SCIERC_NER.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Serbernari/NER-summarization/blob/main/SCIERC_NER.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Dataset Preprocessing"
      ],
      "metadata": {
        "id": "8gVprtkbH9ga"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Download SCIERC dataset from http://nlp.cs.washington.edu/sciIE/"
      ],
      "metadata": {
        "id": "rNanQcrSHe1E"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RsOEtJCKHUW-",
        "outputId": "b1ae0c72-c98c-4a00-ec26-d30493fc3d18"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2022-05-05 15:31:36--  http://nlp.cs.washington.edu/sciIE/data/sciERC_raw.tar.gz\n",
            "Resolving nlp.cs.washington.edu (nlp.cs.washington.edu)... 128.208.3.120, 2607:4000:200:12::78\n",
            "Connecting to nlp.cs.washington.edu (nlp.cs.washington.edu)|128.208.3.120|:80... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3369626 (3.2M) [application/x-gzip]\n",
            "Saving to: ‘sciERC_raw.tar.gz’\n",
            "\n",
            "sciERC_raw.tar.gz   100%[===================>]   3.21M  2.48MB/s    in 1.3s    \n",
            "\n",
            "2022-05-05 15:31:37 (2.48 MB/s) - ‘sciERC_raw.tar.gz’ saved [3369626/3369626]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget http://nlp.cs.washington.edu/sciIE/data/sciERC_raw.tar.gz"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!tar -xf sciERC_raw.tar.gz"
      ],
      "metadata": {
        "id": "aoHetNOFHnhx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/Serbernari/brat"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UQoWlY_UJjMQ",
        "outputId": "35db9858-4686-41e2-f6d7-fadb0908cb47"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'brat'...\n",
            "remote: Enumerating objects: 17080, done.\u001b[K\n",
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            "remote: Compressing objects: 100% (35/35), done.\u001b[K\n",
            "remote: Total 17080 (delta 21), reused 37 (delta 16), pack-reused 17029\u001b[K\n",
            "Receiving objects: 100% (17080/17080), 84.48 MiB | 30.69 MiB/s, done.\n",
            "Resolving deltas: 100% (10315/10315), done.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Если будет ошибка с импортом sentencepice то в anntoconll.py замени импорт на from brat.tools.sentencesplit import sentencebreaks_to_newlines"
      ],
      "metadata": {
        "id": "HzmKgjZxIfxu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from __future__ import print_function\n",
        "\n",
        "import os\n",
        "import re\n",
        "import sys\n",
        "from collections import namedtuple\n",
        "from io import StringIO\n",
        "from os import path\n",
        "from tqdm import tqdm\n",
        "import glob\n",
        "from brat.tools.anntoconll import process_file\n",
        "import pandas as pd"
      ],
      "metadata": {
        "id": "nefbVvVmH6tG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir \"conll\""
      ],
      "metadata": {
        "id": "3DMpCg_9P5bN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir \"conll/raw_data\""
      ],
      "metadata": {
        "id": "8_9O-8ZEQCB8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "path = \"raw_data/\"\n",
        "directory = os.fsencode(path) #Set directory with both .wav and .txt files here\n",
        "\n",
        "error_count = 0\n",
        "sucsess_count = 0\n",
        "\n",
        "for file in tqdm(os.listdir(directory)):\n",
        "    filename = os.fsdecode(file)\n",
        "    \n",
        "    if filename.endswith(\".txt\"):\n",
        "      try:\n",
        "          print(path+filename)\n",
        "          process_file(path+filename)\n",
        "          sucsess_count +=1\n",
        "      except:\n",
        "          error_count += 1\n",
        "    "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jlRsErk5MMGX",
        "outputId": "e514e9b0-e9cf-4db0-9e5f-d6308f13acf4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "  0%|          | 0/1508 [00:00<?, ?it/s]Eliminate Textbound(start=37, end=59, type='Task', text='loss-aware predictions') due to overlap with Textbound(start=37, end=90, type='Task', text='loss-aware predictions in image segmentation settings')\n",
            "Eliminate Textbound(start=37, end=59, type='Task', text='loss-aware predictions') due to overlap with Textbound(start=37, end=90, type='Task', text='loss-aware predictions in image segmentation settings')\n",
            "Eliminate Textbound(start=63, end=90, type='Task', text='image segmentation settings') due to overlap with Textbound(start=37, end=90, type='Task', text='loss-aware predictions in image segmentation settings')\n",
            "Eliminate Textbound(start=63, end=90, type='Task', text='image segmentation settings') due to overlap with Textbound(start=37, end=90, type='Task', text='loss-aware predictions in image segmentation settings')\n",
            "Eliminate Textbound(start=705, end=710, type='OtherScientificTerm', text='EIoEU') due to overlap with Textbound(start=705, end=724, type='Method', text='EIoEU approximation')\n",
