Pretrain_LM.ipynb

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    "colab": {
      "name": "Fine_tune_Langauge_Model.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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    {
      "cell_type": "code",
      "metadata": {
        "id": "xYQ-PmL2oGEF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install transformers"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7rBZJN0m4SAV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!git clone https://github.com/yuanbit/FinBERT-QA\n",
        "%cd FinBERT-QA\n",
        "from src.utils import *"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pDmaizerpBnR",
        "colab_type": "code",
        "outputId": "3a1b784c-3720-4232-e896-44bd9a6e85b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 146
        }
      },
      "source": [
        "import torch\n",
        "import pickle\n",
        "import csv\n",
        "import regex as re\n",
        "import pandas as pd\n",
        "\n",
        "# Setting device on GPU if available\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "print('Using device:', device)\n",
        "print()\n",
        "\n",
        "if device.type == 'cuda':\n",
        "    print(torch.cuda.get_device_name(0))\n",
        "    print('Memory Usage:')\n",
        "    print('Allocated:', round(torch.cuda.memory_allocated(0)/1024**3,1), 'GB')\n",
        "    print('Cached:   ', round(torch.cuda.memory_cached(0)/1024**3,1), 'GB')\n",
        "\n",
        "torch.backends.cudnn.deterministic = True\n",
        "\n",
        "# Set the random seed manually for reproducibility.\n",
        "torch.manual_seed(1234)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using device: cuda\n",
            "\n",
            "Tesla P100-PCIE-16GB\n",
            "Memory Usage:\n",
            "Allocated: 0.0 GB\n",
            "Cached:    0.0 GB\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<torch._C.Generator at 0x7f3be570c130>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BMKZyF9zdzw3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Collection of answers - docid, text\n",
        "collection = pd.read_csv(\"retriever/collection_cleaned.tsv\", sep=\"\\t\", header=None)\n",
        "collection = collection.rename(columns={0: 'docid', 1: 'doc'})\n",
        "# Questions - qid, text\n",
        "query_df = pd.read_csv(\"data/raw/FiQA_train_question_final.tsv\", sep=\"\\t\")\n",
        "queries = query_df[['qid', 'question']]\n",
        "\n",
        "# List of empty docs\n",
        "empty_docs = load_pickle('data/id_to_text/empty_docs.pickle')\n",
        "\n",
        "# docid to text mapping\n",
        "docid_to_text = load_pickle('data/id_to_text/docid_to_text.pickle')\n",
        "# qid to text mapping\n",
        "qid_to_text = load_pickle('data/id_to_text/qid_to_text.pickle')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7-rNWqp1dsgC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load and process dataset\n",
        "dataset = pd.read_csv(\"data/raw/FiQA_train_question_doc_final.tsv\", sep=\"\\t\")\n",
        "dataset = dataset[[\"qid\", \"docid\"]]\n",
        "dataset = dataset[~dataset['docid'].isin(empty_docs)]\n",
        "dataset['question'] = dataset['qid'].apply(lambda x: qid_to_text[x])\n",
        "dataset['answer'] = dataset['docid'].apply(lambda x: docid_to_text[x])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YOv_l1hIoqtK",
        "colab_type": "code",
        "outputId": "3396b453-61f2-44fa-9cab-08743732cbfb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "source": [
        "dataset.head(5)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   qid  ...                                             answer\n",
              "0    0  ...  The IRS Guidance pertaining to the subject.  I...\n",
              "1    1  ...  Yes you can claim your business deductions if ...\n",
              "2    2  ...  You should have separate files for each of the...\n",
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              "\n",
              "[5 rows x 4 columns]"
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          "metadata": {
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          "execution_count": 6
        }
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    {
      "cell_type": "code",
      "metadata": {
        "id": "AF8OzCLfps7s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def add_ques_token(string):\n",
        "    question = string + \" [SEP] \"\n",
        "\n",
        "    return question"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f4-4w4xowyM9",
        "colab_type": "code",
        "outputId": "b0b29988-0584-4497-e1d5-7b137235469d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "# Concatenate question and answer with a separator\n",
