LLM_MaterialsScience.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPAbRpYhOUuhqsD0JqzySSd",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "b6c39ce1b12f4f95a2b51a75393b8b6e": {
          "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_978401893ddc4148b16864ae30bde403",
              "IPY_MODEL_f7863399efd1450b9446c5ed34183556",
              "IPY_MODEL_ebe97956de284b3f905b07328801bf8a"
            ],
            "layout": "IPY_MODEL_a92515900acc47e992f444007d177ddf"
          }
        },
        "978401893ddc4148b16864ae30bde403": {
          "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_986d4f51a6fa4369a057bf14a26af312",
            "placeholder": "​",
            "style": "IPY_MODEL_899106c6961c445abedd84fe963c8294",
            "value": "config.json: 100%"
          }
        },
        "f7863399efd1450b9446c5ed34183556": {
          "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_cb7b9fb096ea493e8c228547cd99a5b9",
            "max": 1154,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_9dcede2eae7147919b506544e3f45594",
            "value": 1154
          }
        },
        "ebe97956de284b3f905b07328801bf8a": {
          "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_082c36998e7349d38aa05494dd0fff0e",
            "placeholder": "​",
            "style": "IPY_MODEL_afa82a2df9f64e5683e7dfa2e3619e24",
            "value": " 1.15k/1.15k [00:00&lt;00:00, 44.1kB/s]"
          }
        },
        "a92515900acc47e992f444007d177ddf": {
          "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
          }
        },
        "986d4f51a6fa4369a057bf14a26af312": {
          "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
          }
        },
        "899106c6961c445abedd84fe963c8294": {
          "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": ""
          }
        },
        "cb7b9fb096ea493e8c228547cd99a5b9": {
          "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
          }
        },
        "9dcede2eae7147919b506544e3f45594": {
          "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": ""
          }
        },
        "082c36998e7349d38aa05494dd0fff0e": {
          "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
          }
        },
        "afa82a2df9f64e5683e7dfa2e3619e24": {
          "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": ""
          }
        },
        "6f95f9ff27f64e29a4645534e8ad67d2": {
          "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_f92d4bd3a4144951a63ef1a8e4090503",
              "IPY_MODEL_cbf544952aa643dabed0ce2349b9849d",
              "IPY_MODEL_860028eb88f74df0a4b62ec38dacb302"
            ],
            "layout": "IPY_MODEL_8965d4eba1074e489c5fac50c3705c2e"
          }
        },
        "f92d4bd3a4144951a63ef1a8e4090503": {
          "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_5a7fb6a8436c4a42a1953382be5a58c1",
            "placeholder": "​",
            "style": "IPY_MODEL_73aad9b2f4ab4058a5086803fbec79d7",
            "value": "model.safetensors: 100%"
          }
        },
        "cbf544952aa643dabed0ce2349b9849d": {
          "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_304ee41aab814330980d8b650dd2cc5e",
            "max": 1629437147,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_2ff071c4b57e407084c2e688d0963211",
            "value": 1629437147
          }
        },
        "860028eb88f74df0a4b62ec38dacb302": {
          "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_5a01d6d6ff314d0f81be776f4ff12535",
            "placeholder": "​",
            "style": "IPY_MODEL_0bbc9e2903174cdda5c74644b67e30d9",
            "value": " 1.63G/1.63G [00:22&lt;00:00, 107MB/s]"
          }
        },
        "8965d4eba1074e489c5fac50c3705c2e": {
          "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
          }
        },
        "5a7fb6a8436c4a42a1953382be5a58c1": {
          "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
          }
        },
        "73aad9b2f4ab4058a5086803fbec79d7": {
          "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": ""
          }
        },
        "304ee41aab814330980d8b650dd2cc5e": {
          "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
          }
        },
        "2ff071c4b57e407084c2e688d0963211": {
          "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": ""
          }
        },
        "5a01d6d6ff314d0f81be776f4ff12535": {
          "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
          }
        },
        "0bbc9e2903174cdda5c74644b67e30d9": {
          "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": ""
          }
        },
        "6f2f1553b2c34e96afbb5ae394c8e88f": {
          "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_f85ff1f162f34b82a0d8f58a09b45ebb",
              "IPY_MODEL_1fac3de4b0e142a9a63a779b7d6ee56a",
              "IPY_MODEL_1313c804fc474031b19b2178150ed5df"
            ],
            "layout": "IPY_MODEL_0e56a794ff1744e7931aacc214a46ae1"
          }
        },
        "f85ff1f162f34b82a0d8f58a09b45ebb": {
          "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_b2d2b53bd9cd41eb9ad8554bdedc9d13",
            "placeholder": "​",
            "style": "IPY_MODEL_a7c0e991dd1b4b218d890e1c4cc32f7d",
            "value": "tokenizer_config.json: 100%"
          }
        },
        "1fac3de4b0e142a9a63a779b7d6ee56a": {
          "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_fb80730db2a64266999cea30a2fce2db",
            "max": 26,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_29507cfd3a9e45828f77687910ddc695",
            "value": 26
          }
        },
        "1313c804fc474031b19b2178150ed5df": {
          "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_07b2838d8c8e44c0b5aff341b99114ff",
            "placeholder": "​",
            "style": "IPY_MODEL_e9d50c44056c4b7288361e7efa5547da",
            "value": " 26.0/26.0 [00:00&lt;00:00, 904B/s]"
          }
        },
        "0e56a794ff1744e7931aacc214a46ae1": {
          "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
          }
        },
        "b2d2b53bd9cd41eb9ad8554bdedc9d13": {
          "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
          }
        },
        "a7c0e991dd1b4b218d890e1c4cc32f7d": {
          "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": ""
          }
        },
        "fb80730db2a64266999cea30a2fce2db": {
          "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
          }
        },
        "29507cfd3a9e45828f77687910ddc695": {
          "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": ""
          }
        },
        "07b2838d8c8e44c0b5aff341b99114ff": {
          "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
          }
        },
        "e9d50c44056c4b7288361e7efa5547da": {
          "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": ""
          }
        },
        "e2be9be7173245068c6465f3d2374ca6": {
          "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_d7b4d7d94f30486f94ec169ea4491fcd",
              "IPY_MODEL_0234d09780214132a66317ca417f6ef4",
              "IPY_MODEL_090ad16c01f143fbb0d3fd6c923144ff"
            ],
            "layout": "IPY_MODEL_1e0f0626edd048b3b19cc02e2e1e34b7"
          }
        },
        "d7b4d7d94f30486f94ec169ea4491fcd": {
          "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_0c70f5623eab49d0bfbad1f0ae4ed2f4",
            "placeholder": "​",
            "style": "IPY_MODEL_e0a37fa2608e42849c5ba4e201184f19",
            "value": "vocab.json: 100%"
          }
        },
        "0234d09780214132a66317ca417f6ef4": {
          "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_b96b4fc340544da1bfd9518348726ae6",
            "max": 898822,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_32d11226e19f43daa835d0696fab83cc",
            "value": 898822
          }
        },
        "090ad16c01f143fbb0d3fd6c923144ff": {
          "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_6ead52c2b4654f5696d7fa18b6bda08e",
            "placeholder": "​",
            "style": "IPY_MODEL_4ce502282ed64c5ab3749f29dfe32882",
            "value": " 899k/899k [00:00&lt;00:00, 24.5MB/s]"
          }
        },
        "1e0f0626edd048b3b19cc02e2e1e34b7": {
          "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
          }
        },
        "0c70f5623eab49d0bfbad1f0ae4ed2f4": {
          "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
          }
        },
        "e0a37fa2608e42849c5ba4e201184f19": {
          "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": ""
          }
        },
        "b96b4fc340544da1bfd9518348726ae6": {
          "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
          }
        },
        "32d11226e19f43daa835d0696fab83cc": {