            "Eliminate Textbound(start=705, end=710, type='OtherScientificTerm', text='EIoEU') due to overlap with Textbound(start=705, end=724, type='Method', text='EIoEU approximation')\n",
            "Eliminate Textbound(start=115, end=139, type='Method', text='Natural Language systems') due to overlap with Textbound(start=101, end=139, type='Task', text='evaluation of Natural Language systems')\n",
            "Eliminate Textbound(start=115, end=139, type='Method', text='Natural Language systems') due to overlap with Textbound(start=101, end=139, type='Task', text='evaluation of Natural Language systems')\n",
            "  9%|▉         | 142/1508 [00:00<00:00, 1410.65it/s]Eliminate Textbound(start=52, end=79, type='OtherScientificTerm', text='rare behaviours of interest') due to overlap with Textbound(start=40, end=79, type='Task', text='identifying rare behaviours of interest')\n",
            "Eliminate Textbound(start=52, end=79, type='OtherScientificTerm', text='rare behaviours of interest') due to overlap with Textbound(start=40, end=79, type='Task', text='identifying rare behaviours of interest')\n",
            "Eliminate Textbound(start=484, end=504, type='OtherScientificTerm', text='temporal consistency') due to overlap with Textbound(start=476, end=504, type='OtherScientificTerm', text='lack of temporal consistency')\n",
            "Eliminate Textbound(start=484, end=504, type='OtherScientificTerm', text='temporal consistency') due to overlap with Textbound(start=476, end=504, type='OtherScientificTerm', text='lack of temporal consistency')\n",
            "Eliminate Textbound(start=570, end=596, type='OtherScientificTerm', text='dominant motion assumption') due to overlap with Textbound(start=554, end=596, type='OtherScientificTerm', text='validity of the dominant motion assumption')\n",
            "Eliminate Textbound(start=570, end=596, type='OtherScientificTerm', text='dominant motion assumption') due to overlap with Textbound(start=554, end=596, type='OtherScientificTerm', text='validity of the dominant motion assumption')\n",
            "Eliminate Textbound(start=766, end=777, type='Method', text='SLS systems') due to overlap with Textbound(start=751, end=777, type='Task', text='evaluation of  SLS systems')\n",
            "Eliminate Textbound(start=766, end=777, type='Method', text='SLS systems') due to overlap with Textbound(start=751, end=777, type='Task', text='evaluation of  SLS systems')\n",
            "Eliminate Textbound(start=657, end=660, type='OtherScientificTerm', text='NSI') due to overlap with Textbound(start=630, end=660, type='OtherScientificTerm', text='distribution of speech and NSI')\n",
            "Eliminate Textbound(start=657, end=660, type='OtherScientificTerm', text='NSI') due to overlap with Textbound(start=630, end=660, type='OtherScientificTerm', text='distribution of speech and NSI')\n",
            "Eliminate Textbound(start=637, end=668, type='Material', text='multilingually aligned wordnets') due to overlap with Textbound(start=615, end=668, type='OtherScientificTerm', text='alignment errors  in  multilingually aligned wordnets')\n",
            "Eliminate Textbound(start=637, end=668, type='Material', text='multilingually aligned wordnets') due to overlap with Textbound(start=615, end=668, type='OtherScientificTerm', text='alignment errors  in  multilingually aligned wordnets')\n",
            "Eliminate Textbound(start=376, end=413, type='OtherScientificTerm', text='discriminative co-occurrence patterns') due to overlap with Textbound(start=364, end=413, type='Task', text='identifying discriminative co-occurrence patterns')\n",
            "Eliminate Textbound(start=376, end=413, type='OtherScientificTerm', text='discriminative co-occurrence patterns') due to overlap with Textbound(start=364, end=413, type='Task', text='identifying discriminative co-occurrence patterns')\n",
            " 19%|█▉        | 284/1508 [00:00<00:00, 1390.85it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
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            "raw_data/P03-1005.txt\n",
            "raw_data/P03-1070.txt\n",
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            "raw_data/H01-1049.txt\n",
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            "raw_data/CVPR_2009_21_abs.txt\n",
            "raw_data/P01-1047.txt\n",
            "raw_data/ICASSP_2016_3_abs.txt\n",
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            "raw_data/C04-1103.txt\n",
            "raw_data/P80-1026.txt\n",
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            "raw_data/I05-5004.txt\n",
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            "raw_data/ICCV_1999_47_abs.txt\n",
            "raw_data/ICCV_1999_47_abs.txt\n",
            "raw_data/CVPR_2009_10_abs.txt\n",
            "raw_data/CVPR_2009_10_abs.txt\n",
            "raw_data/NIPS_2016_80_abs.txt\n",
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            "raw_data/E99-1023.txt\n",