        "dataset['question'] = dataset['question'].apply(add_ques_token)\n",
        "dataset['seq'] = dataset['question'] + dataset['answer']\n",
        "dataset = dataset[['seq']]\n",
        "\n",
        "dataset.at[17081, \"seq\"]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "\"Is it wise to switch investment strategy frequently? [SEP] My super fund and I would say many other funds give you one free switch of strategies per year.  Some suggest you should change from high growth option to a more balance option once you are say about 10 to 15 years from retirement, and then change to a more capital guaranteed option a few years from retirement. This is a more passive approach and has benefits as well as disadvantages. The benefit is that there is not much work involved, you just change your investment option based on your life stage, 2 to 3 times during your lifetime. This allows you to take more risk when you are young to aim for higher returns, take a balanced approach with moderate risk and returns during the middle part of your working life, and take less risk with lower returns (above inflation) during the latter part of your working life. A possible disadvantage of this strategy is you may be in the higher risk/ higher growth option during a market correction and then change to a more balanced option just when the market starts to pick up again. So your funds will be hit with large losses whilst the market is in retreat and just when things look to be getting better you change to a more balanced portfolio and miss out on the big gains. A second more active approach would be to track the market and change investment option as the market changes. One approach which shouldn't take much time is to track the index such as the ASX200 (if you investment option is mainly invested in the Australian stock market) with a 200 day Simple Moving Average (SMA). The concept is that if the index crosses above the 200 day SMA the market is bullish and if it crosses below it is bearish. See the chart below:  This strategy will work well when the market is trending up or down but not very well when the market is going sideways, as you will be changing from aggressive to balanced and back too often. Possibly a more appropriate option would be a combination of the two. Use the first passive approach to change investment option from aggressive to balanced to capital guaranteed with your life stages, however use the second active approach to time the change. For example, if you were say in your late 40s now and were looking to change from aggressive to balanced in the near future, you could wait until the ASX200 crosses below the 200 day SMA before making the change. This way you could capture the majority of the uptrend (which could go on for years) before changing from the high growth/aggressive option to the balanced option. If you where after more control over your superannuation assets another option open to you is to start a SMSF, however I would recommend having at least $300K to $400K in assets before starting a SMSF, or else the annual costs would be too high as a percentage of your total super assets.\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "70xxevtRhOds",
        "colab": {}
      },
      "source": [
        "# Write data to file\n",
        "dataset.to_csv('data/data.txt',index=False,header=False, sep=\"\\t\", quoting=csv.QUOTE_NONE)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AisyaywRqjI_",
        "colab_type": "code",
        "outputId": "03f87925-a1ff-4963-b040-376fd3e82def",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 146
        }
      },
      "source": [
        "!git clone https://github.com/huggingface/transformers.git"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'transformers'...\n",
            "remote: Enumerating objects: 19, done.\u001b[K\n",
            "remote: Counting objects: 100% (19/19), done.\u001b[K\n",
            "remote: Compressing objects: 100% (18/18), done.\u001b[K\n",
            "remote: Total 23768 (delta 0), reused 9 (delta 0), pack-reused 23749\u001b[K\n",
            "Receiving objects: 100% (23768/23768), 14.22 MiB | 14.48 MiB/s, done.\n",
            "Resolving deltas: 100% (16827/16827), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "siEH8D4d5LBM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!mkdir model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qIblCY05tY0r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cmd = '''python /content/transformers/examples/run_language_modeling.py \\\n",
        "    --train_data_file /content/FinBERT-QA/data/data.txt \\\n",
        "    --output_dir /content/FinBERT-QA/model \\ \n",
        "    --model_type bert \\\n",
        "    --mlm \\\n",
        "    --model_name_or_path bert-base-uncased \\\n",
        "    --do_train \\\n",
        "    --line_by_line \\\n",
        "    --overwrite_output_dir \\\n",