          "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": ""
          }
        },
        "6ead52c2b4654f5696d7fa18b6bda08e": {
          "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
          }
        },
        "4ce502282ed64c5ab3749f29dfe32882": {
          "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": ""
          }
        },
        "3e88ad0c6b454e7c84c37a83a0b3e061": {
          "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_b65e01ecc8df486691bb83368f014fd7",
              "IPY_MODEL_6cfca064765e4f998039c2ab5c1b4db1",
              "IPY_MODEL_21f0d5e13a684d2ea87d4055ce7cbfec"
            ],
            "layout": "IPY_MODEL_8fe9f1497c394a798d44d4697736d200"
          }
        },
        "b65e01ecc8df486691bb83368f014fd7": {
          "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_fa828dcde76b4f6e9da5d1238528715f",
            "placeholder": "​",
            "style": "IPY_MODEL_d2ea2f78f3fe4e36a20bae4d9c81ebe9",
            "value": "merges.txt: 100%"
          }
        },
        "6cfca064765e4f998039c2ab5c1b4db1": {
          "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_a7cc5e8c6f0a4728811a09d2adb89199",
            "max": 456318,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_ef828a90bb8c48e8b7624225b5a00c17",
            "value": 456318
          }
        },
        "21f0d5e13a684d2ea87d4055ce7cbfec": {
          "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_4fcc51b7106a40dc90f3d168d223ddc3",
            "placeholder": "​",
            "style": "IPY_MODEL_87595d8793c14ad3a5554c34cfb1c15b",
            "value": " 456k/456k [00:00&lt;00:00, 16.0MB/s]"
          }
        },
        "8fe9f1497c394a798d44d4697736d200": {
          "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
          }
        },
        "fa828dcde76b4f6e9da5d1238528715f": {
          "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
          }
        },
        "d2ea2f78f3fe4e36a20bae4d9c81ebe9": {
          "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": ""
          }
        },
        "a7cc5e8c6f0a4728811a09d2adb89199": {
          "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
          }
        },
        "ef828a90bb8c48e8b7624225b5a00c17": {
          "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": ""
          }
        },
        "4fcc51b7106a40dc90f3d168d223ddc3": {
          "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
          }
        },
        "87595d8793c14ad3a5554c34cfb1c15b": {
          "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": ""
          }
        },
        "761d2409251d41d8be789b104e4b9fd3": {
          "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_b27bce151c0b426ca1781f41518f0a6d",
              "IPY_MODEL_0ef8205aa5be41efb4362de02240f73e",
              "IPY_MODEL_4b067bf46c4f4d34b8d09c99aaa5175d"
            ],
            "layout": "IPY_MODEL_47eae7f02ea3456d918aa043daaf8a20"
          }
        },
        "b27bce151c0b426ca1781f41518f0a6d": {
          "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_aacea4879e60430fac902ef8ad47a0e8",
            "placeholder": "​",
            "style": "IPY_MODEL_578280577199468e871ab73fb6771e8d",
            "value": "tokenizer.json: 100%"
          }
        },
        "0ef8205aa5be41efb4362de02240f73e": {
          "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_de93a0b015a54388929fa7721240d7d7",
            "max": 1355863,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_932da31cf11b439393e9c233331be17b",
            "value": 1355863
          }
        },
        "4b067bf46c4f4d34b8d09c99aaa5175d": {
          "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_e6705bf2b42d4ab7936ba04cb17f314a",
            "placeholder": "​",
            "style": "IPY_MODEL_3998f20ef1f04edb9264138a53352858",
            "value": " 1.36M/1.36M [00:00&lt;00:00, 46.6MB/s]"
          }
        },
        "47eae7f02ea3456d918aa043daaf8a20": {
          "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
          }
        },
        "aacea4879e60430fac902ef8ad47a0e8": {
          "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
          }
        },
        "578280577199468e871ab73fb6771e8d": {
          "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": ""
          }
        },
        "de93a0b015a54388929fa7721240d7d7": {
          "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
          }
        },
        "932da31cf11b439393e9c233331be17b": {
          "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": ""
          }
        },
        "e6705bf2b42d4ab7936ba04cb17f314a": {
          "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
          }
        },
        "3998f20ef1f04edb9264138a53352858": {
          "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": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/MehrdadJalali-KIT/LLM-ELN/blob/main/LLM_MaterialsScience.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install openai==0.28\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LMkxdbiDrTdz",
        "outputId": "139294b3-8be4-418b-f9e0-b0c8b626c129"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: openai==0.28 in /usr/local/lib/python3.10/dist-packages (0.28.0)\n",
            "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.10/dist-packages (from openai==0.28) (2.32.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from openai==0.28) (4.66.5)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from openai==0.28) (3.10.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai==0.28) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai==0.28) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai==0.28) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai==0.28) (2024.8.30)\n",
            "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai==0.28) (2.4.0)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai==0.28) (1.3.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai==0.28) (24.2.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai==0.28) (1.4.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai==0.28) (6.1.0)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai==0.28) (1.11.1)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai==0.28) (4.0.3)\n",
            "Requirement already satisfied: typing-extensions>=4.1.0 in /usr/local/lib/python3.10/dist-packages (from multidict<7.0,>=4.5->aiohttp->openai==0.28) (4.12.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Problem Statement\n",
        "\n",
        "The task involves summarizing a complex text on materials science research, specifically focusing on the development of nanomaterials. These materials exhibit unique properties at the nanoscale, primarily due to their high surface-to-volume ratio. The challenge is to succinctly summarize various methods for synthesizing nanomaterials and their applications, leveraging the capabilities of an AI model.\n",
        "\n",
        "## Purpose of the Code\n",
        "\n",
        "The code is designed to interact with OpenAI's GPT-4 turbo model to generate a concise summary of the provided research text. This summary is intended to encapsulate key points such as synthesis methods and applications of nanomaterials, making the information more accessible and easier to understand.\n",
        "\n",
        "## Parameters Explanation for OpenAI's GPT-4 Turbo Model Interaction\n",
        "\n",
        "### 1. `model`\n",
        "- **Description**: Specifies the model to be used for generating responses.\n",
        "- **Usage**: \"gpt-4-turbo\" is selected for its advanced capabilities in understanding and generating natural language, particularly suitable for complex tasks like summarizing scientific content.\n",
        "\n",
        "### 2. `messages`\n",
        "- **Description**: A sequence of dictionaries that simulate a conversation flow, which guides the AI in processing the user's request.\n",
        "- **Components**:\n",
        "  - `role`: Distinguishes whether the message is from the 'system' or the 'user'.\n",
        "    - `system`: Sets the context or provides instructions to the AI, guiding its response generation.\n",
        "    - `user`: Simulates a user's query or input that the AI needs to address, in this case, summarizing a specific text.\n",
        "  - `content`: Contains the actual text relevant to each role. For the system, this could be instructions or contextual information; for the user, this typically includes the text to be summarized or analyzed.\n",
        "\n",
        "## Interaction Flow\n",