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            "raw_data/CVPR_2008_256_abs.txt\n",
            "raw_data/CVPR_2008_256_abs.txt\n",
            "raw_data/E93-1025.txt\n",
            "raw_data/H05-1101.txt\n",
            "raw_data/P06-2012.txt\n",
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          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Eliminate Textbound(start=498, end=501, type='Material', text='NAM') due to overlap with Textbound(start=498, end=529, type='Material', text='NAM and electrolaryngeal speech')\n",
            "Eliminate Textbound(start=498, end=501, type='Material', text='NAM') due to overlap with Textbound(start=498, end=529, type='Material', text='NAM and electrolaryngeal speech')\n",
            "Eliminate Textbound(start=416, end=435, type='Method', text='active shape models') due to overlap with Textbound(start=391, end=435, type='Method', text='non-linear extensions of active shape models')\n",
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            "Eliminate Textbound(start=389, end=395, type='Generic', text='scenes') due to overlap with Textbound(start=356, end=395, type='Task', text='modeling and subtraction of such scenes')\n",
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            "\r 28%|██▊       | 425/1508 [00:00<00:00, 1392.55it/s]Eliminate Textbound(start=39, end=60, type='Method', text='Kikuchi approximation') due to overlap with Textbound(start=13, end=60, type='Method', text='reweighted version of the Kikuchi approximation')\n",
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            "Eliminate Textbound(start=65, end=99, type='OtherScientificTerm', text='subcategorization frames  ( scfs )') due to overlap with Textbound(start=44, end=99, type='Task', text='acquiring adjectival subcategorization frames  ( scfs )')\n",
            "Eliminate Textbound(start=65, end=99, type='OtherScientificTerm', text='subcategorization frames  ( scfs )') due to overlap with Textbound(start=44, end=99, type='Task', text='acquiring adjectival subcategorization frames  ( scfs )')\n",
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            "Eliminate Textbound(start=118, end=124, type='Material', text='images') due to overlap with Textbound(start=94, end=124, type='Task', text='automatically detecting images')\n",
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            "Eliminate Textbound(start=380, end=411, type='Method', text='object intrinsic representation') due to overlap with Textbound(start=355, end=411, type='Task', text='unsupervised learning of object intrinsic representation')\n",
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            "raw_data/ECCV_2006_13_abs.txt\n",
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            "raw_data/P05-2016.txt\n",
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            "raw_data/N03-1012.txt\n",
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            "raw_data/I05-2021.txt\n",
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            "raw_data/CVPR_1999_15_abs.txt\n",
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            "raw_data/P06-4011.txt\n",
            "raw_data/NIPS_1996_15_abs.txt\n",
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            "raw_data/P05-3030.txt\n",
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            "raw_data/INTERSPEECH_2008_21_abs.txt\n",
            "raw_data/INTERSPEECH_2008_21_abs.txt\n",
            "raw_data/I05-6011.txt\n",
            "raw_data/ICML_2006_122_abs.txt\n",
            "raw_data/ICML_2006_122_abs.txt\n",
            "raw_data/H92-1026.txt\n",
            "raw_data/C90-1002.txt\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Eliminate Textbound(start=55, end=67, type='Task', text='trend survey') due to overlap with Textbound(start=55, end=116, type='Task', text='trend survey on  Japanese natural language processing studies')\n",
            "Eliminate Textbound(start=55, end=67, type='Task', text='trend survey') due to overlap with Textbound(start=55, end=116, type='Task', text='trend survey on  Japanese natural language processing studies')\n",
            "Eliminate Textbound(start=72, end=116, type='Material', text='Japanese natural language processing studies') due to overlap with Textbound(start=55, end=116, type='Task', text='trend survey on  Japanese natural language processing studies')\n",
            "Eliminate Textbound(start=72, end=116, type='Material', text='Japanese natural language processing studies') due to overlap with Textbound(start=55, end=116, type='Task', text='trend survey on  Japanese natural language processing studies')\n",
            "100%|██████████| 1508/1508 [00:01<00:00, 1349.94it/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "raw_data/E85-1041.txt\n",
            "raw_data/E85-1041.txt\n",
            "raw_data/CVPR_2012_10_abs.txt\n",
            "raw_data/CVPR_2012_10_abs.txt\n",
            "raw_data/P06-2059.txt\n",
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            "raw_data/ICCV_2001_50_abs.txt\n",
            "raw_data/ICCV_2001_50_abs.txt\n",
