MeasuredEntity_HasQuantity_Extraction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "MeasuredEntity_HasQuantity_Extraction.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "c74df28c2cb54dd9b15562671a547fda": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_4f3fe720c61d4b038db56cab59aceca6",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_7b908a77d79f42f593254c6b64909f9a",
              "IPY_MODEL_d27b2a7585f64806a073f69022c5182b"
            ]
          }
        },
        "4f3fe720c61d4b038db56cab59aceca6": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "7b908a77d79f42f593254c6b64909f9a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_a3a680def1e7447a8cacb4fb50ae63f7",
            "_dom_classes": [],
            "description": "Downloading: 100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 227845,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 227845,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_347fdc47044b487c97ce5f17f09fee4b"
          }
        },
        "d27b2a7585f64806a073f69022c5182b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_df15f57662204a07877230d96ebdae00",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 228k/228k [00:01&lt;00:00, 205kB/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_738d250ce92b43d6b4ce8e057db244ca"
          }
        },
        "a3a680def1e7447a8cacb4fb50ae63f7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "347fdc47044b487c97ce5f17f09fee4b": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "df15f57662204a07877230d96ebdae00": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "738d250ce92b43d6b4ce8e057db244ca": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "c33c2ba37194467e9450dd84191025a9": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_a24a6d1080b94f53b3f290ba672a1737",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_590fc71f666c45a9ad8fb4246e36dce4",
              "IPY_MODEL_63871ee0b90b423996867bb199257dc7"
            ]
          }
        },
        "a24a6d1080b94f53b3f290ba672a1737": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "590fc71f666c45a9ad8fb4246e36dce4": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_03a2e91bd7d04462bcc8c6f54e4aa1f1",
            "_dom_classes": [],
            "description": "Downloading: 100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 385,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 385,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_88bdea44042346aeaacf5fbf5b2bcc31"
          }
        },
        "63871ee0b90b423996867bb199257dc7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_6035f1faea5845708d33e2f81cd0c6e2",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 385/385 [00:08&lt;00:00, 44.4B/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_4ef9131728c1475daa6f01932c4b737e"
          }
        },
        "03a2e91bd7d04462bcc8c6f54e4aa1f1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "88bdea44042346aeaacf5fbf5b2bcc31": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "6035f1faea5845708d33e2f81cd0c6e2": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "4ef9131728c1475daa6f01932c4b737e": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "afca76bb77ce4b9ba9e124cb347a3e18": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "state": {
            "_view_name": "HBoxView",
            "_dom_classes": [],
            "_model_name": "HBoxModel",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "box_style": "",
            "layout": "IPY_MODEL_63e915e463a2426bb66389d2217d6199",
            "_model_module": "@jupyter-widgets/controls",
            "children": [
              "IPY_MODEL_1fd36dbda84643e68c55ee040c92c24c",
              "IPY_MODEL_cc285e61c5ea452493a04c43c9a0fc1c"
            ]
          }
        },
        "63e915e463a2426bb66389d2217d6199": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "1fd36dbda84643e68c55ee040c92c24c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "state": {
            "_view_name": "ProgressView",
            "style": "IPY_MODEL_9902f39014aa4847aa17ab7c4c8a994c",
            "_dom_classes": [],
            "description": "Downloading: 100%",
            "_model_name": "FloatProgressModel",
            "bar_style": "success",
            "max": 442221694,
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": 442221694,
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "orientation": "horizontal",
            "min": 0,
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_3fc404a45b62452598271cc2eb04e572"
          }
        },
        "cc285e61c5ea452493a04c43c9a0fc1c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "state": {
            "_view_name": "HTMLView",
            "style": "IPY_MODEL_bb67c58a7f51469b9837ad052a089198",
            "_dom_classes": [],
            "description": "",
            "_model_name": "HTMLModel",
            "placeholder": "​",
            "_view_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "value": " 442M/442M [00:08&lt;00:00, 53.9MB/s]",
            "_view_count": null,
            "_view_module_version": "1.5.0",
            "description_tooltip": null,
            "_model_module": "@jupyter-widgets/controls",
            "layout": "IPY_MODEL_7e776cc57836438b856a0c7aa965321b"
          }
        },
        "9902f39014aa4847aa17ab7c4c8a994c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "ProgressStyleModel",
            "description_width": "initial",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "bar_color": null,
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "3fc404a45b62452598271cc2eb04e572": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        },
        "bb67c58a7f51469b9837ad052a089198": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "state": {
            "_view_name": "StyleView",
            "_model_name": "DescriptionStyleModel",
            "description_width": "",
            "_view_module": "@jupyter-widgets/base",
            "_model_module_version": "1.5.0",
            "_view_count": null,
            "_view_module_version": "1.2.0",
            "_model_module": "@jupyter-widgets/controls"
          }
        },
        "7e776cc57836438b856a0c7aa965321b": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "state": {
            "_view_name": "LayoutView",
            "grid_template_rows": null,
            "right": null,
            "justify_content": null,
            "_view_module": "@jupyter-widgets/base",
            "overflow": null,
            "_model_module_version": "1.2.0",
            "_view_count": null,
            "flex_flow": null,
            "width": null,
            "min_width": null,
            "border": null,
            "align_items": null,
            "bottom": null,
            "_model_module": "@jupyter-widgets/base",
            "top": null,
            "grid_column": null,
            "overflow_y": null,
            "overflow_x": null,
            "grid_auto_flow": null,
            "grid_area": null,
            "grid_template_columns": null,
            "flex": null,
            "_model_name": "LayoutModel",
            "justify_items": null,
            "grid_row": null,
            "max_height": null,
            "align_content": null,
            "visibility": null,
            "align_self": null,
            "height": null,
            "min_height": null,
            "padding": null,
            "grid_auto_rows": null,
            "grid_gap": null,
            "max_width": null,
            "order": null,
            "_view_module_version": "1.2.0",
            "grid_template_areas": null,
            "object_position": null,
            "object_fit": null,
            "grid_auto_columns": null,
            "margin": null,
            "display": null,
            "left": null
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ygALYnvcxClb"
      },
      "source": [
        "# Training code for MeasuredEntity and HasQuantity Extraction\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "__RlG01crbrP",
        "outputId": "38cda688-9076-42f8-ddaa-2a0fb6af00ae"
      },
      "source": [
        "!pip install transformers\n",
        "!pip install nltk\n",
        "!pip install cookiecutter\n",
        "!pip install stanza\n",
        "!pip install scispacy==0.3.0\n",
        "!pip install https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.3.0/en_core_sci_sm-0.3.0.tar.gz\n",
        "!pip install pytorch-crf"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting transformers\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ed/d5/f4157a376b8a79489a76ce6cfe147f4f3be1e029b7144fa7b8432e8acb26/transformers-4.4.2-py3-none-any.whl (2.0MB)\n",
            "\u001b[K     |████████████████████████████████| 2.0MB 8.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.7/dist-packages (from transformers) (3.0.12)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from transformers) (20.9)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (1.19.5)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.7/dist-packages (from transformers) (2019.12.20)\n",
            "Collecting tokenizers<0.11,>=0.10.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/71/23/2ddc317b2121117bf34dd00f5b0de194158f2a44ee2bf5e47c7166878a97/tokenizers-0.10.1-cp37-cp37m-manylinux2010_x86_64.whl (3.2MB)\n",
            "\u001b[K     |████████████████████████████████| 3.2MB 51.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.7/dist-packages (from transformers) (4.41.1)\n",
            "Collecting sacremoses\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/08/cd/342e584ee544d044fb573ae697404ce22ede086c9e87ce5960772084cad0/sacremoses-0.0.44.tar.gz (862kB)\n",
            "\u001b[K     |████████████████████████████████| 870kB 55.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: importlib-metadata; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from transformers) (3.8.1)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->transformers) (2.4.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2020.12.5)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.15.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (7.1.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from sacremoses->transformers) (1.0.1)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.7.4.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < \"3.8\"->transformers) (3.4.1)\n",
            "Building wheels for collected packages: sacremoses\n",
            "  Building wheel for sacremoses (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for sacremoses: filename=sacremoses-0.0.44-cp37-none-any.whl size=886084 sha256=a177ba460da1873322919b2ae4b927346200fa26fc6af757835d7b3cab217ad7\n",
            "  Stored in directory: /root/.cache/pip/wheels/3e/fb/c0/13ab4d63d537658f448366744654323077c4d90069b6512f3c\n",
