RoBERTa_LSTM_Empathy.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard",
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "923f4080bc924e1ea1f88e72f6478825": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_957a05d4a09847018d11f8af53733d24",
              "IPY_MODEL_a5cbd5aa81b74f5f937a28e88f17751f",
              "IPY_MODEL_e35afc50ddac4cac9d68052a114b7fd5"
            ],
            "layout": "IPY_MODEL_0b83d94736cf40c797baade2a6fa1717"
          }
        },
        "957a05d4a09847018d11f8af53733d24": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_28118bd95fbb4c3e8369d3c494e86122",
            "placeholder": "​",
            "style": "IPY_MODEL_9819643396b34bdbaa1bc32f230fe074",
            "value": "Downloading (…)lve/main/config.json: 100%"
          }
        },
        "a5cbd5aa81b74f5f937a28e88f17751f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_6dced27bb0bc478fa89287cfc4714587",
            "max": 481,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_f9bb46ba3e6a4c79875edb3c13e0518b",
            "value": 481
          }
        },
        "e35afc50ddac4cac9d68052a114b7fd5": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_bcaf32a5cc584061ac53f4601d06c8d9",
            "placeholder": "​",
            "style": "IPY_MODEL_b977713037e64ff58669da05f21814ab",
            "value": " 481/481 [00:00&lt;00:00, 18.5kB/s]"
          }
        },
        "0b83d94736cf40c797baade2a6fa1717": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "28118bd95fbb4c3e8369d3c494e86122": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "9819643396b34bdbaa1bc32f230fe074": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "6dced27bb0bc478fa89287cfc4714587": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "f9bb46ba3e6a4c79875edb3c13e0518b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "bcaf32a5cc584061ac53f4601d06c8d9": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "b977713037e64ff58669da05f21814ab": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "0ecdf6f72d5045ec9bb6c919a2a8dcef": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_476e4cc3f2af47a78bcf5977afeedfcd",
              "IPY_MODEL_c93375a172104b1e84df05b8504a9770",
              "IPY_MODEL_1c24285ece8d411a8f2f7083954e992c"
            ],
            "layout": "IPY_MODEL_858ce1ede7e0486e9d4d1bccae3b10e2"
          }
        },
        "476e4cc3f2af47a78bcf5977afeedfcd": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_3f8d5919d8b642ee97864bd05c60e597",
            "placeholder": "​",
            "style": "IPY_MODEL_4b675d5dbe8742bd8c603af8e5d8d1cf",
            "value": "Downloading (…)olve/main/vocab.json: 100%"
          }
        },
        "c93375a172104b1e84df05b8504a9770": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_61785f21342e4c1e991c519e0bc3d87e",
            "max": 898823,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_4cf38bdc9dd749db8d1332e2a01500bc",
            "value": 898823
          }
        },
        "1c24285ece8d411a8f2f7083954e992c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_7bf3cf31a11e49198cc8ad4a434e0334",
            "placeholder": "​",
            "style": "IPY_MODEL_3bdfa528f2a247049413d397004f801d",
            "value": " 899k/899k [00:00&lt;00:00, 12.8MB/s]"
          }
        },
        "858ce1ede7e0486e9d4d1bccae3b10e2": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "3f8d5919d8b642ee97864bd05c60e597": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "4b675d5dbe8742bd8c603af8e5d8d1cf": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "61785f21342e4c1e991c519e0bc3d87e": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "4cf38bdc9dd749db8d1332e2a01500bc": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "7bf3cf31a11e49198cc8ad4a434e0334": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "3bdfa528f2a247049413d397004f801d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "2e7b36a2ce864dbc9b2a2ed843057e63": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_706cf1df7ac44714852b7a7cb56c8616",
              "IPY_MODEL_9aaa1006db674a1392ae4748609c4cdd",
              "IPY_MODEL_dc20b7a8ff5e413996d1ac38ace30d04"
            ],
            "layout": "IPY_MODEL_f9b8a4f569394b01bec8921c89ec5490"
          }
        },
        "706cf1df7ac44714852b7a7cb56c8616": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_cfca149b797d41c792f576e9a844ace8",
            "placeholder": "​",
            "style": "IPY_MODEL_3437a807ea51418e82dd162a2723673f",
            "value": "Downloading (…)olve/main/merges.txt: 100%"
          }
        },
        "9aaa1006db674a1392ae4748609c4cdd": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_18cac49e6c3141a7832cd95d9464b26a",
            "max": 456318,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_a983e71f2a4a47e29eff613cb853ba2e",
            "value": 456318
          }
        },