            "raw_data/ECCV_2014_50_abs.txt\n",
            "raw_data/ECCV_2014_50_abs.txt\n",
            "raw_data/A88-1001.txt\n",
            "raw_data/ICCV_2015_400_abs.txt\n",
            "raw_data/ICCV_2015_400_abs.txt\n",
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            "raw_data/C04-1024.txt\n",
            "raw_data/C04-1024.txt\n",
            "raw_data/NIPS_1998_18_abs.txt\n",
            "raw_data/NIPS_1998_18_abs.txt\n",
            "raw_data/P05-1057.txt\n",
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            "raw_data/CVPR_2012_18_abs.txt\n",
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            "raw_data/P08-2034.txt\n",
            "raw_data/INTERSPEECH_2006_21_abs.txt\n",
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            "raw_data/C04-1080.txt\n",
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            "raw_data/CVPR_2003_30_abs.txt\n",
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            "raw_data/C88-1007.txt\n",
            "raw_data/CVPR_2010_11_abs.txt\n",
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            "raw_data/CVPR_2005_10_abs.txt\n",
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            "raw_data/AAAI_2008_262_abs.txt\n",
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            "raw_data/INTERSPEECH_2013_28_abs.txt\n",
            "raw_data/INTERSPEECH_2013_28_abs.txt\n",
            "raw_data/N04-2005.txt\n",
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            "raw_data/ICASSP_1997_714_abs.txt\n",
            "raw_data/ICASSP_1997_714_abs.txt\n",
            "raw_data/C02-1071.txt\n",
            "raw_data/C02-1071.txt\n",
            "raw_data/E87-1037.txt\n",
            "raw_data/E87-1037.txt\n"
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        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
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    {
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      "source": [
        "error_count"
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      "metadata": {
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          "base_uri": "https://localhost:8080/"
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        "id": "BOHYWs2jPGg8",
        "outputId": "8ebb9729-e2a1-4bf2-b208-08b309693dbd"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "164"
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          },
          "metadata": {},
          "execution_count": 6
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      "cell_type": "code",
      "source": [
        "sucsess_count"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "jBYHKr0xPQE-",
        "outputId": "931104e0-7c83-4d6b-c858-f1f8f436e1de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "336"
            ]
          },
          "metadata": {},
          "execution_count": 7
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      ]
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    {
      "cell_type": "code",
      "source": [
        "data_df = pd.read_csv(\"conll/raw_data/A00-2023.txt\", delimiter=\"\\t\", header = None) \n",
        "data_df = data_df.drop([data_df.columns[1],data_df.columns[2]], axis=1)\n",
        "data_df = data_df[[3, 0]]\n",
        "data_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ffATvoFlQcqb",
        "outputId": "bf3aaf59-8f38-4d56-ed90-bf919094e14c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "           3         0\n",
              "0       This         O\n",
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              "85         -  I-Method\n",
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              "                                                     [key], {});\n",
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              "\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "path = \"./conll/raw_data/\"\n",
        "directory = os.fsencode(path) #Set directory with both .wav and .txt files here\n",
        "all_data_df = pd.DataFrame()\n",
        "\n",
        "all_lines = list()\n",
        "\n",
        "for file in os.listdir(directory):\n",
        "    filename = os.fsdecode(file)    \n",
        "    if filename.endswith(\".txt\"):\n",
        "        with open(path+filename) as f:\n",
        "            content = f.readlines()\n",
        "            all_lines.extend(content)"
      ],
      "metadata": {
        "id": "K9wJEdp_QkSk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!touch finfile.txt"
      ],
      "metadata": {
        "id": "WQ9qhs54Q3ER"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open('finfile.txt', 'a') as the_file:\n",
        "    for line in all_lines:\n",
        "        if line != '\\n':\n",
        "            line = line.split('\\t')\n",
        "            line = [line[-1][:-1], line[0]]\n",
        "            #print(new_line)\n",
        "            the_file.write((line[0]+' '+line[1]+'\\n'))\n",
        "        else:\n",
        "            the_file.write(line)"