        "- The `messages` parameter effectively creates a structured interaction, initiating with a system prompt that sets the task for the AI, followed by a user input that contains the specific text. This approach ensures that the AI's responses are aligned with the requirements of the summarization task, providing outputs that are contextually appropriate and informationally concise.\n",
        "\n",
        "The use of these parameters within the `ChatCompletion.create()` method facilitates a dialogue-like interaction with the AI, enabling it to produce summaries that are not only accurate but also context-aware.\n"
      ],
      "metadata": {
        "id": "_GHA4DFyohoD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import openai\n",
        "\n",
        "# Set the API key (Remember to keep your API keys secure and do not expose them publicly)\n",
        "openai.api_key = ''\n",
        "\n",
        "# Input text to be summarized\n",
        "input_text = \"\"\"\n",
        "In recent materials science research, the development of nanomaterials has garnered significant attention. Nanomaterials exhibit unique properties at the nanoscale due to their high surface-to-volume ratio. Researchers have explored various methods for synthesizing nanomaterials, including ‎chemical vapor deposition, sol-gel processes, and ball milling. These methods allow for precise control over nanomaterials' size, shape, and ‎composition, making them versatile for a wide range of applications, such as drug delivery, energy storage, and catalysis. ‎\n",
        "\"\"\"\n",
        "\n",
        "# System prompt\n",
        "system_prompt = \"You are an AI assistant tasked with summarizing materials science research on nanomaterials development.\"\n",
        "\n",
        "# Generate AI response for short summarization using chat completions endpoint\n",
        "response = openai.ChatCompletion.create(\n",
        "    model=\"gpt-4-turbo\",  # Ensure using GPT-4 Turbo\n",
        "    messages=[\n",
        "        {\"role\": \"system\", \"content\": system_prompt},\n",
        "        {\"role\": \"user\", \"content\": input_text}\n",
        "    ],\n",
        "    temperature=0.7,         # Make the output creative\n",
        "    top_p=0.9,               # Focus on more probable responses\n",
        "    max_tokens=30,          # Limit response length\n",
        "    presence_penalty=0.6,    # Encourage the model to introduce new concepts\n",
        "    frequency_penalty=0.5    # Discourage repetition\n",
        ")\n",
        "\n",
        "# Extract the content from the response\n",
        "summary = response['choices'][0]['message']['content'].strip()\n",
        "\n",
        "# Print the summary in a clean format\n",
        "print(f\"Summary of the Materials Science Research:\\n{'-'*40}\\n{summary}\\n{'-'*40}\")\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "eiHZUYNu6zWV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d4feea0a-84f8-4ee0-fdd2-7a6a4d762d3f"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Summary of the Materials Science Research:\n",
            "----------------------------------------\n",
            "The unique properties of nanomaterials derived from their nanoscale dimensions open up a myriad of possibilities across various scientific and engineering disciplines. The high\n",
            "----------------------------------------\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Problem Statement: Zero-Shot Classification for TEM Sample Preparation\n",
        "\n",
        "This task uses **zero-shot classification** to categorize a TEM sample preparation description using a pre-trained model, `facebook/bart-large-mnli`, without task-specific training.\n",
        "\n",
        "#### Categories:\n",
        "- Grid Coating\n",
        "- Embedding Methods\n",
        "- TEM Sample Mounting\n",
        "- Electron Beam Damage Mitigation\n",
        "- Cryo-TEM Sample Preparation\n",
        "\n",
        "#### Input:\n",
        "A sample preparation description:  \n",
        "*\"In this method, the biological sample is embedded in a resin to facilitate ultra-thin sectioning for TEM imaging.\"*\n",
        "\n",
        "#### Output:\n",
        "The model predicts the most relevant category and provides a confidence score based on the description.\n"
      ],
      "metadata": {
        "id": "9kYzgMp9uWpP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "\n",
        "# Load the zero-shot classification pipeline with a pretrained model suitable for the task\n",
        "classifier = pipeline(\"zero-shot-classification\", model=\"facebook/bart-large-mnli\")\n",
        "\n",
        "# Define possible labels/categories\n",
        "labels = [\"Grid Coating\", \"Embedding Methods\", \"TEM Sample Mounting\", \"Electron Beam Damage Mitigation\", \"Cryo-TEM Sample Preparation\"]\n",
        "\n",
        "# New sample preparation description\n",
        "new_sample_prep_description = \"In this method, the biological sample is embedded in a resin to facilitate ultra-thin sectioning for TEM imaging.\"\n",
        "\n",
        "# Predict the label for the new sample preparation description\n",
        "result = classifier(new_sample_prep_description, candidate_labels=labels, hypothesis_template=\"This example is about {}.\")\n",
        "\n",
        "# Extract the predicted label and confidence score\n",
        "predicted_label = result['labels'][0]\n",
        "confidence_score = result['scores'][0]\n",
        "\n",
        "# Print the results\n",
        "print(f\"Predicted Label: {predicted_label}\")\n",
        "print(f\"Confidence Score: {confidence_score:.2f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352,
          "referenced_widgets": [
            "b6c39ce1b12f4f95a2b51a75393b8b6e",
            "978401893ddc4148b16864ae30bde403",
            "f7863399efd1450b9446c5ed34183556",
            "ebe97956de284b3f905b07328801bf8a",
            "a92515900acc47e992f444007d177ddf",
            "986d4f51a6fa4369a057bf14a26af312",
            "899106c6961c445abedd84fe963c8294",
            "cb7b9fb096ea493e8c228547cd99a5b9",
            "9dcede2eae7147919b506544e3f45594",
            "082c36998e7349d38aa05494dd0fff0e",
            "afa82a2df9f64e5683e7dfa2e3619e24",
            "6f95f9ff27f64e29a4645534e8ad67d2",
            "f92d4bd3a4144951a63ef1a8e4090503",
            "cbf544952aa643dabed0ce2349b9849d",
            "860028eb88f74df0a4b62ec38dacb302",
            "8965d4eba1074e489c5fac50c3705c2e",
            "5a7fb6a8436c4a42a1953382be5a58c1",
            "73aad9b2f4ab4058a5086803fbec79d7",
            "304ee41aab814330980d8b650dd2cc5e",
            "2ff071c4b57e407084c2e688d0963211",
            "5a01d6d6ff314d0f81be776f4ff12535",
            "0bbc9e2903174cdda5c74644b67e30d9",
            "6f2f1553b2c34e96afbb5ae394c8e88f",
            "f85ff1f162f34b82a0d8f58a09b45ebb",
            "1fac3de4b0e142a9a63a779b7d6ee56a",
            "1313c804fc474031b19b2178150ed5df",
            "0e56a794ff1744e7931aacc214a46ae1",
            "b2d2b53bd9cd41eb9ad8554bdedc9d13",
            "a7c0e991dd1b4b218d890e1c4cc32f7d",
            "fb80730db2a64266999cea30a2fce2db",
            "29507cfd3a9e45828f77687910ddc695",
            "07b2838d8c8e44c0b5aff341b99114ff",
            "e9d50c44056c4b7288361e7efa5547da",
            "e2be9be7173245068c6465f3d2374ca6",
            "d7b4d7d94f30486f94ec169ea4491fcd",
            "0234d09780214132a66317ca417f6ef4",
            "090ad16c01f143fbb0d3fd6c923144ff",
            "1e0f0626edd048b3b19cc02e2e1e34b7",
            "0c70f5623eab49d0bfbad1f0ae4ed2f4",
            "e0a37fa2608e42849c5ba4e201184f19",
            "b96b4fc340544da1bfd9518348726ae6",
            "32d11226e19f43daa835d0696fab83cc",
            "6ead52c2b4654f5696d7fa18b6bda08e",
            "4ce502282ed64c5ab3749f29dfe32882",
            "3e88ad0c6b454e7c84c37a83a0b3e061",
            "b65e01ecc8df486691bb83368f014fd7",
            "6cfca064765e4f998039c2ab5c1b4db1",
            "21f0d5e13a684d2ea87d4055ce7cbfec",
            "8fe9f1497c394a798d44d4697736d200",
            "fa828dcde76b4f6e9da5d1238528715f",
            "d2ea2f78f3fe4e36a20bae4d9c81ebe9",
            "a7cc5e8c6f0a4728811a09d2adb89199",
            "ef828a90bb8c48e8b7624225b5a00c17",
            "4fcc51b7106a40dc90f3d168d223ddc3",
            "87595d8793c14ad3a5554c34cfb1c15b",
            "761d2409251d41d8be789b104e4b9fd3",
            "b27bce151c0b426ca1781f41518f0a6d",
            "0ef8205aa5be41efb4362de02240f73e",
            "4b067bf46c4f4d34b8d09c99aaa5175d",
            "47eae7f02ea3456d918aa043daaf8a20",
            "aacea4879e60430fac902ef8ad47a0e8",
            "578280577199468e871ab73fb6771e8d",
            "de93a0b015a54388929fa7721240d7d7",
            "932da31cf11b439393e9c233331be17b",
            "e6705bf2b42d4ab7936ba04cb17f314a",
            "3998f20ef1f04edb9264138a53352858"
          ]
        },
        "id": "7ejDRKBPayAx",
