{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [], "gpuType": "T4", "collapsed_sections": [ "rFhWCFb9gx-x", "T8OlGbEk7-Z7", "qH4fOB6P_GJ9", "pWuyZ0gD_rK4", "6F2pR0QyAOsc" ] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" }, "accelerator": "GPU", "widgets": { "application/vnd.jupyter.widget-state+json": { "d33d346f333f429cb490ddcca7699621": { "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_f37e42a524254bc8952993846f15aa6f", "IPY_MODEL_d296fcafb59e49a2a493f3e2bc10b1e4", "IPY_MODEL_8ef4ee8fc6f643399adfa478e042db0d" ], "layout": "IPY_MODEL_2ec91705c97148c991982e6405fc9425" } }, "f37e42a524254bc8952993846f15aa6f": { "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_b171faeb93fb4fdfa77494b5d8a67184", "placeholder": "​", "style": "IPY_MODEL_4bf9470574334436a7fa1d1d9ac47ff7", "value": "config.json: 100%" } }, "d296fcafb59e49a2a493f3e2bc10b1e4": { "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_ce2f5b9efc2e443399181a3ab0e2367c", "max": 570, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_04e7e6f6811443eeb5d4c82e09c67c3d", "value": 570 } }, "8ef4ee8fc6f643399adfa478e042db0d": { "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_d9cfa88446b346d8973540cc151eb3a3", "placeholder": "​", "style": "IPY_MODEL_47be254f458740b38b079f87bc0afe03", "value": " 570/570 [00:00<00:00, 55.6kB/s]" } }, "2ec91705c97148c991982e6405fc9425": { "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 } }, "b171faeb93fb4fdfa77494b5d8a67184": { "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 } }, "4bf9470574334436a7fa1d1d9ac47ff7": { "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": "" } }, "ce2f5b9efc2e443399181a3ab0e2367c": { "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 } }, "04e7e6f6811443eeb5d4c82e09c67c3d": { "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": "" } }, "d9cfa88446b346d8973540cc151eb3a3": { "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 } }, "47be254f458740b38b079f87bc0afe03": { "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": "" } }, "2768c10d7b19495dbb528d44b46f26af": { "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_a396f46cc84c417d8601264f6f8b1fe9", "IPY_MODEL_1a31ea4721674b3090bdf9a22ba29102", "IPY_MODEL_98af28300cf747e783a5074fc154fa76" ], "layout": "IPY_MODEL_c3bb6540427b4bfebb2d7618938e50b7" } }, "a396f46cc84c417d8601264f6f8b1fe9": { "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_bf724b974ca54e1da974bc97e45a3efa", "placeholder": "​", "style": "IPY_MODEL_67dd4c4347914a5581a4ffa8e58d4563", "value": "tokenizer_config.json: 100%" } }, "1a31ea4721674b3090bdf9a22ba29102": { "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_a93de41ff69d41a5bdae522159edc351", "max": 48, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_3ca0e5a8796f44e9b3b27934bf73ed22", "value": 48 } }, "98af28300cf747e783a5074fc154fa76": { "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_7c6b6e6a8ae04271844f173ae02db580", "placeholder": "​", "style": "IPY_MODEL_53c2cb5d4c7d4c24b171c0101ace3f03", "value": " 48.0/48.0 [00:00<00:00, 5.33kB/s]" } }, "c3bb6540427b4bfebb2d7618938e50b7": { "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 } }, "bf724b974ca54e1da974bc97e45a3efa": { "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 } }, "67dd4c4347914a5581a4ffa8e58d4563": { "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": "" } }, "a93de41ff69d41a5bdae522159edc351": { "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 } }, "3ca0e5a8796f44e9b3b27934bf73ed22": { "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": "" } }, "7c6b6e6a8ae04271844f173ae02db580": { "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 } }, "53c2cb5d4c7d4c24b171c0101ace3f03": { "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": "" } }, "a5877d8ca0014332a1e377bb1c1f717d": { "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_ca896b7ddb5d4ed48b8713d569ed1684", "IPY_MODEL_3e3c838fbe5942a6be78e7c2dadd25b5", "IPY_MODEL_f8554323b60344c3a436e60e996ef902" ], "layout": "IPY_MODEL_66b1f1e419904c4ca77107f0229eb8c8" } }, "ca896b7ddb5d4ed48b8713d569ed1684": { "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_1c80893d53df42e4bc04a40efe9d66b6", "placeholder": "​", "style": "IPY_MODEL_fad2798008aa41658c046f2b13aa6c8f", "value": "vocab.txt: 100%" } }, "3e3c838fbe5942a6be78e7c2dadd25b5": { "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_a582e4b9485d4e7b956368f900aa90b3", "max": 231508, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_b2955efecb9d4f39a941abf0d71b3310", "value": 231508 } }, "f8554323b60344c3a436e60e996ef902": { "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_fea923a80b024ab0a4b9f06f6df84157", "placeholder": "​", "style": "IPY_MODEL_815d4e4ad63646b29b4ef13f390cf121", "value": " 232k/232k [00:00<00:00, 6.91MB/s]" } }, "66b1f1e419904c4ca77107f0229eb8c8": { "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 } }, "1c80893d53df42e4bc04a40efe9d66b6": { "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 } }, "fad2798008aa41658c046f2b13aa6c8f": { "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": "" } }, "a582e4b9485d4e7b956368f900aa90b3": { "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 } }, "b2955efecb9d4f39a941abf0d71b3310": { "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": "" } }, "fea923a80b024ab0a4b9f06f6df84157": { "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 } }, "815d4e4ad63646b29b4ef13f390cf121": { "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": "" } }, "7bc122a1017d4f0aa88ac60791a48e45": { "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_5e18732844244af1b205bfa1efae34d8", "IPY_MODEL_8b502946536e47e28c86df8afe42d4ec", "IPY_MODEL_9a818e5af7e74fcea28c8a5b7fdbaccf" ], "layout": "IPY_MODEL_6ee55375e3a3462ba9abc80b30635416" } }, "5e18732844244af1b205bfa1efae34d8": { "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_93140103bcc444be8d1f5384fb5f42f6", "placeholder": "​", "style": "IPY_MODEL_63f4e0b71fa14900b2a4e78a2b44ab6b", "value": "tokenizer.json: 100%" } }, "8b502946536e47e28c86df8afe42d4ec": { "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_6195058dcb2a4a0cbb21dff29efc239a", "max": 466062, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_5c52dc05f97f4fc2bc3e23cf34014f78", "value": 466062 } }, "9a818e5af7e74fcea28c8a5b7fdbaccf": { "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_7e73f670e09f4993bf41a590d2cc34c4", "placeholder": "​", "style": "IPY_MODEL_5dd0a98753e14ef0878dd47f9a7c23c0", "value": " 466k/466k [00:00<00:00, 22.8MB/s]" } }, "6ee55375e3a3462ba9abc80b30635416": { "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 } }, "93140103bcc444be8d1f5384fb5f42f6": { "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 } }, "63f4e0b71fa14900b2a4e78a2b44ab6b": { "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": "" } }, "6195058dcb2a4a0cbb21dff29efc239a": { "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 } }, "5c52dc05f97f4fc2bc3e23cf34014f78": { "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": "" } }, "7e73f670e09f4993bf41a590d2cc34c4": { "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 } }, "5dd0a98753e14ef0878dd47f9a7c23c0": { "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": "" } }, "2cce4b453f5e420db9daa75ae463b092": { "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_ec7e4553a190460f870ee58d87611ead", "IPY_MODEL_46637d29d5d64bcdb6f4880edd1f31e0", "IPY_MODEL_7560006e7c3c4900accb852967bcd485" ], "layout": "IPY_MODEL_25cb5f07f0e54c15bd67e796299f1b28" } }, "ec7e4553a190460f870ee58d87611ead": { "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_976eb45666904abf9270697f9fc1a24e", "placeholder": "​", "style": "IPY_MODEL_7d1e3f586f024e1ab10efb76eb6a8293", "value": "config.json: 100%" } }, "46637d29d5d64bcdb6f4880edd1f31e0": { "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_3da22ad9a76248f0b9aaa4bda4907db1", "max": 571, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_d31f211efa9646cba699ff98ee2d9a37", "value": 571 } }, "7560006e7c3c4900accb852967bcd485": { "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_4551ba608a3b457a9daa391c1eb2841d", "placeholder": "​", "style": "IPY_MODEL_91232cb357a04d6eb00457e121380125", "value": " 571/571 [00:00<00:00, 61.2kB/s]" } }, "25cb5f07f0e54c15bd67e796299f1b28": { "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 } }, "976eb45666904abf9270697f9fc1a24e": { "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 } }, "7d1e3f586f024e1ab10efb76eb6a8293": { "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": "" } }, "3da22ad9a76248f0b9aaa4bda4907db1": { "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 } }, "d31f211efa9646cba699ff98ee2d9a37": { "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": "" } }, "4551ba608a3b457a9daa391c1eb2841d": { "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 } }, "91232cb357a04d6eb00457e121380125": { "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": "" } }, "7df07550166049fda50fe7e8c24288a8": { "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_4764d36b0011446cb8a1fe8db18cffd9", "IPY_MODEL_b58c5ebc5d064e559583bfdfe8c15ec0", "IPY_MODEL_0582f6c5def049e08e0b7e7e1217eaee" ], "layout": "IPY_MODEL_fc21d98adac348769df48b681686b924" } }, "4764d36b0011446cb8a1fe8db18cffd9": { "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_252c9bd913f040e99ad0802f212996d7", "placeholder": "​", "style": "IPY_MODEL_e98a60d500d44c37adfd3fc0aa6fd3df", "value": "tokenizer_config.json: 100%" } }, "b58c5ebc5d064e559583bfdfe8c15ec0": { "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_008d6ee4f2fd429587d95b0bc6263c2f", "max": 363, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_6e87f7f9f3fd479f845dee4d9df8117d", "value": 363 } }, "0582f6c5def049e08e0b7e7e1217eaee": { "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_9430d231b5d44fb692b6efd8cb7bef8e", "placeholder": "​", "style": "IPY_MODEL_beac5b225ca44302a1ad4bd59c2df240", "value": " 363/363 [00:00<00:00, 37.5kB/s]" } }, "fc21d98adac348769df48b681686b924": { "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 } }, "252c9bd913f040e99ad0802f212996d7": { "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 } }, "e98a60d500d44c37adfd3fc0aa6fd3df": { "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": "" } }, "008d6ee4f2fd429587d95b0bc6263c2f": { "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 } }, "6e87f7f9f3fd479f845dee4d9df8117d": { "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": "" } }, "9430d231b5d44fb692b6efd8cb7bef8e": { "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 } }, "beac5b225ca44302a1ad4bd59c2df240": { "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": "" } }, "b26b7b9f9bfe4173b6fac89718a0a92f": { "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_0dbd438e3d984da596af8706af959c88", "IPY_MODEL_ef455ba23f84412abfbdf698dba697b4", "IPY_MODEL_730a1222cf6c4d4496d36f578144997e" ], "layout": "IPY_MODEL_bd9784c61d8e4ad38bf22d6186c65626" } }, "0dbd438e3d984da596af8706af959c88": { "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_682c57f92eb148238a1988bd083df37b", "placeholder": "​", "style": "IPY_MODEL_3aca79cba7cd4e5fb0fd51cec1f80aff", "value": "vocab.txt: 100%" } }, "ef455ba23f84412abfbdf698dba697b4": { "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_1c7f7084f0f24c69abc55b6bcf9d3d77", "max": 231536, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_c1a9abb06e254b5da1fce3dc360fb0b4", "value": 231536 } }, "730a1222cf6c4d4496d36f578144997e": { "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_3b8a803abc0b4607906eb99a0f950931", "placeholder": "​", "style": "IPY_MODEL_de095574f6f24abba6ec3a22263e5280", "value": " 232k/232k [00:00<00:00, 15.4MB/s]" } }, "bd9784c61d8e4ad38bf22d6186c65626": { "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 } }, "682c57f92eb148238a1988bd083df37b": { "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 } }, "3aca79cba7cd4e5fb0fd51cec1f80aff": { "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": "" } }, "1c7f7084f0f24c69abc55b6bcf9d3d77": { "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 } }, "c1a9abb06e254b5da1fce3dc360fb0b4": { "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": "" } }, "3b8a803abc0b4607906eb99a0f950931": { "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 } }, "de095574f6f24abba6ec3a22263e5280": { "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": "" } }, "49cb7634429c4074b792a1771281b948": { "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_2807ad0679be4297bce9567246ca157e", "IPY_MODEL_f7df6a3de8ba4d9da2671ff69c2d8889", "IPY_MODEL_8e74518e415f4f479922c0762561a84a" ], "layout": "IPY_MODEL_cb1ebf0159ae4e8ebe1cdcaacb3c1e57" } }, "2807ad0679be4297bce9567246ca157e": { "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_3d924dd7211b45dbae5c3e6d18c70f15", "placeholder": "​", "style": "IPY_MODEL_ffbcd6627e8c471e94a69ee83f4acd6b", "value": "tokenizer.json: 100%" } }, "f7df6a3de8ba4d9da2671ff69c2d8889": { "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_823ff629ab5c43448b03c0ec0ef2f617", "max": 466021, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_3fdb18dc3fc149bfad401359be17c6d9", "value": 466021 } }, "8e74518e415f4f479922c0762561a84a": { "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_707807e6379a41e38d9c767e91457201", "placeholder": "​", "style": "IPY_MODEL_a8a250e472c84cde815fe5cad73a1fa8", "value": " 466k/466k [00:00<00:00, 27.9MB/s]" } }, "cb1ebf0159ae4e8ebe1cdcaacb3c1e57": { "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 } }, "3d924dd7211b45dbae5c3e6d18c70f15": { "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 } }, "ffbcd6627e8c471e94a69ee83f4acd6b": { "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": "" } }, "823ff629ab5c43448b03c0ec0ef2f617": { "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 } }, "3fdb18dc3fc149bfad401359be17c6d9": { "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": "" } }, "707807e6379a41e38d9c767e91457201": { "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 } }, "a8a250e472c84cde815fe5cad73a1fa8": { "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": "" } }, "b73ebcbe52344d1781ce41fafd4b98a2": { "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_52cac3b916d34e8bab16f3b6b9bb3aa5", "IPY_MODEL_b7ca55abd83d48edb993f9b7fae1f4de", "IPY_MODEL_1cc57387d3f8482996b19d69ef567c8f" ], "layout": "IPY_MODEL_21d3fccc89b9454f93c1d42e27fc2e6f" } }, "52cac3b916d34e8bab16f3b6b9bb3aa5": { "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_c8906e0463ae4256aca8d93df27c8b8c", "placeholder": "​", "style": "IPY_MODEL_0006acbb860d492fa7b094b01cdaf459", "value": "special_tokens_map.json: 100%" } }, "b7ca55abd83d48edb993f9b7fae1f4de": { "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_877e1431f061488d867ce73c7742f8c0", "max": 239, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_be8fa5e173ba439aaf591096002fb1a0", "value": 239 } }, "1cc57387d3f8482996b19d69ef567c8f": { "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_aec6a5e55d2f4d74ae92a45ebc144cd3", "placeholder": "​", "style": "IPY_MODEL_8e4f0290b3014b12a088c6e592afde30", "value": " 239/239 [00:00<00:00, 23.4kB/s]" } }, "21d3fccc89b9454f93c1d42e27fc2e6f": { "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 } }, "c8906e0463ae4256aca8d93df27c8b8c": { "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 } }, "0006acbb860d492fa7b094b01cdaf459": { "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": "" } }, "877e1431f061488d867ce73c7742f8c0": { "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 } }, "be8fa5e173ba439aaf591096002fb1a0": { "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": "" } }, "aec6a5e55d2f4d74ae92a45ebc144cd3": { "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 } }, "8e4f0290b3014b12a088c6e592afde30": { "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": "" } }, "3dc7b6d8d4334771b912f3cc4ac2f24b": { "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_4abbed3718cc4dec9f7d154025db1245", "IPY_MODEL_4ab959c3e3f741be8b6078bab217115c", "IPY_MODEL_805c260860da493daa27b2f3fcfb384c" ], "layout": "IPY_MODEL_58f9a0e7e5b14d328f18d9cab5d156b4" } }, "4abbed3718cc4dec9f7d154025db1245": { "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_8f577de3d5424034a5b175c4b1459ae6", "placeholder": "​", "style": "IPY_MODEL_f3c3983867e64f67b34fb7beee83b764", "value": "config.json: 100%" } }, "4ab959c3e3f741be8b6078bab217115c": { "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_eb0418f6f19a4c6ea04e5fb15cc73242", "max": 612, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_1a5950617a134f83ab50a34494ea799f", "value": 612 } }, "805c260860da493daa27b2f3fcfb384c": { "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_29af3687441a4eca977a49cb8d820d39", "placeholder": "​", "style": "IPY_MODEL_21454dc2e95d4d3084818701bb388ee3", "value": " 612/612 [00:00<00:00, 67.0kB/s]" } }, "58f9a0e7e5b14d328f18d9cab5d156b4": { "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 } }, "8f577de3d5424034a5b175c4b1459ae6": { "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 } }, "f3c3983867e64f67b34fb7beee83b764": { "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": "" } }, "eb0418f6f19a4c6ea04e5fb15cc73242": { "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 } }, "1a5950617a134f83ab50a34494ea799f": { "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": "" } }, "29af3687441a4eca977a49cb8d820d39": { "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 } }, "21454dc2e95d4d3084818701bb388ee3": { "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": "" } }, "088c579a23304e67b80bc4532caaa918": { "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_8e6a029cd02b40b4b42b65c5ff81f0c9", "IPY_MODEL_1819f8db365943c7b165c505a85f9059", "IPY_MODEL_ef043a8ecea54da0bf9fbd5533414189" ], "layout": "IPY_MODEL_0cab4876eae64f5dbadc9407c19645bb" } }, "8e6a029cd02b40b4b42b65c5ff81f0c9": { "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_42015f941c4d4595a24273fa7a573168", "placeholder": "​", "style": "IPY_MODEL_74ce1eeb3333418dac5d47fae2b13459", "value": "tokenizer_config.json: 100%" } }, "1819f8db365943c7b165c505a85f9059": { "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_e570847f0d4246df90266c02b94812b7", "max": 350, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_fab34b84b65f45188c2dab3f190d9427", "value": 350 } }, "ef043a8ecea54da0bf9fbd5533414189": { "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_483ac012db0d4f0bb4ce6d2318cd3f48", "placeholder": "​", "style": "IPY_MODEL_df6bcb73aae5472393289ae329c5da63", "value": " 350/350 [00:00<00:00, 36.9kB/s]" } }, "0cab4876eae64f5dbadc9407c19645bb": { "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 } }, "42015f941c4d4595a24273fa7a573168": { "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 } }, "74ce1eeb3333418dac5d47fae2b13459": { "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": "" } }, "e570847f0d4246df90266c02b94812b7": { "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 } }, "fab34b84b65f45188c2dab3f190d9427": { "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": "" } }, "483ac012db0d4f0bb4ce6d2318cd3f48": { "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 } }, "df6bcb73aae5472393289ae329c5da63": { "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": "" } }, "127580b2218c4622b1df09bc2dece359": { "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_f473dba0b5c1449cb9863dfa47bf4884", "IPY_MODEL_aa5fd41275f64eea8e27879eca9eb566", "IPY_MODEL_cad46f22d1004c09bbfa2c3f5601f25a" ], "layout": "IPY_MODEL_934567710e70481498b630d04f3028e1" } }, "f473dba0b5c1449cb9863dfa47bf4884": { "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_46befe6ebe3f41c59be09fbdcb7cf0a4", "placeholder": "​", "style": "IPY_MODEL_d255e29011f4486e9f4618868321c034", "value": "vocab.txt: 100%" } }, "aa5fd41275f64eea8e27879eca9eb566": { "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_6d153212b999440587c7380d5027fdf5", "max": 231508, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_a4accceb01a6475495f9e097c7da05e2", "value": 231508 } }, "cad46f22d1004c09bbfa2c3f5601f25a": { "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_b13aff45ec424043a2751149ab14b07b", "placeholder": "​", "style": "IPY_MODEL_1badd1ba063e4a468fdf80adfc16d585", "value": " 232k/232k [00:00<00:00, 13.7MB/s]" } }, "934567710e70481498b630d04f3028e1": { "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 } }, "46befe6ebe3f41c59be09fbdcb7cf0a4": { "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 } }, "d255e29011f4486e9f4618868321c034": { "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": "" } }, "6d153212b999440587c7380d5027fdf5": { "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 } }, "a4accceb01a6475495f9e097c7da05e2": { "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": "" } }, "b13aff45ec424043a2751149ab14b07b": { "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 } }, "1badd1ba063e4a468fdf80adfc16d585": { "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": "" } }, "089d9b1283f14a8f9ec35906ca251b66": { "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_777cf0361d2041a28a9ad97cc82dca41", "IPY_MODEL_1efe9e40981e4421bca9282277af981e", "IPY_MODEL_791830aa732d46bdae414c52a8f06b0d" ], "layout": "IPY_MODEL_5fe15dd7bc854adba1fe32ce030ccd1c" } }, "777cf0361d2041a28a9ad97cc82dca41": { "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_34ffec0a789143ef921c0e65eb39b2df", "placeholder": "​", "style": "IPY_MODEL_cc81b2d1c9084876b375fa780cbbf077", "value": "tokenizer.json: 100%" } }, "1efe9e40981e4421bca9282277af981e": { "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_273e64a7fdaa41ddbf87cf4797dd9639", "max": 466247, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_066989efe995420491df8cfb563af3a4", "value": 466247 } }, "791830aa732d46bdae414c52a8f06b0d": { "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_d409977a86194c3585734453d64f2686", "placeholder": "​", "style": "IPY_MODEL_b875f56a5d3b4e27877c98c2e0db5151", "value": " 466k/466k [00:00<00:00, 24.7MB/s]" } }, "5fe15dd7bc854adba1fe32ce030ccd1c": { "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 } }, "34ffec0a789143ef921c0e65eb39b2df": { "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 } }, "cc81b2d1c9084876b375fa780cbbf077": { "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": "" } }, "273e64a7fdaa41ddbf87cf4797dd9639": { "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 } }, "066989efe995420491df8cfb563af3a4": { "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": "" } }, "d409977a86194c3585734453d64f2686": { "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 } }, "b875f56a5d3b4e27877c98c2e0db5151": { "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": "" } }, "4a533c99a3724a3382fc0565f7cba05e": { "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_2039b497b8144f16a8e7b6429898c0b1", "IPY_MODEL_8d4d033be4b04efdb03cf69463440fca", "IPY_MODEL_2aa472f432d94379a85af45af02c98b1" ], "layout": "IPY_MODEL_110828b1523241369b5f9ee342482a63" } }, "2039b497b8144f16a8e7b6429898c0b1": { "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_375c9b4ff9814a6886d5052e038ccba9", "placeholder": "​", "style": "IPY_MODEL_00d3227c953645249747a5d7576702b3", "value": "special_tokens_map.json: 100%" } }, "8d4d033be4b04efdb03cf69463440fca": { "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_56c095db3441460b80d712c3604b4828", "max": 112, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_c33ed0cc05ff4d1a9e7e63ea60dce780", "value": 112 } }, "2aa472f432d94379a85af45af02c98b1": { "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_79df65241e9e4ed4ab9371e495496ddd", "placeholder": "​", "style": "IPY_MODEL_a4ed9199cae247b69d90b668cd1304da", "value": " 112/112 [00:00<00:00, 4.88kB/s]" } }, "110828b1523241369b5f9ee342482a63": { "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 } }, "375c9b4ff9814a6886d5052e038ccba9": { "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 } }, "00d3227c953645249747a5d7576702b3": { "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": "" } }, "56c095db3441460b80d712c3604b4828": { "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 } }, "c33ed0cc05ff4d1a9e7e63ea60dce780": { "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": "" } }, "79df65241e9e4ed4ab9371e495496ddd": { "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 } }, "a4ed9199cae247b69d90b668cd1304da": { "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": "" } }, "0a2459c8a7de4294a1ade237676c96ab": { "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_6934f03c261f4ce89cec74025add01bb", "IPY_MODEL_675d00a8eb5d406391f384f23d1f12b0", "IPY_MODEL_205bf6184a9640e69121ef97338be1f0" ], "layout": "IPY_MODEL_0f17531695ba4a91a228659a24b4cea5" } }, "6934f03c261f4ce89cec74025add01bb": { "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_1abbfdd8144c4545bd5858677d32ca73", "placeholder": "​", "style": "IPY_MODEL_9f5c4c9922f24ca68de36427ac06ea5e", "value": "model.safetensors: 100%" } }, "675d00a8eb5d406391f384f23d1f12b0": { "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_aa5d032a46734c6f86a558d92e799f32", "max": 440449768, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_d85fff2a40aa43078811dd69f64add3d", "value": 440449768 } }, "205bf6184a9640e69121ef97338be1f0": { "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_7ec3e909ebdb4070b0edb1cd6c53f5ca", "placeholder": "​", "style": "IPY_MODEL_3ccf81d28773416fb3d54a06021bf85d", "value": " 440M/440M [00:05<00:00, 95.2MB/s]" } }, "0f17531695ba4a91a228659a24b4cea5": { "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 } }, "1abbfdd8144c4545bd5858677d32ca73": { "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 } }, "9f5c4c9922f24ca68de36427ac06ea5e": { "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": "" } }, "aa5d032a46734c6f86a558d92e799f32": { "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 } }, "d85fff2a40aa43078811dd69f64add3d": { "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": "" } }, "7ec3e909ebdb4070b0edb1cd6c53f5ca": { "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 } }, "3ccf81d28773416fb3d54a06021bf85d": { "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": "" } }, "0a81951dcc464eefb1177cb38a2f73a3": { "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_de64b156784b425a8ca67545da3a8ac4", "IPY_MODEL_d4da9324dd354becb0f38b8221037ad6", "IPY_MODEL_c87d01a2893841a8908e3b316994911b" ], "layout": "IPY_MODEL_06b8f31470ea4d989140aeb88abc7fc5" } }, "de64b156784b425a8ca67545da3a8ac4": { "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_3c71a0d4ebb74eb1a17a1a5b0c5957f1", "placeholder": "​", "style": "IPY_MODEL_faf347c958b54bfca10cc4df65151e89", "value": "Loading weights: 100%" } }, "d4da9324dd354becb0f38b8221037ad6": { "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_d8dcad34c5ff449992bf30e4e8a92d48", "max": 199, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_b5aac165360b47a2b7fae1d8871a908b", "value": 199 } }, "c87d01a2893841a8908e3b316994911b": { "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_ad2958c3a71f477c977b768dbb3c4f1a", "placeholder": "​", "style": "IPY_MODEL_1c5c7539f57145888d310d060e4c7074", "value": " 199/199 [00:00<00:00, 4417.95it/s]" } }, "06b8f31470ea4d989140aeb88abc7fc5": { "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 } }, "3c71a0d4ebb74eb1a17a1a5b0c5957f1": { "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 } }, "faf347c958b54bfca10cc4df65151e89": { "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": "" } }, "d8dcad34c5ff449992bf30e4e8a92d48": { "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 } }, "b5aac165360b47a2b7fae1d8871a908b": { "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": "" } }, "ad2958c3a71f477c977b768dbb3c4f1a": { "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 } }, "1c5c7539f57145888d310d060e4c7074": { "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": "" } }, "ceb65dd460ef4082ad02f44e8a1d3598": { "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_6fb076778c9040689bea2689e7cccacc", "IPY_MODEL_9d34c573558f4b61b2aa448dec8b763b", "IPY_MODEL_0e36a1f93ebd45368150b1c5fcd79d0e" ], "layout": "IPY_MODEL_d4974e0196804bafa6fb8bd875dbe6d2" } }, "6fb076778c9040689bea2689e7cccacc": { "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_404569a966034d66b67d3868f16d355e", "placeholder": "​", "style": "IPY_MODEL_fc390f5f4a3b4ffabeef9b86bd766b86", "value": "Loading weights: 100%" } }, "9d34c573558f4b61b2aa448dec8b763b": { "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_4f1142afcfc9405cb1e841343cba97e2", "max": 199, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_d4ff423744ad440db7addb1f6f1f6079", "value": 199 } }, "0e36a1f93ebd45368150b1c5fcd79d0e": { "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_7345ff3eadb14881a97b8afe739c211a", "placeholder": "​", "style": "IPY_MODEL_909a41d45cdb4b1f9a108779cb820e3f", "value": " 199/199 [00:00<00:00, 4324.92it/s]" } }, "d4974e0196804bafa6fb8bd875dbe6d2": { "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 } }, "404569a966034d66b67d3868f16d355e": { "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 } }, "fc390f5f4a3b4ffabeef9b86bd766b86": { "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": "" } }, "4f1142afcfc9405cb1e841343cba97e2": { "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 } }, "d4ff423744ad440db7addb1f6f1f6079": { "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": "" } }, "7345ff3eadb14881a97b8afe739c211a": { "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 } }, "909a41d45cdb4b1f9a108779cb820e3f": { "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": "" } }, "ca947aba033d4389b5610c35871069e3": { "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_959ab929add9432db6918499f6c24a1f", "IPY_MODEL_973930ba65884315877004531fda4b36", "IPY_MODEL_54e8ad1ad3a846c5a2d18558473146ec" ], "layout": "IPY_MODEL_026a353a30d647ea940ab998d00f86a8" } }, "959ab929add9432db6918499f6c24a1f": { "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_6198c3916fe54eb19bc6876e62e2b241", "placeholder": "​", "style": "IPY_MODEL_818ff352151c42fabe2b2e74b8b0260c", "value": "Loading weights: 100%" } }, "973930ba65884315877004531fda4b36": { "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_8f826e39660c4507a71569a7bc1de69c", "max": 199, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_87424abee7d94bbe85d7022470c10de8", "value": 199 } }, "54e8ad1ad3a846c5a2d18558473146ec": { "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_5d64709161a44ffc86917502917037e3", "placeholder": "​", "style": "IPY_MODEL_49e13101eb3b4d5fae55de4c87a948cb", "value": " 199/199 [00:00<00:00, 3723.73it/s]" } }, "026a353a30d647ea940ab998d00f86a8": { "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 } }, "6198c3916fe54eb19bc6876e62e2b241": { "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 } }, "818ff352151c42fabe2b2e74b8b0260c": { "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": "" } }, "8f826e39660c4507a71569a7bc1de69c": { "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 } }, "87424abee7d94bbe85d7022470c10de8": { "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": "" } }, "5d64709161a44ffc86917502917037e3": { "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 } }, "49e13101eb3b4d5fae55de4c87a948cb": { "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": "" } }, "d9c346c272dd4dd5b5ba5828d8fc7a14": { "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_b9c9e37196724cbd9dfc4aef7e9fd21b", "IPY_MODEL_a7a8ac85f4db46339f9e49e77a0b862f", "IPY_MODEL_d21cf6ff9e9a49d0aa3e805c882b427b" ], "layout": "IPY_MODEL_9abfdc59f1554cdeb784b14a8e27ac5a" } }, "b9c9e37196724cbd9dfc4aef7e9fd21b": { "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_ad64d1a4347f44c098b5b597d8283434", "placeholder": "​", "style": "IPY_MODEL_67b84e8ee9bd4a5aac51e855f8b1727b", "value": "model.safetensors: 100%" } }, "a7a8ac85f4db46339f9e49e77a0b862f": { "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_32a2610322814c40991f4dcfb875b2b4", "max": 437971872, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_5d54853fadd142d69a50f6026d51070f", "value": 437971872 } }, "d21cf6ff9e9a49d0aa3e805c882b427b": { "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_3a970b6e0ddf469aa18ef560bc22af47", "placeholder": "​", "style": "IPY_MODEL_0b0230e9ae144bd1b11f8350d071d33f", "value": " 438M/438M [00:04<00:00, 118MB/s]" } }, "9abfdc59f1554cdeb784b14a8e27ac5a": { "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 } }, "ad64d1a4347f44c098b5b597d8283434": { "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 } }, "67b84e8ee9bd4a5aac51e855f8b1727b": { "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": "" } }, "32a2610322814c40991f4dcfb875b2b4": { "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 } }, "5d54853fadd142d69a50f6026d51070f": { "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": "" } }, "3a970b6e0ddf469aa18ef560bc22af47": { "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 } }, "0b0230e9ae144bd1b11f8350d071d33f": { "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": "" } }, "0fab78b21ab240d9af21d641440ea852": { "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_ba7664d475a14807b08c9e9ce5197882", "IPY_MODEL_e375a2795f344a79898f726db31b8252", "IPY_MODEL_36f8ba2cfb9743b1951122c4da776853" ], "layout": "IPY_MODEL_0659e7e48c8e48468748d049d474d5f3" } }, "ba7664d475a14807b08c9e9ce5197882": { "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_5a173e77a4994906b22b050c22172cde", "placeholder": "​", "style": "IPY_MODEL_85ae76732d914725bd9a0296f1d476c9", "value": "Loading weights: 100%" } }, "e375a2795f344a79898f726db31b8252": { "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_3202b875060c4619b6e8195abb7ac3fb", "max": 199, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_eea2563836d24c849b0e814546641c7e", "value": 199 } }, "36f8ba2cfb9743b1951122c4da776853": { "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_feb4d8d89e8f4af990a44fb6df645ddd", "placeholder": "​", "style": "IPY_MODEL_d01ad345983048cfbf1cef6b916418f1", "value": " 199/199 [00:00<00:00, 3660.87it/s]" } }, "0659e7e48c8e48468748d049d474d5f3": { "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 } }, "5a173e77a4994906b22b050c22172cde": { "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 } }, "85ae76732d914725bd9a0296f1d476c9": { "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": "" } }, "3202b875060c4619b6e8195abb7ac3fb": { "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 } }, "eea2563836d24c849b0e814546641c7e": { "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": "" } }, "feb4d8d89e8f4af990a44fb6df645ddd": { "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 } }, "d01ad345983048cfbf1cef6b916418f1": { "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": "" } }, "33d5cbfaac0f468f9a8305794090395f": { "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_6200d55c4760432688e9ae4a29fa39c1", "IPY_MODEL_14272e3e1aa944689858defbd16a94ce", "IPY_MODEL_9882a131eeab4c5fa98312688d2e3d5f" ], "layout": "IPY_MODEL_8281a9208f76405cb099a18c6ca47b6e" } }, "6200d55c4760432688e9ae4a29fa39c1": { "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_22e1247cbf524ee188a5dd9336e4bcc6", "placeholder": "​", "style": "IPY_MODEL_0a96a5fcc3e04eb2bec33aa60e7a22c9", "value": "Loading weights: 100%" } }, "14272e3e1aa944689858defbd16a94ce": { "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_c04faa843460478eaadd2a504f4ac6bf", "max": 199, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_d8804f836e7b4c22afc424b76e024af7", "value": 199 } }, "9882a131eeab4c5fa98312688d2e3d5f": { "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_24c7b94c68dd4e5db46e1341896f4da1", "placeholder": "​", "style": "IPY_MODEL_f0bd78cbdc5644d984b4d2390d661b47", "value": " 199/199 [00:00<00:00, 3541.86it/s]" } }, "8281a9208f76405cb099a18c6ca47b6e": { "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 } }, "22e1247cbf524ee188a5dd9336e4bcc6": { "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 } }, "0a96a5fcc3e04eb2bec33aa60e7a22c9": { "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": "" } }, "c04faa843460478eaadd2a504f4ac6bf": { "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 } }, "d8804f836e7b4c22afc424b76e024af7": { "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": "" } }, "24c7b94c68dd4e5db46e1341896f4da1": { "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 } }, "f0bd78cbdc5644d984b4d2390d661b47": { "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": "" } }, "87624f7fc8884bc9a561fe65aee84041": { "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_993fde76d7cd4e7e9bc5e6472d16b81b", "IPY_MODEL_a72b412fa185403480ae1d6965002f56", "IPY_MODEL_fb782bd8ba9a461694095663595360cf" ], "layout": "IPY_MODEL_db30b2192d8f438a9962498d53325446" } }, "993fde76d7cd4e7e9bc5e6472d16b81b": { "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_23937754810a4d9baf0b9dfb67764518", "placeholder": "​", "style": "IPY_MODEL_89c9768ffc0c414fb515e3497444fdcd", "value": "Loading weights: 100%" } }, "a72b412fa185403480ae1d6965002f56": { "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_cafd5efea55447bb83e0885cc4532b3a", "max": 199, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_0448c0ea2ef944f1891fe2e4fc12266d", "value": 199 } }, "fb782bd8ba9a461694095663595360cf": { "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_4163cc5170d1420e869687f8e1a89f61", "placeholder": "​", "style": "IPY_MODEL_308b771ddd9f42a486ae293d036c4de2", "value": " 199/199 [00:00<00:00, 4593.93it/s]" } }, "db30b2192d8f438a9962498d53325446": { "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 } }, "23937754810a4d9baf0b9dfb67764518": { "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 } }, "89c9768ffc0c414fb515e3497444fdcd": { "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": "" } }, "cafd5efea55447bb83e0885cc4532b3a": { "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 } }, "0448c0ea2ef944f1891fe2e4fc12266d": { "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": "" } }, "4163cc5170d1420e869687f8e1a89f61": { "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 } }, "308b771ddd9f42a486ae293d036c4de2": { "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": "" } }, "2878e32542514dfdaca0c4bde7b06ee2": { "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_9b238cea7bdb4420b8dcb4d2c1c335bb", "IPY_MODEL_686b21e2a338434f865ec64b3aa321fd", "IPY_MODEL_7ea095b045554a679da813a78e0b095d" ], "layout": "IPY_MODEL_a21b25f3c6d94952b1e96eed073288c7" } }, "9b238cea7bdb4420b8dcb4d2c1c335bb": { "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_929985ce83784ec89e187554fca09201", "placeholder": "​", "style": "IPY_MODEL_e8fb0125c5434e50891e76074057ffa3", "value": "model.safetensors: 100%" } }, "686b21e2a338434f865ec64b3aa321fd": { "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_f0d7a9703b254be1b9053693f637f05a", "max": 90868376, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_497522dfd573430cabba7b35814fb1c7", "value": 90868376 } }, "7ea095b045554a679da813a78e0b095d": { "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_2be9a54daf2446df994c6f18a0a06ee5", "placeholder": "​", "style": "IPY_MODEL_e9e287ea183e4db393aeb0050af6ac1d", "value": " 90.9M/90.9M [00:00<00:00, 131MB/s]" } }, "a21b25f3c6d94952b1e96eed073288c7": { "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 } }, "929985ce83784ec89e187554fca09201": { "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 } }, "e8fb0125c5434e50891e76074057ffa3": { "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": "" } }, "f0d7a9703b254be1b9053693f637f05a": { "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 } }, "497522dfd573430cabba7b35814fb1c7": { "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": "" } }, "2be9a54daf2446df994c6f18a0a06ee5": { "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 } }, "e9e287ea183e4db393aeb0050af6ac1d": { "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": "" } }, "f7fd2b042d56491daa2954c4ef3dd011": { "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_813acde1c375446a994949feca447534", "IPY_MODEL_824a75ac93b74928ab14b286d032295c", "IPY_MODEL_d1cfeb39eb3746ba9cdd2edd0f8535dc" ], "layout": "IPY_MODEL_cedd0f15954841f2b9a24a6e4a29e37a" } }, "813acde1c375446a994949feca447534": { "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_5c7356aac4514c9f9054309104e5c5fb", "placeholder": "​", "style": "IPY_MODEL_e07645f50b334daaa9751e69ec39460d", "value": "Loading weights: 100%" } }, "824a75ac93b74928ab14b286d032295c": { "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_1584125524884ba2b3acf4aa499d12ec", "max": 103, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_62cdfe58af2845d9a88ebe6fb2fafd6d", "value": 103 } }, "d1cfeb39eb3746ba9cdd2edd0f8535dc": { "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_630e8b71890841eb95e253bd2893429f", "placeholder": "​", "style": "IPY_MODEL_58258727d77e452f8b617e45b78add01", "value": " 103/103 [00:00<00:00, 3317.06it/s]" } }, "cedd0f15954841f2b9a24a6e4a29e37a": { "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 } }, "5c7356aac4514c9f9054309104e5c5fb": { "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 } }, "e07645f50b334daaa9751e69ec39460d": { "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": "" } }, "1584125524884ba2b3acf4aa499d12ec": { "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 } }, "62cdfe58af2845d9a88ebe6fb2fafd6d": { "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": "" } }, "630e8b71890841eb95e253bd2893429f": { "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 } }, "58258727d77e452f8b617e45b78add01": { "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": "" } }, "ec449273f52043dc9a2bf9d4aa815c53": { "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_08e71b3c16b54e18b34bd2900f1edacf", "IPY_MODEL_29a1b33db7864a27b5b91325b8c37daa", "IPY_MODEL_3be6215158774061bf3dbaf3b04656cf" ], "layout": "IPY_MODEL_fa1fe69a39b84111b5e07c24dc683f0e" } }, "08e71b3c16b54e18b34bd2900f1edacf": { "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_ff75f42878aa49749c5dfd54629a6aca", "placeholder": "​", "style": "IPY_MODEL_2cdbf055519441ceb36eae315a3a1c00", "value": "Loading weights: 100%" } }, "29a1b33db7864a27b5b91325b8c37daa": { "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_9ab72b6ace0049b0b790fe51ab8d05e3", "max": 103, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_448da33a337e4348bcee458d6ca3187d", "value": 103 } }, "3be6215158774061bf3dbaf3b04656cf": { "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_3261634785fb4f179c161cf8a434f8de", "placeholder": "​", "style": "IPY_MODEL_848d6593ffb342ce9b780bd4dc7c3c1d", "value": " 103/103 [00:00<00:00, 4558.50it/s]" } }, "fa1fe69a39b84111b5e07c24dc683f0e": { "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 } }, "ff75f42878aa49749c5dfd54629a6aca": { "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 } }, "2cdbf055519441ceb36eae315a3a1c00": { "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": "" } }, "9ab72b6ace0049b0b790fe51ab8d05e3": { "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 } }, "448da33a337e4348bcee458d6ca3187d": { "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": "" } }, "3261634785fb4f179c161cf8a434f8de": { "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 } }, "848d6593ffb342ce9b780bd4dc7c3c1d": { "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": "" } }, "4d2b2ed7db2643de97c1ea6c68805a33": { "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_471c265fd24549abbcdec5cfe2e69742", "IPY_MODEL_abf7c91b9b2244af950c87006a18b4ce", "IPY_MODEL_01f0559f24df4f6ab07d0d28c67c015f" ], "layout": "IPY_MODEL_5e6f43f24e8c425baa5bb242a06e9072" } }, "471c265fd24549abbcdec5cfe2e69742": { "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_975a73b46b4d4837b05dcb974d9da05c", "placeholder": "​", "style": "IPY_MODEL_5dd5de5562de411ba666d9f2f2d0f5cf", "value": "Loading weights: 100%" } }, "abf7c91b9b2244af950c87006a18b4ce": { "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_778e229cdebf45288ba1612b8c96e8df", "max": 103, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_4fd9da345f4e43688ee150784a21fdce", "value": 103 } }, "01f0559f24df4f6ab07d0d28c67c015f": { "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_53e5e44bfcee4a1fadd5608d906fc05f", "placeholder": "​", "style": "IPY_MODEL_911cdc0f296a4cf38f2219b9c451799b", "value": " 103/103 [00:00<00:00, 3185.49it/s]" } }, "5e6f43f24e8c425baa5bb242a06e9072": { "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 } }, "975a73b46b4d4837b05dcb974d9da05c": { "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 } }, "5dd5de5562de411ba666d9f2f2d0f5cf": { "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": "" } }, "778e229cdebf45288ba1612b8c96e8df": { "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 } }, "4fd9da345f4e43688ee150784a21fdce": { "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": "" } }, "53e5e44bfcee4a1fadd5608d906fc05f": { "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 } }, "911cdc0f296a4cf38f2219b9c451799b": { "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": "" } }, "e535fa1057044bdca9a902e03c36f1aa": { "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_370b091db53744b19d6ed87e7031d8a4", "IPY_MODEL_0bca46a07e124ca588294052dfd09e9a", "IPY_MODEL_e4acc782baef42af8b707030d4a7f9d6" ], "layout": "IPY_MODEL_280aff8760f24e058a5f91720ff56b4d" } }, "370b091db53744b19d6ed87e7031d8a4": { "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_47510542985a4c029fd476bb22c2e4c3", "placeholder": "​", "style": "IPY_MODEL_716672a27e774c34baefc98750b3dd85", "value": "Loading weights: 100%" } }, "0bca46a07e124ca588294052dfd09e9a": { "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_abad2c605c37441e9a62a1b53249fc3c", "max": 199, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_df1c5460a60f4ac2b70a851d64322e3f", "value": 199 } }, "e4acc782baef42af8b707030d4a7f9d6": { "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_edd8ece504454be6a194b65c008d3283", "placeholder": "​", "style": "IPY_MODEL_0b3831a4f78b4c90b4f4cffb45eb0a4e", "value": " 199/199 [00:00<00:00, 3934.10it/s]" } }, "280aff8760f24e058a5f91720ff56b4d": { "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 } }, "47510542985a4c029fd476bb22c2e4c3": { "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 } }, "716672a27e774c34baefc98750b3dd85": { "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": "" } }, "abad2c605c37441e9a62a1b53249fc3c": { "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 } }, "df1c5460a60f4ac2b70a851d64322e3f": { "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": "" } }, "edd8ece504454be6a194b65c008d3283": { "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 } }, "0b3831a4f78b4c90b4f4cffb45eb0a4e": { "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": "" } }, "824b572d37284b96bebe7ba679af9f68": { "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_845222cc81724a888967af20af091cb6", "IPY_MODEL_22448ee8bcf14a2d9fc04bf93b19edd3", "IPY_MODEL_8040e587fd444b23a0ffc74023c39cfe" ], "layout": "IPY_MODEL_1cb05a936e3441ab8f71edfc180a7ab6" } }, "845222cc81724a888967af20af091cb6": { "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_ca5e66967e564c63bc88d07b6d8b1d63", "placeholder": "​", "style": "IPY_MODEL_a722985e957243d9b1a76ded8823530c", "value": "Loading weights: 100%" } }, "22448ee8bcf14a2d9fc04bf93b19edd3": { "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_2e908da1909440e9ba74c0b01c79f30e", "max": 199, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_ca9e791240654d75b2e72e7440c9cef4", "value": 199 } }, "8040e587fd444b23a0ffc74023c39cfe": { "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_2275b145a55c4a859d0cf142d5328da9", "placeholder": "​", "style": "IPY_MODEL_546171091aea43b0873e0dc890396dff", "value": " 199/199 [00:00<00:00, 3127.84it/s]" } }, "1cb05a936e3441ab8f71edfc180a7ab6": { "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 } }, "ca5e66967e564c63bc88d07b6d8b1d63": { "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 } }, "a722985e957243d9b1a76ded8823530c": { "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": "" } }, "2e908da1909440e9ba74c0b01c79f30e": { "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 } }, "ca9e791240654d75b2e72e7440c9cef4": { "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": "" } }, "2275b145a55c4a859d0cf142d5328da9": { "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 } }, "546171091aea43b0873e0dc890396dff": { "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": "" } }, "7c591f6cc64f4a78b74a7ec50af9ba5e": { "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_fc2b9bc0deff4252acf24cadbfc9c135", "IPY_MODEL_0838323997734193b8f7b82ecacdb5b0", "IPY_MODEL_71bf6d9f70f746208b3b27a1aaf8b99d" ], "layout": "IPY_MODEL_a97b71c26e854c8c824f0a04b59901f7" } }, "fc2b9bc0deff4252acf24cadbfc9c135": { "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_3118e559e1d64a2ba4799a013519025f", "placeholder": "​", "style": "IPY_MODEL_f850c24272a4498e91b3a72a334b6c18", "value": "Loading weights: 100%" } }, "0838323997734193b8f7b82ecacdb5b0": { "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_e6ee5220b44044b9bff2660991d83a4d", "max": 199, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_58e076ba1ef74c12a44b1e3641fd936a", "value": 199 } }, "71bf6d9f70f746208b3b27a1aaf8b99d": { "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_eabd69bca63f456c9992229760a6a194", "placeholder": "​", "style": "IPY_MODEL_0e30e967956d49fea74645c45b626a58", "value": " 199/199 [00:00<00:00, 3574.71it/s]" } }, "a97b71c26e854c8c824f0a04b59901f7": { "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 } }, "3118e559e1d64a2ba4799a013519025f": { "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 } }, "f850c24272a4498e91b3a72a334b6c18": { "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": "" } }, "e6ee5220b44044b9bff2660991d83a4d": { "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 } }, "58e076ba1ef74c12a44b1e3641fd936a": { "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": "" } }, "eabd69bca63f456c9992229760a6a194": { "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 } }, "0e30e967956d49fea74645c45b626a58": { "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": "" } }, "42adffbac6434c1f8c596ca16e654ffa": { "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_3d8275b2a8ce47a09dd9540310cd8448", "IPY_MODEL_09b96b16d0ed46a18fb3ef435a75514c", "IPY_MODEL_6b3fa17ac09e41faa66b680d3983e5fb" ], "layout": "IPY_MODEL_5da6d7950232437e83e256d15a72821a" } }, "3d8275b2a8ce47a09dd9540310cd8448": { "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_4a1e3995b178456a8ebc2e086fa917a4", "placeholder": "​", "style": "IPY_MODEL_2a147001cda942a4bde6bb2bc976f0b7", "value": "Loading weights: 100%" } }, "09b96b16d0ed46a18fb3ef435a75514c": { "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_262fce0b6b284d29af415e22909f55b0", "max": 199, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_67ea54085424479b8b2601db749c164d", "value": 199 } }, "6b3fa17ac09e41faa66b680d3983e5fb": { "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_91a53be9185543fbb315684cc5200c72", "placeholder": "​", "style": "IPY_MODEL_ee8beb4b54644d8ca3f114c974104f36", "value": " 199/199 [00:00<00:00, 3834.53it/s]" } }, "5da6d7950232437e83e256d15a72821a": { "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 } }, "4a1e3995b178456a8ebc2e086fa917a4": { "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 } }, "2a147001cda942a4bde6bb2bc976f0b7": { "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": "" } }, "262fce0b6b284d29af415e22909f55b0": { "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 } }, "67ea54085424479b8b2601db749c164d": { "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": "" } }, "91a53be9185543fbb315684cc5200c72": { "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 } }, "ee8beb4b54644d8ca3f114c974104f36": { "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": "" } }, "a6635fb873bb42da9e782af28814ef13": { "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_ec33e406e1ee4c0486bcf03a701c2819", "IPY_MODEL_fe7e42aa3e784b0e9eb56a1080194c4a", "IPY_MODEL_7325f0bad69a4df38faf20722bbd3d67" ], "layout": "IPY_MODEL_ced18e6e03204f4186657eb0be4216a5" } }, "ec33e406e1ee4c0486bcf03a701c2819": { "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_9c2e20be3a5e42ecb5ed018a34dba314", "placeholder": "​", "style": "IPY_MODEL_e9f57987bc4b486f8c0a5f0fe49f0ce5", "value": "Loading weights: 100%" } }, "fe7e42aa3e784b0e9eb56a1080194c4a": { "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_1ba4835a0fb843f88c7fd5ed6c9731ba", "max": 199, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_0388c6cebe4f4f5193db1eaab0752565", "value": 199 } }, "7325f0bad69a4df38faf20722bbd3d67": { "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_18753db1c8d6423aa54990312cd597bd", "placeholder": "​", "style": "IPY_MODEL_c2b0a5873d644fbbbe4b18947bc9b7cc", "value": " 199/199 [00:00<00:00, 4741.40it/s]" } }, "ced18e6e03204f4186657eb0be4216a5": { "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 } }, "9c2e20be3a5e42ecb5ed018a34dba314": { "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 } }, "e9f57987bc4b486f8c0a5f0fe49f0ce5": { "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": "" } }, "1ba4835a0fb843f88c7fd5ed6c9731ba": { "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 } }, "0388c6cebe4f4f5193db1eaab0752565": { "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": "" } }, "18753db1c8d6423aa54990312cd597bd": { "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 } }, "c2b0a5873d644fbbbe4b18947bc9b7cc": { "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": "" } }, "65f54fc10c41402b8fa9bb5d36f7e5e5": { "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_1112196e941c4d4ab1dd1260cae19c09", "IPY_MODEL_accc25dc5efb43a99ef4e5ef4acbd9c7", "IPY_MODEL_a077870634f84a88b957e9c5baa8f067" ], "layout": "IPY_MODEL_f93320e7c250436f9cebdd2d58345048" } }, "1112196e941c4d4ab1dd1260cae19c09": { "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_81fbfc3e510c46f0b5cd2a02bbdcaf09", "placeholder": "​", "style": "IPY_MODEL_707439897b3d4d12b89b765a0d3f0530", "value": "Loading weights: 100%" } }, "accc25dc5efb43a99ef4e5ef4acbd9c7": { "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_6f8efa8cbb30447b8dec5015553ba7b2", "max": 199, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_a4ec015060c948fe868df77b45dea664", "value": 199 } }, "a077870634f84a88b957e9c5baa8f067": { "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_fc70f34b68964e6ca8084171133914d3", "placeholder": "​", "style": "IPY_MODEL_2969238ef13c47f78e92752d2965c460", "value": " 199/199 [00:00<00:00, 2454.87it/s]" } }, "f93320e7c250436f9cebdd2d58345048": { "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 } }, "81fbfc3e510c46f0b5cd2a02bbdcaf09": { "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 } }, "707439897b3d4d12b89b765a0d3f0530": { "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": "" } }, "6f8efa8cbb30447b8dec5015553ba7b2": { "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 } }, "a4ec015060c948fe868df77b45dea664": { "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": "" } }, "fc70f34b68964e6ca8084171133914d3": { "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 } }, "2969238ef13c47f78e92752d2965c460": { "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": "" } }, "568d02dd3230470782ccd07601901030": { "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_f2645974758e48c8a9fcc8b7af019f6a", "IPY_MODEL_f1c38312f074469c8ffd979c170a9b77", "IPY_MODEL_62cbcb6b5d81441d80054adaf1bbdd94" ], "layout": "IPY_MODEL_7dd4abccc0664ed48e025749f2b036d8" } }, "f2645974758e48c8a9fcc8b7af019f6a": { "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_2c718501d9644f918ec5a339b650cc75", "placeholder": "​", "style": "IPY_MODEL_bbafbe1bee764fbfaca60f9708c06b2a", "value": "Loading weights: 100%" } }, "f1c38312f074469c8ffd979c170a9b77": { "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_e39c47a6564d4a73b126bc87ad3b46a9", "max": 199, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_749c923302da46fd840165f14b58ce2b", "value": 199 } }, "62cbcb6b5d81441d80054adaf1bbdd94": { "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_d494f3ed335443e49d178de99093bac5", "placeholder": "​", "style": "IPY_MODEL_1a72f7a912ff4f4c9b64c7d63ed7ea34", "value": " 199/199 [00:00<00:00, 4210.85it/s]" } }, "7dd4abccc0664ed48e025749f2b036d8": { "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 } }, "2c718501d9644f918ec5a339b650cc75": { "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 } }, "bbafbe1bee764fbfaca60f9708c06b2a": { "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": "" } }, "e39c47a6564d4a73b126bc87ad3b46a9": { "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 } }, "749c923302da46fd840165f14b58ce2b": { "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": "" } }, "d494f3ed335443e49d178de99093bac5": { "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 } }, "1a72f7a912ff4f4c9b64c7d63ed7ea34": { "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": "" } }, "38bdd92592934db5b25ba0580cec5fda": { "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_9ab319d71fbc40baa086dc6a60add71a", "IPY_MODEL_ea50a117323546af9b73e4009ee1ce2c", "IPY_MODEL_9b2361422eee430dbeaebee00f85a92c" ], "layout": "IPY_MODEL_57c66f9e291d4dc2a42b6e0e9772d0cb" } }, "9ab319d71fbc40baa086dc6a60add71a": { "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_3937ece8b29144a385cab475ee5a5995", "placeholder": "​", "style": "IPY_MODEL_7d4b79d7ee71470eb7d8bcee704762ed", "value": "Loading weights: 100%" } }, "ea50a117323546af9b73e4009ee1ce2c": { "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_ebcdd6b311764b01933f494fbbce4f9a", "max": 199, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_17936403b8d84d22aede55c1ebee0428", "value": 199 } }, "9b2361422eee430dbeaebee00f85a92c": { "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_d377708450c94aedbe52a540a0adf4e1", "placeholder": "​", "style": "IPY_MODEL_2c5a458d317b408b8e6acf002b1247cf", "value": " 199/199 [00:00<00:00, 3966.50it/s]" } }, "57c66f9e291d4dc2a42b6e0e9772d0cb": { "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 } }, "3937ece8b29144a385cab475ee5a5995": { "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 } }, "7d4b79d7ee71470eb7d8bcee704762ed": { "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": "" } }, "ebcdd6b311764b01933f494fbbce4f9a": { "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 } }, "17936403b8d84d22aede55c1ebee0428": { "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": "" } }, "d377708450c94aedbe52a540a0adf4e1": { "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 } }, "2c5a458d317b408b8e6acf002b1247cf": { "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": "" } }, "2bbd4dff6a47437e8d91455b2b61237f": { "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_ca2fcf8403884589b0a76d2d254f5fc2", "IPY_MODEL_49212e5215914e328dc033f58580b854", "IPY_MODEL_4af979031150487782f7c922d5876458" ], "layout": "IPY_MODEL_297dc3cfd1cc44c88df5b86505209729" } }, "ca2fcf8403884589b0a76d2d254f5fc2": { "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_cdb85b6e499a4973afa5aeb93a2506d4", "placeholder": "​", "style": "IPY_MODEL_ca8cebbe6b1840fab983f35852a106a4", "value": "Loading weights: 100%" } }, "49212e5215914e328dc033f58580b854": { "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_9b1e53da0f7448348c8024891ddccf37", "max": 199, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_75cb898336d44c75ae6314e5be4db9f9", "value": 199 } }, "4af979031150487782f7c922d5876458": { "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_55c9c2302dac41d7ad03b7840226f26e", "placeholder": "​", "style": "IPY_MODEL_7c21b560ab5d47468c6c5e90ff1e0d76", "value": " 199/199 [00:00<00:00, 4235.81it/s]" } }, "297dc3cfd1cc44c88df5b86505209729": { "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 } }, "cdb85b6e499a4973afa5aeb93a2506d4": { "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 } }, "ca8cebbe6b1840fab983f35852a106a4": { "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": "" } }, "9b1e53da0f7448348c8024891ddccf37": { "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 } }, "75cb898336d44c75ae6314e5be4db9f9": { "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": "" } }, "55c9c2302dac41d7ad03b7840226f26e": { "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 } }, "7c21b560ab5d47468c6c5e90ff1e0d76": { "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", "source": [ "# Import Library" ], "metadata": { "id": "rFhWCFb9gx-x" } }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "xwtuvp0U03e5" }, "outputs": [], "source": [ "!pip install transformers torch scikit-learn wordcloud matplotlib seaborn pandas tqdm -q" ] }, { "cell_type": "code", "source": [ "import os\n", "os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"expandable_segments:True\"" ], "metadata": { "id": "_gD296Dd0Mob" }, "execution_count": 4, "outputs": [] }, { "cell_type": "code", "source": [ "# warnings config\n", "import warnings\n", "warnings.filterwarnings('ignore')" ], "metadata": { "id": "Zzu-k95Ic_ey" }, "execution_count": 5, "outputs": [] }, { "cell_type": "code", "source": [ "import nltk\n", "nltk.download('wordnet')\n", "nltk.download('omw-1.4')\n", "nltk.download('stopwords') # ADDED\n", "nltk.download('punkt') # ADDED\n", "nltk.download('punkt_tab') # ADDED" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "OLXDXRDOFjKu", "outputId": "23568cb8-83a8-4e1f-a801-f8f9b05004bd" }, "execution_count": 6, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "[nltk_data] Downloading package wordnet to /root/nltk_data...\n", "[nltk_data] Downloading package omw-1.4 to /root/nltk_data...\n", "[nltk_data] Downloading package stopwords to /root/nltk_data...\n", "[nltk_data] Unzipping corpora/stopwords.zip.\n", "[nltk_data] Downloading package punkt to /root/nltk_data...\n", "[nltk_data] Unzipping tokenizers/punkt.zip.\n", "[nltk_data] Downloading package punkt_tab to /root/nltk_data...\n", "[nltk_data] Unzipping tokenizers/punkt_tab.zip.\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "True" ] }, "metadata": {}, "execution_count": 6 } ] }, { "cell_type": "code", "source": [ "import os\n", "import re\n", "import time\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import matplotlib.gridspec as gridspec\n", "import seaborn as sns\n", "from collections import Counter\n", "from tqdm import tqdm\n", "\n", "# NLP\n", "from wordcloud import WordCloud\n", "from nltk.stem import WordNetLemmatizer\n", "from nltk.corpus import stopwords\n", "\n", "# Sklearn\n", "from sklearn.model_selection import train_test_split, KFold, cross_val_score\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n", "from sklearn.linear_model import Ridge\n", "from sklearn.metrics import (\n", " mean_absolute_error, mean_squared_error, r2_score\n", ")\n", "from sklearn.pipeline import Pipeline\n", "\n", "# Deep Learning\n", "import torch\n", "import torch.nn as nn\n", "import torch.optim as optim\n", "from torch.utils.data import Dataset, DataLoader\n", "from transformers import BertTokenizer, BertModel, get_linear_schedule_with_warmup\n", "\n", "# Reproducibility — LENGKAP untuk GPU\n", "SEED = 42\n", "np.random.seed(SEED)\n", "torch.manual_seed(SEED)\n", "torch.cuda.manual_seed(SEED)\n", "torch.cuda.manual_seed_all(SEED)\n", "torch.backends.cudnn.deterministic = True\n", "torch.backends.cudnn.benchmark = False\n", "\n", "DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", "print(f\"Using device: {DEVICE}\")\n", "print(f\"PyTorch version: {torch.__version__}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "x1r0jTak17ow", "outputId": "29693461-a5e2-45ae-992d-5310f072f0cb" }, "execution_count": 7, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Using device: cuda\n", "PyTorch version: 2.11.0+cu128\n" ] } ] }, { "cell_type": "code", "source": [ "begin = time.time()" ], "metadata": { "id": "LyeIWAORdMOc" }, "execution_count": 8, "outputs": [] }, { "cell_type": "markdown", "source": [ "# 1. Data Loading" ], "metadata": { "id": "qIV9oC132Izk" } }, { "cell_type": "markdown", "source": [ "## 1.1 Take Data" ], "metadata": { "id": "iHZH-Cfu2SJf" } }, { "cell_type": "code", "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Es7jvORJ1_3h", "outputId": "30d67fdd-e43e-45b2-bf15-0891f9f47d0c" }, "execution_count": 9, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/drive\n" ] } ] }, { "cell_type": "code", "source": [ "import os\n", "\n", "zip_path = \"/content/drive/MyDrive/dataset/daic-woz.zip\"\n", "extract_path = \"./dataset\"\n", "\n", "if os.path.exists(zip_path):\n", " print(f\"📦 Unzipping {zip_path}...\")\n", " !unzip -q -o {zip_path} -d {extract_path}\n", " print(\"✅ Dataset berhasil diekstrak!\")\n", "else:\n", " print(f\"❌ File tidak ditemukan: '{zip_path}'\")\n", " print(\" Pastikan file sudah diunggah ke Google Drive.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "bktrhzei2B3m", "outputId": "779f86e6-a8a0-4ab3-8bdd-66b1b6849f26" }, "execution_count": 10, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "📦 Unzipping /content/drive/MyDrive/dataset/daic-woz.zip...\n", "✅ Dataset berhasil diekstrak!\n" ] } ] }, { "cell_type": "code", "source": [ "DATA_DIR = \"/content/dataset\"\n", "LABELS_PATH = os.path.join(DATA_DIR, \"labels.csv\")\n", "\n", "IGNORE_IDS = set() # Kosongkan dulu, biarkan fungsi load yang handle missing files\n", "\n", "print(f\"Dataset path: {DATA_DIR}\")\n", "print(f\"Labels path: {LABELS_PATH}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "WQsoCJlV2E2-", "outputId": "485639b7-4f79-443d-fbcc-e2cba63245ea" }, "execution_count": 11, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Dataset path: /content/dataset\n", "Labels path: /content/dataset/labels.csv\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 1.2 Load Label (PHQ-8)" ], "metadata": { "id": "J86cCRmP2Wf2" } }, { "cell_type": "code", "source": [ "label_df = pd.read_csv(LABELS_PATH)\n", "label_df.head(5)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 226 }, "id": "keTtFr1P2xYl", "outputId": "f635b4be-e3c6-4223-85bf-fcf1a7c0e579" }, "execution_count": 12, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " Participant PHQ8_1_NoInterest PHQ8_2_Depressed PHQ8_3_Sleep \\\n", "0 300 NaN NaN NaN \n", "1 301 NaN NaN NaN \n", "2 302 1.0 1.0 0.0 \n", "3 303 0.0 0.0 0.0 \n", "4 304 0.0 1.0 1.0 \n", "\n", " PHQ8_4_Tired PHQ8_5_Appetite PHQ8_6_Failure PHQ8_7_Concentration \\\n", "0 NaN NaN NaN NaN \n", "1 NaN NaN NaN NaN \n", "2 1.0 0.0 1.0 0.0 \n", "3 0.0 0.0 0.0 0.0 \n", "4 2.0 2.0 0.0 0.0 \n", "\n", " PHQ8_8_Psychomotor Depression_severity gender Depression_label split \n", "0 NaN 2 male 0 test \n", "1 NaN 3 male 0 test \n", "2 0.0 4 male 0 dev \n", "3 0.0 0 female 0 train \n", "4 0.0 6 female 0 train " ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
ParticipantPHQ8_1_NoInterestPHQ8_2_DepressedPHQ8_3_SleepPHQ8_4_TiredPHQ8_5_AppetitePHQ8_6_FailurePHQ8_7_ConcentrationPHQ8_8_PsychomotorDepression_severitygenderDepression_labelsplit
0300NaNNaNNaNNaNNaNNaNNaNNaN2male0test
1301NaNNaNNaNNaNNaNNaNNaNNaN3male0test
23021.01.00.01.00.01.00.00.04male0dev