      ],
      "metadata": {
        "id": "ziXlrkCsQymy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# NER training"
      ],
      "metadata": {
        "id": "OPpa0bKCRa9q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install simpletransformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "WJXwkjopR1h2",
        "outputId": "bc41fdf1-2dcd-4a25-e8b1-c16ab9b9c994"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting simpletransformers\n",
            "  Downloading simpletransformers-0.63.6-py3-none-any.whl (249 kB)\n",
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            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython>=7.23.1->ipykernel>=5.1.2->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.2.5)\n",
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            "Requirement already satisfied: terminado>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.13.3)\n",
            "Requirement already satisfied: Send2Trash in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (1.8.0)\n",
            "Requirement already satisfied: nbconvert in /usr/local/lib/python3.7/dist-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (5.6.1)\n",
            "Requirement already satisfied: bleach in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (5.0.0)\n",
            "Requirement already satisfied: testpath in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.6.0)\n",
            "Requirement already satisfied: defusedxml in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.7.1)\n",
            "Requirement already satisfied: pandocfilters>=1.4.1 in /usr/local/lib/python3.7/dist-packages (from nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (1.5.0)\n",
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            "Requirement already satisfied: webencodings in /usr/local/lib/python3.7/dist-packages (from bleach->nbconvert->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets>=7.0.0->pydeck>=0.1.dev5->streamlit->simpletransformers) (0.5.1)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.7/dist-packages (from tensorboard->simpletransformers) (0.37.1)\n",
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            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard->simpletransformers) (0.2.8)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard->simpletransformers) (1.3.1)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard->simpletransformers) (0.4.8)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard->simpletransformers) (3.2.0)\n",
            "Building wheels for collected packages: pathtools, sacremoses, seqeval, blinker, validators\n",
            "  Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pathtools: filename=pathtools-0.1.2-py3-none-any.whl size=8806 sha256=38e992736dd24397ef35dad3e5059a200c936ebcd7bbfd725879499262db4b6d\n",
            "  Stored in directory: /root/.cache/pip/wheels/3e/31/09/fa59cef12cdcfecc627b3d24273699f390e71828921b2cbba2\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.53-py3-none-any.whl size=895260 sha256=e91f5e30a3d7db50391d28392e5e54a8d42f312ebb64b896e7d89db2aa83bea7\n",
            "  Stored in directory: /root/.cache/pip/wheels/87/39/dd/a83eeef36d0bf98e7a4d1933a4ad2d660295a40613079bafc9\n",
            "  Building wheel for seqeval (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for seqeval: filename=seqeval-1.2.2-py3-none-any.whl size=16180 sha256=3210709b8b9830d1f1d5d3432c297c0d04857e193be4f708df21d944e827c030\n",
            "  Stored in directory: /root/.cache/pip/wheels/05/96/ee/7cac4e74f3b19e3158dce26a20a1c86b3533c43ec72a549fd7\n",
            "  Building wheel for blinker (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for blinker: filename=blinker-1.4-py3-none-any.whl size=13478 sha256=27e630fcbe3563e2a37b4d7fddeef4dffb2ff0657245e8d559e2dca76ce55a7a\n",
            "  Stored in directory: /root/.cache/pip/wheels/22/f5/18/df711b66eb25b21325c132757d4314db9ac5e8dabeaf196eab\n",
            "  Building wheel for validators (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for validators: filename=validators-0.19.0-py3-none-any.whl size=19553 sha256=037b56d7d48501d506e2681ee8d9505b8780cdaec336d588c3c91cb694fb1d3c\n",
            "  Stored in directory: /root/.cache/pip/wheels/fe/5d/69/ff53a908b9f14fb7730a58fdede0fac4cdc99ef3624ec76d05\n",
            "Successfully built pathtools sacremoses seqeval blinker validators\n",
            "Installing collected packages: tornado, prompt-toolkit, jupyter-client, ipython, ipykernel, urllib3, multidict, frozenlist, yarl, smmap, asynctest, async-timeout, aiosignal, pyyaml, gitdb, fsspec, aiohttp, xxhash, watchdog, validators, toml, tokenizers, shortuuid, setproctitle, sentry-sdk, sacremoses, responses, pympler, pydeck, pathtools, huggingface-hub, GitPython, docker-pycreds, blinker, wandb, transformers, streamlit, seqeval, sentencepiece, datasets, simpletransformers\n",
            "  Attempting uninstall: tornado\n",
            "    Found existing installation: tornado 5.1.1\n",
            "    Uninstalling tornado-5.1.1:\n",
