output.fmeasure, 4),\n",
        "    }"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "2cd3d553b6b84c079b36e4f21abb837f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1656.0, style=ProgressStyle(description…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1ik4hZb2yV-b"
      },
      "source": [
        "Cool! Finally, we start training."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LAaTxUpdzshF",
        "outputId": "3bf1b099-f9b9-4b29-803c-bd45331af2ae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        }
      },
      "source": [
        "# set training arguments - these params are not really tuned, feel free to change\n",
        "training_args = Seq2SeqTrainingArguments(\n",
        "    output_dir=\"./\",\n",
        "    per_device_train_batch_size=batch_size,\n",
        "    per_device_eval_batch_size=batch_size,\n",
        "    predict_with_generate=True,\n",
        "    evaluate_during_training=True,\n",
        "    do_train=True,\n",
        "    do_eval=True,\n",
        "    logging_steps=2,  # set to 1000 for full training\n",
        "    save_steps=16,  # set to 500 for full training\n",
        "    eval_steps=4,  # set to 8000 for full training\n",
        "    warmup_steps=1,  # set to 2000 for full training\n",
        "    max_steps=16, # delete for full training\n",
        "    overwrite_output_dir=True,\n",
        "    save_total_limit=3,\n",
        "    fp16=True, \n",
        ")\n",
        "\n",
        "# instantiate trainer\n",
        "trainer = Seq2SeqTrainer(\n",
        "    model=bert2bert,\n",
        "    args=training_args,\n",
        "    compute_metrics=compute_metrics,\n",
        "    train_dataset=train_data,\n",
        "    eval_dataset=val_data,\n",
        ")\n",
        "trainer.train()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/transformers/training_args.py:339: FutureWarning: The `evaluate_during_training` argument is deprecated in favor of `evaluation_strategy` (which has more options)\n",
            "  FutureWarning,\n",
            "/usr/local/lib/python3.6/dist-packages/datasets/arrow_dataset.py:835: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at  /pytorch/torch/csrc/utils/tensor_numpy.cpp:141.)\n",
            "  return torch.tensor(x, **format_kwargs)\n",
            "/usr/local/lib/python3.6/dist-packages/torch/optim/lr_scheduler.py:123: UserWarning: Detected call of `lr_scheduler.step()` before `optimizer.step()`. In PyTorch 1.1.0 and later, you should call them in the opposite order: `optimizer.step()` before `lr_scheduler.step()`.  Failure to do this will result in PyTorch skipping the first value of the learning rate schedule. See more details at https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\n",
            "  \"https://pytorch.org/docs/stable/optim.html#how-to-adjust-learning-rate\", UserWarning)\n"
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              "                background-size: auto;\n",
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              "        </style>\n",
              "      \n",
              "      <progress value='16' max='16' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [16/16 08:55, Epoch 2/2]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Rouge2 Precision</th>\n",
              "      <th>Rouge2 Recall</th>\n",
              "      <th>Rouge2 Fmeasure</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>4</td>\n",
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              "      <td>7.960626</td>\n",
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              "      <td>0.002300</td>\n",
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              "      <td>0.015100</td>\n",
              "      <td>0.008000</td>\n",
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        {
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            "text/plain": [
              "TrainOutput(global_step=16, training_loss=8.646506309509277)"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZwQIEhKOrJpl"
      },
      "source": [
        "### **Evaluation**\n",
        "\n",
        "Awesome, we finished training our dummy model. Let's now evaluated the model on the test data. We make use of the dataset's handy `.map()` function to generate a summary of each sample of the test data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oOoSrwWarJAC",
        "outputId": "fdbf6e2a-ed91-4902-e1b8-5938311d3af3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85,
          "referenced_widgets": [
            "26e7fd38a2404fdfa35cd85aeaed344e",
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            "8f3600b5620b45b39d7e0b940f39960e",
            "a2ea693d77dd483e85604dcead910fc1"
          ]
        }
      },
      "source": [
        "import datasets\n",
        "from transformers import BertTokenizer, EncoderDecoderModel\n",
        "\n",
        "tokenizer = BertTokenizer.from_pretrained(\"bert-base-uncased\")\n",
        "model = EncoderDecoderModel.from_pretrained(\"./checkpoint-16\")\n",
        "model.to(\"cuda\")\n",
        "\n",
        "test_data = datasets.load_dataset(\"cnn_dailymail\", \"3.0.0\", split=\"test\")\n",
        "\n",
        "# only use 16 training examples for notebook - DELETE LINE FOR FULL TRAINING\n",
        "test_data = test_data.select(range(16))\n",
        "\n",
        "batch_size = 16  # change to 64 for full evaluation\n",
        "\n",
        "# map data correctly\n",
        "def generate_summary(batch):\n",
        "    # Tokenizer will automatically set [BOS] <text> [EOS]\n",
        "    # cut off at BERT max length 512\n",
        "    inputs = tokenizer(batch[\"article\"], padding=\"max_length\", truncation=True, max_length=512, return_tensors=\"pt\")\n",
        "    input_ids = inputs.input_ids.to(\"cuda\")\n",
        "    attention_mask = inputs.attention_mask.to(\"cuda\")\n",
        "\n",
        "    outputs = model.generate(input_ids, attention_mask=attention_mask)\n",
        "\n",
        "    # all special tokens including will be removed\n",
        "    output_str = tokenizer.batch_decode(outputs, skip_special_tokens=True)\n",
        "\n",
        "    batch[\"pred\"] = output_str\n",
        "\n",
        "    return batch\n",
        "\n",
        "results = test_data.map(generate_summary, batched=True, batch_size=batch_size, remove_columns=[\"article\"])\n",
        "\n",
        "pred_str = results[\"pred\"]\n",
        "label_str = results[\"highlights\"]\n",
        "\n",
        "rouge_output = rouge.compute(predictions=pred_str, references=label_str, rouge_types=[\"rouge2\"])[\"rouge2\"].mid\n",
        "\n",
        "print(rouge_output)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "26e7fd38a2404fdfa35cd85aeaed344e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=1.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Score(precision=0.005856995548236219, recall=0.020109209844549343, fmeasure=0.00887036509937936)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7zdm50ZotZqb"
      },
      "source": [
        "The fully trained *BERT2BERT* model is uploaded to the 🤗model hub under [patrickvonplaten/bert2bert_cnn_daily_mail](https://huggingface.co/patrickvonplaten/bert2bert_cnn_daily_mail). \n",
        "\n",
        "The model achieves a ROUGE-2 score of **18.22**, which is even a little better than reported in the paper.\n",
        "\n",
        "For some summarization examples, the reader is advised to use the online inference API of the model, [here](https://huggingface.co/patrickvonplaten/bert2bert_cnn_daily_mail)."
      ]
    }
  ]
}