33030.00.00.00.00.00.00.00.00female0train
43040.01.01.02.02.00.00.00.06female0train
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", "
\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "label_df", "summary": "{\n \"name\": \"label_df\",\n \"rows\": 189,\n \"fields\": [\n {\n \"column\": \"Participant\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 56,\n \"min\": 300,\n \"max\": 492,\n \"num_unique_values\": 189,\n \"samples\": [\n 488,\n 467,\n 318\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PHQ8_1_NoInterest\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.8263769184261883,\n \"min\": 0.0,\n \"max\": 3.0,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.0,\n 3.0,\n 1.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PHQ8_2_Depressed\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.888359105266613,\n \"min\": 0.0,\n \"max\": 3.0,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.0,\n 2.0,\n 1.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PHQ8_3_Sleep\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.0695520857674088,\n \"min\": 0.0,\n \"max\": 3.0,\n \"num_unique_values\": 4,\n \"samples\": [\n 1.0,\n 3.0,\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PHQ8_4_Tired\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.9580398611680276,\n \"min\": 0.0,\n \"max\": 3.0,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.0,\n 3.0,\n 1.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PHQ8_5_Appetite\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.0374083873873179,\n \"min\": 0.0,\n \"max\": 3.0,\n \"num_unique_values\": 4,\n \"samples\": [\n 2.0,\n 3.0,\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PHQ8_6_Failure\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.040389037675481,\n \"min\": 0.0,\n \"max\": 3.0,\n \"num_unique_values\": 4,\n \"samples\": [\n 0.0,\n 3.0,\n 1.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PHQ8_7_Concentration\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.9669392593723531,\n \"min\": 0.0,\n \"max\": 3.0,\n \"num_unique_values\": 4,\n \"samples\": [\n 1.0,\n 3.0,\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PHQ8_8_Psychomotor\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.6933045951183561,\n \"min\": 0.0,\n \"max\": 3.0,\n \"num_unique_values\": 4,\n \"samples\": [\n 1.0,\n 3.0,\n 0.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Depression_severity\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5,\n \"min\": 0,\n \"max\": 23,\n \"num_unique_values\": 24,\n \"samples\": [\n 21,\n 12,\n 2\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"gender\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"female\",\n \"male\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Depression_label\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1,\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"split\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"test\",\n \"dev\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 12 } ] }, { "cell_type": "code", "source": [ "print(\"Shape:\", label_df.shape)\n", "print(\"Columns:\", label_df.columns.tolist())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "szTbFcdO2pN0", "outputId": "943abd2d-51ac-43e5-ee6d-035ae8481770" }, "execution_count": 13, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Shape: (189, 13)\n", "Columns: ['Participant', 'PHQ8_1_NoInterest', 'PHQ8_2_Depressed', 'PHQ8_3_Sleep', 'PHQ8_4_Tired', 'PHQ8_5_Appetite', 'PHQ8_6_Failure', 'PHQ8_7_Concentration', 'PHQ8_8_Psychomotor', 'Depression_severity', 'gender', 'Depression_label', 'split']\n" ] } ] }, { "cell_type": "code", "source": [ "# cek column PHQ8\n", "phq8_items = [col for col in label_df.columns if col.startswith('PHQ8_')]\n", "print(\"PHQ-8 item columns:\", phq8_items)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "0N9Th_As3VQA", "outputId": "d29e27bd-0a28-4895-e277-12e378569d35" }, "execution_count": 14, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "PHQ-8 item columns: ['PHQ8_1_NoInterest', 'PHQ8_2_Depressed', 'PHQ8_3_Sleep', 'PHQ8_4_Tired', 'PHQ8_5_Appetite', 'PHQ8_6_Failure', 'PHQ8_7_Concentration', 'PHQ8_8_Psychomotor']\n" ] } ] }, { "cell_type": "code", "source": [ "# Rename kolom agar konsisten di seluruh notebook\n", "label_df = label_df.rename(columns={\n", " 'Participant': 'Participant_ID',\n", " 'Depression_severity': 'PHQ8_Score',\n", " 'Depression_label': 'label'\n", "})" ], "metadata": { "id": "43oekgEW37dI" }, "execution_count": 15, "outputs": [] }, { "cell_type": "code", "source": [ "#define target\n", "label_df['phq8_target'] = label_df['PHQ8_Score']" ], "metadata": { "id": "4G_l8pde4A3g" }, "execution_count": 16, "outputs": [] }, { "cell_type": "markdown", "source": [ "## 1.3 Load & Merge All Transcript" ], "metadata": { "id": "T8OlGbEk7-Z7" } }, { "cell_type": "code", "source": [ "# NONVERBAL_TAGS = ['laugh', 'sigh', 'cough', 'breath', 'sniff', 'groan', 'pause', 'um', 'uh']\n", "\n", "# def extract_nonverbal_features(raw_utterances):\n", "# features = {f'nv_{tag}': 0 for tag in NONVERBAL_TAGS}\n", "# features['nv_total'] = 0\n", "# for utt in raw_utterances:\n", "# utt_str = str(utt).lower()\n", "# for tag in re.findall(r'<([^>]+)>', utt_str):\n", "# tag_clean = tag.strip().split()[0]\n", "# for known_tag in NONVERBAL_TAGS:\n", "# if known_tag in tag_clean:\n", "# features[f'nv_{known_tag}'] += 1\n", "# features['nv_total'] += 1\n", "# break\n", "# all_text = ' '.join(str(u) for u in raw_utterances)\n", "# features['filler_count'] = len(re.findall(r'\\b(um|uh|uhm|hmm|hm)\\b', all_text.lower()))\n", "# return features\n", "\n", "# def load_participant_transcripts(data_dir, ignore_ids=set()):\n", "# records = []\n", "# participant_folders = sorted([\n", "# f for f in os.listdir(data_dir)\n", "# if os.path.isdir(os.path.join(data_dir, f)) and f.isdigit()\n", "# ], key=lambda x: int(x))\n", "# print(f\"Total folder ditemukan: {len(participant_folders)}\")\n", "\n", "# for pid_str in tqdm(participant_folders, desc=\"Loading transcripts\"):\n", "# pid = int(pid_str)\n", "# if pid in ignore_ids:\n", "# continue\n", "# transcript_path = os.path.join(data_dir, pid_str, f\"{pid_str}_TRANSCRIPT.csv\")\n", "# if not os.path.exists(transcript_path):\n", "# continue\n", "# try:\n", "# df = pd.read_csv(transcript_path, sep='\\t')\n", "# except Exception:\n", "# try:\n", "# df = pd.read_csv(transcript_path, sep=',')\n", "# except Exception as e:\n", "# print(f\"⚠️ Skip {pid_str}: {e}\")\n", "# continue\n", "\n", "# part_df = df[df['speaker'] == 'Participant'].copy()\n", "# raw_utts = part_df['value'].dropna().tolist()\n", "# nv_feats = extract_nonverbal_features(raw_utts)\n", "\n", "# noise_patterns = ['', '', 'scrubbed_entry', '',\n", "# '', '[', ']', '(', ')']\n", "# def is_valid_utterance(text):\n", "# return not any(p in str(text).lower() for p in noise_patterns)\n", "\n", "# part_df = part_df[part_df['value'].apply(is_valid_utterance)]\n", "# if len(part_df) == 0:\n", "# continue\n", "\n", "# full_text = ' '.join(part_df['value'].dropna().astype(str).tolist())\n", "# record = {\n", "# 'Participant_ID': pid,\n", "# 'text': full_text,\n", "# 'utterance_count': len(part_df),\n", "# 'word_count': len(full_text.split())\n", "# }\n", "# record.update(nv_feats)\n", "# records.append(record)\n", "\n", "# return pd.DataFrame(records)" ], "metadata": { "id": "Uhg9dHSE8Cv1" }, "execution_count": 17, "outputs": [] }, { "cell_type": "code", "source": [ "NONVERBAL_TAGS = ['laugh', 'sigh', 'cough', 'breath', 'sniff', 'groan', 'pause', 'um', 'uh']\n", "\n", "def extract_nonverbal_features(raw_utterances):\n", " features = {f'nv_{tag}': 0 for tag in NONVERBAL_TAGS}\n", " features['nv_total'] = 0\n", " for utt in raw_utterances:\n", " utt_str = str(utt).lower()\n", " for tag in re.findall(r'<([^>]+)>', utt_str):\n", " tag_clean = tag.strip().split()[0]\n", " for known_tag in NONVERBAL_TAGS:\n", " if known_tag in tag_clean:\n", " features[f'nv_{known_tag}'] += 1\n", " features['nv_total'] += 1\n", " break\n", " all_text = ' '.join(str(u) for u in raw_utterances)\n", " features['filler_count'] = len(re.findall(r'\\b(um|uh|uhm|hmm|hm)\\b', all_text.lower()))\n", " return features\n", "\n", "def load_participant_transcripts(data_dir, ignore_ids=set()):\n", " records = []\n", " participant_folders = sorted([\n", " f for f in os.listdir(data_dir)\n", " if os.path.isdir(os.path.join(data_dir, f)) and f.isdigit()\n", " ], key=lambda x: int(x))\n", " print(f\"Total folder ditemukan: {len(participant_folders)}\")\n", "\n", " for pid_str in tqdm(participant_folders, desc=\"Loading transcripts\"):\n", " pid = int(pid_str)\n", " if pid in ignore_ids:\n", " continue\n", " transcript_path = os.path.join(data_dir, pid_str, f\"{pid_str}_TRANSCRIPT.csv\")\n", " if not os.path.exists(transcript_path):\n", " continue\n", " try:\n", " df = pd.read_csv(transcript_path, sep='\\t')\n", " except Exception:\n", " try:\n", " df = pd.read_csv(transcript_path, sep=',')\n", " except Exception as e:\n", " print(f\"⚠️ Skip {pid_str}: {e}\")\n", " continue\n", "\n", " part_df = df[df['speaker'] == 'Participant'].copy()\n", " raw_utts = part_df['value'].dropna().tolist()\n", " nv_feats = extract_nonverbal_features(raw_utts)\n", "\n", " # DIGANTI: buang hanya utterance yang seluruhnya tag, bukan yang mengandung tag\n", " verbal_utts = [\n", " u for u in raw_utts\n", " if not re.match(r'^\\s*<.*>\\s*$', str(u), re.IGNORECASE)\n", " and 'scrubbed_entry' not in str(u).lower()\n", " ]\n", " # DIGANTI: strip inline tags dari utterance yang tersisa\n", " verbal_utts = [re.sub(r'<[^>]+>', '', str(u)).strip() for u in verbal_utts]\n", " verbal_utts = [u for u in verbal_utts if len(u) > 0]\n", "\n", " if len(verbal_utts) == 0:\n", " continue\n", "\n", " full_text = ' '.join(verbal_utts)\n", " record = {\n", " 'Participant_ID': pid,\n", " 'text': full_text,\n", " 'utterance_count': len(verbal_utts),\n", " 'word_count': len(full_text.split())\n", " }\n", " record.update(nv_feats)\n", " records.append(record)\n", "\n", " return pd.DataFrame(records)" ], "metadata": { "id": "3EQMJqctPVJm" }, "execution_count": 18, "outputs": [] }, { "cell_type": "code", "source": [ "transcripts_df = load_participant_transcripts(DATA_DIR, ignore_ids=IGNORE_IDS)\n", "print(transcripts_df.columns.tolist()) # harus sudah ada nv_*" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "C9_RH0dKyEaC", "outputId": "ea180a9c-7dc1-4afc-9153-9208d01fac00" }, "execution_count": 19, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Total folder ditemukan: 189\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "Loading transcripts: 100%|██████████| 189/189 [00:00<00:00, 351.22it/s]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "['Participant_ID', 'text', 'utterance_count', 'word_count', 'nv_laugh', 'nv_sigh', 'nv_cough', 'nv_breath', 'nv_sniff', 'nv_groan', 'nv_pause', 'nv_um', 'nv_uh', 'nv_total', 'filler_count']\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n" ] } ] }, { "cell_type": "code", "source": [ "# Load semua transcript\n", "transcripts_df = load_participant_transcripts(DATA_DIR, ignore_ids=IGNORE_IDS)\n", "print(f\"Berhasil load {len(transcripts_df)} participant transcripts\")\n", "transcripts_df.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 801 }, "id": "kFy7-14Z9HKF", "outputId": "1c38d012-2cae-4a65-f069-d9e07532f745" }, "execution_count": 20, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Total folder ditemukan: 189\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "Loading transcripts: 100%|██████████| 189/189 [00:00<00:00, 313.76it/s]" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Berhasil load 189 participant transcripts\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ " Participant_ID text \\\n", "0 300 good atlanta georgia um my parents are from he... \n", "1 301 thank you mmm k i'm doing good thank you i'm f... \n", "2 302 i'm fine how about yourself i'm from los angel... \n", "3 303 okay how 'bout yourself here in california yea... \n", "4 304 i'm doing good um from los angeles california ... \n", "\n", " utterance_count word_count nv_laugh nv_sigh nv_cough nv_breath \\\n", "0 87 352 0 0 0 0 \n", "1 104 1465 3 0 0 0 \n", "2 96 611 1 0 0 0 \n", "3 103 1960 3 0 0 0 \n", "4 104 976 11 0 0 0 \n", "\n", " nv_sniff nv_groan nv_pause nv_um nv_uh nv_total filler_count \n", "0 0 0 0 0 0 0 31 \n", "1 0 0 0 0 0 3 20 \n", "2 0 0 0 0 0 1 25 \n", "3 0 0 0 0 0 3 33 \n", "4 0 0 0 0 0 11 28 " ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Participant_IDtextutterance_countword_countnv_laughnv_sighnv_coughnv_breathnv_sniffnv_groannv_pausenv_umnv_uhnv_totalfiller_count
0300good atlanta georgia um my parents are from he...87352000000000031
1301thank you mmm k i'm doing good thank you i'm f...1041465300000000320
2302i'm fine how about yourself i'm from los angel...96611100000000125
3303okay how 'bout yourself here in california yea...1031960300000000333
4304i'm doing good um from los angeles california ...10497611000000001128
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", "
\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "transcripts_df", "summary": "{\n \"name\": \"transcripts_df\",\n \"rows\": 189,\n \"fields\": [\n {\n \"column\": \"Participant_ID\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 56,\n \"min\": 300,\n \"max\": 492,\n \"num_unique_values\": 189,\n \"samples\": [\n 488,\n 467,\n 318\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 189,\n \"samples\": [\n \"yes fine oh san fernando valley uh well i really like the culture i love going to museums and being able to go at night and having lots of people around it's spread out a lot and it's a pain in the ass to have to drive everywhere and not have a good public transportation system uh not really i like to go camping but i don't really travel um i think it might've been around six months ago i went on a trip with a geology class yeah it was fun hmm i think it's tough to say um well i think the first time i went camping was probably really memorable i went to death valley and i got to see a lot of really cool things and hang out with my friends at night philosophy yes um hmm i don't know i think it just sort of happened i wanted to be a journalist for a really long time and then i kind of grew disillusioned with that and i liked all the different ways or all the different things i could do with philosophy huh i don't know but i really like children and i really like studying their behavior so i think something along those lines might be it i think i'm generally pretty outgoing i tend to be shy around my family but when i'm around friends and stuff i i think i'm pretty friendly um a lot of times i'll watch t_v or read um yeah i think i'm pretty good i don't really get mad i try to think about other things if i start getting mad or um i just start trying to focus on my breathing i think the last time i argued with someone it was probably with my boyfriend um but i'm having trouble remembering what the last thing we argued about was um i think it was i wasn't feeling very well and i was having trouble deciding whether i wanted to go to his department's end of year party and he was getting frustrated which i thought was very inconsiderate 'cause i wasn't feeling well to begin with well i felt like he wasn't really taking my feelings into consideration and i really hate when he gets stressed out or gets angry easily because i just see that as unnecessary and it stresses me out hmm i think a lot of times when my oldest youngest brother or my oldest little brother uh does something wrong i tend to be harsh with him um we're not as close as my sister and i or my youngest brother and i and so he tends to be hostile back and i think that if i were a little bit more gentle it might be easier to communicate with him hmm um when i was nineteen i came home from a party i think around four a_m on a sunday morning and my dad got really mad at me and yelled at me said a lot of not very nice things and then he didn't speak to me for about a year uh well my relationship with my mom is a lot better now that i don't live at home um i used to be really frustrated with her all the time 'cause she has a really quick temper but i think now that i've got a little bit more distance i can see that she did the best that she could um and i love my siblings i again my little sister and i get along really well she's four years younger than me and when when she was around twelve and i was around sixteen it was really difficult 'cause she had a really harsh temper and she was always moody and i wasn't in a place to be patient with that sort of stuff so we fought all the time but we don't anymore and that's really great and we get along okay with my uh my older little brother who's fifteen and i think i get along pretty well with my youngest brother who's twelve one of the first not the first but one of my first philosophy professors and i really loved her classes and it was really good uh being in a class with a professor who really cared and who really engaged people that was really motivating since not all my classes were like that i think it's pretty easy i mean sometimes i'll have trouble going to sleep but generally it's not that bad it's i think it's usually harder for me to wake up in the morning um well i'm really tired i can get grumpy but i think usually i'm just groggy pretty good i'm a little anxious about um about getting a job and i'm gonna be taking summer school about it's only one class so i'll have a lot of free time um yeah well i mean it's not the end of the world if i don't get a job but i know that i will eventually so mostly i think i just try not to think about it too much and i try not to be pessimistic no no hmm well i think the last time i felt really happy i went to a party with my boyfriend the end of department party and um i was the you know i knew a couple people there so i felt comfortable but i was also able to meet new people and there were a couple kids there splashing around in the pool and i had a couple drinks and ate some food and it was fun um she would probably say that i'm smart and uh pretty intellectual i love to read and to watch really like serious films and t_v um and she would probably also say that i'm fun 'cause we like to go out and have fun a lot well i'm kinda i can be really disorganized and that gets me in trouble sometimes so um so that's a major one that and procrastinating hmm um i got into a little bit of trouble a few months back and had to go to court and was uh uh given eh sixty some hours of community service and of course i procrastinated on doing that so i had to show up to court again to get an extension and the judge was very mean and he didn't seem i mean he yelled at me but it didn't seem like he gave it much thought it seemed like it was just sort of a stock speech he gave everyone that he thought needed some yelling at so i understand 'cause that's his job but it was was not great for me 'cause i wasn't i i was really anxious in that situation well i would've been twelve and two um but i think i think i would've told myself at twelve not to be so hard on myself and not to be so hard on my parents 'cause i think i was a little selfish and self-involved back then i can't really think of anything i mean i regret how i treated my mother when i was a kid but everyone does that and um i do regret a couple of semesters ago i didn't do so well in school and i got a a few ws which i now have to work to get off on my transcript i think so i'm i think i i wasn't focusing in school at that time and i didn't see any counselors at school or anything so i definitely could've done that hmm i think i'd probably spend my ideal weekend sleeping in saturday and then going hiking or maybe just going to the duck pond by my house and feeding ducks and picking some oranges and then maybe going out to breakfast sunday and just like window shopping and maybe watching a little bit of t_v huh well my youngest brother is autistic and my mom and dad were always working when he was growing up i'm ten years older than him so and being the oldest i always i always took care of him which made me really really patient because he was a handful but i'm really proud of how he turned out and um and how close we are\",\n \"yeah i'm okay with it uh i feel i feel pretty good little little nervous because i've never done anything like this before that's why yeah from ohio cincinnati yeah uh two months ago two and a half months uh was just looking for just wanted to be in a new environment oh yeah i'm ecstatic very happy uh it took a few weeks just to get adjusted 'cause i didn't know anyone the weather is probably the best thing yeah and uh i mean other than that it's pretty much the same as everywhere else i've been there's nothing i don't really really like about l_a it's just you know just the weather is the best part of it yeah uh i like to travel somewhat uh i haven't traveled a lot in my life but over the past uh year and a half i've done done quite a bit of traveling just seeing different places different people new people new faces new places uh let's see last well last summer i spent the uh summer in cleveland doing uh urban farming uh in the city yeah and uh i stayed at a hostel the first hostel in cleveland and i was uh doing a program with the uh and it was basically we would go to different urban farms throughout the city and volunteer about twenty twenty five hours a week so yeah really a spur of the moment type of thing um wanted to try something different something i'd never done before and farming is one of those things that you know most people would never try 'cause of a you know 'cause of a certain stigma that comes along with it uh and you know i just thought it would be fun and interesting to do something like that yeah what in the uh uh mm urban farming or in general in urban farming uh i don't know i guess uh i don't know just just the overall experience just going you know eating uh going to the farmers markets and eating all the fresh food and just seeing the difference in you know how fresh natural food taste eh you know as opposed to the food you buy in a grocery store yeah yeah it is really is uh i was i took two semesters of uh college and i was undecided so i was just taking general classes my dream job huh i guess my dream job would to be anything that involves