        "outputId": "b9560c32-06a5-468c-c131-ca2fd2ce7dd6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/1.15k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b6c39ce1b12f4f95a2b51a75393b8b6e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/1.63G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6f95f9ff27f64e29a4645534e8ad67d2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6f2f1553b2c34e96afbb5ae394c8e88f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e2be9be7173245068c6465f3d2374ca6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3e88ad0c6b454e7c84c37a83a0b3e061"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "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": "761d2409251d41d8be789b104e4b9fd3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Label: TEM Sample Mounting\n",
            "Confidence Score: 0.42\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Automatic Classification of MOF Preparation Steps Using GPT-4 Turbo\n",
        "\n",
        "The aim of the code is to classify a description of a Metal-Organic Framework (MOF) preparation step into one of several predefined categories using OpenAI's GPT-4 Turbo model.\n",
        "\n",
        "#### Objective:\n",
        "1. **Text Classification**: The code utilizes a function that takes a textual description of a MOF preparation process and classifies it into a relevant category from a given list.\n",
        "   \n",
        "2. **Utilizing OpenAI's GPT-4 Turbo**: The OpenAI `ChatCompletion` API is used to query GPT-4 Turbo, which analyzes the description and selects the best-fitting category based on its understanding of the language.\n",
        "\n",
        "3. **Error Handling**: The code includes mechanisms to catch and handle any potential errors during API calls, ensuring that meaningful feedback is provided if something goes wrong.\n",
        "\n",
        "4. **Practical Application**: This method is particularly useful for automating the classification of technical processes, such as categorizing MOF preparation steps, aiding in the organization and further analysis of scientific workflows.\n"
      ],
      "metadata": {
        "id": "mUIODuojwPNN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import openai\n",
        "\n",
        "# Set your OpenAI API key\n",
        "openai.api_key = 'ReplacewithYourAPI'\n",
        "\n",
        "\n",
        "# Define a function to classify text into predefined categories using OpenAI's GPT-4 Turbo\n",
        "def classify_mof_preparation(description, labels):\n",
        "    # Craft a prompt asking the model to categorize the description\n",
        "    prompt = f\"Given the description: '{description}'\\nWhich of the following categories does this belong to? {', '.join(labels)}\"\n",
        "\n",
        "    try:\n",
        "        # Use the OpenAI ChatCompletion to query the model\n",
        "        response = openai.ChatCompletion.create(\n",
        "            model=\"gpt-4-turbo\",\n",
        "            messages=[\n",
        "                {\"role\": \"system\", \"content\": \"You are a helpful assistant capable of categorizing descriptions into specific labels.\"},\n",
        "                {\"role\": \"user\", \"content\": prompt}\n",
        "            ]\n",
        "        )\n",
        "        # Extract the assistant's response\n",
        "        return response.choices[0].message['content']\n",
        "    except Exception as e:\n",
        "        # Handle exceptions by returning an error message\n",
        "        return f\"An error occurred: {str(e)}\"\n",
        "\n",
        "# Example description of an MOF preparation step\n",
        "description = \"The particles are dried under vacuum to remove any solvents before TEM analysis.\"\n",
        "\n",
        "# Possible labels for MOF TEM preparation\n",
        "labels = [\"Synthesis of MOFs\", \"Purification and Isolation\", \"Drying Process\", \"Dispersion for TEM\", \"Deposition on TEM Grids\"]\n",
        "\n",
        "# Classify the example description\n",
        "predicted_category = classify_mof_preparation(description, labels)\n",
        "\n",
        "# Print the predicted category\n",
        "print(f\"Predicted Category: {predicted_category}\")\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BMJWt4nba_ef",
        "outputId": "c222b413-ac4d-4b4c-830f-23046cd74638"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Category: This description belongs to the category: Drying Process\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Improving the Conciseness of MOF Gas Storage Prediction\n",
        "\n",
        "The goal of this modification is to ensure that the model provides a simple and concise answer, predicting the gas storage capacity of a MOF as \"high,\" \"medium,\" or \"low\" without additional text.\n",
        "\n",
        "#### Key Adjustments:\n",
        "1. **System Message**:\n",
        "   The system message is now explicit, instructing the model to only respond with one of three possible options—\"high,\" \"medium,\" or \"low\"—based on the provided MOF properties.\n",
        "\n",
        "2. **User Message**:\n",
        "   The user still provides specific MOF characteristics (pore size and composition), but no further elaboration is requested to keep the model's response concise.\n",
        "\n",
        "3. **Limiting Response Length**:\n",
        "   The `max_tokens` parameter is set to a very low number (e.g., `10`) to restrict the response to a single word or short phrase.\n",
        "\n",
        "This ensures that the output is strictly focused on the gas storage prediction and avoids any unrelated or unnecessary text.\n"
      ],
      "metadata": {
        "id": "Nx55S-aPzJQY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import openai\n",
        "\n",
        "\n",
        "# Your OpenAI API key\n",
        "openai.api_key = 'ReplacewithYourAPI'\n",
        "\n",
        "\n",
        "\n",
        "response = openai.ChatCompletion.create(\n",
        "    model=\"gpt-4-turbo\",\n",
        "    messages=[\n",
        "        {\"role\": \"system\", \"content\": \"You are a concise assistant. Only respond with 'high', 'medium', or 'low' gas storage capacity based on the provided MOF properties.\"},\n",
        "        {\"role\": \"user\", \"content\": \"Predict the gas storage capacity of a MOF with a pore size of 1.2 nm and composed of Zn4O(BDC)3.\"}\n",
        "    ],\n",
        "    max_tokens=10,  # Small limit for short and concise output\n",
        ")\n",
        "\n",
        "# Print the generated text\n",
        "print(response.choices[0].message['content'].strip())\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EOvO0H4jgMwH",
        "outputId": "d4c2e9ca-ffbf-4426-912e-ccde37bff6f8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "High\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### MOF Pore Size Classification Using GPT-4 Turbo\n",
        "\n",
        "This Python script uses OpenAI's GPT-4 Turbo model to classify the pore size of a Metal-Organic Framework (MOF) based on a given description. The pore sizes are classified into predefined categories with specific size ranges.\n",
        "\n",
        "#### Key Components:\n",
        "1. **API Key**: The OpenAI API key is required to interact with GPT-4 Turbo.\n",
        "   \n",
        "2. **Pore Size Labels**:\n",
        "   The model is provided with labeled pore size categories:\n",
        "   - **Non-porous**: 0 nm (does not contain pores)\n",
        "   - **Small pores**: Less than 2 nm\n",
        "   - **Medium pores**: 2 nm to 5 nm\n",
        "   - **Large pores**: Greater than 5 nm\n",
        "   \n",
        "3. **MOF Description**:\n",
        "   A specific description of the MOF is provided as input, asking the model to predict the pore size. In this example, the description states that the MOF has linkers of 3 nm and a Zn4O node.\n",
        "\n",
        "4. **System Prompt**:\n",
        "   The system prompt informs the model about the task and provides the defined pore size ranges, ensuring the model understands how to classify the MOF.\n",
        "\n",
        "5. **Response**:\n",
        "   The response generated by the model is expected to classify the MOF into one of the predefined categories (e.g., \"medium pores\").\n",
        "\n",
        "6. **Error Handling**:\n",
        "   If any error occurs during the API call, it is captured and printed.\n",
        "\n",
        "This approach helps automate the classification of MOF pore sizes based on their structural description.\n"
      ],
      "metadata": {
        "id": "HpLf0lM_zerf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import openai\n",
        "\n",
        "# Replace 'your-api-key' with your actual OpenAI API key\n",