            "      Successfully uninstalled tornado-5.1.1\n",
            "  Attempting uninstall: prompt-toolkit\n",
            "    Found existing installation: prompt-toolkit 1.0.18\n",
            "    Uninstalling prompt-toolkit-1.0.18:\n",
            "      Successfully uninstalled prompt-toolkit-1.0.18\n",
            "  Attempting uninstall: jupyter-client\n",
            "    Found existing installation: jupyter-client 5.3.5\n",
            "    Uninstalling jupyter-client-5.3.5:\n",
            "      Successfully uninstalled jupyter-client-5.3.5\n",
            "  Attempting uninstall: ipython\n",
            "    Found existing installation: ipython 5.5.0\n",
            "    Uninstalling ipython-5.5.0:\n",
            "      Successfully uninstalled ipython-5.5.0\n",
            "  Attempting uninstall: ipykernel\n",
            "    Found existing installation: ipykernel 4.10.1\n",
            "    Uninstalling ipykernel-4.10.1:\n",
            "      Successfully uninstalled ipykernel-4.10.1\n",
            "  Attempting uninstall: urllib3\n",
            "    Found existing installation: urllib3 1.24.3\n",
            "    Uninstalling urllib3-1.24.3:\n",
            "      Successfully uninstalled urllib3-1.24.3\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "jupyter-console 5.2.0 requires prompt-toolkit<2.0.0,>=1.0.0, but you have prompt-toolkit 3.0.29 which is incompatible.\n",
            "google-colab 1.0.0 requires ipykernel~=4.10, but you have ipykernel 6.13.0 which is incompatible.\n",
            "google-colab 1.0.0 requires ipython~=5.5.0, but you have ipython 7.33.0 which is incompatible.\n",
            "google-colab 1.0.0 requires tornado~=5.1.0; python_version >= \"3.0\", but you have tornado 6.1 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "Successfully installed GitPython-3.1.27 aiohttp-3.8.1 aiosignal-1.2.0 async-timeout-4.0.2 asynctest-0.13.0 blinker-1.4 datasets-2.1.0 docker-pycreds-0.4.0 frozenlist-1.3.0 fsspec-2022.3.0 gitdb-4.0.9 huggingface-hub-0.5.1 ipykernel-6.13.0 ipython-7.33.0 jupyter-client-7.3.0 multidict-6.0.2 pathtools-0.1.2 prompt-toolkit-3.0.29 pydeck-0.7.1 pympler-1.0.1 pyyaml-6.0 responses-0.18.0 sacremoses-0.0.53 sentencepiece-0.1.96 sentry-sdk-1.5.11 seqeval-1.2.2 setproctitle-1.2.3 shortuuid-1.0.8 simpletransformers-0.63.6 smmap-5.0.0 streamlit-1.9.0 tokenizers-0.12.1 toml-0.10.2 tornado-6.1 transformers-4.18.0 urllib3-1.25.11 validators-0.19.0 wandb-0.12.16 watchdog-2.1.7 xxhash-3.0.0 yarl-1.7.2\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "IPython",
                  "ipykernel",
                  "jupyter_client",
                  "prompt_toolkit",
                  "tornado"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from simpletransformers.ner import NERModel, NERArgs"
      ],
      "metadata": {
        "id": "N07NPJARRSBq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "model_args = NERArgs()\n",
        "model_args.labels_list = [\"O\", \"B-Generic\", \"B-Task\", \"B-Method\", \"B-OtherScientificTerm\", \"B-Metric\", \"B-Material\", \n",
        "                        \"I-Generic\", \"I-Task\", \"I-Method\", \"I-OtherScientificTerm\", \"I-Metric\", \"I-Material\"]\n",
        "model_args.num_train_epochs = 7\n",
        "model_args.overwrite_output_dir = True\n",
        "# model_args.evaluate_during_training_verbose = True\n",
        "# model_args.evaluate_during_training = True\n",
        "\n",
        "model = NERModel(\n",
        "  \"roberta\", #electra\n",
        "  \"roberta-base\",\n",
        "  args=model_args\n",
        ")\n",
        "\n",
        "model.train_model(\"/content/drive/MyDrive/Diplom/finfile.txt\", eval_data='/content/drive/MyDrive/Diplom/finfile.txt')\n",
        "result, model_outputs, wrong_preds = model.eval_model(\"/content/drive/MyDrive/Diplom/finfile.txt\")\n",
        "print(result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 493,
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            "8ff872e4ba2e483ba904485969472d42"
          ]
        },
        "id": "irQU1TINrQOs",
        "outputId": "51bb36c9-94bd-4800-dbbf-9b9c5d2e9311"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at roberta-base were not used when initializing RobertaForTokenClassification: ['lm_head.decoder.weight', 'lm_head.layer_norm.bias', 'lm_head.bias', 'lm_head.layer_norm.weight', 'lm_head.dense.bias', 'lm_head.dense.weight']\n",