uh i don't have like a specific dream job but anything that involves nature just being outside uh i would like to uh i wouldn't mind being like a like a forest ranger or something like that just as long as i you know yeah consider myself more shy uh i don't i don't know why it's just the way it is uh huh i read um listen to music uh i draw watch movies stuff like that yeah i think i'm pretty good at it i think yeah wow uh yeah i can't remember the last time i had an argument with someone i don't know again i don't know because i don't dwell on things that happened in the past yeah mm maybe whether or not to come to california i guess that was a hard decision well i don't know i've been thinking about it you know moving to a new place for a long time and i guess the hardest part of the decision was thinking whether whether or not i would be secure or not you know what i mean um just going to a new place where you don't know anyone uh it's kinda uh you really don't know what's gonna happen so uh there was a lot of uh you know fear in that in that decision of should i go to a place where i don't know anyone i don't i'm not familiar with the area you know only only perception ever had that i have of this area is you know what i've seen on in media media and t_v so you know eh yeah guess that was hardest hardest decision decision that i can think of yeah eh guess so uh nothing absolutely nothing yeah uh nothing i don't feel guilty about anything uh relationship with my family uh i'm not really close to my family um i guess the person i'm closest to is my mom but you know i don't you know i don't i never really talk to her much uh i don't really know my dad that well so you know uh i guess my grandmother yeah she yeah she's uh she's very um xxx how should i say it uh she's very she's a thinker i put it like that like she's very insightful so um yeah i look to her for a lot of advice because she's been through a lot through a lot in her life and i feel like she you know she's been through a lot so she understands a lot so eh you know she's a very positive influence very easy uh maybe unsure i would say because right now i really don't know what direction i wanna go in with my life and there's been a lot of confusion with that um but you know it's not it's not something that overly affects me but you know it's always a thought in the back of my mind it feels feels like somehow maybe i'm running out of time and i know that's not like that's not xxx that's not a realistic thought because i don't see how you could ever run out of time but yeah i guess yeah just general not knowing what i want but i know like how do i cope with that how do i cope with that i uh i don't know i just do a lot of thinking a lot of soul searching it's not hard i mean i mean i'm it's xxx like i can't say it's hard because it's one of those things that i feel like it's necessary and you know certain things in life you just you're forced to face so it's harder to i think it's harder to avoid those type of things yeah no no no uh xxx today when i talked when i uh talked to my friend earlier yeah how would my best friend describe me uh i guess he would say i'm quiet uh uh guess he would describe as a nice guy uh guess insightful mm uh goofy and yeah oh i don't really think like that no ten or twenty years ago uh guess i would tell myself to stick with what what stick with what i love hmm uh i did uh acting workshop in um yeah i don't know it's always always been an interest of mine and i decided why not you know just try it see how you like it what am i most proud of like an event or a an accomplishment i guess i pride myself on being independent mm yeah bye\",\n \"yes i'm alright los angeles california yep um the lights big city it's always something going on the traffic and that's it uh business administration and business management uh not that i'm doing something else i'm doing networking at the moment hmm uh to open up a big clothing line and just supply the whole world with clothing yeah definitely uh 'cause i'm all about myself it's all about me not often but more times than not yeah being lied to and i guess when people think you're dumber than you than what you are uh try to get away from everything go into my own space very well i'm great at controlling my temper i um probably like two days ago and it was probably just over some sports it wasn't a real argument not a lot but i travel enough just being able to see to get a change of pace see somebody new see new faces new environment mm let me see the last trip i had was vegas you know how vegas goes it was pretty fun it was a lotta lotta drinking and a lot of partying going on um pretty open-minded i'm kind to pretty much everybody i'm a pretty even-keel guy it's alright it could be better no no ma'am nope no uh lately it's been pretty tough i guess because i've been staying up so late but uh that's my fault i don't know i guess i'm a night owl all of a sudden i'm turning into an owl sluggish and tired all day if i don't sleep well all day i'm just laying around no not too much mm no oh stay focused and listen to the adults yeah i mean it's self-explanatory i could've just listened to parents or other adults that tried to steer you in the right way and you know you're being a stubborn kid at that uh at those ages so it's like what what are you talking about i this is my life i can do what i want i don't have to listen to you but now i see that what they were telling me was correct um hilarious pretty close uh both my parents mother and father mm recently what did i do um new year's eve party i had i had the time of my life it was so fun uh this new year's eve i went to a party at a friend's house in baldwin hills area he had it at a a pretty nice house it's a three story house and it was like three hundred and fifty people in there it was pretty fun but it was also not that fun because there was so many people and you couldn't really move so that was the only downfall but besides that i brought my new year in great with tons of friends and that's how it's supposed to be done um i'm generally in a good mood every day i mean waking up puts me in a good mood i i guess you could say that but um i don't know a good a good meal ooh one of my most memorable experiences were probably uh when i was younger i was in the boy scouts and we used to go camping all the time and one year we went to yellowstone or yosemite one of those places and we had to work on a mountain biking merit badge and of course being from the city i'm like a mountain biking i can ride a mountain bike but once we got in the real mountains on got on real mountain bikes i gained a new respect for the mountain bikers it was pretty it was fun and pretty intense going downhill doing about forty five miles an hour in dirt on a bicycle was pretty intense and that was i'll i'll never forget that mm i don't know that's a tough one to to answer what am i most proud of i don't know honestly bye\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"utterance_count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 73,\n \"min\": 42,\n \"max\": 385,\n \"num_unique_values\": 128,\n \"samples\": [\n 155,\n 116,\n 161\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"word_count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 801,\n \"min\": 165,\n \"max\": 4551,\n \"num_unique_values\": 183,\n \"samples\": [\n 755,\n 1912,\n 2640\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_laugh\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 7,\n \"min\": 0,\n \"max\": 38,\n \"num_unique_values\": 29,\n \"samples\": [\n 17,\n 27,\n 18\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_sigh\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5,\n \"min\": 0,\n \"max\": 31,\n \"num_unique_values\": 22,\n \"samples\": [\n 0,\n 21,\n 4\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_cough\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 4,\n \"num_unique_values\": 5,\n \"samples\": [\n 1,\n 4,\n 3\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_breath\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_sniff\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2,\n \"min\": 0,\n \"max\": 23,\n \"num_unique_values\": 12,\n \"samples\": [\n 23\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_groan\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_pause\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_um\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 3,\n \"num_unique_values\": 4,\n \"samples\": [\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_uh\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_total\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 11,\n \"min\": 0,\n \"max\": 61,\n \"num_unique_values\": 42,\n \"samples\": [\n 14\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"filler_count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 49,\n \"min\": 1,\n \"max\": 379,\n \"num_unique_values\": 107,\n \"samples\": [\n 47\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 20 } ] }, { "cell_type": "code", "source": [ "df = pd.merge(\n", " transcripts_df,\n", " label_df[['Participant_ID', 'PHQ8_Score', 'phq8_target', 'label', 'gender', 'split']],\n", " on='Participant_ID',\n", " how='inner'\n", ")\n", "\n", "print(f\"Dataset setelah merge: {df.shape}\")\n", "print(f\"\\nDistribusi label:\")\n", "print(df['label'].value_counts())\n", "print(f\"\\nDistribusi split:\")\n", "print(df['split'].value_counts())\n", "df.head()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 997 }, "id": "zGz3NjAf9L3s", "outputId": "5676f1f1-8188-463e-8809-14d9c73d2c3b" }, "execution_count": 21, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Dataset setelah merge: (189, 20)\n", "\n", "Distribusi label:\n", "label\n", "0 132\n", "1 57\n", "Name: count, dtype: int64\n", "\n", "Distribusi split:\n", "split\n", "train 107\n", "test 47\n", "dev 35\n", "Name: count, dtype: int64\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ " Participant_ID text \\\n", "0 300 good atlanta georgia um my parents are from he... \n", "1 301 thank you mmm k i'm doing good thank you i'm f... \n", "2 302 i'm fine how about yourself i'm from los angel... \n", "3 303 okay how 'bout yourself here in california yea... \n", "4 304 i'm doing good um from los angeles california ... \n", "\n", " utterance_count word_count nv_laugh nv_sigh nv_cough nv_breath \\\n", "0 87 352 0 0 0 0 \n", "1 104 1465 3 0 0 0 \n", "2 96 611 1 0 0 0 \n", "3 103 1960 3 0 0 0 \n", "4 104 976 11 0 0 0 \n", "\n", " nv_sniff nv_groan nv_pause nv_um nv_uh nv_total filler_count \\\n", "0 0 0 0 0 0 0 31 \n", "1 0 0 0 0 0 3 20 \n", "2 0 0 0 0 0 1 25 \n", "3 0 0 0 0 0 3 33 \n", "4 0 0 0 0 0 11 28 \n", "\n", " PHQ8_Score phq8_target label gender split \n", "0 2 2 0 male test \n", "1 3 3 0 male test \n", "2 4 4 0 male dev \n", "3 0 0 0 female train \n", "4 6 6 0 female train " ], "text/html": [ "\n", "
\n", "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
Participant_IDtextutterance_countword_countnv_laughnv_sighnv_coughnv_breathnv_sniffnv_groannv_pausenv_umnv_uhnv_totalfiller_countPHQ8_Scorephq8_targetlabelgendersplit
0300good atlanta georgia um my parents are from he...87352000000000031220maletest
1301thank you mmm k i'm doing good thank you i'm f...1041465300000000320330maletest
2302i'm fine how about yourself i'm from los angel...96611100000000125440maledev
3303okay how 'bout yourself here in california yea...1031960300000000333000femaletrain
4304i'm doing good um from los angeles california ...10497611000000001128660femaletrain
\n", "
\n", "
\n", "\n", "
\n", " \n", "\n", " \n", "\n", " \n", "
\n", "\n", "\n", "
\n", "
\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "df", "summary": "{\n \"name\": \"df\",\n \"rows\": 189,\n \"fields\": [\n {\n \"column\": \"Participant_ID\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 56,\n \"min\": 300,\n \"max\": 492,\n \"num_unique_values\": 189,\n \"samples\": [\n 488,\n 467,\n 318\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 189,\n \"samples\": [\n \"yes fine oh san fernando valley uh well i really like the culture i love going to museums and being able to go at night and having lots of people around it's spread out a lot and it's a pain in the ass to have to drive everywhere and not have a good public transportation system uh not really i like to go camping but i don't really travel um i think it might've been around six months ago i went on a trip with a geology class yeah it was fun hmm i think it's tough to say um well i think the first time i went camping was probably really memorable i went to death valley and i got to see a lot of really cool things and hang out with my friends at night philosophy yes um hmm i don't know i think it just sort of happened i wanted to be a journalist for a really long time and then i kind of grew disillusioned with that and i liked all the different ways or all the different things i could do with philosophy huh i don't know but i really like children and i really like studying their behavior so i think something along those lines might be it i think i'm generally pretty outgoing i tend to be shy around my family but when i'm around friends and stuff i i think i'm pretty friendly um a lot of times i'll watch t_v or read um yeah i think i'm pretty good i don't really get mad i try to think about other things if i start getting mad or um i just start trying to focus on my breathing i think the last time i argued with someone it was probably with my boyfriend um but i'm having trouble remembering what the last thing we argued about was um i think it was i wasn't feeling very well and i was having trouble deciding whether i wanted to go to his department's end of year party and he was getting frustrated which i thought was very inconsiderate 'cause i wasn't feeling well to begin with well i felt like he wasn't really taking my feelings into consideration and i really hate when he gets stressed out or gets angry easily because i just see that as unnecessary and it stresses me out hmm i think a lot of times when my oldest youngest brother or my oldest little brother uh does something wrong i tend to be harsh with him um we're not as close as my sister and i or my youngest brother and i and so he tends to be hostile back and i think that if i were a little bit more gentle it might be easier to communicate with him hmm um when i was nineteen i came home from a party i think around four a_m on a sunday morning and my dad got really mad at me and yelled at me said a lot of not very nice things and then he didn't speak to me for about a year uh well my relationship with my mom is a lot better now that i don't live at home um i used to be really frustrated with her all the time 'cause she has a really quick temper but i think now that i've got a little bit more distance i can see that she did the best that she could um and i love my siblings i again my little sister and i get along really well she's four years younger than me and when when she was around twelve and i was around sixteen it was really difficult 'cause she had a really harsh temper and she was always moody and i wasn't in a place to be patient with that sort of stuff so we fought all the time but we don't anymore and that's really great and we get along okay with my uh my older little brother who's fifteen and i think i get along pretty well with my youngest brother who's twelve one of the first not the first but one of my first philosophy professors and i really loved her classes and it was really good uh being in a class with a professor who really cared and who really engaged people that was really motivating since not all my classes were like that i think it's pretty easy i mean sometimes i'll have trouble going to sleep but generally it's not that bad it's i think it's usually harder for me to wake up in the morning um well i'm really tired i can get grumpy but i think usually i'm just groggy pretty good i'm a little anxious about um about getting a job and i'm gonna be taking summer school about it's only one class so i'll have a lot of free time um yeah well i mean it's not the end of the world if i don't get a job but i know that i will eventually so mostly i think i just try not to think about it too much and i try not to be pessimistic no no hmm well i think the last time i felt really happy i went to a party with my boyfriend the end of department party and um i was the you know i knew a couple people there so i felt comfortable but i was also able to meet new people and there were a couple kids there splashing around in the pool and i had a couple drinks and ate some food and it was fun um she would probably say that i'm smart and uh pretty intellectual i love to read and to watch really like serious films and t_v um and she would probably also say that i'm fun 'cause we like to go out and have fun a lot well i'm kinda i can be really disorganized and that gets me in trouble sometimes so um so that's a major one that and procrastinating hmm um i got into a little bit of trouble a few months back and had to go to court and was uh uh given eh sixty some hours of community service and of course i procrastinated on doing that so i had to show up to court again to get an extension and the judge was very mean and he didn't seem i mean he yelled at me but it didn't seem like he gave it much thought it seemed like it was just sort of a stock speech he gave everyone that he thought needed some yelling at so i understand 'cause that's his job but it was was not great for me 'cause i wasn't i i was really anxious in that situation well i would've been twelve and two um but i think i think i would've told myself at twelve not to be so hard on myself and not to be so hard on my parents 'cause i think i was a little selfish and self-involved back then i can't really think of anything i mean i regret how i treated my mother when i was a kid but everyone does that and um i do regret a couple of semesters ago i didn't do so well in school and i got a a few ws which i now have to work to get off on my transcript i think so i'm i think i i wasn't focusing in school at that time and i didn't see any counselors at school or anything so i definitely could've done that hmm i think i'd probably spend my ideal weekend sleeping in saturday and then going hiking or maybe just going to the duck pond by my house and feeding ducks and picking some oranges and then maybe going out to breakfast sunday and just like window shopping and maybe watching a little bit of t_v huh well my youngest brother is autistic and my mom and dad were always working when he was growing up i'm ten years older than him so and being the oldest i always i always took care of him which made me really really patient because he was a handful but i'm really proud of how he turned out and um and how close we are\",\n \"yeah i'm okay with it uh i feel i feel pretty good little little nervous because i've never done anything like this before that's why yeah from ohio cincinnati yeah uh two months ago two and a half months uh was just looking for just wanted to be in a new environment oh yeah i'm ecstatic very happy uh it took a few weeks just to get adjusted 'cause i didn't know anyone the weather is probably the best thing yeah and uh i mean other than that it's pretty much the same as everywhere else i've been there's nothing i don't really really like about l_a it's just you know just the weather is the best part of it yeah uh i like to travel somewhat uh i haven't traveled a lot in my life but over the past uh year and a half i've done done quite a bit of traveling just seeing different places different people new people new faces new places uh let's see last well last summer i spent the uh summer in cleveland doing uh urban farming uh in the city yeah and uh i stayed at a hostel the first hostel in cleveland and i was uh doing a program with the uh and it was basically we would go to different urban farms throughout the city and volunteer about twenty twenty five hours a week so yeah really a spur of the moment type of thing um wanted to try something different something i'd never done before and farming is one of those things that you know most people would never try 'cause of a you know 'cause of a certain stigma that comes along with it uh and you know i just thought it would be fun and interesting to do something like that yeah what in the uh uh mm urban farming or in general in urban farming uh i don't know i guess uh i don't know just just the overall experience just going you know eating uh going to the farmers markets and eating all the fresh food and just seeing the difference in you know how fresh natural food taste eh you know as opposed to the food you buy in a grocery store yeah yeah it is really is uh i was i took two semesters of uh college and i was undecided so i was just taking general classes my dream job huh i guess my dream job would to be anything that involves uh i don't have like a specific dream job but anything that involves nature just being outside uh i would like to uh i wouldn't mind being like a like a forest ranger or something like that just as long as i you know yeah consider myself more shy uh i don't i don't know why it's just the way it is uh huh i read um listen to music uh i draw watch movies stuff like that yeah i think i'm pretty good at it i think yeah wow uh yeah i can't remember the last time i had an argument with someone i don't know again i don't know because i don't dwell on things that happened in the past yeah mm maybe whether or not to come to california i guess that was a hard decision well i don't know i've been thinking about it you know moving to a new place for a long time and i guess the hardest part of the decision was thinking whether whether or not i would be secure or not you know what i mean um just going to a new place where you don't know anyone uh it's kinda uh you really don't know what's gonna happen so uh there was a lot of uh you know fear in that in that decision of should i go to a place where i don't know anyone i don't i'm not familiar with the area you know only only perception ever had that i have of this area is you know what i've seen on in media media and t_v so you know eh yeah guess that was hardest hardest decision decision that i can think of yeah eh guess so uh nothing