        "openai.api_key = 'ReplacewithYourAPI'\n",
        "# Labels with defined pore size ranges\n",
        "labels_with_ranges = {\n",
        "    \"non-porous\": \"0 nm – does not contain pores\",\n",
        "    \"small pores\": \"less than 2 nm\",\n",
        "    \"medium pores\": \"2 nm to 5 nm\",\n",
        "    \"large pores\": \"greater than 5 nm\"\n",
        "}\n",
        "\n",
        "# Convert labels with definitions into a formatted string for the prompt\n",
        "label_definitions = \", \".join([f\"{key} ({value})\" for key, value in labels_with_ranges.items()])\n",
        "\n",
        "# Example MOF description\n",
        "mof_description = \"Predict the pore size of a MOF with a framework incorporating linkers of 3 nm and a Zn4O node.\"\n",
        "\n",
        "# Setup the system prompt to explain the task to the model\n",
        "system_prompt = f\"You are a helpful assistant capable of classifying pore sizes of MOFs. Here are the possible categories with their size ranges: {label_definitions}. Based on the description, classify the pore size.\"\n",
        "\n",
        "try:\n",
        "    response = openai.ChatCompletion.create(\n",
        "        model=\"gpt-4-turbo\",  # Ensure this model is accessible with your API key\n",
        "        messages=[\n",
        "            {\"role\": \"system\", \"content\": system_prompt},\n",
        "            {\"role\": \"user\", \"content\": mof_description}\n",
        "        ]\n",
        "    )\n",
        "\n",
        "    # Print the generated text\n",
        "    # print(response.choices[0].message['content'])\n",
        "    print(response.choices[0].message['content'].strip())\n",
        "\n",
        "\n",
        "except Exception as e:\n",
        "    print(f\"An error occurred: {str(e)}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p22gY_P2j2Pa",
        "outputId": "6fe9724c-4f22-4d8c-dc4d-e8971124da7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "To predict the pore size of the metal-organic framework (MOF) you described, it's essential to consider the typical arrangement of the linkers and the metal nodes within the structure. In MOFs, the pore size is not exclusively determined by the length of the linkers but also by how the linkers and nodes are arranged spatially.\n",
            "\n",
            "You mentioned the MOF framework includes linkers that are 3 nm long and Zn4O nodes. Assuming typical coordination and spacing:\n",
            "\n",
            "1. **Length of Linkers (3 nm)**: These create potential for medium to large pores, depending on how they are oriented and connected in the framework.\n",
            "\n",
            "2. **Zn4O Nodes**: These are generally smaller and could act as corners or junctions where the linkers meet. \n",
            "\n",
            "MOFs constructed with 3 nm linkers, depending on their geometry and connectivity, could potentially form medium to large pores. The node does not necessarily limit the maximum pore diameter unless it causes a more compact structure. Therefore, without detailed information on structural geometry (e.g., whether it forms a cubic, tetrahedral, or another type of geometry), one might expect that the pores formed by 3 nm linkers typically fall in the range of medium pores (2 nm to 5 nm), possibly extending into the large pore category if the geometry is particularly open.\n",
            "\n",
            "**Prediction**: Based on general considerations and typical linker length alone, I would predict this MOF to have medium to potentially large pores. Without specific structural data indicating tighter linkage or denser node arrangement, a prediction of medium pore size (2 nm to 5 nm) is most reasonable.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Predicting Material Properties Using GPT-4 Turbo\n",
        "\n",
        "This Python script demonstrates how to use OpenAI's GPT-4 Turbo model to predict the potential properties of a material based on its composition and structure.\n",
        "\n",
        "#### Aim:\n",
        "To predict key material properties such as mechanical strength, conductivity, and thermal stability for a new material, specifically a Metal-Organic Framework (MOF).\n",
        "\n",
        "#### Key Components:\n",
        "\n",
        "1. **System Prompt**:\n",
        "   The system prompt instructs the model to act as an expert assistant in materials science, capable of predicting various material properties based on composition and structure.\n",
        "\n",
        "2. **Material Description**:\n",
        "   A specific material description is provided, in this case, a Metal-Organic Framework (MOF) with copper ions and benzenedicarboxylate (BDC) linkers, characterized by high porosity and a pore size range of 2-3 nm.\n",
        "\n",
        "3. **API Call**:\n",
        "   The script uses OpenAI's `ChatCompletion.create()` method to generate predictions from the model, specifying the material details and expecting properties such as mechanical strength and conductivity in return.\n",
        "\n",
        "4. **Predicted Output**:\n",
        "   The model is expected to return a concise prediction about the material’s properties, such as high gas storage capacity, moderate thermal stability, or potential catalytic activity.\n",
        "\n",
        "#### Applications:\n",
        "This approach can help materials scientists quickly predict potential properties of new materials, facilitating rapid experimentation and discovery in the field of materials science.\n"
      ],
      "metadata": {
        "id": "GX-3GnCK2J7g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import openai\n",
        "\n",
        "# Your OpenAI API key\n",
        "openai.api_key = 'ReplacewithYourAPI'\n",
        "\n",
        "\n",
        "# System prompt defining the task for the LLM\n",
        "system_prompt = \"\"\"\n",
        "You are an expert assistant in materials science. Based on the composition and structure of a material, predict the material's potential properties, such as mechanical strength, conductivity, thermal stability, and other relevant characteristics.\n",
        "\"\"\"\n",
        "\n",
        "# Description of a new material\n",
        "material_description = \"\"\"\n",
        "This is a metal-organic framework (MOF) with a high concentration of copper ions connected by benzenedicarboxylate (BDC) linkers. The framework exhibits high porosity with pore sizes in the range of 2-3 nm.\n",
        "\"\"\"\n",
        "\n",
        "# Make the API call to predict the material's properties\n",
        "response = openai.ChatCompletion.create(\n",
        "    model=\"gpt-4-turbo\",\n",
        "    messages=[\n",
        "        {\"role\": \"system\", \"content\": system_prompt},\n",
        "        {\"role\": \"user\", \"content\": material_description}\n",
        "    ]\n",
        ")\n",
        "\n",
        "# Print the predicted material properties\n",
        "print(response.choices[0].message['content'].strip())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l5eJcnV30qFX",
        "outputId": "f9d290e4-8102-4d0b-d4ca-1ff7953a5218"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For a metal-organic framework (MOF) consisting primarily of copper ions connected by benzenedicarboxylate (BDC) linkers such as the one described, we can predict its properties based on its unique structure and composition:\n",
            "\n",
            "### Mechanical Strength\n",
            "- **Robustness**: Copper-based MOFs can have decent mechanical strength due to the presence of metal links, but generally, MOFs are less strong mechanically compared to dense metals or ceramics because of their porous structure.\n",
            "- **Brittleness**: Like many MOFs, this structure may be brittle under stress due to the nature of the metal-linker bonds and the large void spaces within the crystal structure.\n",
            "\n",
            "### Conductivity\n",
            "- **Electrical Conductivity**: MOFs are typically insulators. However, the presence of copper ions could potentially introduce some level of electrical conductivity, especially if the MOF's structure provides pathways for electron transfer. This is less common and would depend significantly on the exact nature of the electronic interactions in the framework.\n",
            "- **Thermal Conductivity**: Generally low due to the porous structure disrupting heat transfer pathways. This trait makes MOFs suitable for applications requiring insulation.\n",
            "\n",
            "### Thermal Stability\n",
            "- **Decomposition/Decay**: Copper-based MOFs can decompose upon heating, often starting from the breakdown of organic linkers (BDC in this case) at elevated temperatures. Their precise thermal stability will depend on the specific structure and bonding environment.\n",