            "- This IS expected if you are initializing RobertaForTokenClassification 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 RobertaForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of RobertaForTokenClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4d81c45bf2294165a75014e1f3e5e8fb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Epoch:   0%|          | 0/7 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "05c904ce54f743a1b92de612f2e57b4d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 0 of 7:   0%|          | 0/196 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7458f75206d842a6bfee4885c1fcf4a5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 1 of 7:   0%|          | 0/196 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ffd8f901e0b34d5cb44890ef6c5076fb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 2 of 7:   0%|          | 0/196 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9af3f00b8ff743b4a0b19e9f905e09bb"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 3 of 7:   0%|          | 0/196 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cb202f2a6188453689a07c361f0c1bee"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 4 of 7:   0%|          | 0/196 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9ac19f260b44467b93f7dbd3da8acf93"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 5 of 7:   0%|          | 0/196 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "99e3808e4bfc425b9504baac78f38b9a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Epoch 6 of 7:   0%|          | 0/196 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7db4fb48c9e548da811b6d4a0aae267e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "239c9da0952f48148e825240eed79576"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Running Evaluation:   0%|          | 0/196 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5a599264b93149c0895fe4273bf2fa48"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'eval_loss': 0.03688295679855902, 'precision': 0.9341577540106952, 'recall': 0.9674627898926964, 'f1_score': 0.9505186192824351}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "model_args = NERArgs()\n",
        "model_args.labels_list = [\"O\", \"B-Generic\", \"B-Task\", \"B-Method\", \"B-OtherScientificTerm\", \"B-Metric\", \"B-Material\", \n",
        "                        \"I-Generic\", \"I-Task\", \"I-Method\", \"I-OtherScientificTerm\", \"I-Metric\", \"I-Material\"]\n",
        "model_args.num_train_epochs = 7\n",
        "model_args.overwrite_output_dir = True\n",
        "# model_args.evaluate_during_training_verbose = True\n",
        "# model_args.evaluate_during_training = True\n",
        "\n",
        "model = NERModel(\n",
        "  \"roberta\", #electra\n",
        "  \"roberta-base\",\n",
        "  args=model_args\n",
        ")\n",
        "\n",
        "model.train_model(\"finfile.txt\", eval_data='finfile.txt')\n",
        "result, model_outputs, wrong_preds = model.eval_model(\"finfile.txt\")\n",
        "print(result)"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "CzFhgcVlR0WT",
        "outputId": "1ad98673-9bd1-4061-f021-67d4193070ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4ef2f4c2c1f3408c919fac64ace21436",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/481 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9d159ccc6a844d03b635b93a2368ed6e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/478M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at roberta-base were not used when initializing RobertaForTokenClassification: ['lm_head.decoder.weight', 'lm_head.bias', 'lm_head.layer_norm.weight', 'lm_head.dense.bias', 'lm_head.dense.weight', 'lm_head.layer_norm.bias']\n",
            "- This IS expected if you are initializing RobertaForTokenClassification 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 RobertaForTokenClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of RobertaForTokenClassification were not initialized from the model checkpoint at roberta-base and are newly initialized: ['classifier.bias', 'classifier.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4a284a4113a64f6b911e14e2c889d9fd",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "Downloading:   0%|          | 0.00/878k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9e78d804ee854a3e93c44b6eb0c934ee",
              "version_minor": 0,
              "version_major": 2
            },
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        },
        {
          "output_type": "stream",
          "name": "stderr",
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            "/usr/local/lib/python3.7/dist-packages/torch/optim/lr_scheduler.py:134: 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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        {
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              "model_id": "91f35fdf43284e67befb18463ac6ea04",
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          "text": [
            "{'eval_loss': 0.036517561458488355, 'precision': 0.9319056382800737, 'recall': 0.9636678200692042, 'f1_score': 0.9475206260100366}\n"
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      "cell_type": "code",
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        "predictions, raw_outputs = model.predict([\"\"\"We introduce a multitask setup of identifying and classifying\n",
        "  entities, relations, and coreference clusters in scientific articles. We create SCIERC, a dataset\n",
        "  that includes annotations for all three tasks and develop a unified framewor  called Scientific Information Extractor (SCIIE) \n",
        "  for with shared span representations. The multitask setup reduces cascading errors between tasks and leverages cross-sentence \n",