            "Successfully built sacremoses\n",
            "Installing collected packages: tokenizers, sacremoses, transformers\n",
            "Successfully installed sacremoses-0.0.44 tokenizers-0.10.1 transformers-4.4.2\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.7/dist-packages (3.2.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from nltk) (1.15.0)\n",
            "Collecting cookiecutter\n",
            "  Downloading https://files.pythonhosted.org/packages/95/83/83ebf950ec99b02c61719ccb116462844ba2e873df7c4d40afc962494312/cookiecutter-1.7.2-py2.py3-none-any.whl\n",
            "Requirement already satisfied: MarkupSafe<2.0.0 in /usr/local/lib/python3.7/dist-packages (from cookiecutter) (1.1.1)\n",
            "Requirement already satisfied: python-slugify>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from cookiecutter) (4.0.1)\n",
            "Collecting binaryornot>=0.4.4\n",
            "  Downloading https://files.pythonhosted.org/packages/24/7e/f7b6f453e6481d1e233540262ccbfcf89adcd43606f44a028d7f5fae5eb2/binaryornot-0.4.4-py2.py3-none-any.whl\n",
            "Requirement already satisfied: six>=1.10 in /usr/local/lib/python3.7/dist-packages (from cookiecutter) (1.15.0)\n",
            "Collecting poyo>=0.5.0\n",
            "  Downloading https://files.pythonhosted.org/packages/42/50/0b0820601bde2eda403f47b9a4a1f270098ed0dd4c00c443d883164bdccc/poyo-0.5.0-py2.py3-none-any.whl\n",
            "Collecting jinja2-time>=0.2.0\n",
            "  Downloading https://files.pythonhosted.org/packages/6a/a1/d44fa38306ffa34a7e1af09632b158e13ec89670ce491f8a15af3ebcb4e4/jinja2_time-0.2.0-py2.py3-none-any.whl\n",
            "Requirement already satisfied: Jinja2<3.0.0 in /usr/local/lib/python3.7/dist-packages (from cookiecutter) (2.11.3)\n",
            "Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.7/dist-packages (from cookiecutter) (2.23.0)\n",
            "Requirement already satisfied: click>=7.0 in /usr/local/lib/python3.7/dist-packages (from cookiecutter) (7.1.2)\n",
            "Requirement already satisfied: text-unidecode>=1.3 in /usr/local/lib/python3.7/dist-packages (from python-slugify>=4.0.0->cookiecutter) (1.3)\n",
            "Requirement already satisfied: chardet>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from binaryornot>=0.4.4->cookiecutter) (3.0.4)\n",
            "Collecting arrow\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ea/22/482a480cbee1aa7795a58b411159a4df249d6d4ceacdd9987ab946f78210/arrow-1.0.3-py3-none-any.whl (54kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 6.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests>=2.23.0->cookiecutter) (2020.12.5)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests>=2.23.0->cookiecutter) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests>=2.23.0->cookiecutter) (1.24.3)\n",
            "Requirement already satisfied: python-dateutil>=2.7.0 in /usr/local/lib/python3.7/dist-packages (from arrow->jinja2-time>=0.2.0->cookiecutter) (2.8.1)\n",
            "Requirement already satisfied: typing-extensions; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from arrow->jinja2-time>=0.2.0->cookiecutter) (3.7.4.3)\n",
            "Installing collected packages: binaryornot, poyo, arrow, jinja2-time, cookiecutter\n",
            "Successfully installed arrow-1.0.3 binaryornot-0.4.4 cookiecutter-1.7.2 jinja2-time-0.2.0 poyo-0.5.0\n",
            "Collecting stanza\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/50/ae/a70a58ce6b4e2daad538688806ee0f238dbe601954582a74ea57cde6c532/stanza-1.2-py3-none-any.whl (282kB)\n",
            "\u001b[K     |████████████████████████████████| 286kB 7.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: tqdm in /usr/local/lib/python3.7/dist-packages (from stanza) (4.41.1)\n",
            "Requirement already satisfied: torch>=1.3.0 in /usr/local/lib/python3.7/dist-packages (from stanza) (1.8.1+cu101)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from stanza) (1.19.5)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from stanza) (2.23.0)\n",
            "Requirement already satisfied: protobuf in /usr/local/lib/python3.7/dist-packages (from stanza) (3.12.4)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch>=1.3.0->stanza) (3.7.4.3)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->stanza) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->stanza) (2.10)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->stanza) (2020.12.5)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->stanza) (3.0.4)\n",
            "Requirement already satisfied: six>=1.9 in /usr/local/lib/python3.7/dist-packages (from protobuf->stanza) (1.15.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from protobuf->stanza) (54.2.0)\n",
            "Installing collected packages: stanza\n",
            "Successfully installed stanza-1.2\n",
            "Collecting scispacy==0.3.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/7b/0c/e1dc89d73122677cdee8a2d4e734c608c5f112e0c4ea2886f0944ceff1b6/scispacy-0.3.0-py3-none-any.whl (42kB)\n",
            "\u001b[K     |████████████████████████████████| 51kB 4.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from scispacy==0.3.0) (1.0.1)\n",
            "Requirement already satisfied: scikit-learn>=0.20.3 in /usr/local/lib/python3.7/dist-packages (from scispacy==0.3.0) (0.22.2.post1)\n",
            "Collecting spacy<3.0.0,>=2.3.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/95/89/1539c4024c339650c222b0b2ca2b3e3f13523b7a02671f8001b7b1cee6f2/spacy-2.3.5-cp37-cp37m-manylinux2014_x86_64.whl (10.4MB)\n",
            "\u001b[K     |████████████████████████████████| 10.4MB 7.5MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from scispacy==0.3.0) (1.19.5)\n",
            "Requirement already satisfied: requests<3.0.0conllu,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scispacy==0.3.0) (2.23.0)\n",
            "Collecting pysbd\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/48/0a/c99fb7d7e176f8b176ef19704a32e6a9c6aafdf19ef75a187f701fc15801/pysbd-0.3.4-py3-none-any.whl (71kB)\n",
            "\u001b[K     |████████████████████████████████| 71kB 8.5MB/s \n",
            "\u001b[?25hCollecting nmslib>=1.7.3.6\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/be/77/aebbd03a32488024d2ae2230b47a28f6fa83c887318e673fa5d3234f7772/nmslib-2.1.1-cp37-cp37m-manylinux2010_x86_64.whl (13.5MB)\n",
            "\u001b[K     |████████████████████████████████| 13.5MB 225kB/s \n",
            "\u001b[?25hRequirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.20.3->scispacy==0.3.0) (1.4.1)\n",
            "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<3.0.0,>=2.3.0->scispacy==0.3.0) (1.0.5)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.0.0,>=2.3.0->scispacy==0.3.0) (0.8.2)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<3.0.0,>=2.3.0->scispacy==0.3.0) (3.0.5)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy<3.0.0,>=2.3.0->scispacy==0.3.0) (54.2.0)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.0.0,>=2.3.0->scispacy==0.3.0) (4.41.1)\n",
            "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.7/dist-packages (from spacy<3.0.0,>=2.3.0->scispacy==0.3.0) (1.1.3)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy<3.0.0,>=2.3.0->scispacy==0.3.0) (2.0.5)\n",
            "Collecting thinc<7.5.0,>=7.4.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9a/92/71ab278f865f7565c37ed6917d0f23342e4f9a0633013113bd435cf0a691/thinc-7.4.5-cp37-cp37m-manylinux2014_x86_64.whl (1.0MB)\n",
            "\u001b[K     |████████████████████████████████| 1.1MB 44.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: blis<0.8.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.0.0,>=2.3.0->scispacy==0.3.0) (0.4.1)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy<3.0.0,>=2.3.0->scispacy==0.3.0) (1.0.5)\n",
            "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from spacy<3.0.0,>=2.3.0->scispacy==0.3.0) (1.0.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0conllu,>=2.0.0->scispacy==0.3.0) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0conllu,>=2.0.0->scispacy==0.3.0) (2020.12.5)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0conllu,>=2.0.0->scispacy==0.3.0) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0conllu,>=2.0.0->scispacy==0.3.0) (2.10)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.7/dist-packages (from nmslib>=1.7.3.6->scispacy==0.3.0) (5.4.8)\n",
            "Collecting pybind11<2.6.2\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/00/84/fc9dc13ee536ba5e6b8fd10ce368fea5b738fe394c3b296cde7c9b144a92/pybind11-2.6.1-py2.py3-none-any.whl (188kB)\n",
            "\u001b[K     |████████████████████████████████| 194kB 57.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: importlib-metadata>=0.20; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy<3.0.0,>=2.3.0->scispacy==0.3.0) (3.8.1)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy<3.0.0,>=2.3.0->scispacy==0.3.0) (3.7.4.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy<3.0.0,>=2.3.0->scispacy==0.3.0) (3.4.1)\n",
            "Installing collected packages: thinc, spacy, pysbd, pybind11, nmslib, scispacy\n",
            "  Found existing installation: thinc 7.4.0\n",
            "    Uninstalling thinc-7.4.0:\n",
            "      Successfully uninstalled thinc-7.4.0\n",
            "  Found existing installation: spacy 2.2.4\n",
            "    Uninstalling spacy-2.2.4:\n",
            "      Successfully uninstalled spacy-2.2.4\n",
            "Successfully installed nmslib-2.1.1 pybind11-2.6.1 pysbd-0.3.4 scispacy-0.3.0 spacy-2.3.5 thinc-7.4.5\n",
            "Collecting https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.3.0/en_core_sci_sm-0.3.0.tar.gz\n",
            "\u001b[?25l  Downloading https://s3-us-west-2.amazonaws.com/ai2-s2-scispacy/releases/v0.3.0/en_core_sci_sm-0.3.0.tar.gz (33.1MB)\n",
            "\u001b[K     |████████████████████████████████| 33.1MB 95kB/s \n",
            "\u001b[?25hRequirement already satisfied: spacy>=2.3.1 in /usr/local/lib/python3.7/dist-packages (from en-core-sci-sm==0.3.0) (2.3.5)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.3.1->en-core-sci-sm==0.3.0) (3.0.5)\n",
            "Requirement already satisfied: srsly<1.1.0,>=1.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.3.1->en-core-sci-sm==0.3.0) (1.0.5)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from spacy>=2.3.1->en-core-sci-sm==0.3.0) (54.2.0)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.3.1->en-core-sci-sm==0.3.0) (1.0.5)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.3.1->en-core-sci-sm==0.3.0) (2.0.5)\n",