        "    --num_train_epochs 1 \\\n",
        "    --save_total_limit 2 \\\n",
        "    --block_size 512 \\\n",
        "    --save_steps 2000 \\\n",
        "    --per_gpu_train_batch_size 8 \\\n",
        "    --seed 42'''"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4-wUC508_88k",
        "colab_type": "code",
        "outputId": "76dd7375-d9ff-4d91-ffc4-8228baa2681f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "%%time\n",
        "!{cmd}"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "03/27/2020 12:18:14 - WARNING - __main__ -   Process rank: -1, device: cuda, n_gpu: 1, distributed training: False, 16-bits training: False\n",
            "03/27/2020 12:18:14 - INFO - filelock -   Lock 140196865260568 acquired on /root/.cache/torch/transformers/4dad0251492946e18ac39290fcfe91b89d370fee250efe9521476438fe8ca185.8f56353af4a709bf5ff0fbc915d8f5b42bfff892cbb6ac98c3c45f481a03c685.lock\n",
            "03/27/2020 12:18:14 - INFO - transformers.file_utils -   https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-config.json not found in cache or force_download set to True, downloading to /root/.cache/torch/transformers/tmpnsdmynxv\n",
            "Downloading: 100% 361/361 [00:00<00:00, 393kB/s]\n",
            "03/27/2020 12:18:14 - INFO - transformers.file_utils -   storing https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-config.json in cache at /root/.cache/torch/transformers/4dad0251492946e18ac39290fcfe91b89d370fee250efe9521476438fe8ca185.8f56353af4a709bf5ff0fbc915d8f5b42bfff892cbb6ac98c3c45f481a03c685\n",
            "03/27/2020 12:18:14 - INFO - transformers.file_utils -   creating metadata file for /root/.cache/torch/transformers/4dad0251492946e18ac39290fcfe91b89d370fee250efe9521476438fe8ca185.8f56353af4a709bf5ff0fbc915d8f5b42bfff892cbb6ac98c3c45f481a03c685\n",
            "03/27/2020 12:18:14 - INFO - filelock -   Lock 140196865260568 released on /root/.cache/torch/transformers/4dad0251492946e18ac39290fcfe91b89d370fee250efe9521476438fe8ca185.8f56353af4a709bf5ff0fbc915d8f5b42bfff892cbb6ac98c3c45f481a03c685.lock\n",
            "03/27/2020 12:18:14 - INFO - transformers.configuration_utils -   loading configuration file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-config.json from cache at /root/.cache/torch/transformers/4dad0251492946e18ac39290fcfe91b89d370fee250efe9521476438fe8ca185.8f56353af4a709bf5ff0fbc915d8f5b42bfff892cbb6ac98c3c45f481a03c685\n",
            "03/27/2020 12:18:14 - INFO - transformers.configuration_utils -   Model config BertConfig {\n",
            "  \"_num_labels\": 2,\n",
            "  \"architectures\": [\n",
            "    \"BertForMaskedLM\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"bos_token_id\": null,\n",
            "  \"do_sample\": false,\n",
            "  \"early_stopping\": false,\n",
            "  \"eos_token_id\": null,\n",
            "  \"finetuning_task\": 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-12,\n",
            "  \"length_penalty\": 1.0,\n",
            "  \"max_length\": 20,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"min_length\": 0,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"no_repeat_ngram_size\": 0,\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_beams\": 1,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"num_return_sequences\": 1,\n",
            "  \"output_attentions\": false,\n",
            "  \"output_hidden_states\": false,\n",
            "  \"output_past\": true,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"pruned_heads\": {},\n",
            "  \"repetition_penalty\": 1.0,\n",
            "  \"temperature\": 1.0,\n",
            "  \"top_k\": 50,\n",
            "  \"top_p\": 1.0,\n",
            "  \"torchscript\": false,\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_bfloat16\": false,\n",
            "  \"vocab_size\": 30522\n",
            "}\n",
            "\n",
            "03/27/2020 12:18:14 - INFO - transformers.configuration_utils -   loading configuration file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-config.json from cache at /root/.cache/torch/transformers/4dad0251492946e18ac39290fcfe91b89d370fee250efe9521476438fe8ca185.8f56353af4a709bf5ff0fbc915d8f5b42bfff892cbb6ac98c3c45f481a03c685\n",
            "03/27/2020 12:18:14 - INFO - transformers.configuration_utils -   Model config BertConfig {\n",
            "  \"_num_labels\": 2,\n",
            "  \"architectures\": [\n",
            "    \"BertForMaskedLM\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"bos_token_id\": null,\n",
            "  \"do_sample\": false,\n",
            "  \"early_stopping\": false,\n",
            "  \"eos_token_id\": null,\n",
            "  \"finetuning_task\": 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-12,\n",
            "  \"length_penalty\": 1.0,\n",