            "- **Operating Temperature Range**: Typically, MOFs tend to have lower thermal stability compared to inorganic materials. This specific MOF might start degrading or losing its structural integrity at temperatures commonly used in industrial processes.\n",
            "\n",
            "### Porosity and Surface Area\n",
            "- **Gas Storage and Separation**: High porosity and the pore size range of 2-3 nm could make this MOF excellent for applications involving gas storage (like hydrogen or methane) or separation (such as carbon dioxide from nitrogen in air). The size and polarity of the pores can influence selectivity and capacity.\n",
            "- **Catalysis**: The accessibility of copper sites and the framework's ability to stabilize reactive intermediates can make it suitable for catalytic applications, including oxidation reactions or facilitating coupling reactions.\n",
            "\n",
            "### Chemical Stability\n",
            "- **Sensitivity to Water and Air**: Depending on the exact coordination environment of the copper and the nature of the BDC linkers, the MOF might be sensitive to moisture and oxygen, which could degrade the framework or modify its properties over time.\n",
            "\n",
            "### Potential Applications\n",
            "Given these properties, this type of MOF might be particularly suited to:\n",
            "- **Environmental Applications**: Gas storage, CO2 capture, and separation processes.\n",
            "- **Catalysis**: Especially where copper's redox properties are advantageous.\n",
            "- **Sensors**: Detection of gases or chemicals that can interact specifically with copper sites or the organic linkers.\n",
            "\n",
            "### Conclusion\n",
            "The utility of this MOF in practical applications will depend greatly on optimizing its synthesis to enhance desirable properties such as stability and specific pore functionalities and mitigating limitations such as mechanical strength and sensitivity to environmental conditions.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Generating a Material Synthesis Procedure Using GPT-4 Turbo\n",
        "\n",
        "This Python script uses OpenAI's GPT-4 Turbo model to generate a detailed experimental procedure for synthesizing a material based on its composition.\n",
        "\n",
        "#### Aim:\n",
        "To automatically generate a step-by-step experimental synthesis procedure for a specific material, in this case, a Metal-Organic Framework (MOF) for gas adsorption applications.\n",
        "\n",
        "#### Key Components:\n",
        "\n",
        "1. **System Prompt**:\n",
        "   The system prompt instructs the model to act as a materials science expert capable of designing synthesis procedures. It specifies that the output should be a detailed step-by-step experimental process based on the provided material description.\n",
        "\n",
        "2. **Material Description**:\n",
        "   The material to be synthesized is described as a Metal-Organic Framework (MOF) composed of iron(III) ions and terephthalic acid (BDC) linkers, with a pore size of 1.5 nm, for gas adsorption applications.\n",
        "\n",
        "3. **API Call**:\n",
        "   The script uses OpenAI's `ChatCompletion.create()` method to prompt the model for an experimental procedure, based on the system prompt and the material description.\n",
        "\n",
        "4. **Predicted Output**:\n",
        "   The model is expected to return a detailed synthesis procedure, which could include steps such as:\n",
        "   - Dissolution of iron(III) ions and terephthalic acid.\n",
        "   - Heating, crystallization, washing, and drying steps.\n",
        "\n",
        "#### Applications:\n",
        "This approach helps researchers and scientists to generate synthesis procedures for new materials automatically, reducing time spent on planning experiments and accelerating the material discovery process.\n"
      ],
      "metadata": {
        "id": "ZoJqOi-R2UPg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import openai\n",
        "\n",
        "# Your OpenAI API key\n",
        "openai.api_key = 'ReplacewithYourAPI'\n",
        "\n",
        "\n",
        "# System prompt defining the task for the LLM\n",
        "system_prompt = \"\"\"\n",
        "You are a materials science expert capable of designing synthesis procedures for materials. Based on a material's composition, generate a detailed experimental procedure for synthesizing it.\n",
        "\"\"\"\n",
        "\n",
        "# Description of the material to be synthesized\n",
        "material_description = \"\"\"\n",
        "Synthesize a metal-organic framework (MOF) composed of iron(III) ions and terephthalic acid (BDC) linkers, with a pore size of 1.5 nm, for gas adsorption applications.\n",
        "\"\"\"\n",
        "\n",
        "# Make the API call to generate the experimental procedure\n",
        "response = openai.ChatCompletion.create(\n",
        "    model=\"gpt-4-turbo\",\n",
        "    messages=[\n",
        "        {\"role\": \"system\", \"content\": system_prompt},\n",
        "        {\"role\": \"user\", \"content\": material_description}\n",
        "    ]\n",
        ")\n",
        "\n",
        "# Print the generated experimental procedure\n",
        "print(response.choices[0].message['content'].strip())\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pUsZxeoP116Y",
        "outputId": "3da5d1c1-14ca-44da-bbdb-0673f38ac8c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "To synthesize a metal-organic framework (MOF) with iron(III) (Fe(III)) ions as the metal centers and terephthalic acid (benzene-1,4-dicarboxylic acid, BDC) as the organic linkers that leads to a pore size of approximately 1.5 nm, you can follow this experimental procedure. This type of MOF could be particularly useful for gas adsorption applications due to its suitable pore size and robust framework functionality.\n",
            "\n",
            "### Materials Required:\n",
            "1. Iron(III) chloride hexahydrate (FeCl₃·6H₂O)\n",
            "2. Terephthalic acid (H₂BDC)\n",
            "3. N,N-dimethylformamide (DMF)\n",
            "4. Distilled water\n",
            "5. Ethanol\n",
            "6. Hydrochloric acid (HCl, optional, for pH adjustment)\n",
            "7. Oven or microwave for heating\n",
            "8. Vacuum filtration apparatus\n",
            "9. Schlenk line (optional for inert atmosphere)\n",
            "\n",
            "### Procedure:\n",
            "#### Step 1: Preparation of Solutions\n",
            "1. **Iron(III) Chloride Solution:** Dissolve 2.70 grams of FeCl₃·6H₂O in 60 mL of DMF. This solution provides the iron(III) ions needed as metal nodes.\n",
            "2. **Terephthalic Acid Solution:** Dissolve 1.66 grams of H₂BDC in 40 mL of DMF. Heating the solution to 50 °C can assist in fully dissolving the BDC.\n",
            "\n",
            "#### Step 2: Synthesis of the MOF\n",
            "1. Mix the two solutions prepared in Step 1 in a 100 mL Schlenk flask or a similar reaction vessel. Ensure the solutions are thoroughly mixed to form a clear homogeneous mixture.\n",
            "2. Optionally, adjust the pH of the mixture to around 5 using dilute HCl to promote better crystallization conditions.\n",
            "3. Transfer the mixture into an oven or a microwave reactor. Heat the mixture under reflux at 120 °C for 24 hours. If using a microwave reactor, the reaction time could be reduced due to higher energy efficiency.\n",
            "4. After heating, allow the mixture to cool down to room temperature slowly.\n",
            "5. Centrifuge or vacuum filter the resulting precipitate to separate the solid MOF from the mother liquor.\n",
            "  \n",
            "#### Step 3: Washing and Activation\n",
            "1. Wash the precipitate three times with fresh DMF to remove any unreacted starting materials or byproducts.\n",
            "2. Subsequently, wash three more times with ethanol to exchange the solvent from DMF to ethanol, which may help in removing any residual DMF effectively.\n",
            "3. Dry the solid under vacuum at 80 °C for 12 hours to remove solvent molecules trapped in the pore structure. This step is critical for ensuring the pores in the MOF are open and accessible for gas adsorption.\n",
            "\n",
            "#### Step 4: Characterization (Optional)\n",
            "1. Characterize the resulting MOF using powder X-ray diffraction (XRD) to confirm the crystal structure.\n",
            "2. Use gas adsorption studies such as BET and N₂ sorption isotherms to confirm the pore size and to estimate the surface area.\n",