        "dc20b7a8ff5e413996d1ac38ace30d04": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_4cfe9ec36212434d9ab9d8c98baf674b",
            "placeholder": "​",
            "style": "IPY_MODEL_7934104904fe4cdbabd05207af572049",
            "value": " 456k/456k [00:00&lt;00:00, 26.7MB/s]"
          }
        },
        "f9b8a4f569394b01bec8921c89ec5490": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "cfca149b797d41c792f576e9a844ace8": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "3437a807ea51418e82dd162a2723673f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "18cac49e6c3141a7832cd95d9464b26a": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "a983e71f2a4a47e29eff613cb853ba2e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "4cfe9ec36212434d9ab9d8c98baf674b": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "7934104904fe4cdbabd05207af572049": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "5414977470fb4f3f8fc1edbf7ea78207": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_49a7d8e169c4472ab8a318a2d480aba0",
              "IPY_MODEL_86b3e39e9c0f437c8aa7993da55860e1",
              "IPY_MODEL_7f79100b4cdf4410a9c31910ff998a47"
            ],
            "layout": "IPY_MODEL_c1dd19f512f14989adeb7b76d8ec7171"
          }
        },
        "49a7d8e169c4472ab8a318a2d480aba0": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_1086446043fe48e083be63a8277c5f1a",
            "placeholder": "​",
            "style": "IPY_MODEL_a068f1b1c02d46259b61c0285ecc00d2",
            "value": "Downloading (…)/main/tokenizer.json: 100%"
          }
        },
        "86b3e39e9c0f437c8aa7993da55860e1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_1639ce214ff74b7196ef0f07cbfe4354",
            "max": 1355863,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_83e860c839444971b640fe9184d31b41",
            "value": 1355863
          }
        },
        "7f79100b4cdf4410a9c31910ff998a47": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_15dbcd1a825e45e7aa9568873e8c2a6e",
            "placeholder": "​",
            "style": "IPY_MODEL_a70dc194868a4d7689aaca3d1bff3297",
            "value": " 1.36M/1.36M [00:00&lt;00:00, 44.9MB/s]"
          }
        },
        "c1dd19f512f14989adeb7b76d8ec7171": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "1086446043fe48e083be63a8277c5f1a": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "a068f1b1c02d46259b61c0285ecc00d2": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "1639ce214ff74b7196ef0f07cbfe4354": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "83e860c839444971b640fe9184d31b41": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "15dbcd1a825e45e7aa9568873e8c2a6e": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "a70dc194868a4d7689aaca3d1bff3297": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "e042ef9be16649d3a514217bbccd51cf": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_8d4370bdbc9545cf9df47e3ff1086fc4",
              "IPY_MODEL_2426039b29e545fd8ab7f813db7bc27f",
              "IPY_MODEL_97396977f7ff4476ac9906561dba1d03"
            ],
            "layout": "IPY_MODEL_416c2a97733b4e519abf1518e6ff700c"
          }
        },
        "8d4370bdbc9545cf9df47e3ff1086fc4": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_0fa28fc0617a401289b8c38f8c5def9b",
            "placeholder": "​",
            "style": "IPY_MODEL_872688ddd03c48c79d03e7ff544ac4dc",
            "value": "100%"
          }
        },
        "2426039b29e545fd8ab7f813db7bc27f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_77093a59fe034c3fb5e23af123add4cd",
            "max": 38,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_342f5d1fa0214ccd9ea65962c3fc1601",
            "value": 38
          }
        },
        "97396977f7ff4476ac9906561dba1d03": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_d7d20328e11d4023909acbb9fd911bcb",
            "placeholder": "​",
            "style": "IPY_MODEL_ad9fb508fb9147c5a56754e69311f160",
            "value": " 38/38 [00:11&lt;00:00,  3.79it/s]"
          }
        },
        "416c2a97733b4e519abf1518e6ff700c": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "0fa28fc0617a401289b8c38f8c5def9b": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "872688ddd03c48c79d03e7ff544ac4dc": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "77093a59fe034c3fb5e23af123add4cd": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "342f5d1fa0214ccd9ea65962c3fc1601": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "d7d20328e11d4023909acbb9fd911bcb": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "ad9fb508fb9147c5a56754e69311f160": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S0IYl0s_yfux",