            "Requirement already satisfied: plac<1.2.0,>=0.9.6 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.3.1->en-core-sci-sm==0.3.0) (1.1.3)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.3.1->en-core-sci-sm==0.3.0) (2.23.0)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.3.1->en-core-sci-sm==0.3.0) (4.41.1)\n",
            "Requirement already satisfied: numpy>=1.15.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.3.1->en-core-sci-sm==0.3.0) (1.19.5)\n",
            "Requirement already satisfied: thinc<7.5.0,>=7.4.1 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.3.1->en-core-sci-sm==0.3.0) (7.4.5)\n",
            "Requirement already satisfied: blis<0.8.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.3.1->en-core-sci-sm==0.3.0) (0.4.1)\n",
            "Requirement already satisfied: wasabi<1.1.0,>=0.4.0 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.3.1->en-core-sci-sm==0.3.0) (0.8.2)\n",
            "Requirement already satisfied: catalogue<1.1.0,>=0.0.7 in /usr/local/lib/python3.7/dist-packages (from spacy>=2.3.1->en-core-sci-sm==0.3.0) (1.0.0)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.3.1->en-core-sci-sm==0.3.0) (1.24.3)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.3.1->en-core-sci-sm==0.3.0) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.3.1->en-core-sci-sm==0.3.0) (2020.12.5)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3.0.0,>=2.13.0->spacy>=2.3.1->en-core-sci-sm==0.3.0) (2.10)\n",
            "Requirement already satisfied: importlib-metadata>=0.20; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from catalogue<1.1.0,>=0.0.7->spacy>=2.3.1->en-core-sci-sm==0.3.0) (3.8.1)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4; python_version < \"3.8\" in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy>=2.3.1->en-core-sci-sm==0.3.0) (3.7.4.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.20; python_version < \"3.8\"->catalogue<1.1.0,>=0.0.7->spacy>=2.3.1->en-core-sci-sm==0.3.0) (3.4.1)\n",
            "Building wheels for collected packages: en-core-sci-sm\n",
            "  Building wheel for en-core-sci-sm (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for en-core-sci-sm: filename=en_core_sci_sm-0.3.0-cp37-none-any.whl size=33119280 sha256=fc7e6aaf6eeae7b9e9ccb68883b9e97b0debb2aff6f3cc332951c9012f010f84\n",
            "  Stored in directory: /root/.cache/pip/wheels/92/d2/d2/5aef870612ebd315c4b7b191f7ab5ff544281ef019d7c10c5d\n",
            "Successfully built en-core-sci-sm\n",
            "Installing collected packages: en-core-sci-sm\n",
            "Successfully installed en-core-sci-sm-0.3.0\n",
            "Collecting pytorch-crf\n",
            "  Downloading https://files.pythonhosted.org/packages/96/7d/4c4688e26ea015fc118a0327e5726e6596836abce9182d3738be8ec2e32a/pytorch_crf-0.7.2-py3-none-any.whl\n",
            "Installing collected packages: pytorch-crf\n",
            "Successfully installed pytorch-crf-0.7.2\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ng-d0eprreSE"
      },
      "source": [
        "import os\n",
        "import io\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import torch\n",
        "from transformers import BertTokenizer, BertModel, BertForPreTraining\n",
        "from sklearn.model_selection import train_test_split\n",
        "import stanza\n",
        "import spacy\n",
        "import re\n",
        "import en_core_sci_sm\n",
        "from torch.utils.data import TensorDataset, DataLoader\n",
        "from transformers import BertTokenizer, BertModel\n",
        "from transformers import BertForTokenClassification, AdamW\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "from transformers import BertForTokenClassification, AdamW\n",
        "from torchcrf import CRF"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "miaYrOHMr3r0"
      },
      "source": [
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66,
          "referenced_widgets": [
            "c74df28c2cb54dd9b15562671a547fda",
            "4f3fe720c61d4b038db56cab59aceca6",
            "7b908a77d79f42f593254c6b64909f9a",
            "d27b2a7585f64806a073f69022c5182b",
            "a3a680def1e7447a8cacb4fb50ae63f7",
            "347fdc47044b487c97ce5f17f09fee4b",
            "df15f57662204a07877230d96ebdae00",
            "738d250ce92b43d6b4ce8e057db244ca"
          ]
        },
        "id": "wy1m57w-r7gr",
        "outputId": "8e1c8161-6e66-48a8-99d4-03852e13716a"
      },
      "source": [
        "tokenizer = BertTokenizer.from_pretrained('allenai/scibert_scivocab_uncased')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c74df28c2cb54dd9b15562671a547fda",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=227845.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "55fFQd0LxReV"
      },
      "source": [
        "### Mounting the drive containing the training and dev data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ALGYsZar9ZG",
        "outputId": "cf1529d6-d19f-4346-c815-b014a87165b4"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ASKGhA7VsCex"
      },
      "source": [
        "train_path = \"/content/drive/MyDrive/final_data/train/\"\n",
        "val_path = \"/content/drive/MyDrive/final_data/dev/\""
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UvX9OCqDsMG5"
      },
      "source": [
        "files=os.listdir(train_path + \"text\")\n",
        "files_val = os.listdir(val_path + \"text\")"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AgkB7BGnxW_u"
      },
      "source": [
        "#### Function to split the scientific articles into sentences"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f2CmuMr9sONG"
      },
      "source": [
        "nlp = en_core_sci_sm.load()\n",
        "def sen_split(text):\n",
        "  doc = nlp(text)\n",
        "  sen = [s.text for s in doc.sents]\n",
        "  return sen"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4vK4ztrQxTn9"
      },
      "source": [
        "### Training data preparation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uDojmsKYsP3E",
        "outputId": "26171a52-a8c1-4e84-8797-82f0eb8e55f5"
      },
      "source": [
        "ans = []\n",
        "itr = 0\n",
        "for fil in files:\n",
        "  pathtxt=os.path.join(train_path+\"text\",fil)\n",
        "  pathtsv=os.path.join(train_path+\"tsv\",fil[:-3]+\"tsv\")\n",
        "  if(not(os.path.exists(pathtsv))):\n",
        "    continue\n",
        "  filetsv=pd.read_csv(pathtsv,sep = '\\t')\n",
        "  tsv_lis = filetsv.values\n",
        "  with open(pathtxt, 'r') as f:\n",
        "    text_in=f.read()\n",
        "  sen_lis = sen_split(text=text_in)\n",
        "  len_p = 0\n",
        "  for sen in sen_lis:\n",
        "    for i in range(len(tsv_lis)):\n",
        "      if tsv_lis[i][2] == \"Quantity\" and tsv_lis[i][3]>=len_p and tsv_lis[i][4]<=len_p+len(sen):\n",
        "        val = []\n",
        "        val.append(sen)\n",
        "        val.append((tsv_lis[i][3] - len_p, tsv_lis[i][4] - len_p))\n",
        "        c = 0\n",
        "        for j in range(len(tsv_lis)):\n",
        "          if tsv_lis[j][2] == \"MeasuredEntity\" and tsv_lis[j][3]>=len_p and tsv_lis[j][4]<=len_p+len(sen) and tsv_lis[i][1] == tsv_lis[j][1]:\n",
        "            c += 1\n",
        "            val.append((tsv_lis[j][3] - len_p, tsv_lis[j][4] - len_p))\n",
        "            ans.append(val)\n",
        "        if c == 0:\n",
        "          ans.append(val)\n",
        "    len_p += len(sen)+1\n",
        "  print(itr)\n",
        "  print(fil)\n",
        "  itr += 1"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n",
            "S0167577X14001256-389.txt\n",
            "1\n",
            "S030881461301604X-1001.txt\n",
            "2\n",
            "S0006322312001096-1136.txt\n",
            "3\n",
            "S095741741101342X-2624.txt\n",
            "4\n",
            "S0927024813002961-1334.txt\n",
            "5\n",
            "S0019103512001388-3081.txt\n",
            "6\n",
            "S0960896612001022-1223.txt\n",
            "7\n",
            "S0012821X12004384-1265.txt\n",
            "8\n",
            "S2213671113001306-885.txt\n",
            "9\n",
            "S0378383912000130-3745.txt\n",
            "10\n",
            "S0038071713001971-1427.txt\n",
            "11\n",
            "S0168945213001805-4775.txt\n",
            "12\n",
            "S0032063312003054-2467.txt\n",
            "13\n",
            "S0921818113002245-882.txt\n",
            "14\n",
            "S0167880913001229-1304.txt\n",
            "15\n",
            "S095741741101342X-726.txt\n",
            "16\n",
            "S0032063312003054-2501.txt\n",
            "17\n",
            "S2213671113001306-1398.txt\n",
            "18\n",
            "S0016236113008041-961.txt\n",
            "19\n",
            "S2211124712002884-1060.txt\n",
            "20\n",
            "S0012821X12004384-1249.txt\n",
            "21\n",
            "S0019103512003995-1283.txt\n",
            "22\n",
            "S2213671113000738-684.txt\n",
            "23\n",
            "S0022399913003358-943.txt\n",
            "24\n",
            "S0019103512004009-3976.txt\n",
            "25\n",
            "S0927024813002420-1032.txt\n",
            "26\n",
            "S2213671113000738-738.txt\n",
            "27\n",
            "S0378383912000130-3827.txt\n",
            "28\n",
            "S0960148113004989-3258.txt\n",
            "29\n",
            "S175058361300203X-1280.txt\n",
            "30\n",
            "S0012821X12004384-1148.txt\n",
            "31\n",
            "S0016236113008041-872.txt\n",
            "32\n",
            "S0378383912000130-1096.txt\n",
            "33\n",
            "S0378112713005288-1720.txt\n",
            "34\n",
            "S0031405612000728-769.txt\n",
            "35\n",
            "S1359645413009816-2973.txt\n",
            "36\n",
            "S0012821X12004384-1284.txt\n",
            "37\n",
            "S0921818113002245-1571.txt\n",
            "38\n",
            "S2213158213000582-1327.txt\n",
            "39\n",
            "S016412121300188X-4392.txt\n",
            "40\n",
            "S0019103512003995-2681.txt\n",
            "41\n",
            "S0019103513005058-3154.txt\n",
            "42\n",
            "S0167577X13006393-399.txt\n",
            "43\n",
            "S0167577X14001256-517.txt\n",
            "44\n",
            "S2213671113000908-810.txt\n",
            "45\n",
            "S2213671113001306-1520.txt\n",
            "46\n",
            "S2213158213000582-1340.txt\n",
            "47\n",
            "S016412121300188X-5038.txt\n",
            "48\n",
            "S0032063312003054-2264.txt\n",
            "49\n",
            "S0378383912000130-1054.txt\n",
            "50\n",
            "S2211124713006475-841.txt\n",
            "51\n",
            "S2211124712002884-903.txt\n",
            "52\n",
            "S2213671113001306-910.txt\n",