            "  \"max_length\": 20,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"min_length\": 0,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"no_repeat_ngram_size\": 0,\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_beams\": 1,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"num_return_sequences\": 1,\n",
            "  \"output_attentions\": false,\n",
            "  \"output_hidden_states\": false,\n",
            "  \"output_past\": true,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"pruned_heads\": {},\n",
            "  \"repetition_penalty\": 1.0,\n",
            "  \"temperature\": 1.0,\n",
            "  \"top_k\": 50,\n",
            "  \"top_p\": 1.0,\n",
            "  \"torchscript\": false,\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_bfloat16\": false,\n",
            "  \"vocab_size\": 30522\n",
            "}\n",
            "\n",
            "03/27/2020 12:18:14 - INFO - filelock -   Lock 140196492586008 acquired on /root/.cache/torch/transformers/26bc1ad6c0ac742e9b52263248f6d0f00068293b33709fae12320c0e35ccfbbb.542ce4285a40d23a559526243235df47c5f75c197f04f37d1a0c124c32c9a084.lock\n",
            "03/27/2020 12:18:14 - INFO - transformers.file_utils -   https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-vocab.txt not found in cache or force_download set to True, downloading to /root/.cache/torch/transformers/tmpog0e8ug9\n",
            "Downloading: 100% 232k/232k [00:00<00:00, 5.39MB/s]\n",
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            "03/27/2020 12:18:23 - INFO - transformers.modeling_utils -   loading weights file https://s3.amazonaws.com/models.huggingface.co/bert/bert-base-uncased-pytorch_model.bin from cache at /root/.cache/torch/transformers/aa1ef1aede4482d0dbcd4d52baad8ae300e60902e88fcb0bebdec09afd232066.36ca03ab34a1a5d5fa7bc3d03d55c4fa650fed07220e2eeebc06ce58d0e9a157\n",
            "03/27/2020 12:18:27 - INFO - transformers.modeling_utils -   Weights of BertForMaskedLM not initialized from pretrained model: ['cls.predictions.decoder.bias']\n",
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            "03/27/2020 12:18:31 - INFO - __main__ -   Training/evaluation parameters Namespace(adam_epsilon=1e-08, block_size=512, cache_dir=None, config_name=None, device=device(type='cuda'), do_eval=False, do_train=True, eval_all_checkpoints=False, eval_data_file=None, evaluate_during_training=False, fp16=False, fp16_opt_level='O1', gradient_accumulation_steps=1, learning_rate=5e-05, line_by_line=True, local_rank=-1, logging_steps=500, max_grad_norm=1.0, max_steps=-1, mlm=True, mlm_probability=0.15, model_name_or_path='bert-base-uncased', model_type='bert', n_gpu=1, no_cuda=False, num_train_epochs=1.0, output_dir='/content/drive/My Drive/fiqa/bert-lm/lm_model_bert', overwrite_cache=False, overwrite_output_dir=True, per_gpu_eval_batch_size=4, per_gpu_train_batch_size=8, save_steps=2000, save_total_limit=2, seed=42, server_ip='', server_port='', should_continue=False, tokenizer_name=None, train_data_file='/content/drive/My Drive/fiqa/bert-lm/data.txt', warmup_steps=0, weight_decay=0.0)\n",
            "03/27/2020 12:18:31 - INFO - __main__ -   Creating features from dataset file at /content/drive/My Drive/fiqa/bert-lm/data.txt\n",
            "03/27/2020 12:19:44 - INFO - __main__ -   ***** Running training *****\n",
            "03/27/2020 12:19:44 - INFO - __main__ -     Num examples = 17072\n",
            "03/27/2020 12:19:44 - INFO - __main__ -     Num Epochs = 1\n",
            "03/27/2020 12:19:44 - INFO - __main__ -     Instantaneous batch size per GPU = 8\n",
            "03/27/2020 12:19:44 - INFO - __main__ -     Total train batch size (w. parallel, distributed & accumulation) = 8\n",
            "03/27/2020 12:19:44 - INFO - __main__ -     Gradient Accumulation steps = 1\n",
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            "03/27/2020 12:38:00 - INFO - __main__ -    global_step = 2134, average loss = 2.104712081715227\n",
            "03/27/2020 12:38:00 - INFO - __main__ -   Saving model checkpoint to /content/drive/My Drive/fiqa/bert-lm/lm_model_bert\n",
            "03/27/2020 12:38:00 - INFO - transformers.configuration_utils -   Configuration saved in /content/drive/My Drive/fiqa/bert-lm/lm_model_bert/config.json\n",
            "03/27/2020 12:38:01 - INFO - transformers.modeling_utils -   Model weights saved in /content/drive/My Drive/fiqa/bert-lm/lm_model_bert/pytorch_model.bin\n",
            "03/27/2020 12:38:02 - INFO - transformers.configuration_utils -   loading configuration file /content/drive/My Drive/fiqa/bert-lm/lm_model_bert/config.json\n",
            "03/27/2020 12:38:02 - INFO - transformers.configuration_utils -   Model config BertConfig {\n",
            "  \"_num_labels\": 2,\n",
            "  \"architectures\": [\n",
            "    \"BertForMaskedLM\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"bos_token_id\": null,\n",
            "  \"do_sample\": false,\n",