        "outputId": "d42b11d6-9217-40a4-8af9-f3584f4867c1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.0.0+cu118)\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.28.1-py3-none-any.whl (7.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.0/7.0 MB\u001b[0m \u001b[31m58.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.12.0)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.2)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.10/dist-packages (from torch) (4.5.0)\n",
            "Requirement already satisfied: triton==2.0.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.0.0)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.1)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.11.1)\n",
            "Requirement already satisfied: cmake in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch) (3.25.2)\n",
            "Requirement already satisfied: lit in /usr/local/lib/python3.10/dist-packages (from triton==2.0.0->torch) (16.0.2)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.65.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0)\n",
            "Collecting huggingface-hub<1.0,>=0.11.0\n",
            "  Downloading huggingface_hub-0.14.1-py3-none-any.whl (224 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m224.5/224.5 kB\u001b[0m \u001b[31m28.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.27.1)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m103.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2022.10.31)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.22.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.11.0->transformers) (2023.4.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.2)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.12)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (1.26.15)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2022.12.7)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n",
            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.14.1 tokenizers-0.13.3 transformers-4.28.1\n",
            "--2023-05-06 17:58:14--  https://raw.githubusercontent.com/00ber/ml-projects/main/data/wassa2023-shared-task.tar.gz\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.111.133, 185.199.108.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 4321231 (4.1M) [application/octet-stream]\n",
            "Saving to: ‘wassa2023-shared-task.tar.gz’\n",
            "\n",
            "wassa2023-shared-ta 100%[===================>]   4.12M  --.-KB/s    in 0.07s   \n",
            "\n",
            "2023-05-06 17:58:14 (58.6 MB/s) - ‘wassa2023-shared-task.tar.gz’ saved [4321231/4321231]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!pip install torch transformers\n",
        "!rm -f wassa2023-shared-task.tar.gz\n",
        "!wget 'https://raw.githubusercontent.com/00ber/ml-projects/main/data/wassa2023-shared-task.tar.gz'\n",
        "!tar -xf wassa2023-shared-task.tar.gz"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KqeNheNENmsa",
        "outputId": "dba968db-6583-4513-c4eb-2eee069f4f83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import torch\n",
        "from sklearn.preprocessing import OneHotEncoder, LabelEncoder\n",
        "\n",
        "class OrdinalLabelEncoder:\n",
        "    def get_feature_names_out(self, prefix=\"X\"):\n",
        "      return list(map(lambda x: f\"{prefix}_{x}\", self.classes_))\n",
        "    \n",
        "    def fit(self, y):\n",
        "      self.classes_ = sorted(list(set(y)))\n",
        "      self.sorter = np.argsort(self.classes_)\n",
        "      \n",
        "    def fit_transform(self, y):\n",
        "        y = y.to_numpy()\n",
        "        self.classes_ = sorted(list(set(y.reshape(-1))))\n",
        "        self.sorter = np.argsort(self.classes_)\n",
        "        idx = self.sorter[np.searchsorted(self.classes_, y, sorter=self.sorter)]\n",
        "        ordinal_labels = np.zeros((y.shape[0], len(self.classes_)))\n",
        "\n",
        "        for row, i in enumerate(idx):\n",
        "          ordinal_labels[row,:i[0]+1] = 1\n",
        "        return ordinal_labels\n",
        "\n",
        "    def transform(self, y):\n",
        "        idx = self.sorter[np.searchsorted(self.classes_, y, sorter=self.sorter)]\n",
        "        ordinal_labels = np.zeros((y.shape[0], len(self.classes_)), dtype=\"int32\")\n",
        "\n",
        "        for row, i in enumerate(idx):\n",
        "          ordinal_labels[row][:i[0]+1] = 1\n",
        "        ordinal_labels = ordinal_labels.astype(\"int32\")\n",
        "        return ordinal_labels\n",
        "\n",
        "    def inverse_transform(self, y_ordinal):\n",
        "        idx = y_ordinal.sum(axis=1, keepdims=True)\n",
        "        idx = idx.numpy()\n",
        "        idx = idx.astype(\"int32\")\n",
        "        classes = np.array(self.classes_)\n",
        "        return classes[idx - 1]\n",
        "\n",
        "\n",
        "class Encoder:\n",
        "  def __init__(self, enc_type=None):\n",
        "    self.enc_type = enc_type\n",
        "    if enc_type is None:\n",
        "      self.enc = None\n",
