Train_MLM.ipynb

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            "\u001b[K     |████████████████████████████████| 778kB 4.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.6/dist-packages (from transformers) (2019.12.20)\n",
            "Collecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7d/34/09d19aff26edcc8eb2a01bed8e98f13a1537005d31e95233fd48216eed10/sacremoses-0.0.43.tar.gz (883kB)\n",
            "\u001b[K     |████████████████████████████████| 890kB 8.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.6/dist-packages (from transformers) (3.0.12)\n",
            "Collecting tokenizers==0.8.1.rc1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/40/d0/30d5f8d221a0ed981a186c8eb986ce1c94e3a6e87f994eae9f4aa5250217/tokenizers-0.8.1rc1-cp36-cp36m-manylinux1_x86_64.whl (3.0MB)\n",
            "\u001b[K     |████████████████████████████████| 3.0MB 20.3MB/s \n",
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            "Collecting sentencepiece!=0.1.92\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d4/a4/d0a884c4300004a78cca907a6ff9a5e9fe4f090f5d95ab341c53d28cbc58/sentencepiece-0.1.91-cp36-cp36m-manylinux1_x86_64.whl (1.1MB)\n",
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            "Building wheels for collected packages: sacremoses\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.43-cp36-none-any.whl size=893260 sha256=24faa68d6ff1853d0d3aafdb90ed27793a525ac8a4eb964986ca483d3646a3c4\n",
            "  Stored in directory: /root/.cache/pip/wheels/29/3c/fd/7ce5c3f0666dab31a50123635e6fb5e19ceb42ce38d4e58f45\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: sacremoses, tokenizers, sentencepiece, transformers\n",
            "Successfully installed sacremoses-0.0.43 sentencepiece-0.1.91 tokenizers-0.8.1rc1 transformers-3.0.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EiTU6trYDal1",
        "outputId": "93ec3034-2037-4203-b8df-083d93e2e036",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266,
          "referenced_widgets": [
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            "f2e21964c5e04b4195baab3512b6da90",
            "85440a5e07ad45f8b9904c315cb8fefb",
            "873b0cd3cde94c328340797edbe52f6c",
            "5a01145fbde841909cf24665d001a779",
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            "8fb6377a08dc4c3a8c01f8ce84b70094"
          ]
        }
      },
      "source": [
        "from transformers import LongformerForMaskedLM, Trainer, TrainingArguments, LongformerTokenizer\n",
        "from datasets import load_dataset\n",
        "\n",
        "model = LongformerForMaskedLM.from_pretrained(\"allenai/longformer-large-4096\")\n",
        "tokenizer = LongformerTokenizer.from_pretrained(\"allenai/longformer-large-4096\")\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "model_id": "3a10a7da6f4b401584ce7fa3e0ba21a7",
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=803.0, style=ProgressStyle(description_…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "336d31b0fc9448c4891636d08ed4d98a",
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=1742910431.0, style=ProgressStyle(descr…"
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Some weights of LongformerForMaskedLM were not initialized from the model checkpoint at allenai/longformer-large-4096 and are newly initialized: ['lm_head.decoder.bias']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
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        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
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          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P96LEyq7DeHv"
      },
      "source": [
        "training_args = TrainingArguments(\n",
        "    output_dir='./results',          # output directory\n",
        "    num_train_epochs=3,              # total # of training epochs\n",
        "    per_device_train_batch_size=16,  # batch size per device during training\n",
        "    per_device_eval_batch_size=64,   # batch size for evaluation\n",
        "    warmup_steps=500,                # number of warmup steps for learning rate scheduler\n",
        "    weight_decay=0.01,               # strength of weight decay\n",
        "    logging_dir='./logs',            # directory for storing logs\n",
        ")\n",
        "\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q1nqwnlLNdmm"
      },
      "source": [
        "def load_data(path):\n",
        "  files_path = sorted(os.listdir(path))\n",
        "  dataset = load_dataset('text', data_files={'train': files_path})\n",
        "  return dataset"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0qSDryf_FNE4",
        "outputId": "1ebe1574-f2da-4680-e2b1-bba9fbf5ac7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "%%time\n",
        "from transformers import LineByLineTextDataset\n",
        "\n",
        "# dataset = LineByLineTextDataset(\n",
        "#     tokenizer=tokenizer,\n",
        "#     file_path=\"/content/sample_data/patent.txt\",\n",
        "#     block_size=128,\n",
        "# )\n",
        "train_dataset = load_data(path)\n",
        "test_dataset = train_dataset[1000:]\n",
        "from transformers import DataCollatorForLanguageModeling\n",
        "\n",
        "data_collator = DataCollatorForLanguageModeling(\n",
        "    tokenizer=tokenizer, mlm=True, mlm_probability=0.15\n",
        ")\n",
        "\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,                         # the instantiated 🤗 Transformers model to be trained\n",
        "    args=training_args,                  # training arguments, defined above\n",
        "    data_collator=data_collator,\n",
        "    train_dataset=train_dataset,         # training dataset\n",
        "    eval_dataset=test_dataset            # evaluation dataset\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 3.78 s, sys: 1.86 s, total: 5.65 s\n",
            "Wall time: 14.3 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
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      "metadata": {
        "id": "GkGqomyiDhf0",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 229,
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        }
      },
      "source": [
        "trainer.train()"
      ],
      "execution_count": null,
      "outputs": [
        {
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              "HBox(children=(FloatProgress(value=0.0, description='Epoch', max=3.0, style=ProgressStyle(description_width='i…"
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        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=3, training_loss=2.456737518310547)"
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          },
          "metadata": {
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          "execution_count": 8
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      ]
    }
  ]
}