            "  \"early_stopping\": false,\n",
            "  \"eos_token_id\": null,\n",
            "  \"finetuning_task\": 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-12,\n",
            "  \"length_penalty\": 1.0,\n",
            "  \"max_length\": 20,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"min_length\": 0,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"no_repeat_ngram_size\": 0,\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_beams\": 1,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"num_return_sequences\": 1,\n",
            "  \"output_attentions\": false,\n",
            "  \"output_hidden_states\": false,\n",
            "  \"output_past\": true,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"pruned_heads\": {},\n",
            "  \"repetition_penalty\": 1.0,\n",
            "  \"temperature\": 1.0,\n",
            "  \"top_k\": 50,\n",
            "  \"top_p\": 1.0,\n",
            "  \"torchscript\": false,\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_bfloat16\": false,\n",
            "  \"vocab_size\": 30522\n",
            "}\n",
            "\n",
            "03/27/2020 12:38:02 - INFO - transformers.modeling_utils -   loading weights file /content/drive/My Drive/fiqa/bert-lm/lm_model_bert/pytorch_model.bin\n",
            "03/27/2020 12:38:06 - INFO - transformers.configuration_utils -   loading configuration file /content/drive/My Drive/fiqa/bert-lm/lm_model_bert/config.json\n",
            "03/27/2020 12:38:06 - INFO - transformers.configuration_utils -   Model config BertConfig {\n",
            "  \"_num_labels\": 2,\n",
            "  \"architectures\": [\n",
            "    \"BertForMaskedLM\"\n",
            "  ],\n",
            "  \"attention_probs_dropout_prob\": 0.1,\n",
            "  \"bos_token_id\": null,\n",
            "  \"do_sample\": false,\n",
            "  \"early_stopping\": false,\n",
            "  \"eos_token_id\": null,\n",
            "  \"finetuning_task\": 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-12,\n",
            "  \"length_penalty\": 1.0,\n",
            "  \"max_length\": 20,\n",
            "  \"max_position_embeddings\": 512,\n",
            "  \"min_length\": 0,\n",
            "  \"model_type\": \"bert\",\n",
            "  \"no_repeat_ngram_size\": 0,\n",
            "  \"num_attention_heads\": 12,\n",
            "  \"num_beams\": 1,\n",
            "  \"num_hidden_layers\": 12,\n",
            "  \"num_return_sequences\": 1,\n",
            "  \"output_attentions\": false,\n",
            "  \"output_hidden_states\": false,\n",
            "  \"output_past\": true,\n",
            "  \"pad_token_id\": 0,\n",
            "  \"pruned_heads\": {},\n",
            "  \"repetition_penalty\": 1.0,\n",
            "  \"temperature\": 1.0,\n",
            "  \"top_k\": 50,\n",
            "  \"top_p\": 1.0,\n",
            "  \"torchscript\": false,\n",
            "  \"type_vocab_size\": 2,\n",
            "  \"use_bfloat16\": false,\n",
            "  \"vocab_size\": 30522\n",
            "}\n",
            "\n",
            "03/27/2020 12:38:06 - INFO - transformers.tokenization_utils -   Model name '/content/drive/My Drive/fiqa/bert-lm/lm_model_bert' not found in model shortcut name list (bert-base-uncased, bert-large-uncased, bert-base-cased, bert-large-cased, bert-base-multilingual-uncased, bert-base-multilingual-cased, bert-base-chinese, bert-base-german-cased, bert-large-uncased-whole-word-masking, bert-large-cased-whole-word-masking, bert-large-uncased-whole-word-masking-finetuned-squad, bert-large-cased-whole-word-masking-finetuned-squad, bert-base-cased-finetuned-mrpc, bert-base-german-dbmdz-cased, bert-base-german-dbmdz-uncased, bert-base-finnish-cased-v1, bert-base-finnish-uncased-v1, bert-base-dutch-cased). Assuming '/content/drive/My Drive/fiqa/bert-lm/lm_model_bert' is a path, a model identifier, or url to a directory containing tokenizer files.\n",
            "03/27/2020 12:38:06 - INFO - transformers.tokenization_utils -   Didn't find file /content/drive/My Drive/fiqa/bert-lm/lm_model_bert/added_tokens.json. We won't load it.\n",
            "03/27/2020 12:38:06 - INFO - transformers.tokenization_utils -   loading file /content/drive/My Drive/fiqa/bert-lm/lm_model_bert/vocab.txt\n",
            "03/27/2020 12:38:06 - INFO - transformers.tokenization_utils -   loading file None\n",
            "03/27/2020 12:38:06 - INFO - transformers.tokenization_utils -   loading file /content/drive/My Drive/fiqa/bert-lm/lm_model_bert/special_tokens_map.json\n",
            "03/27/2020 12:38:06 - INFO - transformers.tokenization_utils -   loading file /content/drive/My Drive/fiqa/bert-lm/lm_model_bert/tokenizer_config.json\n",
            "CPU times: user 7.22 s, sys: 1.08 s, total: 8.31 s\n",
            "Wall time: 19min 57s\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}