            "3. Scanning electron microscopy (SEM) or transmission electron microscopy (TEM) can be used for morphological studies.\n",
            "\n",
            "### Safety and Disposal:\n",
            "- Handle all chemicals with appropriate safety protocol (gloves, goggles, lab coat).\n",
            "- DMF should be handled with care due to its toxicity.\n",
            "- Dispose of all waste according to local environmental regulations.\n",
            "\n",
            "This procedure should yield a MOF with Fe(III) ions coordinated to BDC linkers, expected to exhibit a pore size suitable for efficient gas adsorption applications.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Sentiment Analysis of MOF Comments Using GPT-4 Turbo\n",
        "\n",
        "This Python script demonstrates how to use OpenAI's GPT-4 Turbo model to perform sentiment analysis on a comment regarding Metal-Organic Frameworks (MOFs).\n",
        "\n",
        "#### Aim:\n",
        "To classify the sentiment of a comment about MOF materials as **positive**, **neutral**, or **negative**.\n",
        "\n",
        "#### Key Components:\n",
        "\n",
        "1. **System Prompt**:\n",
        "   The system prompt instructs the model to analyze the sentiment of the given comment about MOF materials and classify it into one of three categories: positive, neutral, or negative.\n",
        "\n",
        "2. **MOF Comment**:\n",
        "   The comment expresses concern about the stability of MOFs under operational conditions, mentioning that progress in enhancing their robustness has been slow, which limits practical applications.\n",
        "\n",
        "3. **API Call**:\n",
        "   The script uses OpenAI's `ChatCompletion.create()` method to perform the sentiment analysis by passing the MOF-related comment along with the system prompt to the GPT-4 Turbo model.\n",
        "\n",
        "4. **Predicted Output**:\n",
        "   The model is expected to return a sentiment classification based on the tone of the comment. In this case, it would likely be classified as **negative** due to the expressed disappointment in MOF stability.\n",
        "\n",
        "#### Applications:\n",
        "This method can be used to automatically gauge sentiment in scientific discourse, helping researchers understand the community's perception of specific materials or challenges in the field.\n"
      ],
      "metadata": {
        "id": "_oC9OTUA2eM5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import openai\n",
        "\n",
        "# Replace 'your-api-key' with your actual OpenAI API key\n",
        "openai.api_key = 'ReplacewithYourAPI'\n",
        "\n",
        "# Example MOF description\n",
        "mof_comment = \"The stability of MOFs under operational conditions remains a major disappointment. Despite years of research, there's little progress in enhancing their robustness, which limits their practical applications.\"\n",
        "\n",
        "# System prompt to analyze sentiment\n",
        "system_prompt = \"Analyze the sentiment of the following comment about MOF materials and classify it as positive, neutral, or negative.\"\n",
        "\n",
        "response = openai.ChatCompletion.create(\n",
        "    model=\"gpt-4-turbo\",\n",
        "    messages=[\n",
        "        {\"role\": \"system\", \"content\": system_prompt},\n",
        "        {\"role\": \"user\", \"content\": mof_comment}\n",
        "    ]\n",
        ")\n",
        "\n",
        "# Print the AI's sentiment analysis\n",
        "print(response.choices[0].message['content'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mf3mx-c_k6oc",
        "outputId": "07b36360-27da-434c-b902-7ac09770f74a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The sentiment of the comment about MOF materials is negative. The comment expresses disappointment regarding the stability of MOFs under operational conditions and criticizes the lack of progress in enhancing their robustness, which it says limits their practical applications.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Generating Innovative MOF Designs Using GPT-4 Turbo\n",
        "\n",
        "This Python script demonstrates how to use OpenAI's GPT-4 Turbo model to generate innovative combinations of metal ions and organic ligands for the design of new Metal-Organic Frameworks (MOFs) with unique properties.\n",
        "\n",
        "#### Aim:\n",
        "To automatically generate new MOF designs by suggesting combinations of metal ions and organic ligands, which may lead to the discovery of MOFs with novel applications or properties.\n",
        "\n",
        "#### Key Components:\n",
        "\n",
        "1. **System Prompt**:\n",
        "   The system prompt instructs the model to generate three suggestions for innovative combinations of metal ions and organic ligands that could lead to the creation of new MOFs. These combinations are expected to offer potential unique properties or applications.\n",
        "\n",
        "2. **API Call**:\n",
        "   The script uses OpenAI's `ChatCompletion.create()` method to request the generation of new MOF combinations. The model is expected to return three novel design suggestions, including possible metals and organic linkers.\n",
        "\n",
        "3. **Predicted Output**:\n",
        "   The model will generate three combinations of metal ions and organic ligands that could potentially result in MOFs with unique properties, such as high gas adsorption capacity, enhanced thermal stability, or catalytic activity.\n",
        "\n",
        "#### Applications:\n",
        "This method can aid materials scientists in discovering new MOF designs by providing creative suggestions for material synthesis, which could accelerate the development of MOFs with specific desired properties for applications in fields like catalysis, gas storage, or drug delivery.\n"
      ],
      "metadata": {
        "id": "iBw8ht2j2m6f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import openai\n",
        "\n",
        "# Replace 'your-api-key' with your actual OpenAI API key\n",
        "openai.api_key = 'ReplacewithYourAPI'\n",
        "\n",
        "# Setup the system prompt to generate new MOF designs\n",
        "system_prompt = \"Generate innovative combinations of metal ions and organic ligands to create new MOFs with potential unique properties or applications. Provide three suggestions.\"\n",
        "\n",
        "try:\n",
        "    response = openai.ChatCompletion.create(\n",
        "        model=\"gpt-4-turbo\",  # Ensure this model is accessible with your API key\n",
        "        messages=[\n",
        "            {\"role\": \"system\", \"content\": system_prompt}\n",
        "        ]\n",
        "    )\n",
        "\n",
        "    # Print the innovative MOF combinations\n",
        "    print(response.choices[0].message['content'].strip())\n",
        "\n",
        "except Exception as e:\n",
        "    print(f\"An error occurred: {str(e)}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z0XEZCWulm7o",
        "outputId": "d82d659c-075f-4aaa-efd4-864cfa8bc30d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Creating unique metal-organic frameworks (MOFs) involves pairing metal ions with organic ligands in ways that may lead to novel structural properties and applications. Here are three innovative combinations that have potential in different applications:\n",
            "\n",
            "### 1. **Combination: Bismuth Ions (Bi3+) with Pyrazine-2,3-dicarboxylic Acid (PZDC)**\n",
            "   **Properties and Applications:**\n",
            "   - **Structural Uniqueness:** Bismuth-based MOFs are relatively rare and coupling Bi3+ with an aromatic dicarboxylic acid like PZDC might lead to highly stable, porous structures capable of accommodating large molecules.\n",
            "   - **Potential Application in Photonics and Luminescence:** Bismuth compounds are known for their optical properties. The potential luminescence of Bi-PZDC MOF could be utilized in photonics, sensors, or as markers in biomedical imaging.\n",
            "   - **Catalysis:** The Lewis acidity of bismuth and the availability of coordination sites in PZDC can make this MOF useful as a heterogeneous catalyst for organic transformations.\n",
            "\n",
            "### 2. **Combination: Titanium Ions (Ti4+) with Tetrathiafulvalene Tetracarboxylate (TTF-TC)**\n",
            "   **Properties and Applications:**\n",
            "   - **Structural Uniqueness:** Combining Ti4+, known for its high coordination numbers and variability, with an electroactive ligand like TTF-TC, can lead to electrically conductive frameworks.\n",