            "53\n",
            "S0019103512001388-1070.txt\n",
            "54\n",
            "S0021979713004438-1401.txt\n",
            "55\n",
            "S0378383912000130-3907.txt\n",
            "56\n",
            "S2213671113000738-647.txt\n",
            "57\n",
            "S1359645413009816-2243.txt\n",
            "58\n",
            "S0927024813002420-975.txt\n",
            "59\n",
            "S0167610512002292-3187.txt\n",
            "60\n",
            "S2213671113001306-907.txt\n",
            "61\n",
            "S0925443913003037-1397.txt\n",
            "62\n",
            "S0006322312001096-1230.txt\n",
            "63\n",
            "S2213671113001306-1286.txt\n",
            "64\n",
            "S0006322312001096-1190.txt\n",
            "65\n",
            "S0168945213001805-5026.txt\n",
            "66\n",
            "S0019103513005058-3917.txt\n",
            "67\n",
            "S0925443913001385-1638.txt\n",
            "68\n",
            "S0378383912000130-3732.txt\n",
            "69\n",
            "S0006322312001096-1253.txt\n",
            "70\n",
            "S0006322312001096-1221.txt\n",
            "71\n",
            "S2213158213000302-1597.txt\n",
            "72\n",
            "S0019103511004994-996.txt\n",
            "73\n",
            "S0032386113005454-2055.txt\n",
            "74\n",
            "S0169433213008933-689.txt\n",
            "75\n",
            "S0167610513001001-739.txt\n",
            "76\n",
            "S0012821X12004384-1221.txt\n",
            "77\n",
            "S2213671113000738-485.txt\n",
            "78\n",
            "S0378383912000130-1048.txt\n",
            "79\n",
            "S0019103512004009-2821.txt\n",
            "80\n",
            "S0019103511004994-1399.txt\n",
            "81\n",
            "S0012821X12004384-1232.txt\n",
            "82\n",
            "S1750583613004192-1267.txt\n",
            "83\n",
            "S0925443913001385-839.txt\n",
            "84\n",
            "S0022399913003358-931.txt\n",
            "85\n",
            "S016412121300188X-4069.txt\n",
            "86\n",
            "S0378112713005288-2036.txt\n",
            "87\n",
            "S0038071712001010-918.txt\n",
            "88\n",
            "S0960148113005727-904.txt\n",
            "89\n",
            "S0012821X13007309-1691.txt\n",
            "90\n",
            "S1389128612002496-6119.txt\n",
            "91\n",
            "S0019103512004009-4350.txt\n",
            "92\n",
            "S0038071712001010-944.txt\n",
            "93\n",
            "S0167577X13006393-644.txt\n",
            "94\n",
            "S027737911400050X-2401.txt\n",
            "95\n",
            "S0019103513005058-4098.txt\n",
            "96\n",
            "S2213671113000908-640.txt\n",
            "97\n",
            "S0006322312001096-1205.txt\n",
            "98\n",
            "S0378383913001567-2462.txt\n",
            "99\n",
            "S0012821X13002185-994.txt\n",
            "100\n",
            "S0925443913001385-1646.txt\n",
            "101\n",
            "S0925443913001385-1429.txt\n",
            "102\n",
            "S1359835X13001875-1359.txt\n",
            "103\n",
            "S0960148113004989-3203.txt\n",
            "104\n",
            "S2211124713006475-1205.txt\n",
            "105\n",
            "S0378383911001669-1088.txt\n",
            "106\n",
            "S0006322312001096-1202.txt\n",
            "107\n",
            "S0019103513005058-4175.txt\n",
            "108\n",
            "S1873506113001116-1204.txt\n",
            "109\n",
            "S0016236113008041-3012.txt\n",
            "110\n",
            "S0025322712001600-2406.txt\n",
            "111\n",
            "S0167819113001051-1247.txt\n",
            "112\n",
            "S0032386113005454-2865.txt\n",
            "113\n",
            "S0016236113008041-3159.txt\n",
            "114\n",
            "S2211124712002884-1110.txt\n",
            "115\n",
            "S1873506113001116-978.txt\n",
            "116\n",
            "S1389128612002496-6138.txt\n",
            "117\n",
            "S0168945213001805-4536.txt\n",
            "118\n",
            "S2211124712002884-705.txt\n",
            "119\n",
            "S2213671113001306-1404.txt\n",
            "120\n",
            "S0019103512002801-1716.txt\n",
            "121\n",
            "S0019103512003995-2737.txt\n",
            "122\n",
            "S0167610512002292-3305.txt\n",
            "123\n",
            "S1367912013002277-1213.txt\n",
            "124\n",
            "S0378383911001669-2260.txt\n",
            "125\n",
            "S0022459611006116-547.txt\n",
            "126\n",
            "S0019103513005058-4302.txt\n",
            "127\n",
            "S0006322312001096-1197.txt\n",
            "128\n",
            "S0378383911001669-1634.txt\n",
            "129\n",
            "S2213671113000738-435.txt\n",
            "130\n",
            "S2213671113000908-979.txt\n",
            "131\n",
            "S0032063313003218-6651.txt\n",
            "132\n",
            "S1873506113001116-710.txt\n",
            "133\n",
            "S016412121300188X-4640.txt\n",
            "134\n",
            "S0016236113008041-2924.txt\n",
            "135\n",
            "S016412121300188X-4545.txt\n",
            "136\n",
            "S0960148113004989-3277.txt\n",
            "137\n",
            "S2211124712002884-682.txt\n",
            "138\n",
            "S0927024813003036-2011.txt\n",
            "139\n",
            "S0927024813001955-679.txt\n",
            "140\n",
            "S0022459611006116-1448.txt\n",
            "141\n",
            "S0927024813002420-1202.txt\n",
            "142\n",
            "S0167610513002729-1062.txt\n",
            "143\n",
            "S0032386113005454-2308.txt\n",
            "144\n",
            "S1873506114000075-1132.txt\n",
            "145\n",
            "S1161030113001950-923.txt\n",
            "146\n",
            "S0019103512004009-5033.txt\n",
            "147\n",
            "S0167610513001001-1751.txt\n",
            "148\n",
            "S0165587612003680-1078.txt\n",
            "149\n",
            "S0031405612000728-1621.txt\n",
            "150\n",
            "S2213671113000921-994.txt\n",
            "151\n",
            "S0019103513005058-4158.txt\n",
            "152\n",
            "S016412121300188X-4937.txt\n",
            "153\n",
            "S0165587612003680-998.txt\n",
            "154\n",
            "S0006322312001096-1278.txt\n",
            "155\n",
            "S0038071713001971-1388.txt\n",
            "156\n",
            "S0378112713005288-2062.txt\n",
            "157\n",
            "S0012821X12004384-952.txt\n",
            "158\n",
            "S0038071712001010-1044.txt\n",
            "159\n",
            "S175058361300203X-1542.txt\n",
            "160\n",
            "S0168945213001805-4454.txt\n",
            "161\n",
            "S0019103512003533-3306.txt\n",
            "162\n",
            "S0006322312001096-1271.txt\n",
            "163\n",
            "S0921818113002245-859.txt\n",
            "164\n",
            "S2211124712002884-649.txt\n",
            "165\n",
            "S016412121300188X-4617.txt\n",
            "166\n",
            "S1387700313001822-661.txt\n",
            "167\n",
            "S2213671113000738-445.txt\n",
            "168\n",
            "S0019103512004009-5271.txt\n",
            "169\n",
            "S0960148113005727-1494.txt\n",
            "170\n",
            "S0006322312001096-1275.txt\n",
            "171\n",
            "S0167610513002729-1127.txt\n",
            "172\n",
            "S2213671113000921-1279.txt\n",
            "173\n",
            "S0927775713009606-1361.txt\n",
            "174\n",
            "S0927775713009606-1074.txt\n",
            "175\n",
            "S030881461301604X-1002.txt\n",
            "176\n",
            "S2213671113000921-714.txt\n",
            "177\n",
            "S1873506114000075-1242.txt\n",
            "178\n",
            "S0006322312001096-1260.txt\n",
            "179\n",
            "S0921818113002245-1752.txt\n",
            "180\n",
            "S0927024813001955-576.txt\n",
            "181\n",
            "S0032063312003054-2483.txt\n",
            "182\n",
            "S0927024813002961-1051.txt\n",
            "183\n",
            "S0167819113001051-1550.txt\n",
            "184\n",
            "S175058361300203X-1556.txt\n",
            "185\n",
            "S0167880913001229-1021.txt\n",
            "186\n",
            "S0925443913003037-654.txt\n",
            "187\n",
            "S0006322312001096-1194.txt\n",
            "188\n",
            "S0168945213001805-3964.txt\n",
            "189\n",
            "S0167880913001229-1225.txt\n",
            "190\n",
            "S0032386113005454-2008.txt\n",
            "191\n",
            "S0960148113004989-3327.txt\n",
            "192\n",
            "S0019103512002801-2018.txt\n",
            "193\n",
            "S0012821X12004384-1178.txt\n",
            "194\n",
            "S0960148113005727-1181.txt\n",
            "195\n",
            "S2213671113001306-908.txt\n",
            "196\n",
            "S0378383912000130-3755.txt\n",
            "197\n",
            "S0006322312001096-1248.txt\n",
            "198\n",
            "S0012821X12004384-1640.txt\n",
            "199\n",
            "S0167880913001229-1033.txt\n",
            "200\n",
            "S0032063312003054-1990.txt\n",
            "201\n",
            "S0006322312001096-1177.txt\n",
            "202\n",
            "S1873506113001116-1369.txt\n",
            "203\n",
            "S1550413113004920-1509.txt\n",
            "204\n",
            "S0378383911001669-1112.txt\n",
            "205\n",
            "S0960148113002048-3775.txt\n",
            "206\n",
            "S0927024813001955-1005.txt\n",
            "207\n",
            "S1388248113001951-339.txt\n",
            "208\n",
            "S0378112713005288-1948.txt\n",
            "209\n",
            "S0019103512003995-1767.txt\n",
            "210\n",
            "S0019103513005058-4349.txt\n",
            "211\n",
            "S0016236113008041-3161.txt\n",
            "212\n",
            "S0032063312002437-627.txt\n",
            "213\n",
            "S175058361300203X-1483.txt\n",
            "214\n",
            "S0378383912000130-3662.txt\n",
            "215\n",
            "S0032386113009889-2123.txt\n",
            "216\n",
            "S0167577X13006393-787.txt\n",
            "217\n",
            "S0950705113001895-23699.txt\n",
            "218\n",
            "S0006322312001096-626.txt\n",
            "219\n",
            "S0032386113005454-2601.txt\n",
            "220\n",
            "S0927775713009606-1216.txt\n",
            "221\n",