absolutely nothing yeah uh nothing i don't feel guilty about anything uh relationship with my family uh i'm not really close to my family um i guess the person i'm closest to is my mom but you know i don't you know i don't i never really talk to her much uh i don't really know my dad that well so you know uh i guess my grandmother yeah she yeah she's uh she's very um xxx how should i say it uh she's very she's a thinker i put it like that like she's very insightful so um yeah i look to her for a lot of advice because she's been through a lot through a lot in her life and i feel like she you know she's been through a lot so she understands a lot so eh you know she's a very positive influence very easy uh maybe unsure i would say because right now i really don't know what direction i wanna go in with my life and there's been a lot of confusion with that um but you know it's not it's not something that overly affects me but you know it's always a thought in the back of my mind it feels feels like somehow maybe i'm running out of time and i know that's not like that's not xxx that's not a realistic thought because i don't see how you could ever run out of time but yeah i guess yeah just general not knowing what i want but i know like how do i cope with that how do i cope with that i uh i don't know i just do a lot of thinking a lot of soul searching it's not hard i mean i mean i'm it's xxx like i can't say it's hard because it's one of those things that i feel like it's necessary and you know certain things in life you just you're forced to face so it's harder to i think it's harder to avoid those type of things yeah no no no uh xxx today when i talked when i uh talked to my friend earlier yeah how would my best friend describe me uh i guess he would say i'm quiet uh uh guess he would describe as a nice guy uh guess insightful mm uh goofy and yeah oh i don't really think like that no ten or twenty years ago uh guess i would tell myself to stick with what what stick with what i love hmm uh i did uh acting workshop in um yeah i don't know it's always always been an interest of mine and i decided why not you know just try it see how you like it what am i most proud of like an event or a an accomplishment i guess i pride myself on being independent mm yeah bye\",\n \"yes i'm alright los angeles california yep um the lights big city it's always something going on the traffic and that's it uh business administration and business management uh not that i'm doing something else i'm doing networking at the moment hmm uh to open up a big clothing line and just supply the whole world with clothing yeah definitely uh 'cause i'm all about myself it's all about me not often but more times than not yeah being lied to and i guess when people think you're dumber than you than what you are uh try to get away from everything go into my own space very well i'm great at controlling my temper i um probably like two days ago and it was probably just over some sports it wasn't a real argument not a lot but i travel enough just being able to see to get a change of pace see somebody new see new faces new environment mm let me see the last trip i had was vegas you know how vegas goes it was pretty fun it was a lotta lotta drinking and a lot of partying going on um pretty open-minded i'm kind to pretty much everybody i'm a pretty even-keel guy it's alright it could be better no no ma'am nope no uh lately it's been pretty tough i guess because i've been staying up so late but uh that's my fault i don't know i guess i'm a night owl all of a sudden i'm turning into an owl sluggish and tired all day if i don't sleep well all day i'm just laying around no not too much mm no oh stay focused and listen to the adults yeah i mean it's self-explanatory i could've just listened to parents or other adults that tried to steer you in the right way and you know you're being a stubborn kid at that uh at those ages so it's like what what are you talking about i this is my life i can do what i want i don't have to listen to you but now i see that what they were telling me was correct um hilarious pretty close uh both my parents mother and father mm recently what did i do um new year's eve party i had i had the time of my life it was so fun uh this new year's eve i went to a party at a friend's house in baldwin hills area he had it at a a pretty nice house it's a three story house and it was like three hundred and fifty people in there it was pretty fun but it was also not that fun because there was so many people and you couldn't really move so that was the only downfall but besides that i brought my new year in great with tons of friends and that's how it's supposed to be done um i'm generally in a good mood every day i mean waking up puts me in a good mood i i guess you could say that but um i don't know a good a good meal ooh one of my most memorable experiences were probably uh when i was younger i was in the boy scouts and we used to go camping all the time and one year we went to yellowstone or yosemite one of those places and we had to work on a mountain biking merit badge and of course being from the city i'm like a mountain biking i can ride a mountain bike but once we got in the real mountains on got on real mountain bikes i gained a new respect for the mountain bikers it was pretty it was fun and pretty intense going downhill doing about forty five miles an hour in dirt on a bicycle was pretty intense and that was i'll i'll never forget that mm i don't know that's a tough one to to answer what am i most proud of i don't know honestly bye\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"utterance_count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 73,\n \"min\": 42,\n \"max\": 385,\n \"num_unique_values\": 128,\n \"samples\": [\n 155,\n 116,\n 161\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"word_count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 801,\n \"min\": 165,\n \"max\": 4551,\n \"num_unique_values\": 183,\n \"samples\": [\n 755,\n 1912,\n 2640\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_laugh\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 7,\n \"min\": 0,\n \"max\": 38,\n \"num_unique_values\": 29,\n \"samples\": [\n 17,\n 27,\n 18\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_sigh\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5,\n \"min\": 0,\n \"max\": 31,\n \"num_unique_values\": 22,\n \"samples\": [\n 0,\n 21,\n 4\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_cough\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 4,\n \"num_unique_values\": 5,\n \"samples\": [\n 1,\n 4,\n 3\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_breath\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_sniff\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 2,\n \"min\": 0,\n \"max\": 23,\n \"num_unique_values\": 12,\n \"samples\": [\n 23\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_groan\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_pause\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 0,\n \"num_unique_values\": 1,\n \"samples\": [\n 0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_um\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 3,\n \"num_unique_values\": 4,\n \"samples\": [\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_uh\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"nv_total\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 11,\n \"min\": 0,\n \"max\": 61,\n \"num_unique_values\": 42,\n \"samples\": [\n 14\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"filler_count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 49,\n \"min\": 1,\n \"max\": 379,\n \"num_unique_values\": 107,\n \"samples\": [\n 47\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"PHQ8_Score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5,\n \"min\": 0,\n \"max\": 23,\n \"num_unique_values\": 24,\n \"samples\": [\n 21\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"phq8_target\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5,\n \"min\": 0,\n \"max\": 23,\n \"num_unique_values\": 24,\n \"samples\": [\n 21\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"label\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"gender\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"female\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"split\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 3,\n \"samples\": [\n \"test\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 21 } ] }, { "cell_type": "code", "source": [ "NONVERBAL_TAGS = ['laugh', 'sigh', 'cough', 'breath', 'sniff', 'groan', 'pause', 'um', 'uh']\n", "\n", "def extract_nonverbal_features(raw_utterances):\n", " features = {f'nv_{tag}': 0 for tag in NONVERBAL_TAGS}\n", " features['nv_total'] = 0\n", " for utt in raw_utterances:\n", " utt_str = str(utt).lower()\n", " for tag in re.findall(r'<([^>]+)>', utt_str):\n", " tag_clean = tag.strip().split()[0]\n", " for known_tag in NONVERBAL_TAGS:\n", " if known_tag in tag_clean:\n", " features[f'nv_{known_tag}'] += 1\n", " features['nv_total'] += 1\n", " break\n", " all_text = ' '.join(str(u) for u in raw_utterances)\n", " features['filler_count'] = len(re.findall(r'\\b(um|uh|uhm|hmm|hm)\\b', all_text.lower()))\n", " return features\n", "\n", "def load_participant_transcripts(data_dir, ignore_ids=set()):\n", " records = []\n", " participant_folders = sorted([\n", " f for f in os.listdir(data_dir)\n", " if os.path.isdir(os.path.join(data_dir, f)) and f.isdigit()\n", " ], key=lambda x: int(x))\n", " print(f\"Total folder ditemukan: {len(participant_folders)}\")\n", "\n", " for pid_str in tqdm(participant_folders, desc=\"Loading transcripts\"):\n", " pid = int(pid_str)\n", " if pid in ignore_ids:\n", " continue\n", "\n", " transcript_path = os.path.join(data_dir, pid_str, f\"{pid_str}_TRANSCRIPT.csv\")\n", " if not os.path.exists(transcript_path):\n", " continue\n", "\n", " try:\n", " df = pd.read_csv(transcript_path, sep='\\t')\n", " except Exception:\n", " try:\n", " df = pd.read_csv(transcript_path, sep=',')\n", " except Exception as e:\n", " print(f\"⚠️ Skip {pid_str}: {e}\")\n", " continue\n", "\n", " part_df = df[df['speaker'] == 'Participant'].copy()\n", "\n", " # --- TAMBAHAN: extract non-verbal dari raw utterances dulu ---\n", " raw_utts = part_df['value'].dropna().tolist()\n", " nv_feats = extract_nonverbal_features(raw_utts)\n", " # ------------------------------------------------------------\n", "\n", " # filter noise seperti sebelumnya\n", " noise_patterns = ['', '', 'scrubbed_entry', '',\n", " '', '[', ']', '(', ')']\n", " def is_valid_utterance(text):\n", " text_lower = str(text).lower()\n", " return not any(p in text_lower for p in noise_patterns)\n", "\n", " part_df = part_df[part_df['value'].apply(is_valid_utterance)]\n", " if len(part_df) == 0:\n", " continue\n", "\n", " full_text = ' '.join(part_df['value'].dropna().astype(str).tolist())\n", "\n", " record = {\n", " 'Participant_ID': pid,\n", " 'text': full_text,\n", " 'utterance_count': len(part_df),\n", " 'word_count': len(full_text.split())\n", " }\n", " record.update(nv_feats) # gabungkan non-verbal ke record\n", " records.append(record)\n", "\n", " return pd.DataFrame(records)" ], "metadata": { "id": "NSUZMyHYwkOy" }, "execution_count": 22, "outputs": [] }, { "cell_type": "markdown", "source": [ "# 2. Exploritory Data Analysis" ], "metadata": { "id": "qH4fOB6P_GJ9" } }, { "cell_type": "markdown", "source": [ "# 3. Pre-Processing For Baseline Model" ], "metadata": { "id": "eHtr4Unz_Jeq" } }, { "cell_type": "markdown", "source": [ "## 3.1 SLANG WORD DICT" ], "metadata": { "id": "pWuyZ0gD_rK4" } }, { "cell_type": "code", "source": [ "SLANG_DICT = {\n", " # ── yang sudah ada ──────────────────────────────────────────────\n", " \"gonna\": \"going to\",\n", " \"wanna\": \"want to\",\n", " \"gotta\": \"got to\",\n", " \"kinda\": \"kind of\",\n", " \"sorta\": \"sort of\",\n", " \"lotta\": \"a lot of\",\n", " \"hafta\": \"have to\",\n", " \"dunno\": \"do not know\",\n", " \"ain't\": \"is not\",\n", " \"cannot\": \"can not\",\n", " \"wouldn't\": \"would not\",\n", " \"couldn't\": \"could not\",\n", " \"shouldn't\": \"should not\",\n", " \"didn't\": \"did not\",\n", " \"doesn't\": \"does not\",\n", " \"don't\": \"do not\",\n", " \"wasn't\": \"was not\",\n", " \"weren't\": \"were not\",\n", " \"haven't\": \"have not\",\n", " \"hasn't\": \"has not\",\n", " \"hadn't\": \"had not\",\n", " \"won't\": \"will not\",\n", " \"i'm\": \"i am\",\n", " \"i've\": \"i have\",\n", " \"i'll\": \"i will\",\n", " \"i'd\": \"i would\",\n", " \"it's\": \"it is\",\n", " \"that's\": \"that is\",\n", " \"there's\": \"there is\",\n", " \"they're\": \"they are\",\n", " \"they've\": \"they have\",\n", " \"we're\": \"we are\",\n", " \"we've\": \"we have\",\n", " \"you're\": \"you are\",\n", " \"you've\": \"you have\",\n", " \"he's\": \"he is\",\n", " \"she's\": \"she is\",\n", "\n", " # ── contractions yang belum ada ──────────────────────────────────\n", " \"it'd\": \"it would\",\n", " \"it'll\": \"it will\",\n", " \"that'd\": \"that would\",\n", " \"that'll\": \"that will\",\n", " \"who's\": \"who is\",\n", " \"who'd\": \"who would\",\n", " \"who'll\": \"who will\",\n", " \"what's\": \"what is\",\n", " \"what'd\": \"what did\",\n", " \"what'll\": \"what will\",\n", " \"where's\": \"where is\",\n", " \"when's\": \"when is\",\n", " \"how's\": \"how is\",\n", " \"how'd\": \"how did\",\n", " \"why's\": \"why is\",\n", " \"you'd\": \"you would\",\n", " \"you'll\": \"you will\",\n", " \"we'd\": \"we would\",\n", " \"we'll\": \"we will\",\n", " \"he'd\": \"he would\",\n", " \"he'll\": \"he will\",\n", " \"she'd\": \"she would\",\n", " \"she'll\": \"she will\",\n", " \"they'd\": \"they would\",\n", " \"they'll\": \"they will\",\n", " \"let's\": \"let us\",\n", " \"there'd\": \"there would\",\n", " \"there'll\": \"there will\",\n", " \"here's\": \"here is\",\n", " \"could've\": \"could have\",\n", " \"would've\": \"would have\",\n", " \"should've\": \"should have\",\n", " \"might've\": \"might have\",\n", " \"must've\": \"must have\",\n", " \"i'm not\": \"i am not\",\n", " \"aren't\": \"are not\",\n", " \"needn't\": \"need not\",\n", " \"mightn't\": \"might not\",\n", " \"mustn't\": \"must not\",\n", " \"daren't\": \"dare not\",\n", "\n", " # ── casual speech / reduction (khas speech transcript) ───────────\n", " \"tryna\": \"trying to\",\n", " \"finna\": \"fixing to\", # Southern US, \"about to\"\n", " \"lemme\": \"let me\",\n", " \"gimme\": \"give me\",\n", " \"gotcha\": \"got you\",\n", " \"getcha\": \"get you\",\n", " \"betcha\": \"bet you\",\n", " \"whatcha\": \"what are you\",\n", " \"watcha\": \"what are you\",\n", " \"ya\": \"you\",\n", " \"yep\": \"yes\",\n", " \"yup\": \"yes\",\n", " \"nope\": \"no\",\n", " \"nah\": \"no\",\n", " \"yeah\": \"yes\",\n", " \"yea\": \"yes\",\n", " \"cause\": \"because\",\n", " \"'cause\": \"because\",\n", " \"cuz\": \"because\",\n", " \"cos\": \"because\",\n", " \"'bout\": \"about\",\n", " \"bout\": \"about\",\n", " \"'til\": \"until\",\n", " \"til\": \"until\",\n", " \"em\": \"them\",\n", " \"'em\": \"them\",\n", " \"an'\": \"and\",\n", " \"n'\": \"and\",\n", " \"o'\": \"of\",\n", " \"ma\": \"my\", # \"ma family\"\n", " \"hafta\": \"have to\",\n", " \"oughta\": \"ought to\",\n", " \"useta\": \"used to\",\n", " \"supposta\": \"supposed to\",\n", " \"sposta\": \"supposed to\",\n", " \"woulda\": \"would have\",\n", " \"coulda\": \"could have\",\n", " \"shoulda\": \"should have\",\n", " \"mighta\": \"might have\",\n", " \"musta\": \"must have\",\n", "\n", " # ── filler / disfluency yang sering muncul di clinical interview ─\n", " \"y'know\": \"you know\",\n", " \"ya know\": \"you know\",\n", " \"y'all\": \"you all\",\n", " \"c'mon\": \"come on\",\n", " \"c'mere\": \"come here\",\n", " \"somethin'\": \"something\",\n", " \"nothin'\": \"nothing\",\n", " \"everythin'\": \"everything\",\n", " \"anythin'\": \"anything\",\n", " \"somethin\": \"something\",\n", " \"nothin\": \"nothing\",\n", " \"everythin\": \"everything\",\n", " \"anythin\": \"anything\",\n", " \"doin'\": \"doing\",\n", " \"goin'\": \"going\",\n", " \"feelin'\": \"feeling\",\n", " \"thinkin'\": \"thinking\",\n", " \"talkin'\": \"talking\",\n", " \"havin'\": \"having\",\n", " \"bein'\": \"being\",\n", " \"gettin'\": \"getting\",\n", " \"puttin'\": \"putting\",\n", " \"tryin'\": \"trying\",\n", " \"comin'\": \"coming\",\n", " \"workin'\": \"working\",\n", " \"sleepin'\": \"sleeping\",\n", " \"eatin'\": \"eating\",\n", " \"drinkin'\": \"drinking\",\n", " \"livin'\": \"living\",\n", " \"lookin'\": \"looking\",\n", " \"runnin'\": \"running\",\n", " \"walkin'\": \"walking\",\n", "\n", " # ── mental health / mood context (khas DAIC-WoZ) ─────────────────\n", " \"prolly\": \"probably\",\n", " \"prob\": \"probably\",\n", " \"probs\": \"probably\",\n", " \"def\": \"definitely\",\n", " \"defo\": \"definitely\",\n", " \"rly\": \"really\",\n", " \"rlly\": \"really\",\n", " \"tbh\": \"to be honest\",\n", " \"idk\": \"i do not know\",\n", " \"idc\": \"i do not care\",\n", " \"imo\": \"in my opinion\",\n", " \"imho\": \"in my honest opinion\",\n", " \"lol\": \"laughing\", # sering muncul di transcript sebagai marker\n", " \"haha\": \"laughing\",\n", " \"hm\": \"hmm\",\n", " \"uh-huh\": \"yes\",\n", " \"mm-hmm\": \"yes\",\n", " \"uh\": \"\", # disfluency filler → hapus\n", " \"um\": \"\",\n", " \"uh huh\": \"yes\",\n", "}\n", "\n", "print(f\"Total slang entries: {len(SLANG_DICT)}\")\n", "print(\"Contoh:\", list(SLANG_DICT.items())[:5])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "KwTQLKkF_LVc", "outputId": "5a09ca67-4ed5-404a-c726-cc7474b5638d" }, "execution_count": 45, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Total slang entries: 168\n", "Contoh: [('gonna', 'going to'), ('wanna', 'want to'), ('gotta', 'got to'), ('kinda', 'kind of'), ('sorta', 'sort of')]\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 3.2 Normalisasi & Teks Cleaning" ], "metadata": { "id": "6F2pR0QyAOsc" } }, { "cell_type": "code", "source": [ "# # ADDED: Inisialisasi lemmatizer dan stopword list\n", "# lemmatizer = WordNetLemmatizer()\n", "\n", "# STOP_WORDS = set(stopwords.words('english'))\n", "\n", "# # Kata klinis depresi — keluarkan dari stopwords supaya tidak ikut kebuang\n", "# DEPRESSION_KEYWORDS = {\n", "# 'no', 'not', 'never', 'nothing', 'nobody', 'none', 'nor',\n", "# 'neither', 'cannot', 'cant', 'dont', 'doesnt', 'didnt',\n", "# 'wont', 'wouldnt', 'shouldnt', 'couldnt', 'havent', 'hasnt',\n", "# 'hadnt', 'wasnt', 'werent', 'isnt', 'arent',\n", "# 'empty', 'hopeless', 'worthless', 'tired', 'sad', 'lonely',\n", "# 'anxious', 'numb', 'fail', 'useless', 'burden'\n", "# }\n", "# STOP_WORDS -= DEPRESSION_KEYWORDS\n", "\n", "# # Filler words transcript — tambahkan ke stopwords karena noise\n", "# FILLER_WORDS = {'uh', 'um', 'uhm', 'hmm', 'hm', 'like', 'okay', 'ok', 'yeah', 'yep'}\n", "# STOP_WORDS |= FILLER_WORDS\n", "\n", "# print(f\"Total stopwords aktif: {len(STOP_WORDS)}\")\n", "# print(f\"Depression keywords dikecualikan: {DEPRESSION_KEYWORDS}\")" ], "metadata": { "id": "WJzs65j_Kl_5" }, "execution_count": 46, "outputs": [] }, { "cell_type": "code", "source": [ "# Inisialisasi lemmatizer dan stopword list\n", "lemmatizer = WordNetLemmatizer()\n", "\n", "STOP_WORDS = set(stopwords.words('english'))\n", "\n", "# Filler words transcript — tambahkan ke stopwords karena noise\n", "FILLER_WORDS = {'uh', 'um', 'uhm', 'hmm', 'hm', 'like', 'okay', 'ok', 'yeah', 'yep'}\n", "STOP_WORDS |= FILLER_WORDS\n", "\n", "print(f\"Total stopwords aktif: {len(STOP_WORDS)}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "bRBvpkJJOgGF", "outputId": "466c1a70-084e-40c2-dfd5-6ecd61a8320d" }, "execution_count": 47, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Total stopwords aktif: 208\n" ] } ] }, { "cell_type": "code", "source": [ "#fungsi untuk ganti kata slang dengan salng_dict yang sudah dibuat\n", "def normalize_slang(text, slang_dict):\n", "\n", " # Buat regex pattern dari keys slang dict (escape karakter khusus)\n", " pattern = re.compile(\n", " r'\\b(' + '|'.join(map(re.escape, slang_dict.keys())) + r')\\b',\n", " re.IGNORECASE\n", " )\n", " return pattern.sub(lambda m: slang_dict.get(m.group().lower(), m.group()), text)" ], "metadata": { "id": "F-T0XGr3ARD6" }, "execution_count": 48, "outputs": [] }, { "cell_type": "code", "source": [ "def clean_text(text, slang_dict=None, remove_stopwords=True, lemmatize=True):\n", "\n", " text = str(text).lower()\n", "\n", " # Hapus marker sinkronisasi dan tags\n", " text = re.sub(r'<[^>]+>', ' ', text)\n", " text = re.sub(r'\\[.*?\\]', ' ', text)\n", " text = re.sub(r'\\(.*?\\)', ' ', text)\n", " text = re.sub(r'scrubbed_entry', ' ', text)\n", "\n", " # Normalisasi slang\n", " if slang_dict:\n", " text = normalize_slang(text, slang_dict)\n", "\n", " # Hapus karakter khusus\n", " text = re.sub(r\"[^a-z0-9\\s']\", ' ', text)\n", "\n", " # Hapus standalone apostrophe\n", " text = re.sub(r\"(? 