        "    elif enc_type == \"ohe\":\n",
        "      self.enc = OneHotEncoder()\n",
        "    elif enc_type == \"label\":\n",
        "      self.enc = LabelEncoder()\n",
        "    elif enc_type == \"ordinal\":\n",
        "      self.enc = OrdinalLabelEncoder()\n",
        "\n",
        "  def fit(self, df, target_column):\n",
        "    if self.enc_type is None:\n",
        "      self.target_column = target_column\n",
        "      self.classes = []\n",
        "      self.num_classes = 0 \n",
        "    elif self.enc_type == \"ohe\":\n",
        "      self.target_column = target_column\n",
        "      self.enc.fit(df[[target_column]])\n",
        "      self.classes = self.enc.categories_[0]\n",
        "      self.num_classes = len(self.classes)\n",
        "    elif self.enc_type == \"label\":\n",
        "      self.target_column = target_column\n",
        "      self.enc.fit(df[target_column])\n",
        "      self.classes = self.enc.classes_\n",
        "      self.num_classes = len(self.classes)\n",
        "    elif self.enc_type == \"ordinal\":\n",
        "      self.target_column = target_column\n",
        "      self.enc.fit(df[target_column])\n",
        "      self.classes = self.enc.classes_\n",
        "      self.num_classes = len(self.classes)\n",
        "    else:\n",
        "      raise Exception(f\"Unknown encoding type {self.enc_type}\")\n",
        "\n",
        "  def transform(self, df):\n",
        "    if self.enc_type is None:\n",
        "      return df[self.target_column]\n",
        "    elif self.enc_type == \"ohe\":\n",
        "      return self.enc.transform(df[[self.target_column]]).toarray() \n",
        "    elif self.enc_type == \"label\":\n",
        "      return self.enc.transform(df[self.target_column])\n",
        "    elif self.enc_type == \"ordinal\":\n",
        "      return self.enc.transform(df[[self.target_column]])\n",
        "    else:\n",
        "      raise Exception(f\"Unknown encoding type {self.enc_type}\")\n",
        "\n",
        "  def inverse_transform(self, val):\n",
        "    if self.enc_type in [\"ohe\", \"label\", \"ordinal\"]:\n",
        "      return self.enc.inverse_transform(val) \n",
        "    else:\n",
        "      raise Exception(f\"Cannot inverse transform with encoding type {self.enc_type}\")\n",
        "\n",
        "  def get_column_names(self):\n",
        "    if self.enc_type == \"ohe\":\n",
        "      return self.enc.get_feature_names_out()\n",
        "    if self.enc_type == \"ordinal\":\n",
        "      return self.enc.get_feature_names_out(prefix=self.target_column)\n",
        "    else:\n",
        "      return np.array([self.target_column])\n",
        "    \n",
        "class Preprocessor:\n",
        "  def __init__(self, path, tokenizer, text_col, encoder, max_token_len=512):\n",
        "    self.path = path\n",
        "    self.tokenizer = tokenizer\n",
        "    self.text_col = text_col\n",
        "    self.max_token_len = max_token_len \n",
        "    self.encoder = encoder\n",
        "    self._prepare_data()\n",
        "\n",
        "\n",
        "  def _prepare_data(self):\n",
        "    self.df = pd.read_table(self.path[\"convs\"])\n",
        "    if \"conv_labels\" in self.path:\n",
        "      conv_labels_df = pd.read_table(self.path[\"conv_labels\"], names=[\"EmotionalPolarity\", \"Emotion\", \"Empathy\"])\n",
        "      self.df = pd.concat([self.df, conv_labels_df], axis=1)\n",
        "    \n",
        "    def tokenize(target_col):\n",
        "      def _tokenize(row):\n",
        "        text = row[target_col]\n",
        "        tokens = self.tokenizer.encode_plus(\n",
        "            text,\n",
        "            add_special_tokens=True,\n",
        "            return_tensors=\"pt\",\n",
        "            truncation=True,\n",
        "            max_length=self.max_token_len,\n",
        "            padding=\"max_length\",\n",
        "            return_attention_mask=True\n",
        "        )\n",
        "        return tokens[\"input_ids\"].numpy(), tokens[\"attention_mask\"].numpy()\n",
        "      return _tokenize\n",
        "\n",
        "    self.tokenized_input = self.df.apply(tokenize(self.text_col), axis=1)\n",
        "    self.labels = self.encoder.transform(self.df)\n",
        "\n",
        "  def get_tokenized_data(self):\n",
        "    tokenized_series = self.tokenized_input.apply(pd.Series)\n",
        "    input_ids = tokenized_series[0].to_numpy().reshape(-1, 1)\n",
        "    attention_mask = tokenized_series[1].to_numpy().reshape(-1, 1)\n",
        "    labels = self.labels\n",
        "    return input_ids, attention_mask, labels"
      ],
      "metadata": {
        "id": "dkAnaZZ5_w-I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd \n",
        "from transformers import AutoTokenizer\n",
        "\n",