            "S2213671113000738-430.txt\n",
            "222\n",
            "S0165587612003680-953.txt\n",
            "223\n",
            "S0025322712001600-2230.txt\n",
            "224\n",
            "S0031405612000728-1639.txt\n",
            "225\n",
            "S0378383912000130-3601.txt\n",
            "226\n",
            "S175058361300203X-1638.txt\n",
            "227\n",
            "S2211124713006475-1195.txt\n",
            "228\n",
            "S016412121300188X-5066.txt\n",
            "229\n",
            "S1389128612002496-5994.txt\n",
            "230\n",
            "S1750583613004192-714.txt\n",
            "231\n",
            "S016412121300188X-4436.txt\n",
            "232\n",
            "S0038071711004354-2573.txt\n",
            "233\n",
            "S0019103512003995-1807.txt\n",
            "234\n",
            "S0019103512003533-3348.txt\n",
            "235\n",
            "S0019103512003533-5072.txt\n",
            "236\n",
            "S0019103512004009-4492.txt\n",
            "237\n",
            "S0019103512004009-3962.txt\n",
            "238\n",
            "S0019103512003533-5598.txt\n",
            "239\n",
            "S0012821X13002185-835.txt\n",
            "240\n",
            "S0019103512004009-5507.txt\n",
            "241\n",
            "S0019103512003995-2760.txt\n",
            "242\n",
            "S0012821X12004384-1405.txt\n",
            "243\n",
            "S0019103512002801-1824.txt\n",
            "244\n",
            "S0016236113008041-3269.txt\n",
            "245\n",
            "S0012821X13007309-1509.txt\n",
            "246\n",
            "S0022399913003358-1044.txt\n",
            "247\n",
            "S0012821X13007309-1482.txt\n",
            "248\n",
            "S0019103512004009-3488.txt\n",
            "249\n",
            "S0016236113008041-3207.txt\n",
            "250\n",
            "S0016236113008041-3031.txt\n",
            "251\n",
            "S0019103512003533-5031.txt\n",
            "252\n",
            "S0016236113008041-913.txt\n",
            "253\n",
            "S0019103512003995-3548.txt\n",
            "254\n",
            "S0021979713004438-2148.txt\n",
            "255\n",
            "S0016236113008041-3112.txt\n",
            "256\n",
            "S0012821X12004384-1302.txt\n",
            "257\n",
            "S0019103512002801-1927.txt\n",
            "258\n",
            "S0012821X13002185-1061.txt\n",
            "259\n",
            "S0012821X13002185-1217.txt\n",
            "260\n",
            "S0021979713004438-1907.txt\n",
            "261\n",
            "S0019103512003995-1910.txt\n",
            "262\n",
            "S0019103513005058-4210.txt\n",
            "263\n",
            "S0019103512002801-1342.txt\n",
            "264\n",
            "S0012821X12004384-1415.txt\n",
            "265\n",
            "S0019103512002801-1608.txt\n",
            "266\n",
            "S0019103511004994-1565.txt\n",
            "267\n",
            "S0019103512003533-5251.txt\n",
            "268\n",
            "S0019103512002801-1849.txt\n",
            "269\n",
            "S0019103512002801-1496.txt\n",
            "270\n",
            "S0022000014000026-17824.txt\n",
            "271\n",
            "S0016236113008041-3153.txt\n",
            "272\n",
            "S0012821X12004384-1599.txt\n",
            "273\n",
            "S0016236113008041-3290.txt\n",
            "274\n",
            "S0021979713004438-1415.txt\n",
            "275\n",
            "S0019103513005058-1737.txt\n",
            "276\n",
            "S0022000014000026-18167.txt\n",
            "277\n",
            "S0019103512003533-4971.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qLJ504fOsf5I",
        "outputId": "651780f5-b144-422d-dfb3-b44f8a8fa482"
      },
      "source": [
        "ans_val = []\n",
        "for fil in files_val:\n",
        "  pathtxt=os.path.join(val_path+\"text\",fil)\n",
        "  pathtsv=os.path.join(val_path+\"tsv\",fil[:-3]+\"tsv\")\n",
        "  if(not(os.path.exists(pathtsv))):\n",
        "    continue\n",
        "  filetsv=pd.read_csv(pathtsv,sep = '\\t')\n",
        "  tsv_lis = filetsv.values\n",
        "  with open(pathtxt, 'r') as f:\n",
        "    text_in=f.read()\n",
        "  sen_lis = sen_split(text=text_in)\n",
        "  len_p = 0\n",
        "  for sen in sen_lis:\n",
        "    for i in range(len(tsv_lis)):\n",
        "      if tsv_lis[i][2] == \"Quantity\" and tsv_lis[i][3]>=len_p and tsv_lis[i][4]<=len_p+len(sen):\n",
        "        val = []\n",
        "        val.append(sen)\n",
        "        val.append((tsv_lis[i][3] - len_p, tsv_lis[i][4] - len_p))\n",
        "        c = 0\n",
        "        for j in range(len(tsv_lis)):\n",
        "          if tsv_lis[j][2] == \"MeasuredEntity\" and tsv_lis[j][3]>=len_p and tsv_lis[j][4]<=len_p+len(sen) and tsv_lis[i][1] == tsv_lis[j][1]:\n",
        "            c += 1\n",
        "            val.append((tsv_lis[j][3] - len_p, tsv_lis[j][4] - len_p))\n",
        "            ans_val.append(val)\n",
        "        if c == 0:\n",
        "          ans_val.append(val)\n",
        "    len_p += len(sen)+1\n",
        "  print(fil)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "S0016236113008041-3186.txt\n",
            "S0019103512004009-3825.txt\n",
            "S0019103512004009-5019.txt\n",
            "S0019103512003533-4685.txt\n",
            "S0016236113008041-967.txt\n",
            "S0012821X12004384-1594.txt\n",
            "S0019103512004009-4007.txt\n",
            "S0019103513005058-3094.txt\n",
            "S0021979713004438-1969.txt\n",
            "S0012821X13002185-1231.txt\n",
            "S0012821X13007309-1989.txt\n",
            "S0012821X13007309-1605.txt\n",
            "S0016236113008041-890.txt\n",
            "S0019103511004994-1511.txt\n",
            "S0022459611006116-1160.txt\n",
            "S0016236113008041-3171.txt\n",
            "S0022459611006116-1195.txt\n",
            "S0019103511004994-1382.txt\n",
            "S0019103512003533-3299.txt\n",
            "S0012821X13002185-1200.txt\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-KhjYAQYttJx",
        "outputId": "ce0d9bdf-b5ff-4956-f533-baa70f8adc5b"
      },
      "source": [
        "stanza.download('en')\n",
        "nlp2 = stanza.Pipeline(lang='en', processors='tokenize', tokenize_no_ssplit=True)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading https://raw.githubusercontent.com/stanfordnlp/stanza-resources/master/resources_1.2.0.json: 128kB [00:00, 44.8MB/s]                    \n",
            "2021-04-04 11:49:12 INFO: Downloading default packages for language: en (English)...\n",
            "Downloading http://nlp.stanford.edu/software/stanza/1.2.0/en/default.zip: 100%|██████████| 411M/411M [01:13<00:00, 5.60MB/s]\n",
            "2021-04-04 11:50:30 INFO: Finished downloading models and saved to /root/stanza_resources.\n",
            "2021-04-04 11:50:30 INFO: Loading these models for language: en (English):\n",
            "========================\n",
            "| Processor | Package  |\n",
            "------------------------\n",
            "| tokenize  | combined |\n",
            "========================\n",
            "\n",
            "2021-04-04 11:50:30 INFO: Use device: gpu\n",
            "2021-04-04 11:50:30 INFO: Loading: tokenize\n",
            "2021-04-04 11:50:41 INFO: Done loading processors!\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "95nA5HUttyUJ"
      },
      "source": [
        "def length(text):\n",
        "  l = 0\n",
        "  for i in range(len(text)):\n",
        "    if text[i] != '#':\n",
        "      l += 1\n",
        "  return l"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XL_HlRYP59lG"
      },
      "source": [
        "#enclosing the quantity span in \"$\" tokens"
      ],
      "execution_count": 48,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "53Ry4zRkuGdp"
      },
      "source": [
        "pretrain = []\n",
        "i = 0\n",
        "for val in ans:\n",
        "  label_q = []\n",
        "  sen_tok = []\n",
        "  sen = nlp2(val[0])\n",
        "  label_en = []\n",
        "  if len(val) == 3:\n",
        "    for w in sen.sentences[0].words[:]:\n",
        "      rng = re.findall(r'\\d+', w.misc) \n",
        "      bert_tok = tokenizer.tokenize(w.text)\n",
        "      sen_tok.extend(bert_tok)\n",
        "      w_len = int(rng[0]);\n",
        "      for t in bert_tok:\n",
        "        if val[1][0] <= w_len and val[1][1] >= w_len + length(t):\n",
        "          label_q.append(1)\n",
        "        else:\n",
        "          label_q.append(0)\n",
        "        if val[2][0] <= w_len and val[2][1] >= w_len + length(t):\n",
        "          label_en.append(1)\n",
        "        else:\n",
        "          label_en.append(0)\n",
        "        w_len += length(t)\n",
        "    for i in range(len(label_q)):\n",
        "      if label_q[i] == 1 and i == 0:\n",
        "        label_en.insert(i, 0)\n",
        "        sen_tok.insert(i,\"$\")\n",
        "      elif label_q[i] == 1 and label_q[i-1] == 0:\n",
        "        label_en.insert(i, 0)\n",
        "        sen_tok.insert(i,\"$\")\n",
        "      if label_q[i] == 1 and i == len(label_q) - 1:\n",
        "        label_en.insert(i+2, 0)\n",
        "        sen_tok.insert(i+2,\"$\")\n",
        "      elif label_q[i] == 1 and label_q[i+1] == 0:\n",
        "        label_en.insert(i+2, 0)\n",
        "        sen_tok.insert(i+2,\"$\")\n",
        "    pretrain.append([sen_tok, label_en])\n",
        "  else:\n",
        "    for w in sen.sentences[0].words[:]:\n",
        "      rng = re.findall(r'\\d+', w.misc) \n",
        "      bert_tok = tokenizer.tokenize(w.text)\n",
        "      sen_tok.extend(bert_tok)\n",
        "      w_len = int(rng[0]);\n",
        "      for t in bert_tok:\n",
        "        if val[1][0] <= w_len and val[1][1] >= w_len + length(t):\n",
        "          label_q.append(1)\n",
        "        else:\n",
        "          label_q.append(0)\n",
        "        label_en.append(0)\n",
        "        w_len += length(t)\n",
        "    for i in range(len(label_q)):\n",
        "      if label_q[i] == 1 and i == 0:\n",
        "        label_en.insert(i, 0)\n",
        "        sen_tok.insert(i,\"$\")\n",
        "      elif label_q[i] == 1 and label_q[i-1] == 0:\n",
        "        label_en.insert(i, 0)\n",
        "        sen_tok.insert(i,\"$\")\n",
        "      if label_q[i] == 1 and i == len(label_q) - 1:\n",
        "        label_en.insert(i+2, 0)\n",
        "        sen_tok.insert(i+2,\"$\")\n",
        "      elif label_q[i] == 1 and label_q[i+1] == 0:\n",
        "        label_en.insert(i+2, 0)\n",
        "        sen_tok.insert(i+2,\"$\")\n",