1]\n", "\n", " # ADDED: Lemmatisasi\n", " if lemmatize:\n", " tokens = [lemmatizer.lemmatize(t) for t in tokens]\n", "\n", " return ' '.join(tokens)" ], "metadata": { "id": "pVszUOLcaSB4" }, "execution_count": 49, "outputs": [] }, { "cell_type": "code", "source": [ "df['text_clean'] = df['text'].apply(lambda x: clean_text(x, SLANG_DICT))\n", "\n", "print(\"Contoh teks sebelum preprocessing:\")\n", "print(df['text'].iloc[0][:200])\n", "print(\"\\nSetelah preprocessing (slang + stopwords + lemma):\")\n", "print(df['text_clean'].iloc[0][:200])\n", "\n", "# ADDED: cek reduksi kata\n", "df['word_count_raw'] = df['text'].apply(lambda x: len(str(x).split()))\n", "df['word_count_clean_temp'] = df['text_clean'].apply(lambda x: len(x.split()))\n", "reduction = (1 - df['word_count_clean_temp'] / df['word_count_raw']).mean() * 100\n", "print(f\"\\nRata-rata reduksi kata: {reduction:.1f}%\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "eH8OQMcHaw_f", "outputId": "3e7b0224-9010-4fd5-dde3-18035def511e" }, "execution_count": 38, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Contoh teks sebelum preprocessing:\n", "good atlanta georgia um my parents are from here um i love it i like the weather i like the opportunities um yes um it took a minute somewhat easy congestion that's it um i took up business and admini\n", "\n", "Setelah preprocessing (slang + stopwords + lemma):\n", "good atlanta georgia parent love weather opportunity yes took minute somewhat easy congestion took business administration yes break right plan going back next semester probably open business specific\n", "\n", "Rata-rata reduksi kata: 58.7%\n" ] } ] }, { "cell_type": "code", "source": [ "# Cek data setelah cleaning\n", "df['word_count_clean'] = df['text_clean'].apply(lambda x: len(x.split()))\n", "\n", "# Filter participant dengan teks terlalu pendek (mungkin ada masalah data)\n", "MIN_WORDS = 20\n", "df_filtered = df[df['word_count_clean'] >= MIN_WORDS].copy()\n", "print(f\"Data setelah filter min {MIN_WORDS} kata: {len(df_filtered)} dari {len(df)} participant\")\n", "print(f\"Distribusi label: {df_filtered['label'].value_counts().to_dict()}\")\n", "\n", "# Update df utama\n", "df = df_filtered.reset_index(drop=True)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "pK6iPVRTa26I", "outputId": "1059cf9e-3c1a-4463-a0a7-f92badf46432" }, "execution_count": 39, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Data setelah filter min 20 kata: 189 dari 189 participant\n", "Distribusi label: {0: 132, 1: 57}\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 3.3 Future Eng" ], "metadata": { "id": "3ibiRnCW8hG7" } }, { "cell_type": "code", "source": [ "# ── Length Features ──────────────────────────────────────────────────\n", "df['avg_utt_len'] = df['word_count'] / (df['utterance_count'] + 1)\n", "df['response_brevity'] = (df['utterance_count'] < 50).astype(int)\n", "\n", "LENGTH_COLS = ['word_count', 'utterance_count', 'avg_utt_len', 'response_brevity']\n", "print(df[LENGTH_COLS].head())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "5oufGktt8kPs", "outputId": "079f23e2-941a-4437-e74e-f7e8fce73c11" }, "execution_count": 40, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " word_count utterance_count avg_utt_len response_brevity\n", "0 352 87 4.000000 0\n", "1 1465 104 13.952381 0\n", "2 611 96 6.298969 0\n", "3 1960 103 18.846154 0\n", "4 976 104 9.295238 0\n" ] } ] }, { "cell_type": "code", "source": [ "# ── Lexical Sentiment Features ───────────────────────────────────────\n", "NEGATIVE_WORDS = {'hopeless','worthless','empty','tired','sad','lonely',\n", " 'fail','useless','burden','numb','anxious','hate'}\n", "POSITIVE_WORDS = {'happy','good','fine','great','better','enjoy',\n", " 'love','hope','okay','well','improving'}\n", "\n", "def lexical_features(text):\n", " words = set(str(text).lower().split())\n", " neg = len(words & NEGATIVE_WORDS)\n", " pos = len(words & POSITIVE_WORDS)\n", " total = len(words) + 1\n", " return {\n", " 'neg_ratio' : neg / total,\n", " 'pos_ratio' : pos / total,\n", " 'sentiment_gap': (neg - pos) / total\n", " }\n", "\n", "lex = df['text_clean'].apply(lexical_features).apply(pd.Series)\n", "df = pd.concat([df, lex], axis=1)\n", "\n", "LEXICAL_COLS = ['neg_ratio', 'pos_ratio', 'sentiment_gap']\n", "print(df[LEXICAL_COLS].head())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Ny5nB7PU8nyy", "outputId": "971258e0-f072-4869-d0a2-a969760f182c" }, "execution_count": 41, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " neg_ratio pos_ratio sentiment_gap\n", "0 0.000000 0.055046 -0.055046\n", "1 0.003759 0.022556 -0.018797\n", "2 0.000000 0.037433 -0.037433\n", "3 0.005935 0.014837 -0.008902\n", "4 0.008547 0.025641 -0.017094\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 3.4 split data" ], "metadata": { "id": "gPSF1Ua6oCFH" } }, { "cell_type": "code", "source": [ "NV_FEATURE_COLS = [f'nv_{tag}' for tag in NONVERBAL_TAGS] + ['nv_total', 'filler_count']\n", "\n", "train_df = df[df['split'] == 'train'].copy().reset_index(drop=True)\n", "dev_df = df[df['split'] == 'dev'].copy().reset_index(drop=True)\n", "\n", "X_train = train_df['text_clean'].values\n", "y_train = train_df['phq8_target'].values\n", "X_test = dev_df['text_clean'].values\n", "y_test = dev_df['phq8_target'].values\n", "\n", "nv_train = train_df[NV_FEATURE_COLS].fillna(0).values.astype(np.float32)\n", "nv_test = dev_df[NV_FEATURE_COLS].fillna(0).values.astype(np.float32)\n", "\n", "# normalize non-verbal features\n", "from sklearn.preprocessing import StandardScaler\n", "nv_scaler = StandardScaler()\n", "nv_train = nv_scaler.fit_transform(nv_train)\n", "nv_test = nv_scaler.transform(nv_test)\n", "\n", "print(f\"Train size: {len(X_train)} | Dev size: {len(X_test)}\")\n", "print(f\"Train target — mean: {y_train.mean():.2f}, std: {y_train.std():.2f}\")\n", "print(f\"Dev target — mean: {y_test.mean():.2f}, std: {y_test.std():.2f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "2DH0O630oEiq", "outputId": "fd30285e-a775-4070-9af0-423961b8ed22" }, "execution_count": 42, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Train size: 107 | Dev size: 35\n", "Train target — mean: 6.42, std: 5.44\n", "Dev target — mean: 7.43, std: 6.50\n" ] } ] }, { "cell_type": "code", "source": [ "# ── Apply extra features ke train_df & dev_df ────────────────────────\n", "extra_cols = LENGTH_COLS + LEXICAL_COLS\n", "\n", "for col in extra_cols:\n", " train_df[col] = df.loc[df['split'] == 'train', col].values\n", " dev_df[col] = df.loc[df['split'] == 'dev', col].values\n", "\n", "ALL_EXTRA_COLS = NV_FEATURE_COLS + LENGTH_COLS + LEXICAL_COLS\n", "\n", "extra_scaler = StandardScaler()\n", "nv_train = extra_scaler.fit_transform(train_df[ALL_EXTRA_COLS].fillna(0))\n", "nv_test = extra_scaler.transform(dev_df[ALL_EXTRA_COLS].fillna(0))\n", "\n", "NV_SIZE = len(ALL_EXTRA_COLS)\n", "print(f\"Total extra features: {NV_SIZE}\")\n", "print(ALL_EXTRA_COLS)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "NkusPLb-9fll", "outputId": "a98c4a28-a0ba-4593-aeb3-019eaa09a0d9" }, "execution_count": 43, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Total extra features: 18\n", "['nv_laugh', 'nv_sigh', 'nv_cough', 'nv_breath', 'nv_sniff', 'nv_groan', 'nv_pause', 'nv_um', 'nv_uh', 'nv_total', 'filler_count', 'word_count', 'utterance_count', 'avg_utt_len', 'response_brevity', 'neg_ratio', 'pos_ratio', 'sentiment_gap']\n" ] } ] }, { "cell_type": "code", "source": [ "print(\"transcripts_df:\", transcripts_df.columns.tolist())\n", "print(\"df:\", df.columns.tolist())\n", "print(\"train_df:\", train_df.columns.tolist())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "o8YHloURxhoc", "outputId": "e61958d3-c213-44f5-9f7f-9b1b09e9ec96" }, "execution_count": 44, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "transcripts_df: ['Participant_ID', 'text', 'utterance_count', 'word_count', 'nv_laugh', 'nv_sigh', 'nv_cough', 'nv_breath', 'nv_sniff', 'nv_groan', 'nv_pause', 'nv_um', 'nv_uh', 'nv_total', 'filler_count']\n", "df: ['Participant_ID', 'text', 'utterance_count', 'word_count', 'nv_laugh', 'nv_sigh', 'nv_cough', 'nv_breath', 'nv_sniff', 'nv_groan', 'nv_pause', 'nv_um', 'nv_uh', 'nv_total', 'filler_count', 'PHQ8_Score', 'phq8_target', 'label', 'gender', 'split', 'text_clean', 'word_count_raw', 'word_count_clean_temp', 'word_count_clean', 'avg_utt_len', 'response_brevity', 'neg_ratio', 'pos_ratio', 'sentiment_gap']\n", "train_df: ['Participant_ID', 'text', 'utterance_count', 'word_count', 'nv_laugh', 'nv_sigh', 'nv_cough', 'nv_breath', 'nv_sniff', 'nv_groan', 'nv_pause', 'nv_um', 'nv_uh', 'nv_total', 'filler_count', 'PHQ8_Score', 'phq8_target', 'label', 'gender', 'split', 'text_clean', 'word_count_raw', 'word_count_clean_temp', 'word_count_clean', 'avg_utt_len', 'response_brevity', 'neg_ratio', 'pos_ratio', 'sentiment_gap']\n" ] } ] }, { "cell_type": "markdown", "source": [ "# 5. Modeling" ], "metadata": { "id": "BkFpICUbmT9w" } }, { "cell_type": "markdown", "source": [ "## 5.1 Regulasi Max Min" ], "metadata": { "id": "zITbjz6kxbaC" } }, { "cell_type": "code", "source": [ "class DAICWOZDataset(Dataset):\n", " def __init__(self, texts, labels, nv_features, tokenizer, max_len=512, head=256, tail=256):\n", " self.texts = texts\n", " self.labels = labels\n", " self.nv_features = nv_features # shape (N, len(NV_FEATURE_COLS))\n", " self.tokenizer = tokenizer\n", " self.max_len = max_len\n", " self.head = head\n", " self.tail = tail\n", "\n", " def __len__(self):\n", " return len(self.texts)\n", "\n", " def __getitem__(self, idx):\n", " text = str(self.texts[idx])\n", " label = float(self.labels[idx])\n", "\n", " tokens = self.tokenizer(\n", " text,\n", " add_special_tokens=False,\n", " return_attention_mask=False,\n", " return_token_type_ids=False\n", " )['input_ids']\n", "\n", " max_body = self.max_len - 2\n", " if len(tokens) > max_body:\n", " head_len = min(self.head, max_body // 2)\n", " tail_len = max_body - head_len\n", " tokens = tokens[:head_len] + tokens[-tail_len:]\n", "\n", " tokens = [self.tokenizer.cls_token_id] + tokens + [self.tokenizer.sep_token_id]\n", " if len(tokens) > self.max_len:\n", " tokens = tokens[:self.max_len]\n", "\n", " padding_len = self.max_len - len(tokens)\n", " attention_mask = [1] * len(tokens) + [0] * padding_len\n", " tokens = tokens + [self.tokenizer.pad_token_id] * padding_len\n", "\n", " return {\n", " 'input_ids': torch.tensor(tokens, dtype=torch.long),\n", " 'attention_mask': torch.tensor(attention_mask, dtype=torch.long),\n", " 'nv_features': torch.tensor(self.nv_features[idx], dtype=torch.float),\n", " 'labels': torch.tensor(label, dtype=torch.float)\n", " }" ], "metadata": { "id": "ZxfMkQtmr3aR" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "## 5.2 Baseline TF-IDF\n" ], "metadata": { "id": "wcsVKwXEEgly" } }, { "cell_type": "markdown", "source": [ "### import & severity helper" ], "metadata": { "id": "0IlU8cgWMB1e" } }, { "cell_type": "code", "source": [ "from sklearn.svm import SVR\n", "from sklearn.ensemble import GradientBoostingRegressor\n", "from sklearn.decomposition import TruncatedSVD\n", "from sklearn.model_selection import GridSearchCV\n", "import scipy.sparse as sp\n", "\n", "# severity category helper (untuk post-processing prediksi)\n", "def severity_category(score):\n", " if score <= 4: return 'Minimal (0-4)'\n", " elif score <= 9: return 'Mild (5-9)'\n", " elif score <= 14: return 'Moderate (10-14)'\n", " elif score <= 19: return 'Mod-Severe (15-19)'\n", " else: return 'Severe (20-27)'\n", "\n", "all_results = []" ], "metadata": { "id": "K-c-3YyxMBJb" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Persiapan data: buat text_baseline & combined feature matrix" ], "metadata": { "id": "lbJMqzMbMFm1" } }, { "cell_type": "code", "source": [ "# Persiapan data\n", "train_df['text_baseline'] = train_df['text'].apply(lambda x: clean_text(x, slang_dict=None))\n", "dev_df['text_baseline'] = dev_df['text'].apply(lambda x: clean_text(x, slang_dict=None))\n", "\n", "y_train_base = train_df['phq8_target'].values\n", "y_dev_base = dev_df['phq8_target'].values\n", "\n", "# TF-IDF features\n", "tfidf_base = TfidfVectorizer(ngram_range=(1, 2), max_features=5000, min_df=2, sublinear_tf=True)\n", "X_train_tfidf = tfidf_base.fit_transform(train_df['text_baseline'])\n", "X_dev_tfidf = tfidf_base.transform(dev_df['text_baseline'])\n", "print(f\"TF-IDF shape: {X_train_tfidf.shape}\")\n", "\n", "# Non-verbal + extra features\n", "nv_scaler_base = StandardScaler()\n", "X_train_nv = nv_scaler_base.fit_transform(train_df[ALL_EXTRA_COLS].fillna(0))\n", "X_dev_nv = nv_scaler_base.transform(dev_df[ALL_EXTRA_COLS].fillna(0))\n", "\n", "# Combined matrix\n", "X_train_combined = sp.hstack([X_train_tfidf, sp.csr_matrix(X_train_nv)])\n", "X_dev_combined = sp.hstack([X_dev_tfidf, sp.csr_matrix(X_dev_nv)])\n", "print(f\"Combined shape: {X_train_combined.shape}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "NB57EYTZH9Op", "outputId": "4943b77e-e01f-4765-f774-ad8fee50f451" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "TF-IDF shape: (107, 5000)\n", "Combined shape: (107, 5021)\n" ] } ] }, { "cell_type": "markdown", "source": [ "### Helper Eval" ], "metadata": { "id": "Awh7vDyFMIyQ" } }, { "cell_type": "code", "source": [ "def evaluate_regressor(model, X_train, X_dev, y_train, y_dev, model_name='Model'):\n", " model.fit(X_train, y_train)\n", " preds = model.predict(X_dev)\n", " preds_clipped = np.clip(preds, 0, 27)\n", "\n", " mae = mean_absolute_error(y_dev, preds_clipped)\n", " rmse = mean_squared_error(y_dev, preds_clipped) ** 0.5\n", "\n", " pred_categories = [severity_category(round(p)) for p in preds_clipped]\n", " true_categories = [severity_category(int(s)) for s in y_dev]\n", " category_accuracy = sum(p == t for p, t in zip(pred_categories, true_categories)) / len(y_dev)\n", "\n", " print(f\"\\n{'='*60}\")\n", " print(f\" {model_name}\")\n", " print(f\"{'='*60}\")\n", " print(f\" MAE : {mae:.4f}\")\n", " print(f\" RMSE : {rmse:.4f}\")\n", " print(f\" Category Accuracy: {category_accuracy:.4f}\")\n", " print(f\" Pred range: [{preds_clipped.min():.1f}, {preds_clipped.max():.1f}]\")\n", " print(f\" True range: [{y_dev.min()}, {y_dev.max()}]\")\n", "\n", " return {'model': model_name, 'mae': mae, 'rmse': rmse, 'category_accuracy': category_accuracy}, model, preds_clipped" ], "metadata": { "id": "kT0BN0b3MLBN" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Load Model" ], "metadata": { "id": "s64TOcePMMlf" } }, { "cell_type": "code", "source": [ "### Load Model\n", "ALPHA_GRID = [0.1, 0.5, 1.0, 5.0, 10.0, 50.0]\n", "\n", "pipeline_ridge = Pipeline([\n", " ('tfidf', TfidfVectorizer(ngram_range=(1, 2), max_features=5000, min_df=2, sublinear_tf=True)),\n", " ('ridge', Ridge(alpha=1.0))\n", "])" ], "metadata": { "id": "1gqwPsIDMNke" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Optimizer" ], "metadata": { "id": "4lfe6Ox2Mybh" } }, { "cell_type": "code", "source": [ "### Optimizer\n", "param_grid = {'ridge__alpha': ALPHA_GRID}\n", "gs_ridge = GridSearchCV(\n", " pipeline_ridge, param_grid,\n", " cv=5, scoring='neg_mean_absolute_error',\n", " n_jobs=-1, verbose=0\n", ")\n", "gs_ridge.fit(train_df['text_baseline'], y_train_base)\n", "print(f\"Best alpha : {gs_ridge.best_params_['ridge__alpha']}\")\n", "print(f\"Best CV MAE: {-gs_ridge.best_score_:.4f}\")\n", "best_ridge = gs_ridge.best_estimator_" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "TztX-e4HMP6v", "outputId": "85d5607e-f357-4577-9f53-aa7501ffd86c" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Best alpha : 0.1\n", "Best CV MAE: 4.3641\n" ] } ] }, { "cell_type": "markdown", "source": [ "### Ridge" ], "metadata": { "id": "L1lRt-CIMRSh" } }, { "cell_type": "code", "source": [ "### Train Loop\n", "m_ridge, _, pred_ridge = evaluate_regressor(\n", " best_ridge,\n", " train_df['text_baseline'], dev_df['text_baseline'],\n", " y_train_base, y_dev_base,\n", " model_name='TF-IDF + Ridge (tuned)'\n", ")\n", "all_results.append(m_ridge)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "WZ3B0vbVMSSk", "outputId": "385ecc07-3a29-4f65-beb5-62b9cb2b9007" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "============================================================\n", " TF-IDF + Ridge (tuned)\n", "============================================================\n", " MAE : 5.3618\n", " RMSE : 6.4914\n", " Category Accuracy: 0.1429\n", " Pred range: [4.3, 8.2]\n", " True range: [0, 23]\n" ] } ] }, { "cell_type": "markdown", "source": [ "### SVR" ], "metadata": { "id": "Ug63gTDwMUmw" } }, { "cell_type": "code", "source": [ "### Load Model — SVR\n", "pipeline_svr = Pipeline([\n", " ('tfidf', TfidfVectorizer(ngram_range=(1, 2), max_features=5000, min_df=2, sublinear_tf=True)),\n", " ('svd', TruncatedSVD(n_components=100, random_state=SEED)),\n", " ('scaler', StandardScaler()),\n", " ('svr', SVR(kernel='rbf', C=1.0, epsilon=1.0))\n", "])" ], "metadata": { "id": "9O0UJJWFMVeO" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "### Train Loop\n", "m_svr, _, pred_svr = evaluate_regressor(\n", " pipeline_svr,\n", " train_df['text_baseline'], dev_df['text_baseline'],\n", " y_train_base, y_dev_base,\n", " model_name='TF-IDF + SVD + SVR'\n", ")\n", "all_results.append(m_svr)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ebOFmscxMYUh", "outputId": "85391541-b146-477f-bde9-9e8593e9ffa0" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "============================================================\n", " TF-IDF + SVD + SVR\n", "============================================================\n", " MAE : 5.4685\n", " RMSE : 6.8659\n", " Category Accuracy: 0.1714\n", " Pred range: [4.9, 5.5]\n", " True range: [0, 23]\n" ] } ] }, { "cell_type": "markdown", "source": [ "### GBR" ], "metadata": { "id": "fTiE7FvHMZ6X" } }, { "cell_type": "code", "source": [ "### Load Model — Gradient Boosting\n", "pipeline_gbr = Pipeline([\n", " ('tfidf', TfidfVectorizer(ngram_range=(1, 2), max_features=3000, min_df=2, sublinear_tf=True)),\n", " ('svd', TruncatedSVD(n_components=100, random_state=SEED)),\n", " ('gbr', GradientBoostingRegressor(n_estimators=200, learning_rate=0.05, max_depth=3, random_state=SEED))\n", "])" ], "metadata": { "id": "b1O5EbWHMa2Q" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "### Train Loop\n", "m_gbr, _, pred_gbr = evaluate_regressor(\n", " pipeline_gbr,\n", " train_df['text_baseline'], dev_df['text_baseline'],\n", " y_train_base, y_dev_base,\n", " model_name='TF-IDF + SVD + GBR'\n", ")\n", "all_results.append(m_gbr)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "5NfcVC46MdNu", "outputId": "920e2ecb-ee6e-4380-ae06-48e01372aca7" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "============================================================\n", " TF-IDF + SVD + GBR\n", "============================================================\n", " MAE : 5.5499\n", " RMSE : 7.0963\n", " Category Accuracy: 0.2857\n", " Pred range: [3.4, 6.9]\n", " True range: [0, 23]\n" ] } ] }, { "cell_type": "markdown", "source": [ "### Ensamble" ], "metadata": { "id": "vBVhNC4vM5nA" } }, { "cell_type": "code", "source": [ "### Load Model — Combined Features\n", "ridge_combined = Ridge(alpha=5.0)" ], "metadata": { "id": "hwRU9OVkM-D1" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "### Train Loop\n", "m_comb, model_comb, pred_comb = evaluate_regressor(\n", " ridge_combined,\n", " X_train_combined, X_dev_combined,\n", " y_train_base, y_dev_base,\n", " model_name='TF-IDF + Non-Verbal + Ridge'\n", ")\n", "all_results.append(m_comb)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "9x-Dj9W3M8-7", "outputId": "ec723b77-a7a3-4799-bcd5-ce87a5b06bad" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "============================================================\n", " TF-IDF + Non-Verbal + Ridge\n", "============================================================\n", " MAE : 5.5879\n", " RMSE : 7.1236\n", " Category Accuracy: 0.1714\n", " Pred range: [0.6, 9.4]\n", " True range: [0, 23]\n" ] } ] }, { "cell_type": "markdown", "source": [ "### Eval" ], "metadata": { "id": "k8jLd42HM_U3" } }, { "cell_type": "code", "source": [ "### Evaluation\n", "results_df = pd.DataFrame(all_results).sort_values('mae')\n", "\n", "print(\"\\n\" + \"=\"*65)\n", "print(\"MODEL COMPARISON — Dev Set\")\n", "print(\"=\"*65)\n", "print(results_df.to_string(index=False, float_format='{:.4f}'.format))\n", "\n", "# Plot\n", "fig, axes = plt.subplots(1, 3, figsize=(16, 5))\n", "fig.suptitle('5.2 Baseline Regression — Model Comparison (Dev Set)', fontweight='bold')\n", "\n", "axes[0].barh(results_df['model'], results_df['mae'], color='#2196F3', alpha=0.85)\n", "axes[0].set_title('MAE (lower = better)')\n", "axes[0].axvline(x=3, color='red', linestyle='--', label='Target MAE ≤ 3')\n", "axes[0].legend()\n", "\n", "axes[1].barh(results_df['model'], results_df['rmse'], color='#FF9800', alpha=0.85)\n", "axes[1].set_title('RMSE (lower = better)')\n", "\n", "axes[2].barh(results_df['model'], results_df['category_accuracy'], color='#4CAF50', alpha=0.85)\n", "axes[2].set_title('Severity Category Accuracy')\n", "axes[2].axvline(x=0.5, color='red', linestyle='--', label='Random baseline')\n", "axes[2].legend()\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "print(f\"\\n🏆 Best model: {results_df.iloc[0]['model']}\")\n", "print(f\" MAE: {results_df.iloc[0]['mae']:.4f} | RMSE: {results_df.iloc[0]['rmse']:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 393 }, "id": "m086SVKoM_1t", "outputId": "fde1b52e-04b4-48d9-8334-7cc7249d1187" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "=================================================================\n", "MODEL COMPARISON — Dev Set\n", "=================================================================\n", " model mae rmse category_accuracy\n", " TF-IDF + Ridge (tuned) 5.3618 6.4914 0.1429\n", " TF-IDF + SVD + SVR 5.4685 6.8659 0.1714\n", " TF-IDF + SVD + GBR 5.5499 7.0963 0.2857\n", "TF-IDF + Non-Verbal + Ridge 5.5879 7.1236 0.1714\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "🏆 Best model: TF-IDF + Ridge (tuned)\n", " MAE: 5.3618 | RMSE: 6.4914\n" ] } ] }, { "cell_type": "code", "source": [ "print(f\"Train size: {len(train_df)} | Dev size: {len(dev_df)}\")\n", "print(f\"Train target — mean: {y_train_base.mean():.2f}, std: {y_train_base.std():.2f}, min: {y_train_base.min()}, max: {y_train_base.max()}\")\n", "print(f\"Dev target — mean: {y_dev_base.mean():.2f}, std: {y_dev_base.std():.2f}, min: {y_dev_base.min()}, max: {y_dev_base.max()}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ZSMlJz7MOwBG", "outputId": "fe9743cf-8524-451e-cd08-dbf19261fac2" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Train size: 107 | Dev size: 35\n", "Train target — mean: 6.42, std: 5.44, min: 0, max: 20\n", "Dev target — mean: 7.43, std: 6.50, min: 0, max: 23\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 5.3 E-GCN (by paper)" ], "metadata": { "id": "OW9zxae_31GZ" } }, { "cell_type": "code", "source": [ "# !pip install torch_geometric -q" ], "metadata": { "id": "uRis_ky55DD4" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Load Model" ], "metadata": { "id": "vj56w9Qj5Fq0" } }, { "cell_type": "code", "source": [ "EPOCHS_GCN = 200\n", "LR_GCN = 1e-3\n", "WEIGHT_DECAY_GCN = 1e-4\n", "DROPOUT_GCN = 0.2\n", "HIDDEN_GCN = 64\n", "TOP_K_WORDS = 250" ], "metadata": { "id": "6LJlLIBoFMhQ" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "# from torch_geometric.nn import GCNConv\n", "\n", "# NV_SIZE = nv_train.shape[1]\n", "\n", "# class GCNRegressorModel(nn.Module):\n", "# def __init__(self, in_channels, hidden_channels=64, nv_size=NV_SIZE, dropout=0.1):\n", "# super().