        "model_name = \"roberta-base\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "\n",
        "train_path = {\n",
        "    \"convs\": \"/content/WASSA23_conv_level_with_labels_train.tsv\",\n",
        "    \"essays\": \"/content/WASSA23_essay_level_with_labels_train.tsv\",\n",
        "    \"articles\": \"/content/articles_adobe_AMT.csv\"\n",
        "}\n",
        "val_path = {\n",
        "    \"convs\": \"/content/WASSA23_conv_level_dev.tsv\",\n",
        "    \"conv_labels\": \"/content/goldstandard_CONV_dev.tsv\",\n",
        "    \"essays\": \"/content/WASSA23_essay_level_dev.tsv\",\n",
        "    \"essay_labels\": \"/content/goldstandard_dev.tsv\",\n",
        "    \"articles\": \"/content/articles_adobe_AMT.csv\"\n",
        "}\n",
        "\n",
        "max_token_len = 96\n",
        "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
        "label_col = \"Empathy\"\n",
        "encoder = Encoder(\"label\")\n",
        "_df = pd.read_table(\"/content/WASSA23_conv_level_with_labels_train.tsv\")\n",
        "encoder.fit(_df, label_col)\n",
        "\n",
        "preprocessor_train = Preprocessor(train_path, tokenizer, \"text\", encoder, max_token_len=max_token_len)\n",
        "preprocessor_val = Preprocessor(val_path, tokenizer, \"text\", encoder, max_token_len=max_token_len)"
      ],
      "metadata": {
        "id": "nO4o5NrtGu_r",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "923f4080bc924e1ea1f88e72f6478825",
            "957a05d4a09847018d11f8af53733d24",
            "a5cbd5aa81b74f5f937a28e88f17751f",
            "e35afc50ddac4cac9d68052a114b7fd5",
            "0b83d94736cf40c797baade2a6fa1717",
            "28118bd95fbb4c3e8369d3c494e86122",
            "9819643396b34bdbaa1bc32f230fe074",
            "6dced27bb0bc478fa89287cfc4714587",
            "f9bb46ba3e6a4c79875edb3c13e0518b",
            "bcaf32a5cc584061ac53f4601d06c8d9",
            "b977713037e64ff58669da05f21814ab",
            "0ecdf6f72d5045ec9bb6c919a2a8dcef",
            "476e4cc3f2af47a78bcf5977afeedfcd",
            "c93375a172104b1e84df05b8504a9770",
            "1c24285ece8d411a8f2f7083954e992c",
            "858ce1ede7e0486e9d4d1bccae3b10e2",
            "3f8d5919d8b642ee97864bd05c60e597",
            "4b675d5dbe8742bd8c603af8e5d8d1cf",
            "61785f21342e4c1e991c519e0bc3d87e",
            "4cf38bdc9dd749db8d1332e2a01500bc",
            "7bf3cf31a11e49198cc8ad4a434e0334",
            "3bdfa528f2a247049413d397004f801d",
            "2e7b36a2ce864dbc9b2a2ed843057e63",
            "706cf1df7ac44714852b7a7cb56c8616",
            "9aaa1006db674a1392ae4748609c4cdd",
            "dc20b7a8ff5e413996d1ac38ace30d04",
            "f9b8a4f569394b01bec8921c89ec5490",
            "cfca149b797d41c792f576e9a844ace8",
            "3437a807ea51418e82dd162a2723673f",
            "18cac49e6c3141a7832cd95d9464b26a",
            "a983e71f2a4a47e29eff613cb853ba2e",
            "4cfe9ec36212434d9ab9d8c98baf674b",
            "7934104904fe4cdbabd05207af572049",
            "5414977470fb4f3f8fc1edbf7ea78207",
            "49a7d8e169c4472ab8a318a2d480aba0",
            "86b3e39e9c0f437c8aa7993da55860e1",
            "7f79100b4cdf4410a9c31910ff998a47",
            "c1dd19f512f14989adeb7b76d8ec7171",
            "1086446043fe48e083be63a8277c5f1a",
            "a068f1b1c02d46259b61c0285ecc00d2",
            "1639ce214ff74b7196ef0f07cbfe4354",
            "83e860c839444971b640fe9184d31b41",
            "15dbcd1a825e45e7aa9568873e8c2a6e",
            "a70dc194868a4d7689aaca3d1bff3297"
          ]
        },
        "outputId": "0e03f228-61eb-4706-f880-ee1986796c8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/481 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "923f4080bc924e1ea1f88e72f6478825"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0ecdf6f72d5045ec9bb6c919a2a8dcef"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2e7b36a2ce864dbc9b2a2ed843057e63"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5414977470fb4f3f8fc1edbf7ea78207"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.python.ops.numpy_ops import ndarray\n",
        "from tensorflow.python.ops.gen_logging_ops import Print\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import torch\n",
        "from torch.utils.data import Dataset\n",
        "\n",
        "\n",
        "class ConversationDataset(Dataset):\n",
        "  def __init__(self, input_ids, attention_mask, labels):\n",
        "    self.input_ids = input_ids\n",
        "    self.attention_mask = attention_mask \n",
        "    if len(labels.shape) > 1:\n",
        "      self.labels = torch.FloatTensor(labels)\n",
        "    else:\n",
        "      self.labels = labels if isinstance(labels, np.ndarray) else torch.tensor(labels.to_numpy()).float() \n",
        "  def __len__(self):\n",
        "    return len(self.input_ids)\n",
        "\n",
        "  def __getitem__(self, idx):\n",