        "    pretrain.append([sen_tok, label_en])"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aZZ60I1LuLix"
      },
      "source": [
        "validation = []\n",
        "i = 0\n",
        "for val in ans_val:\n",
        "  label_q = []\n",
        "  sen_tok = []\n",
        "  sen = nlp2(val[0])\n",
        "  label_en = []\n",
        "  if len(val) == 3:\n",
        "    for w in sen.sentences[0].words[:]:\n",
        "      rng = re.findall(r'\\d+', w.misc) \n",
        "      bert_tok = tokenizer.tokenize(w.text)\n",
        "      sen_tok.extend(bert_tok)\n",
        "      w_len = int(rng[0]);\n",
        "      for t in bert_tok:\n",
        "        if val[1][0] <= w_len and val[1][1] >= w_len + length(t):\n",
        "          label_q.append(1)\n",
        "        else:\n",
        "          label_q.append(0)\n",
        "        if val[2][0] <= w_len and val[2][1] >= w_len + length(t):\n",
        "          label_en.append(1)\n",
        "        else:\n",
        "          label_en.append(0)\n",
        "        w_len += length(t)\n",
        "    for i in range(len(label_q)):\n",
        "      if label_q[i] == 1 and i == 0:\n",
        "        label_en.insert(i, 0)\n",
        "        sen_tok.insert(i,\"$\")\n",
        "      elif label_q[i] == 1 and label_q[i-1] == 0:\n",
        "        label_en.insert(i, 0)\n",
        "        sen_tok.insert(i,\"$\")\n",
        "      if label_q[i] == 1 and i == len(label_q) - 1:\n",
        "        label_en.insert(i+2, 0)\n",
        "        sen_tok.insert(i+2,\"$\")\n",
        "      elif label_q[i] == 1 and label_q[i+1] == 0:\n",
        "        label_en.insert(i+2, 0)\n",
        "        sen_tok.insert(i+2,\"$\")\n",
        "    validation.append([sen_tok, label_en])\n",
        "  else:\n",
        "    for w in sen.sentences[0].words[:]:\n",
        "      rng = re.findall(r'\\d+', w.misc) \n",
        "      bert_tok = tokenizer.tokenize(w.text)\n",
        "      sen_tok.extend(bert_tok)\n",
        "      w_len = int(rng[0]);\n",
        "      for t in bert_tok:\n",
        "        if val[1][0] <= w_len and val[1][1] >= w_len + length(t):\n",
        "          label_q.append(1)\n",
        "        else:\n",
        "          label_q.append(0)\n",
        "        label_en.append(0)\n",
        "        w_len += length(t)\n",
        "    for i in range(len(label_q)):\n",
        "      if label_q[i] == 1 and i == 0:\n",
        "        label_en.insert(i, 0)\n",
        "        sen_tok.insert(i,\"$\")\n",
        "      elif label_q[i] == 1 and label_q[i-1] == 0:\n",
        "        label_en.insert(i, 0)\n",
        "        sen_tok.insert(i,\"$\")\n",
        "      if label_q[i] == 1 and i == len(label_q) - 1:\n",
        "        label_en.insert(i+2, 0)\n",
        "        sen_tok.insert(i+2,\"$\")\n",
        "      elif label_q[i] == 1 and label_q[i+1] == 0:\n",
        "        label_en.insert(i+2, 0)\n",
        "        sen_tok.insert(i+2,\"$\")\n",
        "    validation.append([sen_tok, label_en])"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MJxP3Lc-uQq8"
      },
      "source": [
        "train = []\n",
        "for val in pretrain:\n",
        "  lab = np.zeros(256)\n",
        "  tok_arr = np.zeros(256)\n",
        "  att_mask = np.zeros(256)\n",
        "  sen_tok = []\n",
        "  tok_arr[0] = 102\n",
        "  att_mask[0] = 1\n",
        "  for tok in val[0]:\n",
        "    sen_tok.append(tokenizer.convert_tokens_to_ids(tok))\n",
        "  for i in range(len(val[1])):\n",
        "    lab[i+1] = val[1][i]\n",
        "    tok_arr[i+1] = sen_tok[i]\n",
        "    att_mask[i+1] = 1\n",
        "  train.append([tok_arr,att_mask, lab])"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P0q5G69PuTwq"
      },
      "source": [
        "val_data = []\n",
        "for val in validation:\n",
        "  lab = np.zeros(256)\n",
        "  tok_arr = np.zeros(256)\n",
        "  att_mask = np.zeros(256)\n",
        "  sen_tok = []\n",
        "  tok_arr[0] = 102\n",
        "  att_mask[0] = 1\n",
        "  for tok in val[0]:\n",
        "    sen_tok.append(tokenizer.convert_tokens_to_ids(tok))\n",
        "  for i in range(len(val[1])):\n",
        "    lab[i+1] = val[1][i]\n",
        "    tok_arr[i+1] = sen_tok[i]\n",
        "    att_mask[i+1] = 1\n",
        "  val_data.append([tok_arr,att_mask, lab])"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LcmNZ5ZguWHG"
      },
      "source": [
        "x_train_id = np.zeros((0,256))\n",
        "x_train_mask = np.zeros((0,256))\n",
        "y_train = np.zeros((0,256))\n",
        "for val in train:\n",
        "  x_train_id = np.vstack((x_train_id, val[0]))\n",
        "  x_train_mask = np.vstack((x_train_mask, val[1]))\n",
        "  y_train = np.vstack((y_train, val[2]))"
      ],
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OY9mRsFGuYlO"
      },
      "source": [
        "for i in range(len(y_train)):\n",
        "  for j in range(1,len(y_train[0])):\n",
        "    if y_train[i][j-1] == 0 and y_train[i][j] == 1:\n",
        "      y_train[i][j] = 2"
      ],
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KsR7wTzHuaYB"
      },
      "source": [
        "x_val_id = np.zeros((0,256))\n",
        "x_val_mask = np.zeros((0,256))\n",
        "y_val = np.zeros((0,256))\n",
        "for val in val_data:\n",
        "  x_val_id = np.vstack((x_val_id, val[0]))\n",
        "  x_val_mask = np.vstack((x_val_mask, val[1]))\n",
        "  y_val = np.vstack((y_val, val[2]))"
      ],
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xK8DT2BsudGc"
      },
      "source": [
        "for i in range(len(y_val)):\n",
        "  for j in range(1,len(y_val[0])):\n",
        "    if y_val[i][j-1] == 0 and y_val[i][j] == 1:\n",
        "      y_val[i][j] = 2"
      ],
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NVaQh-Xau_XJ"
      },
      "source": [
        "train_data = TensorDataset(torch.from_numpy(x_train_id), torch.from_numpy(x_train_mask), torch.from_numpy(y_train))\n",
        "val_data = TensorDataset(torch.from_numpy(x_val_id), torch.from_numpy(x_val_mask), torch.from_numpy(y_val))"
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G4kVq9KMvPTM"
      },
      "source": [
        "batch_size = 24\n",
        "train_loader = DataLoader(train_data, shuffle=True, batch_size = batch_size)\n",
        "val_loader = DataLoader(val_data, shuffle=True, batch_size = batch_size)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 116,
          "referenced_widgets": [
            "c33c2ba37194467e9450dd84191025a9",
            "a24a6d1080b94f53b3f290ba672a1737",
            "590fc71f666c45a9ad8fb4246e36dce4",
            "63871ee0b90b423996867bb199257dc7",
            "03a2e91bd7d04462bcc8c6f54e4aa1f1",
            "88bdea44042346aeaacf5fbf5b2bcc31",
            "6035f1faea5845708d33e2f81cd0c6e2",
            "4ef9131728c1475daa6f01932c4b737e",
            "afca76bb77ce4b9ba9e124cb347a3e18",
            "63e915e463a2426bb66389d2217d6199",
            "1fd36dbda84643e68c55ee040c92c24c",
            "cc285e61c5ea452493a04c43c9a0fc1c",
            "9902f39014aa4847aa17ab7c4c8a994c",
            "3fc404a45b62452598271cc2eb04e572",
            "bb67c58a7f51469b9837ad052a089198",
            "7e776cc57836438b856a0c7aa965321b"
          ]
        },
        "id": "Ae41z16cvRrK",
        "outputId": "6806631d-36d7-4cec-87a9-21cd7b30e4e4"
      },
      "source": [
        "tokenizer = BertTokenizer.from_pretrained('allenai/scibert_scivocab_uncased')\n",
        "model = BertModel.from_pretrained('allenai/scibert_scivocab_uncased')"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c33c2ba37194467e9450dd84191025a9",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=385.0, 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": "afca76bb77ce4b9ba9e124cb347a3e18",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Downloading', max=442221694.0, style=ProgressStyle(descri…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XF8Ni74QyTOo"
      },
      "source": [
        "### Overall Model Architechture of SciBERT + CRF Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7k3uPUXBvVWT"
      },
      "source": [
        "class BERT_Arch(nn.Module):\n",
        "\n",
        "    def __init__(self, bert, embed_dim, hidden_dim, drop_prob, n_layers, out_dim):\n",
        "      \n",
        "      super(BERT_Arch, self).__init__()\n",
        "      self.bert = bert \n",
        "      self.dropout = nn.Dropout(drop_prob)\n",
        "      self.fc1 = nn.Linear(2*embed_dim,out_dim)\n",
        "      self.w1 = nn.Linear(embed_dim, embed_dim)\n",
        "      self.w2 = nn.Linear(embed_dim, embed_dim)\n",
        "      #self.bilstm = nn.LSTM(embed_dim, hidden_dim,  bidirectional=True, batch_first=True)\n",
        "      self.softmax = nn.LogSoftmax(dim = 2)\n",
        "      self.crf = CRF(3, batch_first=True)  \n",
        "      self.tanh = nn.Tanh()\n",
        "\n",
        "    #define the forward pass\n",
        "    def forward(self, sent_id, mask_val, labels=None):\n",
        "      x = self.bert(sent_id, attention_mask=mask_val)\n",
        "      x = x.last_hidden_state\n",
        "      x = self.tanh(x)\n",
        "      cls = x[:,0,:]\n",
        "      cls = cls.unsqueeze(1).repeat(1, 256, 1)\n",
        "      cls = self.w1(cls)\n",
        "      x = self.w2(x)\n",
        "      x = torch.cat([x,cls], dim = 2)\n",
        "      #x,_ = self.bilstm(x)\n",
        "      x = self.dropout(x)\n",
        "      x = self.fc1(x)\n",
        "      mask_val = mask_val.type(torch.uint8)\n",
        "      logit = self.softmax(x)\n",
        "      if labels is not None:\n",
        "          loss = -self.crf(logit, labels, mask=mask_val, reduction='mean')\n",
        "          return loss\n",
        "      else:\n",
        "          prediction = self.crf.decode(x, mask=mask_val)\n",
        "          return prediction"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u8sJ2mAfvZGH"
      },
      "source": [
        "bert_model = BERT_Arch(model, 768, 64, 0.1, 1,3)\n",
        "bert_model = bert_model.to(device)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-PGvGtk9vbL4",
        "outputId": "e69abf64-77ec-4d58-a456-081beb9e758d"
      },
      "source": [
        "print(bert_model)"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "BERT_Arch(\n",
            "  (bert): BertModel(\n",
            "    (embeddings): BertEmbeddings(\n",
            "      (word_embeddings): Embedding(31090, 768, padding_idx=0)\n",
            "      (position_embeddings): Embedding(512, 768)\n",
            "      (token_type_embeddings): Embedding(2, 768)\n",
            "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "      (dropout): Dropout(p=0.1, inplace=False)\n",