        "  relations through coreference links. Experiments show that our multi-task model outperforms previous models in scientific \n",
        "  information extraction without using any domain-specific features. We further show that the framework supports construction of a scientific knowledge graph, \n",
        "  which we use to analyze information in scientific literature\"\"\"])"
      ],
      "metadata": {
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          "height": 81,
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        "id": "s0cjzpAsSAQH",
        "outputId": "57d2808a-cee3-4ddc-cada-5754d0da2f9f"
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      "execution_count": null,
      "outputs": [
        {
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          "data": {
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        {
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          "data": {
            "text/plain": [
              "Running Prediction:   0%|          | 0/1 [00:00<?, ?it/s]"
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    {
      "cell_type": "code",
      "source": [
        "predictions"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JaDldbl-T8n9",
        "outputId": "6a56ba2f-0c26-461c-ad44-f27dd59f71fa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[{'We': 'O'},\n",
              "  {'introduce': 'O'},\n",
              "  {'a': 'O'},\n",
              "  {'multitask': 'B-Method'},\n",
              "  {'setup': 'I-Method'},\n",
              "  {'of': 'O'},\n",
              "  {'identifying': 'B-Task'},\n",
              "  {'and': 'I-Task'},\n",
              "  {'classifying': 'I-Task'},\n",
              "  {'entities,': 'I-Task'},\n",
              "  {'relations,': 'I-Task'},\n",
              "  {'and': 'O'},\n",
              "  {'coreference': 'B-OtherScientificTerm'},\n",
              "  {'clusters': 'I-Task'},\n",
              "  {'in': 'O'},\n",
              "  {'scientific': 'B-Material'},\n",
              "  {'articles.': 'I-Material'},\n",
              "  {'We': 'O'},\n",
              "  {'create': 'O'},\n",
              "  {'SCIERC,': 'B-Material'},\n",
              "  {'a': 'O'},\n",
              "  {'dataset': 'B-Generic'},\n",
              "  {'that': 'O'},\n",
              "  {'includes': 'O'},\n",
              "  {'annotations': 'O'},\n",
              "  {'for': 'O'},\n",
              "  {'all': 'O'},\n",
              "  {'three': 'O'},\n",
              "  {'tasks': 'B-Generic'},\n",
              "  {'and': 'O'},\n",
              "  {'develop': 'O'},\n",
              "  {'a': 'O'},\n",
              "  {'unified': 'B-Method'},\n",
              "  {'framewor': 'I-Method'},\n",
              "  {'called': 'O'},\n",
              "  {'Scientific': 'B-Method'},\n",
              "  {'Information': 'I-Method'},\n",
              "  {'Extractor': 'I-Method'},\n",
              "  {'(SCIIE)': 'I-Method'},\n",
              "  {'for': 'O'},\n",
              "  {'with': 'O'},\n",
              "  {'shared': 'B-Method'},\n",
              "  {'span': 'I-Method'},\n",
              "  {'representations.': 'I-Method'},\n",
              "  {'The': 'O'},\n",
              "  {'multitask': 'B-Method'},\n",
              "  {'setup': 'I-Method'},\n",
              "  {'reduces': 'O'},\n",
              "  {'cascading': 'B-OtherScientificTerm'},\n",
              "  {'errors': 'I-OtherScientificTerm'},\n",
              "  {'between': 'O'},\n",
              "  {'tasks': 'O'},\n",
              "  {'and': 'O'},\n",
              "  {'leverages': 'O'},\n",
              "  {'cross-sentence': 'B-OtherScientificTerm'},\n",
              "  {'relations': 'I-OtherScientificTerm'},\n",
              "  {'through': 'O'},\n",
              "  {'coreference': 'B-OtherScientificTerm'},\n",
              "  {'links.': 'I-OtherScientificTerm'},\n",
              "  {'Experiments': 'O'},\n",
              "  {'show': 'O'},\n",
              "  {'that': 'O'},\n",
              "  {'our': 'O'},\n",
              "  {'multi-task': 'B-Method'},\n",
              "  {'model': 'I-Method'},\n",
              "  {'outperforms': 'O'},\n",
              "  {'previous': 'O'},\n",
              "  {'models': 'B-Generic'},\n",
              "  {'in': 'O'},\n",
              "  {'scientific': 'B-Task'},\n",
              "  {'information': 'I-Task'},\n",
              "  {'extraction': 'I-Task'},\n",
              "  {'without': 'O'},\n",
              "  {'using': 'O'},\n",
              "  {'any': 'O'},\n",
              "  {'domain-specific': 'B-OtherScientificTerm'}]]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save_model(\"drive/MyDrive/Diplom/NER/ner.pt\")"
      ],
      "metadata": {
        "id": "49Mc3MTEZdwa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!cp -r /content/outputs/checkpoint-1372-epoch-7 /content/drive/MyDrive/Diplom/NER/checkpoint-1372-epoch-7 "
      ],
      "metadata": {
        "id": "SKBt9_N_Zgld"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}