        "    input_ids = torch.tensor(self.input_ids[idx][0][0])\n",
        "    attention_mask = torch.tensor(self.attention_mask[idx][0][0])\n",
        "    return {\n",
        "        \"input_ids\": input_ids,\n",
        "        \"attention_mask\": attention_mask,\n",
        "        \"labels\": self.labels[idx]\n",
        "    }"
      ],
      "metadata": {
        "id": "FyQmvx427rWt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from torch.utils.data import DataLoader\n",
        "\n",
        "batch_size = 64\n",
        "\n",
        "train_input, train_attn_mask, train_labels = preprocessor_train.get_tokenized_data()\n",
        "val_input, val_attn_mask, val_labels = preprocessor_val.get_tokenized_data()\n",
        "\n",
        "train_ds = ConversationDataset(train_input, train_attn_mask, train_labels)\n",
        "val_ds = ConversationDataset(val_input, val_attn_mask, val_labels)\n",
        "\n",
        "\n",
        "val_dl = DataLoader(\n",
        "        val_ds,\n",
        "        batch_size=batch_size,\n",
        "        shuffle=False\n",
        ")"
      ],
      "metadata": {
        "id": "efgU6YPHWYP0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch.nn as nn\n",
        "from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss\n",
        "from transformers import AutoModel\n",
        "from transformers.modeling_outputs import SequenceClassifierOutput\n",
        "\n",
        "\n",
        "class CustomClassificationHead(nn.Module):\n",
        "    def __init__(self, hidden_size, hidden_layers_to_use, num_labels, classifier_dropout):\n",
        "        super().__init__()\n",
        "        self.hidden_size = hidden_size \n",
        "        self.num_labels = num_labels\n",
        "        self.classifier_dropout = classifier_dropout\n",
        "        self.hidden_layers_to_use = hidden_layers_to_use\n",
        "\n",
        "        self.dense = nn.Linear(hidden_size * hidden_layers_to_use, hidden_size)\n",
        "        self.dropout = nn.Dropout(classifier_dropout)\n",
        "        self.out_proj = nn.Linear(hidden_size, num_labels)\n",
        "\n",
        "    def forward(self, hidden_states, **kwargs):\n",
        "        hidden_state_cls_vectors = []\n",
        "        for i in range(1, self.hidden_layers_to_use + 1):\n",
        "          hidden_state_cls_vectors.append(hidden_states[-1 * i][:, 0, :])\n",
        "        x = torch.cat(hidden_state_cls_vectors, dim=1)\n",
        "        x = self.dropout(x)\n",
        "        x = self.dense(x)\n",
        "        x = torch.tanh(x)\n",
        "        x = self.dropout(x)\n",
        "        x = self.out_proj(x)\n",
        "        return x\n",
        "\n",
        "class CustomClassifier(nn.Module):\n",
        "    def __init__(self, model_name, num_labels, lstm_cells=12, lstm_stack=1, lstm_bidirectional=True, lstm_dropout=0.1):\n",
        "        super().__init__()\n",
        "        self.model_name = model_name \n",
        "        self.num_labels = num_labels\n",
        "        self.lstm_cells = lstm_cells\n",
        "        self.lstm_stack = lstm_stack\n",
        "        self.lstm_bidirectional = lstm_bidirectional\n",
        "        self.lstm_dropout = lstm_dropout\n",
        "        self.pretrained = AutoModel.from_pretrained(model_name)\n",
        "        self.lstm = nn.LSTM(self.pretrained.config.hidden_size, lstm_cells, lstm_stack, batch_first=True, bidirectional=lstm_bidirectional, dropout=lstm_dropout)\n",
        "        self.dropout = nn.Dropout(lstm_dropout)\n",
        "        self.relu = nn.ReLU()\n",
        "        self.classifier = nn.Linear(lstm_cells * 2, num_labels)\n",
        "\n",
        "    def forward(self,input_ids=None, attention_mask=None,labels=None):\n",
        "        outputs = self.pretrained(input_ids=input_ids, attention_mask=attention_mask)\n",
        "        # out = outputs.last_hidden_state[:, 0, :]\n",
        "        _, (hidden, _) = self.lstm(outputs.last_hidden_state)\n",
        "        out = torch.cat((hidden[-2,:,:], hidden[-1,:,:]), dim = 1)\n",
        "        out = self.relu(self.dropout(out))  \n",
        "        logits = self.classifier(out)\n",
        "\n",
        "        loss = None\n",
        "        if labels is not None:\n",
        "            # move labels to correct device to enable model parallelism\n",
        "            labels = labels.to(logits.device)\n",
        "            loss_fct = CrossEntropyLoss(label_smoothing=0.1)\n",
        "            loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))\n",
        "            # if self.config.problem_type is None:\n",
        "            #     if self.num_labels == 1:\n",
        "            #         self.config.problem_type = \"regression\"\n",
        "            #     elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):\n",