            "    )\n",
            "    (encoder): BertEncoder(\n",
            "      (layer): ModuleList(\n",
            "        (0): BertLayer(\n",
            "          (attention): BertAttention(\n",
            "            (self): BertSelfAttention(\n",
            "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "            (output): BertSelfOutput(\n",
            "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (intermediate): BertIntermediate(\n",
            "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          )\n",
            "          (output): BertOutput(\n",
            "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
            "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "            (dropout): Dropout(p=0.1, inplace=False)\n",
            "          )\n",
            "        )\n",
            "        (1): BertLayer(\n",
            "          (attention): BertAttention(\n",
            "            (self): BertSelfAttention(\n",
            "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "            (output): BertSelfOutput(\n",
            "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (intermediate): BertIntermediate(\n",
            "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          )\n",
            "          (output): BertOutput(\n",
            "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
            "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "            (dropout): Dropout(p=0.1, inplace=False)\n",
            "          )\n",
            "        )\n",
            "        (2): BertLayer(\n",
            "          (attention): BertAttention(\n",
            "            (self): BertSelfAttention(\n",
            "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "            (output): BertSelfOutput(\n",
            "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (intermediate): BertIntermediate(\n",
            "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          )\n",
            "          (output): BertOutput(\n",
            "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
            "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "            (dropout): Dropout(p=0.1, inplace=False)\n",
            "          )\n",
            "        )\n",
            "        (3): BertLayer(\n",
            "          (attention): BertAttention(\n",
            "            (self): BertSelfAttention(\n",
            "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "            (output): BertSelfOutput(\n",
            "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (intermediate): BertIntermediate(\n",
            "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          )\n",
            "          (output): BertOutput(\n",
            "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
            "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "            (dropout): Dropout(p=0.1, inplace=False)\n",
            "          )\n",
            "        )\n",
            "        (4): BertLayer(\n",
            "          (attention): BertAttention(\n",
            "            (self): BertSelfAttention(\n",
            "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "            (output): BertSelfOutput(\n",
            "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (intermediate): BertIntermediate(\n",
            "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          )\n",
            "          (output): BertOutput(\n",
            "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
            "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "            (dropout): Dropout(p=0.1, inplace=False)\n",
            "          )\n",
            "        )\n",
            "        (5): BertLayer(\n",
            "          (attention): BertAttention(\n",
            "            (self): BertSelfAttention(\n",
            "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "            (output): BertSelfOutput(\n",
            "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (intermediate): BertIntermediate(\n",
            "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          )\n",
            "          (output): BertOutput(\n",
            "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
            "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "            (dropout): Dropout(p=0.1, inplace=False)\n",
            "          )\n",
            "        )\n",
            "        (6): BertLayer(\n",
            "          (attention): BertAttention(\n",
            "            (self): BertSelfAttention(\n",
            "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "            (output): BertSelfOutput(\n",
            "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (intermediate): BertIntermediate(\n",
            "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          )\n",
            "          (output): BertOutput(\n",
            "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
            "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "            (dropout): Dropout(p=0.1, inplace=False)\n",
            "          )\n",
            "        )\n",
            "        (7): BertLayer(\n",
            "          (attention): BertAttention(\n",
            "            (self): BertSelfAttention(\n",
            "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "            (output): BertSelfOutput(\n",
            "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (intermediate): BertIntermediate(\n",
            "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          )\n",
            "          (output): BertOutput(\n",
            "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
            "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "            (dropout): Dropout(p=0.1, inplace=False)\n",
            "          )\n",
            "        )\n",
            "        (8): BertLayer(\n",
            "          (attention): BertAttention(\n",
            "            (self): BertSelfAttention(\n",
            "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "            (output): BertSelfOutput(\n",
            "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (intermediate): BertIntermediate(\n",
            "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          )\n",
            "          (output): BertOutput(\n",
            "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
            "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "            (dropout): Dropout(p=0.1, inplace=False)\n",
            "          )\n",
            "        )\n",
            "        (9): BertLayer(\n",
            "          (attention): BertAttention(\n",
            "            (self): BertSelfAttention(\n",
            "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "            (output): BertSelfOutput(\n",
            "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (intermediate): BertIntermediate(\n",
            "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          )\n",
            "          (output): BertOutput(\n",
            "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
            "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "            (dropout): Dropout(p=0.1, inplace=False)\n",
            "          )\n",
            "        )\n",
            "        (10): BertLayer(\n",
            "          (attention): BertAttention(\n",
            "            (self): BertSelfAttention(\n",
            "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "            (output): BertSelfOutput(\n",
            "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (intermediate): BertIntermediate(\n",
            "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          )\n",
            "          (output): BertOutput(\n",
            "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
            "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "            (dropout): Dropout(p=0.1, inplace=False)\n",
            "          )\n",
            "        )\n",
            "        (11): BertLayer(\n",
            "          (attention): BertAttention(\n",
            "            (self): BertSelfAttention(\n",
            "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "            (output): BertSelfOutput(\n",
            "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
            "              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "              (dropout): Dropout(p=0.1, inplace=False)\n",
            "            )\n",
            "          )\n",
            "          (intermediate): BertIntermediate(\n",
            "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
            "          )\n",
            "          (output): BertOutput(\n",
            "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
            "            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "            (dropout): Dropout(p=0.1, inplace=False)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "    (pooler): BertPooler(\n",
            "      (dense): Linear(in_features=768, out_features=768, bias=True)\n",
            "      (activation): Tanh()\n",
            "    )\n",
            "  )\n",
            "  (dropout): Dropout(p=0.1, inplace=False)\n",
            "  (fc1): Linear(in_features=1536, out_features=3, bias=True)\n",
            "  (w1): Linear(in_features=768, out_features=768, bias=True)\n",
            "  (w2): Linear(in_features=768, out_features=768, bias=True)\n",
            "  (softmax): LogSoftmax(dim=2)\n",
            "  (crf): CRF(num_tags=3)\n",
            "  (tanh): Tanh()\n",
            ")\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DP1LwYFPvlca"
      },
      "source": [
        "optimizer = torch.optim.Adam(bert_model.parameters(), lr=1e-5)"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s4pUDyTUyaFF"
      },
      "source": [
        "### Score metric very much similar to F1-Overlap score of the Organizers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AscnhqGZvoPg"
      },
      "source": [
        "def score(val_loader):\n",
        "  p=0\n",
        "  n=0\n",
        "  pos=0\n",
        "  neg=0\n",
        "  prec_num = 0 \n",
        "  prec_den = 0\n",
        "  rec_num = 0\n",
        "  rec_den = 0 \n",
        "  p1 = 0\n",
        "  n1=0\n",
        "  for seq, mask, y in val_loader:\n",
        "      bert_model.zero_grad()\n",
        "      bert_model.eval()\n",
        "      y_pred = bert_model(seq.long().to(device), mask.long().to(device))\n",
        "      np_out = np.zeros((len(y_pred),256, 1))\n",