            "   - **Potential Application in Electronics:** Such MOFs might be useful in the development of MOF-based electronic devices, offering a blend of conductivity, porosity, and stability.\n",
            "   - **Energy Storage:** The redox-active TTF-TC ligand in the structure could enable these MOFs to function as supercapacitors or components in batteries, contributing to energy storage solutions.\n",
            "\n",
            "### 3. **Combination: Scandium Ions (Sc3+) with Isophthalic Acid-N-Oxide (IPAO)**\n",
            "   **Properties and Applications:**\n",
            "   - **Structural Uniqueness:** Scandium is rarely used in MOF synthesis. Combining it with oxygen-rich ligands like IPAO could lead to unique structural motifs with enhanced stability.\n",
            "   - **Potential Application in Gas Separation and Storage:** The polar nature of the N-oxide group in IPAO could enhance interaction with small gas molecules (e.g., CO2, H2), making this MOF ideal for gas separation or storage applications.\n",
            "   - **Catalysis:** The unique acidic and coordination properties of this MOF might make it suitable for acid-catalyzed reactions or as a support medium for catalytically active metal nanoparticles.\n",
            "\n",
            "These combinations are speculative and require empirical investigations to explore their practicality and real-world applications fully. Nonetheless, each presents a promising avenue for creating MOFs with properties tailored for specific, innovative uses.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Generating Innovative MOF Compositions for High Gas Adsorption Using GPT-4 Turbo\n",
        "\n",
        "This Python script demonstrates how to use OpenAI's GPT-4 Turbo model to suggest innovative Metal-Organic Framework (MOF) compositions with potential for high gas adsorption.\n",
        "\n",
        "#### Aim:\n",
        "To generate unique MOF compositions by exploring combinations of metal nodes and organic linkers that are not widely explored in current research, specifically targeting high gas adsorption properties.\n",
        "\n",
        "#### Key Components:\n",
        "\n",
        "1. **System Prompt**:\n",
        "   The system prompt instructs the model to act as a materials science expert, specializing in MOFs. The task is to generate innovative MOF compositions with a focus on high gas adsorption potential, using novel combinations of metal nodes and organic linkers.\n",
        "\n",
        "2. **User Query**:\n",
        "   The user requests specific MOF compositions that are likely to exhibit high gas adsorption properties. The AI is expected to provide innovative ideas that are not common in existing literature.\n",
        "\n",
        "3. **API Call**:\n",
        "   The script uses OpenAI's `ChatCompletion.create()` method to prompt the model for innovative MOF composition ideas based on the system and user input.\n",
        "\n",
        "4. **Predicted Output**:\n",
        "   The AI is expected to return several creative MOF composition ideas, detailing specific combinations of metal nodes and organic linkers that could be used to enhance gas adsorption properties.\n",
        "\n",
        "#### Applications:\n",
        "This approach assists materials scientists in discovering new MOF designs by suggesting creative and unexplored combinations, which could lead to breakthroughs in gas storage, separation, or capture technologies.\n"
      ],
      "metadata": {
        "id": "hC7JSb8t2vdC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import openai\n",
        "from prettytable import PrettyTable\n",
        "\n",
        "# Replace 'your-api-key' with your actual OpenAI API key\n",
        "openai.api_key = 'ReplacewithYourAPI'\n",
        "\n",
        "\n",
        "\n",
        "# System prompt to generate innovative MOF compositions for high gas adsorption\n",
        "system_prompt = \"\"\"\n",
        "You are an assistant with deep expertise in materials science, specializing in Metal-Organic Frameworks (MOFs). Generate innovative MOF compositions that could potentially exhibit high gas adsorption properties. Focus on suggesting unique combinations of metal nodes and organic linkers that have not been widely explored in existing literature.\n",
        "\"\"\"\n",
        "\n",
        "response = openai.ChatCompletion.create(\n",
        "    model=\"gpt-4-turbo\",\n",
        "    messages=[\n",
        "        {\"role\": \"system\", \"content\": system_prompt},\n",
        "        {\"role\": \"user\", \"content\": \"I am looking for MOF compositions with potential for high gas adsorption. Please suggest some innovative ideas.\"}\n",
        "    ]\n",
        ")\n",
        "\n",
        "# Print the AI's innovative MOF composition ideas\n",
        "print(response.choices[0].message['content'])\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pdPjzNGll3uE",
        "outputId": "1e17141b-a588-41bb-d4ab-8ac7fbbd27cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For developing Metal-Organic Frameworks (MOFs) with high gas adsorption capabilities, it's key to consider the potential for facilitating porous structures that can enhance the surface area and incorporate functional groups capable of interacting strongly with specific gas molecules. Here are some innovative MOF compositions that leverage unique combinations of metal nodes and organic linkers, potentially leading to enhanced gas adsorption properties:\n",
            "\n",
            "1. **Scandium-Trimesic Acid MOF**:\n",
            "   - **Metal Node**: Scandium (Sc)\n",
            "   - **Organic Linker**: Trimesic acid (benzene-1,3,5-tricarboxylic acid)\n",
            "   - **Rationale**: Scandium-based MOFs are less studied despite scandium's potential for high coordination numbers and strong metal-linker interactions. Pairing scandium with trimesic acid, a commonly used linker but underexplored with scandium, might yield a structure with high porosity and exceptional stability.\n",
            "\n",
            "2. **Thulium-Phthalocyanine MOF**:\n",
            "   - **Metal Node**: Thulium (Tm)\n",
            "   - **Organic Linker**: Phthalocyanine\n",
            "   - **Rationale**: Phthalocyanines are macrocyclic compounds typically used in dyes and pigments but underutilized in MOFs. Incorporating these with thulium, a lesser-used lanthanide in MOF applications, can potentially provide unique electronic environments conducive to gas interactions due to the extensive π-network of the phthalocyanine.\n",
            "\n",
            "3. **Yttrium-Naphthalenediimide MOF**:\n",
            "   - **Metal Node**: Yttrium (Y)\n",
            "   - **Organic Linker**: Naphthalenediimide (NDI)\n",
            "   - **Rationale**: Yttrium is known for its high coordination chemistry, and when combined with NDI, which is known for its electron-accepting properties, it might create MOFs suitable for capturing electron-rich gases like ammonia or sulfur-containing compounds.\n",
            "\n",
            "4. **Titanium-Squaraine MOF**:\n",
            "   - **Metal Node**: Titanium (Ti)\n",
            "   - **Organic Linker**: Squaraine\n",
            "   - **Rationale**: Titanium is a strong photocatalyst, and combining it with squaraine, an organic compound known for its intense absorption properties in the visible range, could lead to a MOF capable of gas adsorption under light activation, ideal for applications requiring reversible adsorption-desorption cycles influenced by light.\n",
            "\n",
            "5. **Cerium-Porphyrin MOF**:\n",
            "   - **Metal Node**: Cerium (Ce)\n",
            "   - **Organic Linker**: Porphyrin\n",
            "   - **Rationale**: Cerium has multiple oxidation states that can contribute to variable coordination environments. Porphyrins are highly conjugated systems with potential for gas interaction via π-π interactions and electrostatic forces, which could be enhanced by the variable valency of cerium.\n",
            "\n",
            "6. **Gadolinium-Dithiolenes MOF**:\n",
            "   - **Metal Node**: Gadolinium (Gd)\n",
            "   - **Organic Linker**: Dithiolene (example: ethylenedithiolate)\n",
            "   - **Rationale**: Gadolinium's magnetic properties combined with the electron-rich sulfur atoms in dithiolenes could lead to a MOF with unique gas adsorption and separation capabilities, especially for gases like hydrogen sulfide.\n",
            "\n",
            "These MOF concepts are innovative in the combination of metallic elements and organic linkers not typically seen in the most common MOFs. Due to their unique properties, these MOFs could exhibit enhanced and selective gas adsorption properties, suitable for applications in gas storage, separation, and catalysis.\n"
          ]
        }
      ]
    }
  ]
}