        "            #         self.config.problem_type = \"single_label_classification\"\n",
        "            #     else:\n",
        "            #         self.config.problem_type = \"multi_label_classification\"\n",
        "\n",
        "            # if self.config.problem_type == \"regression\":\n",
        "            #     loss_fct = MSELoss()\n",
        "            #     if self.num_labels == 1:\n",
        "            #         loss = loss_fct(logits.squeeze(), labels.squeeze())\n",
        "            #     else:\n",
        "            #         loss = loss_fct(logits, labels)\n",
        "            # elif self.config.problem_type == \"single_label_classification\":\n",
        "            #     loss_fct = CrossEntropyLoss()\n",
        "            #     loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))\n",
        "            # elif self.config.problem_type == \"multi_label_classification\":\n",
        "            #     loss_fct = BCEWithLogitsLoss()\n",
        "            #     loss = loss_fct(logits, labels)\n",
        "\n",
        "        return SequenceClassifierOutput(\n",
        "            loss=loss,\n",
        "            logits=logits,\n",
        "            hidden_states=outputs.hidden_states,\n",
        "            attentions=outputs.attentions,\n",
        "        )\n",
        "\n"
      ],
      "metadata": {
        "id": "_Wg8QSKnbszU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = CustomClassifier(\n",
        "    model_name, \n",
        "    num_labels=encoder.num_classes,\n",
        "    lstm_cells=12,\n",
        "    lstm_stack=1,\n",
        "    lstm_bidirectional=True, \n",
        "    lstm_dropout=0.2\n",
        ").to(device)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0sM7hedHOIYb",
        "outputId": "dbb957c9-9f43-4bda-8f24-2bdcc216f5d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at roberta-base were not used when initializing RobertaModel: ['lm_head.layer_norm.weight', 'lm_head.bias', 'lm_head.dense.bias', 'lm_head.decoder.weight', 'lm_head.layer_norm.bias', 'lm_head.dense.weight']\n",
            "- This IS expected if you are initializing RobertaModel from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing RobertaModel from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/rnn.py:71: UserWarning: dropout option adds dropout after all but last recurrent layer, so non-zero dropout expects num_layers greater than 1, but got dropout=0.2 and num_layers=1\n",
            "  warnings.warn(\"dropout option adds dropout after all but last \"\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments\n",
        "import numpy as np\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir='/content/drive/MyDrive/MSML-641-empathy/models/roberta-lstm-empathy/',\n",
        "    num_train_epochs=3,\n",
        "    per_device_train_batch_size=8,\n",
        "    per_device_eval_batch_size=64,\n",
        "    warmup_steps=500,\n",
        "    learning_rate=1e-5,\n",
        "    label_smoothing_factor=0.1,\n",
        "    weight_decay=0.01,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    eval_steps=300,\n",
        "    logging_steps=300,\n",
        "    save_strategy=\"epoch\",\n",
        "    save_total_limit=1\n",
        ")\n"
      ],
      "metadata": {
        "id": "fdPvTy6zQYdw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Trainer\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_ds,\n",
        "    eval_dataset=val_ds\n",
        ")\n",
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "39YLZieuQkvn",
        "outputId": "a5b5a531-8559-4c04-c56d-468ccde0d614"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='3291' max='3291' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [3291/3291 11:08, Epoch 3/3]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>300</td>\n",
              "      <td>2.708600</td>\n",
              "      <td>2.669830</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>600</td>\n",
              "      <td>2.641700</td>\n",
              "      <td>2.629938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>900</td>\n",
              "      <td>2.592800</td>\n",
              "      <td>2.565517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1200</td>\n",
              "      <td>2.564000</td>\n",
              "      <td>2.559016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1500</td>\n",
              "      <td>2.553100</td>\n",
              "      <td>2.539561</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>1800</td>\n",
              "      <td>2.539200</td>\n",
              "      <td>2.536136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2100</td>\n",
              "      <td>2.534600</td>\n",
              "      <td>2.529781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2400</td>\n",
              "      <td>2.520500</td>\n",