        "      np_act = (y.cpu().data.numpy() >= 1).astype(int).reshape((len(y), len(y[0]),1))\n",
        "      \n",
        "      for i in range(np_out.shape[0]):\n",
        "        prev_2 = 0\n",
        "        for j in range(np_out.shape[1]):\n",
        "          for k in range(np_out.shape[2]):\n",
        "            if j >= len(y_pred[i]):\n",
        "              np_out[i][j][k] = 0\n",
        "            elif y_pred[i][j] == 2:\n",
        "              np_out[i][j][k]=1\n",
        "              prev_2 = 1\n",
        "            elif y_pred[i][j] == 1 and prev_2 == 1:\n",
        "              np_out[i][j][k]=1\n",
        "            else:\n",
        "              prev_2 = 0\n",
        "              np_out[i][j][k]=0\n",
        "\n",
        "      for i in range(np_out.shape[0]):\n",
        "        for j in range(np_out.shape[1]):\n",
        "          if np.max(np_out[i,j,:])==1 or np.max(np_act[i,j,:])==1:\n",
        "            if np.max(np_out[i,j,:])==1 and np.max(np_act[i,j,:])==1:\n",
        "              p1 = p1 + 1\n",
        "            else:\n",
        "              n1 = n1 + 1\n",
        "\n",
        "      for i in range(np_out.shape[0]):\n",
        "        for j in range(np_out.shape[1]):\n",
        "          for k in range(np_out.shape[2]):\n",
        "            if np_out[i,j,k] == np_act[i,j,k]:\n",
        "              p = p +1\n",
        "            else:\n",
        "              n=n+1\n",
        "            if (np_out[i,j,k])==1 or (np_act[i,j,k])==1:\n",
        "              if (np_out[i,j,k])==1 and (np_act[i,j,k])==1:\n",
        "                pos = pos + 1\n",
        "              else:\n",
        "                neg = neg + 1\n",
        "            if np_out[i,j,k]==1:\n",
        "              if np_out[i,j,k]==np_act[i,j,k]:\n",
        "                prec_num = prec_num + 1\n",
        "              else:\n",
        "                prec_den = prec_den + 1\n",
        "            if np_act[i,j,k]==1:\n",
        "              if np_out[i,j,k]==np_act[i,j,k]:\n",
        "                rec_num = rec_num + 1\n",
        "              else:\n",
        "                rec_den = rec_den + 1\n",
        "  if prec_num+prec_den == 0:\n",
        "    return 0\n",
        "  precision = prec_num/(prec_num+prec_den)\n",
        "  if rec_num+rec_den == 0:\n",
        "    return 0\n",
        "  recall = rec_num/(rec_num+rec_den)\n",
        "  if precision == 0 and recall == 0:\n",
        "    return 0\n",
        "  F1 = 2*precision*recall/(precision+recall)\n",
        "  return F1"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s2pO7iBjyb0E"
      },
      "source": [
        "### Loading a pretrained model "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_BWlK7owvqrb"
      },
      "source": [
        "bert_model = torch.load(\"/content/drive/My Drive/scibert_base_measured_entity_crf_0.56.pt\")"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JB4pHFkByi7j"
      },
      "source": [
        "### Uncomment the below code if you want to train the Custom Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bXXYkmTLvtg7"
      },
      "source": [
        "# epochs = 10\n",
        "# for e in range(epochs):\n",
        "  \n",
        "#   bert_model.train()\n",
        "#   i=0\n",
        "#   train_loss=0\n",
        "#   for seq, mask, y in train_loader:\n",
        "#     bert_model.zero_grad()\n",
        "#     loss = bert_model(seq.long().to(device), mask.long().to(device), y.long().to(device))\n",
        "#     train_loss += loss.item()*batch_size\n",
        "#     loss.backward()\n",
        "#     torch.nn.utils.clip_grad_norm_(bert_model.parameters(), 1.0)\n",
        "#     optimizer.step()\n",
        "#     if(i%5==0):\n",
        "#       print(\"Epoch-{}/{} Iterations-{} loss-{}\".format(e+1,epochs,i+1,loss.item()))\n",
        "#     i+=1\n",
        "  \n",
        "  \n",
        "#   bert_model.eval()\n",
        "#   f1_score = score(val_loader)\n",
        "#   val_loss=0\n",
        "#   for seq, mask, y in val_loader:\n",
        "#     bert_model.zero_grad()\n",
        "#     loss = bert_model(seq.long().to(device), mask.long().to(device), y.long().to(device))\n",
        "#     val_loss += loss.item()*batch_size\n",
        "#     i+=1\n",
        "  \n",
        "#   print(\"Epoch-{}/{} train_loss-{} Val_loss-{} F1 Score-{}\".format(e+1,epochs,train_loss/len(train_loader),val_loss/len(val_loader), f1_score))"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zBIeSpwPymUU"
      },
      "source": [
        "### The F1-Score obtained on the dev dataset using the score metric defined above"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hQWvQ3Rsv09Y",
        "outputId": "7bd4d8cc-a852-45a1-e7af-297b9dac4449"
      },
      "source": [
        "p=0\n",
        "n=0\n",
        "pos=0\n",
        "neg=0\n",
        "prec_num = 0 \n",
        "prec_den = 0\n",
        "rec_num = 0\n",
        "rec_den = 0 \n",
        "p1 = 0\n",
        "n1=0\n",
        "for seq, mask, y in val_loader:\n",
        "    bert_model.zero_grad()\n",
        "    bert_model.eval()\n",
        "    y_pred = bert_model(seq.long().to(device), mask.long().to(device))\n",
        "    np_out = np.zeros((len(y_pred),256, 1))\n",
        "    np_act = (y.cpu().data.numpy() >= 1).astype(int).reshape((len(y), len(y[0]),1))\n",
        "    \n",
        "    for i in range(np_out.shape[0]):\n",
        "      prev_2 = 0\n",
        "      for j in range(np_out.shape[1]):\n",
        "        for k in range(np_out.shape[2]):\n",
        "          if j >= len(y_pred[i]):\n",
        "            np_out[i][j][k] = 0\n",
        "          elif y_pred[i][j] == 2:\n",
        "            np_out[i][j][k]=1\n",
        "            prev_2 = 1\n",
        "          elif y_pred[i][j] == 1 and prev_2 == 1:\n",
        "            np_out[i][j][k]=1\n",
        "          else:\n",
        "            prev_2 = 0\n",
        "            np_out[i][j][k]=0\n",
        "\n",
        "    for i in range(np_out.shape[0]):\n",
        "      for j in range(np_out.shape[1]):\n",
        "        if np.max(np_out[i,j,:])==1 or np.max(np_act[i,j,:])==1:\n",
        "          if np.max(np_out[i,j,:])==1 and np.max(np_act[i,j,:])==1:\n",
        "            p1 = p1 + 1\n",
        "          else:\n",
        "            n1 = n1 + 1\n",
        "\n",
        "    for i in range(np_out.shape[0]):\n",
        "      for j in range(np_out.shape[1]):\n",
        "        for k in range(np_out.shape[2]):\n",
        "          if np_out[i,j,k] == np_act[i,j,k]:\n",
        "            p = p +1\n",
        "          else:\n",
        "            n=n+1\n",
        "          if (np_out[i,j,k])==1 or (np_act[i,j,k])==1:\n",
        "            if (np_out[i,j,k])==1 and (np_act[i,j,k])==1:\n",
        "              pos = pos + 1\n",
        "            else:\n",
        "              neg = neg + 1\n",
        "          if np_out[i,j,k]==1:\n",
        "            if np_out[i,j,k]==np_act[i,j,k]:\n",
        "              prec_num = prec_num + 1\n",
        "            else:\n",
        "              prec_den = prec_den + 1\n",
        "          if np_act[i,j,k]==1:\n",
        "            if np_out[i,j,k]==np_act[i,j,k]:\n",
        "              rec_num = rec_num + 1\n",
        "            else:\n",
        "              rec_den = rec_den + 1\n",
        "\n",
        "precision = prec_num/(prec_num+prec_den)\n",
        "recall = rec_num/(rec_num+rec_den)\n",
        "F1 = 2*precision*recall/(precision+recall)\n",
        "\n",
        "print(\"Entity recognition modified accuracy:-\" + str(p1/(p1+n1)))\n",
        "print(\"--------NER RESULTS--------\")\n",
        "print(\"Accuracy:-\" + str(p/(n+p)))\n",
        "print(\"Modified Accuracy:-\" + str(pos/(pos+neg)))\n",
        "print(\"Precision:-\" + str(precision))\n",
        "print(\"Recall:-\" + str(recall))\n",
        "print(\"F1 score:-\"+str(F1))"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Entity recognition modified accuracy:-0.3830409356725146\n",
            "--------NER RESULTS--------\n",
            "Accuracy:-0.9910411005434783\n",
            "Modified Accuracy:-0.3830409356725146\n",
            "Precision:-0.5954545454545455\n",
            "Recall:-0.5177865612648221\n",
            "F1 score:-0.5539112050739958\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IFYDp3c4ys24"
      },
      "source": [
        "### Testing the model on custom sentences"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q4wgWotEv3U1"
      },
      "source": [
        "t = \"The concentration and strength of H2SO4 is $ 2g/ml $ and 98%\""
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o9WAzX4FwjtE"
      },
      "source": [
        "tok_lis = tokenizer.tokenize(t)\n",
        "tok_arr = np.zeros(256)\n",
        "att_mask = np.zeros(256)\n",
        "att_mask[0] = 1\n",
        "tok_arr[0] = 102\n",
        "sen_tok = []\n",
        "for tok in tok_lis:\n",
        "  sen_tok.append(tokenizer.convert_tokens_to_ids(tok))\n",
        "for i in range(len(sen_tok)):\n",
        "  tok_arr[i+1] = sen_tok[i]\n",
        "  att_mask[i+1] = 1\n",
        "tok_arr = torch.from_numpy(tok_arr)\n",
        "att_mask = torch.from_numpy(att_mask)"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YU11iSCjwmIc"
      },
      "source": [
        "bert_model.zero_grad()\n",
        "bert_model.eval()\n",
        "y_pred = bert_model(tok_arr.reshape((1,256)).long().to(device), att_mask.reshape((1,256)).long().to(device))"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GffU6IdQwoVG"
      },
      "source": [
        "tokenized_sen = tokenizer.tokenize(t)"
      ],
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sfO-XMCvwsYn",
        "outputId": "31f1b8d2-39d1-4b8c-a5e8-09e42d8eb959"
      },
      "source": [
        "i = 1\n",
        "for word in tokenized_sen:\n",
        "  if y_pred[0][i] != 0:\n",
        "    print(word + \": \"+\"MeasuredEntity\")\n",
        "  else:\n",
        "    print(word + \": \")\n",
        "  i+=1"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "the: \n",
            "concentration: \n",
            "and: \n",
            "strength: \n",
            "of: \n",
            "h: MeasuredEntity\n",
            "##2: MeasuredEntity\n",
            "##so: MeasuredEntity\n",
            "##4: MeasuredEntity\n",
            "is: \n",
            "$: \n",
            "2: \n",
            "##g: \n",
            "/: \n",
            "ml: \n",
            "$: \n",
            "and: \n",
            "98: \n",
            "%: \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xemgnXB_wvz4"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}