              "      <td>2.535101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2700</td>\n",
              "      <td>2.509100</td>\n",
              "      <td>2.522185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3000</td>\n",
              "      <td>2.524300</td>\n",
              "      <td>2.519941</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=3291, training_loss=2.563996149536615, metrics={'train_runtime': 669.0496, 'train_samples_per_second': 39.351, 'train_steps_per_second': 4.919, 'total_flos': 0.0, 'train_loss': 2.563996149536615, 'epoch': 3.0})"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 900\t2.393300\t2.357900\n",
        "\n",
        "from tqdm.auto import tqdm\n",
        "\n",
        "predictions = np.array([])\n",
        "with torch.no_grad():\n",
        "    model.eval()\n",
        "    val_loop = tqdm(val_dl)\n",
        "    for idx, val_batch in enumerate(val_loop):\n",
        "      input_ids = val_batch[\"input_ids\"].to(device)\n",
        "      attention_mask = val_batch[\"attention_mask\"].to(device)\n",
        "      labels = val_batch[\"labels\"].to(device)\n",
        "      output = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n",
        "      pr = torch.sigmoid(output.logits)\n",
        "      pr_idx = pr.argmax(axis=-1)\n",
        "      labels = encoder.classes[pr_idx.cpu()]\n",
        "      predictions = np.concatenate([predictions, labels], axis=0)"
      ],
      "metadata": {
        "id": "OG5z4fCKBK8K",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "e042ef9be16649d3a514217bbccd51cf",
            "8d4370bdbc9545cf9df47e3ff1086fc4",
            "2426039b29e545fd8ab7f813db7bc27f",
            "97396977f7ff4476ac9906561dba1d03",
            "416c2a97733b4e519abf1518e6ff700c",
            "0fa28fc0617a401289b8c38f8c5def9b",
            "872688ddd03c48c79d03e7ff544ac4dc",
            "77093a59fe034c3fb5e23af123add4cd",
            "342f5d1fa0214ccd9ea65962c3fc1601",
            "d7d20328e11d4023909acbb9fd911bcb",
            "ad9fb508fb9147c5a56754e69311f160"
          ]
        },
        "outputId": "7d2e48d4-e44b-488a-fe27-5f558549121c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/38 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e042ef9be16649d3a514217bbccd51cf"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ohe = OneHotEncoder()\n",
        "_df = pd.read_table(\"/content/WASSA23_conv_level_with_labels_train.tsv\")\n",
        "ohe.fit(_df[[label_col]])\n",
        "num_classes = len(ohe.categories_[0])\n",
        "\n",
        "predictions_tr = pd.DataFrame(predictions, columns=[label_col])\n",
        "\n",
        "_transformed = ohe.transform(predictions_tr[[label_col]])\n",
        "_ohe_df = pd.DataFrame(_transformed.toarray(), columns=ohe.get_feature_names_out())\n",
        "predictions_tr = pd.concat([predictions_tr, _ohe_df], axis=1).drop([label_col], axis=1)\n",
        "final_pr = predictions_tr.to_numpy()\n",
        "\n",
        "val_data = pd.read_table('/content/goldstandard_CONV_dev.tsv', names=[\"EmotionalPolarity\", \"Emotion\", \"Empathy\"])\n",
        "transformed = ohe.transform(val_data[[label_col]]).toarray()\n",
        "ohe_df = pd.DataFrame(transformed, columns=ohe.get_feature_names_out())\n",
        "val_data = pd.concat([val_data, ohe_df], axis=1).drop([\"Empathy\", \"EmotionalPolarity\", \"Emotion\"], axis=1)\n",
        "labels = val_data.to_numpy()\n",
        "\n",
        "final_pr.shape == labels.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6J68SROgQrCb",
        "outputId": "85f9a54c-1129-4094-aef9-77b540de01c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot\n",
        "from sklearn import metrics\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(15,8))\n",
        "for i, label in enumerate(ohe.get_feature_names_out()):\n",
        "  fpr, tpr, _ = metrics.roc_curve(\n",
        "      labels[:,i].astype(float), final_pr[:,i]\n",
        "  )\n",
        "  auc = metrics.roc_auc_score(\n",
        "      labels[:,i].astype(float), final_pr[:,i]\n",
        "  )\n",
        "  plt.plot(fpr, tpr, label=\"%s %g\" %(label, auc))\n",
        "  plt.xlabel(\"False positive rate\")\n",
        "  plt.ylabel(\"True positive rate\")\n",
        "  plt.legend(loc=\"lower right\")\n",
        "  plt.title(\"RoBERTa + LSTM for Empathy\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 718
        },
        "id": "HrTDbmfpDb01",
        "outputId": "ed12342e-0e14-4727-9103-ffdd43943091"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gc \n",
        "gc.collect()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pc__LYrQ1CcQ",
        "outputId": "58476e0e-25df-46ee-dc1d-4286b3a69d7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9867"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "C9rqi_l893WJ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}