__init__()\n", "# self.conv1 = GCNConv(in_channels, hidden_channels)\n", "# self.conv2 = GCNConv(hidden_channels, hidden_channels)\n", "# self.conv3 = GCNConv(hidden_channels, hidden_channels)\n", "# self.bn1 = nn.BatchNorm1d(hidden_channels)\n", "# self.bn2 = nn.BatchNorm1d(hidden_channels)\n", "# self.bn3 = nn.BatchNorm1d(hidden_channels)\n", "# self.dropout = nn.Dropout(dropout)\n", "# self.shared = nn.Linear(hidden_channels + nv_size, 128)\n", "# self.regressor = nn.Linear(128, 1) # PHQ-8 score\n", "# self.classifier = nn.Linear(128, 1) # binary depression label\n", "\n", "# def forward(self, x, edge_index, edge_weight, interview_mask, nv_features):\n", "# x = F.relu(self.bn1(self.conv1(x, edge_index, edge_weight)))\n", "# x = self.dropout(x)\n", "# residual = x\n", "# x = F.relu(self.bn2(self.conv2(x, edge_index, edge_weight)))\n", "# x = self.dropout(x)\n", "# x = F.relu(self.bn3(self.conv3(x, edge_index, edge_weight))) + residual\n", "# x = x[interview_mask]\n", "# x = F.relu(self.shared(torch.cat([x, nv_features], dim=1)))\n", "# return self.regressor(x).squeeze(-1), self.classifier(x).squeeze(-1)\n", "\n", "# model_gcn = GCNRegressorModel(\n", "# in_channels=vocab_size_gcn,\n", "# hidden_channels=HIDDEN_GCN,\n", "# nv_size=NV_SIZE,\n", "# dropout=DROPOUT_GCN\n", "# ).to(DEVICE)\n", "\n", "# trainable = sum(p.numel() for p in model_gcn.parameters() if p.requires_grad)\n", "# print(f\"NV_SIZE: {NV_SIZE}\")\n", "# print(f\"Trainable params: {trainable:,}\")" ], "metadata": { "id": "Sf7pU6rKCG7u" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from torch_geometric.nn import GATConv\n", "\n", "NV_SIZE = nv_train.shape[1]\n", "\n", "class GATRegressorModel(nn.Module):\n", " def __init__(self, in_channels, hidden_channels=64, nv_size=NV_SIZE, dropout=0.1, heads=4):\n", " super().__init__()\n", " self.conv1 = GATConv(in_channels, hidden_channels, heads=heads, dropout=dropout)\n", " self.conv2 = GATConv(hidden_channels * heads, hidden_channels, heads=heads, dropout=dropout)\n", " self.conv3 = GATConv(hidden_channels * heads, hidden_channels, heads=1, dropout=dropout, concat=False)\n", " self.bn1 = nn.BatchNorm1d(hidden_channels * heads)\n", " self.bn2 = nn.BatchNorm1d(hidden_channels * heads)\n", " self.bn3 = nn.BatchNorm1d(hidden_channels)\n", " self.dropout = nn.Dropout(dropout)\n", " self.regressor = nn.Sequential(\n", " nn.Linear(hidden_channels + nv_size, 128),\n", " nn.ReLU(),\n", " nn.Dropout(dropout),\n", " nn.Linear(128, 1)\n", " )\n", "\n", " def forward(self, x, edge_index, edge_weight, interview_mask, nv_features):\n", " x = F.relu(self.bn1(self.conv1(x, edge_index)))\n", " x = self.dropout(x)\n", " residual = x\n", " x = F.relu(self.bn2(self.conv2(x, edge_index)))\n", " x = self.dropout(x)\n", " x = F.relu(self.bn3(self.conv3(x, edge_index))) + residual[:, :64]\n", " x = x[interview_mask]\n", " x = torch.cat([x, nv_features], dim=1)\n", " return self.regressor(x).squeeze(-1)\n", "\n", "model_gcn = GATRegressorModel(\n", " in_channels=vocab_size_gcn,\n", " hidden_channels=HIDDEN_GCN,\n", " nv_size=NV_SIZE,\n", " dropout=DROPOUT_GCN\n", ").to(DEVICE)\n", "\n", "trainable = sum(p.numel() for p in model_gcn.parameters() if p.requires_grad)\n", "print(f\"NV_SIZE: {NV_SIZE}\")\n", "print(f\"Trainable params: {trainable:,}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "7lKxa0EJIU1E", "outputId": "a3887268-27b2-4976-bb1a-fd67cde69f6b" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "NV_SIZE: 27\n", "Trainable params: 160,705\n" ] } ] }, { "cell_type": "code", "source": [ "# EPOCHS_GCN = 50\n", "# LR_GCN = 1e-3\n", "# WEIGHT_DECAY_GCN = 1e-4\n", "# DROPOUT_GCN = 0.2\n", "# HIDDEN_GCN = 64\n", "# TOP_K_WORDS = 250" ], "metadata": { "id": "uuL6xdxg5Kl7" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Build Graph" ], "metadata": { "id": "_SWgPgkN5PDV" } }, { "cell_type": "code", "source": [ "import torch.nn.functional as F\n", "from torch_geometric.utils import from_scipy_sparse_matrix\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n", "from sklearn.feature_selection import SelectKBest, f_classif\n", "import scipy.sparse as sp\n", "\n", "# TF-IDF word-interview edges\n", "tfidf_gcn = TfidfVectorizer(\n", " max_features=5000,\n", " min_df=2,\n", " sublinear_tf=True,\n", " ngram_range=(1, 2)\n", " )\n", "\n", "X_tfidf_train_gcn = tfidf_gcn.fit_transform(train_df['text_clean'])\n", "X_tfidf_dev_gcn = tfidf_gcn.transform(dev_df['text_clean'])\n", "\n", "# Feature selection top-250 kata\n", "selector_gcn = SelectKBest(f_classif, k=TOP_K_WORDS)\n", "selector_gcn.fit(X_tfidf_train_gcn, train_df['label'].values)\n", "X_tfidf_train_gcn = selector_gcn.transform(X_tfidf_train_gcn)\n", "X_tfidf_dev_gcn = selector_gcn.transform(X_tfidf_dev_gcn)\n", "\n", "vocab_size_gcn = X_tfidf_train_gcn.shape[1] # 250\n", "n_train_gcn = len(train_df)\n", "n_nodes_gcn = vocab_size_gcn + n_train_gcn\n", "\n", "# Node features: eye(250) untuk word nodes, tfidf untuk interview nodes\n", "word_feats = torch.eye(vocab_size_gcn)\n", "interview_feats = torch.tensor(X_tfidf_train_gcn.toarray(), dtype=torch.float)\n", "x_gcn = torch.cat([word_feats, interview_feats], dim=0) # (n_nodes, 250)\n", "\n", "# Edges: interview-word dari TF-IDF\n", "cx = X_tfidf_train_gcn.tocoo()\n", "rows = np.concatenate([cx.row + vocab_size_gcn, cx.col])\n", "cols = np.concatenate([cx.col, cx.row + vocab_size_gcn])\n", "vals = np.concatenate([cx.data, cx.data])\n", "\n", "edge_index_gcn = torch.tensor([rows, cols], dtype=torch.long)\n", "edge_weight_gcn = torch.tensor(vals, dtype=torch.float)\n", "\n", "# Mask interview nodes\n", "interview_mask_gcn = torch.zeros(n_nodes_gcn, dtype=torch.bool)\n", "interview_mask_gcn[vocab_size_gcn:] = True\n", "\n", "print(f\"Nodes: {n_nodes_gcn} | Edges: {edge_index_gcn.shape[1]}\")\n", "print(f\"Node feature dim: {x_gcn.shape[1]}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Q5ag9oqq5QO9", "outputId": "609fec76-d58b-48e2-f382-607f8ad46a2e" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Nodes: 357 | Edges: 4506\n", "Node feature dim: 250\n" ] } ] }, { "cell_type": "markdown", "source": [ "### Data Loader" ], "metadata": { "id": "lsxCoNkX5T12" } }, { "cell_type": "code", "source": [ "x_gcn = x_gcn.to(DEVICE)\n", "edge_index_gcn = edge_index_gcn.to(DEVICE)\n", "edge_weight_gcn = edge_weight_gcn.to(DEVICE)\n", "interview_mask_gcn = interview_mask_gcn.to(DEVICE)\n", "y_train_gcn = torch.tensor(y_train, dtype=torch.float).to(DEVICE)\n", "nv_train_tensor = torch.tensor(nv_train, dtype=torch.float).to(DEVICE)" ], "metadata": { "id": "KBXLd8dT5TFn" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Head Reggression" ], "metadata": { "id": "oESRelgY5W_M" } }, { "cell_type": "code", "source": [ "NV_SIZE = nv_train.shape[1] # tambah ini\n", "\n", "model_gcn = GCNRegressorModel(\n", " in_channels=vocab_size_gcn,\n", " hidden_channels=HIDDEN_GCN,\n", " nv_size=NV_SIZE,\n", " dropout=DROPOUT_GCN\n", ").to(DEVICE)\n", "\n", "trainable = sum(p.numel() for p in model_gcn.parameters() if p.requires_grad)\n", "print(f\"NV_SIZE: {NV_SIZE}\")\n", "print(f\"Trainable params: {trainable:,}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MOsgO99O5U-F", "outputId": "e57499a4-2785-41a4-dc04-2d567bde144d" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "NV_SIZE: 27\n", "Trainable params: 36,802\n" ] } ] }, { "cell_type": "markdown", "source": [ "### Optimizer" ], "metadata": { "id": "7dqmre-B5axK" } }, { "cell_type": "code", "source": [ "optimizer_gcn = optim.AdamW(model_gcn.parameters(), lr=LR_GCN, weight_decay=WEIGHT_DECAY_GCN)\n", "loss_fn_reg = nn.MSELoss()\n", "loss_fn_cls = nn.BCEWithLogitsLoss()\n", "\n", "print(f\"Optimizer: AdamW | LR: {LR_GCN} | Epochs: {EPOCHS_GCN}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "4LhMpeSz5cAO", "outputId": "8fe161e5-eddc-4120-ac88-e8fda9ed1422" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Optimizer: AdamW | LR: 0.001 | Epochs: 200\n" ] } ] }, { "cell_type": "code", "source": [ "# optimizer_gcn = optim.AdamW(model_gcn.parameters(), lr=LR_GCN, weight_decay=WEIGHT_DECAY_GCN)\n", "# loss_fn_gcn = nn.HuberLoss(delta=3.0) # ganti dari MSELoss\n", "\n", "# print(f\"Optimizer: AdamW | LR: {LR_GCN} | Epochs: {EPOCHS_GCN}\")" ], "metadata": { "id": "YdWPtMvbBzIh" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Train Loop" ], "metadata": { "id": "LwJeurT65eW_" } }, { "cell_type": "code", "source": [ "history_gcn = {'train_loss': [], 'val_mae': [], 'val_rmse': [], 'val_r2': []}\n", "best_val_loss_gcn = float('inf')\n", "best_epoch_gcn = -1\n", "patience = 20\n", "no_improve = 0\n", "\n", "y_train_label_gcn = torch.tensor(train_df['label'].values, dtype=torch.float).to(DEVICE)\n", "nv_test_tensor = torch.tensor(nv_test, dtype=torch.float).to(DEVICE)\n", "y_test_gcn = torch.tensor(y_test, dtype=torch.float).to(DEVICE)\n", "\n", "for epoch in range(1, EPOCHS_GCN + 1):\n", " # Train\n", " model_gcn.train()\n", " optimizer_gcn.zero_grad()\n", " pred_reg, pred_cls = model_gcn(x_gcn, edge_index_gcn, edge_weight_gcn, interview_mask_gcn, nv_train_tensor)\n", " loss_r = loss_fn_reg(pred_reg, y_train_gcn)\n", " loss_c = loss_fn_cls(pred_cls, y_train_label_gcn)\n", " loss = loss_r + 0.3 * loss_c # multitask: regression dominan\n", " loss.backward()\n", " nn.utils.clip_grad_norm_(model_gcn.parameters(), 1.0)\n", " optimizer_gcn.step()\n", "\n", " # Eval\n", " model_gcn.eval()\n", " with torch.no_grad():\n", " interview_feats_dev_gpu = torch.tensor(X_tfidf_dev_gcn.toarray(), dtype=torch.float).to(DEVICE)\n", " x_eval = torch.cat([word_feats.to(DEVICE), interview_feats_dev_gpu], dim=0)\n", " cx_dev = X_tfidf_dev_gcn.tocoo()\n", " n_word = vocab_size_gcn\n", " rows_d = np.concatenate([cx_dev.row + n_word, cx_dev.col])\n", " cols_d = np.concatenate([cx_dev.col, cx_dev.row + n_word])\n", " vals_d = np.concatenate([cx_dev.data, cx_dev.data])\n", " ei_dev = torch.tensor([rows_d, cols_d], dtype=torch.long).to(DEVICE)\n", " ew_dev = torch.tensor(vals_d, dtype=torch.float).to(DEVICE)\n", " mask_dev = torch.zeros(n_word + len(dev_df), dtype=torch.bool).to(DEVICE)\n", " mask_dev[n_word:] = True\n", " preds_dev, _ = model_gcn(x_eval, ei_dev, ew_dev, mask_dev, nv_test_tensor)\n", " preds_dev = np.clip(preds_dev.cpu().numpy(), 0, 27)\n", "\n", " val_mae = mean_absolute_error(y_test, preds_dev)\n", " val_rmse = mean_squared_error(y_test, preds_dev) ** 0.5\n", " val_r2 = r2_score(y_test, preds_dev)\n", " val_loss = mean_squared_error(y_test, preds_dev)\n", "\n", " history_gcn['train_loss'].append(loss.item())\n", " history_gcn['val_mae'].append(val_mae)\n", " history_gcn['val_rmse'].append(val_rmse)\n", " history_gcn['val_r2'].append(val_r2)\n", "\n", " print(f\"Epoch {epoch:02d}/{EPOCHS_GCN} | Train Loss: {loss.item():.4f} | Val Loss: {val_loss:.4f} | MAE: {val_mae:.4f} | RMSE: {val_rmse:.4f} | R²: {val_r2:.4f}\")\n", "\n", " if val_loss < best_val_loss_gcn:\n", " best_val_loss_gcn = val_loss\n", " best_epoch_gcn = epoch\n", " no_improve = 0\n", " torch.save(model_gcn.state_dict(), \"best_model_gcn.pt\")\n", " print(f\"Best model saved (epoch {epoch})\")\n", " else:\n", " no_improve += 1\n", " if no_improve >= patience:\n", " print(f\"Early stopping at epoch {epoch}\")\n", " break\n", "\n", "print(f\"\\nDone. Best epoch: {best_epoch_gcn} | Best Val Loss: {best_val_loss_gcn:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "j3LDlkbY5fTO", "outputId": "4f7eea3f-32b4-4634-e317-df0f697a2c21" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Epoch 01/200 | Train Loss: 73.4761 | Val Loss: 96.8921 | MAE: 7.4063 | RMSE: 9.8434 | R²: -1.2967\n", "Best model saved (epoch 1)\n", "Epoch 02/200 | Train Loss: 72.2436 | Val Loss: 96.7431 | MAE: 7.3981 | RMSE: 9.8358 | R²: -1.2932\n", "Best model saved (epoch 2)\n", "Epoch 03/200 | Train Loss: 71.0436 | Val Loss: 96.5740 | MAE: 7.3895 | RMSE: 9.8272 | R²: -1.2891\n", "Best model saved (epoch 3)\n", "Epoch 04/200 | Train Loss: 69.9745 | Val Loss: 96.2557 | MAE: 7.3729 | RMSE: 9.8110 | R²: -1.2816\n", "Best model saved (epoch 4)\n", "Epoch 05/200 | Train Loss: 68.7990 | Val Loss: 95.7137 | MAE: 7.3448 | RMSE: 9.7833 | R²: -1.2688\n", "Best model saved (epoch 5)\n", "Epoch 06/200 | Train Loss: 67.6981 | Val Loss: 95.0270 | MAE: 7.3095 | RMSE: 9.7482 | R²: -1.2525\n", "Best model saved (epoch 6)\n", "Epoch 07/200 | Train Loss: 66.6059 | Val Loss: 94.2481 | MAE: 7.2702 | RMSE: 9.7081 | R²: -1.2340\n", "Best model saved (epoch 7)\n", "Epoch 08/200 | Train Loss: 65.4623 | Val Loss: 93.4237 | MAE: 7.2284 | RMSE: 9.6656 | R²: -1.2145\n", "Best model saved (epoch 8)\n", "Epoch 09/200 | Train Loss: 64.3200 | Val Loss: 92.5546 | MAE: 7.1836 | RMSE: 9.6205 | R²: -1.1939\n", "Best model saved (epoch 9)\n", "Epoch 10/200 | Train Loss: 63.2223 | Val Loss: 91.6357 | MAE: 7.1359 | RMSE: 9.5727 | R²: -1.1721\n", "Best model saved (epoch 10)\n", "Epoch 11/200 | Train Loss: 61.8682 | Val Loss: 90.6643 | MAE: 7.0854 | RMSE: 9.5218 | R²: -1.1491\n", "Best model saved (epoch 11)\n", "Epoch 12/200 | Train Loss: 60.6733 | Val Loss: 89.6412 | MAE: 7.0317 | RMSE: 9.4679 | R²: -1.1248\n", "Best model saved (epoch 12)\n", "Epoch 13/200 | Train Loss: 59.4385 | Val Loss: 88.5593 | MAE: 6.9745 | RMSE: 9.4106 | R²: -1.0992\n", "Best model saved (epoch 13)\n", "Epoch 14/200 | Train Loss: 58.0935 | Val Loss: 87.4055 | MAE: 6.9130 | RMSE: 9.3491 | R²: -1.0718\n", "Best model saved (epoch 14)\n", "Epoch 15/200 | Train Loss: 56.5298 | Val Loss: 86.1810 | MAE: 6.8470 | RMSE: 9.2834 | R²: -1.0428\n", "Best model saved (epoch 15)\n", "Epoch 16/200 | Train Loss: 55.1672 | Val Loss: 84.8961 | MAE: 6.7769 | RMSE: 9.2139 | R²: -1.0123\n", "Best model saved (epoch 16)\n", "Epoch 17/200 | Train Loss: 53.6248 | Val Loss: 83.5013 | MAE: 6.7000 | RMSE: 9.1379 | R²: -0.9793\n", "Best model saved (epoch 17)\n", "Epoch 18/200 | Train Loss: 52.3502 | Val Loss: 82.0682 | MAE: 6.6204 | RMSE: 9.0592 | R²: -0.9453\n", "Best model saved (epoch 18)\n", "Epoch 19/200 | Train Loss: 50.0876 | Val Loss: 80.4697 | MAE: 6.5403 | RMSE: 8.9705 | R²: -0.9074\n", "Best model saved (epoch 19)\n", "Epoch 20/200 | Train Loss: 48.7493 | Val Loss: 78.8052 | MAE: 6.4580 | RMSE: 8.8772 | R²: -0.8680\n", "Best model saved (epoch 20)\n", "Epoch 21/200 | Train Loss: 46.5983 | Val Loss: 77.0719 | MAE: 6.3701 | RMSE: 8.7791 | R²: -0.8269\n", "Best model saved (epoch 21)\n", "Epoch 22/200 | Train Loss: 44.3141 | Val Loss: 75.2063 | MAE: 6.2731 | RMSE: 8.6722 | R²: -0.7827\n", "Best model saved (epoch 22)\n", "Epoch 23/200 | Train Loss: 42.1936 | Val Loss: 73.2599 | MAE: 6.1692 | RMSE: 8.5592 | R²: -0.7365\n", "Best model saved (epoch 23)\n", "Epoch 24/200 | Train Loss: 40.5436 | Val Loss: 71.2539 | MAE: 6.0631 | RMSE: 8.4412 | R²: -0.6890\n", "Best model saved (epoch 24)\n", "Epoch 25/200 | Train Loss: 38.1232 | Val Loss: 69.1782 | MAE: 5.9644 | RMSE: 8.3173 | R²: -0.6398\n", "Best model saved (epoch 25)\n", "Epoch 26/200 | Train Loss: 36.2870 | Val Loss: 67.1269 | MAE: 5.8723 | RMSE: 8.1931 | R²: -0.5911\n", "Best model saved (epoch 26)\n", "Epoch 27/200 | Train Loss: 34.6331 | Val Loss: 65.0127 | MAE: 5.7779 | RMSE: 8.0630 | R²: -0.5410\n", "Best model saved (epoch 27)\n", "Epoch 28/200 | Train Loss: 31.9935 | Val Loss: 62.9847 | MAE: 5.6830 | RMSE: 7.9363 | R²: -0.4930\n", "Best model saved (epoch 28)\n", "Epoch 29/200 | Train Loss: 29.8969 | Val Loss: 61.0310 | MAE: 5.5976 | RMSE: 7.8122 | R²: -0.4467\n", "Best model saved (epoch 29)\n", "Epoch 30/200 | Train Loss: 27.5633 | Val Loss: 59.1063 | MAE: 5.5439 | RMSE: 7.6881 | R²: -0.4010\n", "Best model saved (epoch 30)\n", "Epoch 31/200 | Train Loss: 25.8460 | Val Loss: 57.3021 | MAE: 5.5028 | RMSE: 7.5698 | R²: -0.3583\n", "Best model saved (epoch 31)\n", "Epoch 32/200 | Train Loss: 22.6114 | Val Loss: 55.6159 | MAE: 5.4641 | RMSE: 7.4576 | R²: -0.3183\n", "Best model saved (epoch 32)\n", "Epoch 33/200 | Train Loss: 20.9884 | Val Loss: 54.0541 | MAE: 5.4252 | RMSE: 7.3521 | R²: -0.2813\n", "Best model saved (epoch 33)\n", "Epoch 34/200 | Train Loss: 19.2279 | Val Loss: 52.5960 | MAE: 5.3928 | RMSE: 7.2523 | R²: -0.2467\n", "Best model saved (epoch 34)\n", "Epoch 35/200 | Train Loss: 16.2423 | Val Loss: 51.3440 | MAE: 5.3677 | RMSE: 7.1655 | R²: -0.2170\n", "Best model saved (epoch 35)\n", "Epoch 36/200 | Train Loss: 14.9587 | Val Loss: 50.1539 | MAE: 5.3490 | RMSE: 7.0819 | R²: -0.1888\n", "Best model saved (epoch 36)\n", "Epoch 37/200 | Train Loss: 13.0558 | Val Loss: 49.1019 | MAE: 5.3282 | RMSE: 7.0073 | R²: -0.1639\n", "Best model saved (epoch 37)\n", "Epoch 38/200 | Train Loss: 11.1617 | Val Loss: 48.1405 | MAE: 5.3046 | RMSE: 6.9383 | R²: -0.1411\n", "Best model saved (epoch 38)\n", "Epoch 39/200 | Train Loss: 10.4343 | Val Loss: 47.3138 | MAE: 5.2779 | RMSE: 6.8785 | R²: -0.1215\n", "Best model saved (epoch 39)\n", "Epoch 40/200 | Train Loss: 8.4362 | Val Loss: 46.5266 | MAE: 5.2461 | RMSE: 6.8210 | R²: -0.1028\n", "Best model saved (epoch 40)\n", "Epoch 41/200 | Train Loss: 7.8759 | Val Loss: 45.8588 | MAE: 5.2099 | RMSE: 6.7719 | R²: -0.0870\n", "Best model saved (epoch 41)\n", "Epoch 42/200 | Train Loss: 6.3910 | Val Loss: 45.2842 | MAE: 5.1684 | RMSE: 6.7294 | R²: -0.0734\n", "Best model saved (epoch 42)\n", "Epoch 43/200 | Train Loss: 6.4473 | Val Loss: 44.8951 | MAE: 5.1250 | RMSE: 6.7004 | R²: -0.0642\n", "Best model saved (epoch 43)\n", "Epoch 44/200 | Train Loss: 6.0198 | Val Loss: 44.6449 | MAE: 5.0833 | RMSE: 6.6817 | R²: -0.0582\n", "Best model saved (epoch 44)\n", "Epoch 45/200 | Train Loss: 5.5848 | Val Loss: 44.5921 | MAE: 5.0438 | RMSE: 6.6777 | R²: -0.0570\n", "Best model saved (epoch 45)\n", "Epoch 46/200 | Train Loss: 4.9346 | Val Loss: 44.6310 | MAE: 5.0037 | RMSE: 6.6806 | R²: -0.0579\n", "Epoch 47/200 | Train Loss: 5.7220 | Val Loss: 44.8162 | MAE: 4.9695 | RMSE: 6.6945 | R²: -0.0623\n", "Epoch 48/200 | Train Loss: 5.8936 | Val Loss: 45.1502 | MAE: 4.9496 | RMSE: 6.7194 | R²: -0.0702\n", "Epoch 49/200 | Train Loss: 5.2026 | Val Loss: 45.6095 | MAE: 4.9309 | RMSE: 6.7535 | R²: -0.0811\n", "Epoch 50/200 | Train Loss: 5.7482 | Val Loss: 46.2056 | MAE: 4.9145 | RMSE: 6.7975 | R²: -0.0952\n", "Epoch 51/200 | Train Loss: 4.7854 | Val Loss: 46.9053 | MAE: 4.9031 | RMSE: 6.8487 | R²: -0.1118\n", "Epoch 52/200 | Train Loss: 4.2987 | Val Loss: 47.7000 | MAE: 4.9095 | RMSE: 6.9065 | R²: -0.1307\n", "Epoch 53/200 | Train Loss: 4.5246 | Val Loss: 48.5763 | MAE: 4.9240 | RMSE: 6.9697 | R²: -0.1514\n", "Epoch 54/200 | Train Loss: 4.8655 | Val Loss: 49.5219 | MAE: 4.9455 | RMSE: 7.0372 | R²: -0.1738\n", "Epoch 55/200 | Train Loss: 4.1584 | Val Loss: 50.3223 | MAE: 4.9719 | RMSE: 7.0938 | R²: -0.1928\n", "Epoch 56/200 | Train Loss: 4.1676 | Val Loss: 50.9609 | MAE: 4.9952 | RMSE: 7.1387 | R²: -0.2080\n", "Epoch 57/200 | Train Loss: 3.8288 | Val Loss: 51.4643 | MAE: 5.0214 | RMSE: 7.1739 | R²: -0.2199\n", "Epoch 58/200 | Train Loss: 4.0166 | Val Loss: 51.7538 | MAE: 5.0432 | RMSE: 7.1940 | R²: -0.2267\n", "Epoch 59/200 | Train Loss: 3.9768 | Val Loss: 51.8038 | MAE: 5.0598 | RMSE: 7.1975 | R²: -0.2279\n", "Epoch 60/200 | Train Loss: 3.5392 | Val Loss: 51.3765 | MAE: 5.0612 | RMSE: 7.1677 | R²: -0.2178\n", "Epoch 61/200 | Train Loss: 3.4859 | Val Loss: 50.6182 | MAE: 5.0506 | RMSE: 7.1146 | R²: -0.1998\n", "Epoch 62/200 | Train Loss: 3.1583 | Val Loss: 49.5041 | MAE: 5.0267 | RMSE: 7.0359 | R²: -0.1734\n", "Epoch 63/200 | Train Loss: 3.0095 | Val Loss: 48.1478 | MAE: 4.9900 | RMSE: 6.9389 | R²: -0.1413\n", "Epoch 64/200 | Train Loss: 2.8501 | Val Loss: 46.5221 | MAE: 4.9342 | RMSE: 6.8207 | R²: -0.1027\n", "Epoch 65/200 | Train Loss: 3.0698 | Val Loss: 44.9076 | MAE: 4.8699 | RMSE: 6.7013 | R²: -0.0645\n", "Early stopping at epoch 65\n", "\n", "Done. Best epoch: 45 | Best Val Loss: 44.5921\n" ] } ] }, { "cell_type": "code", "source": [ "print(f\"interview_mask_gcn sum: {interview_mask_gcn.cpu().sum()}\")\n", "print(f\"n_train_gcn : {n_train_gcn}\")\n", "print(f\"y_train_label_gcn shape: {y_train_label_gcn.shape}\")\n", "print(f\"nv_train_tensor shape : {nv_train_tensor.shape}\")\n", "print(f\"x_gcn shape : {x_gcn.shape}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "9kVZU7LwG9oc", "outputId": "53ede25a-c30f-4a3b-f994-df659a2ca396" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "interview_mask_gcn sum: 107\n", "n_train_gcn : 107\n", "y_train_label_gcn shape: torch.Size([107])\n", "nv_train_tensor shape : torch.Size([107, 27])\n", "x_gcn shape : torch.Size([357, 250])\n" ] } ] }, { "cell_type": "code", "source": [ "print(nv_train_tensor.shape)\n", "print(nv_test_tensor.shape)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "NhdL1WNpAmB6", "outputId": "70744dcd-79a5-443f-f5e3-01bc918eaa9f" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "torch.Size([107, 27])\n", "torch.Size([35, 27])\n" ] } ] }, { "cell_type": "code", "source": [ "epochs_range = range(1, len(history_gcn['train_loss']) + 1)\n", "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n", "\n", "# Plot Training Loss vs. Validation Loss\n", "axes[0].plot(epochs_range, history_gcn['train_loss'], label='Train Loss', marker='o', markersize=4, alpha=0.8)\n", "axes[0].axvline(best_epoch_gcn, color='red', linestyle='--', label=f'Best Epoch ({best_epoch_gcn})')\n", "axes[0].set_title('Loss — GCN')\n", "axes[0].set_xlabel('Epoch')\n", "axes[0].set_ylabel('Loss')\n", "axes[0].legend()\n", "axes[0].grid(True, linestyle='--', alpha=0.6)\n", "\n", "# Plot MAE & RMSE\n", "axes[1].plot(epochs_range, history_gcn['val_mae'], label='MAE', marker='s', markersize=4, color='orange', alpha=0.8)\n", "axes[1].plot(epochs_range, history_gcn['val_rmse'], label='RMSE', marker='s', markersize=4, color='purple', alpha=0.8)\n", "axes[1].axvline(best_epoch_gcn, color='red', linestyle='--')\n", "axes[1].set_title('MAE & RMSE — GCN')\n", "axes[1].set_xlabel('Epoch')\n", "axes[1].set_ylabel('Metric Value')\n", "axes[1].legend()\n", "axes[1].grid(True, linestyle='--', alpha=0.6)\n", "\n", "# Plot R²\n", "axes[2].plot(epochs_range, history_gcn['val_r2'], label='R²', marker='^', markersize=4, color='green', alpha=0.8)\n", "axes[2].axvline(best_epoch_gcn, color='red', linestyle='--')\n", "axes[2].set_title('R² — GCN')\n", "axes[2].set_xlabel('Epoch')\n", "axes[2].set_ylabel('R² Score')\n", "axes[2].legend()\n", "axes[2].grid(True, linestyle='--', alpha=0.6)\n", "\n", "plt.tight_layout()\n", "plt.show()\n", "\n", "# Save history to CSV\n", "history_df_gcn = pd.DataFrame(history_gcn, index=epochs_range)\n", "history_df_gcn.index.name = 'epoch'\n", "history_df_gcn.to_csv('training_history_gcn.csv')\n", "print(\"Training history saved to training_history_gcn.csv\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 185 }, "id": "SvrTQmuqLkNp", "outputId": "14a92a09-9d73-483e-d44e-8cbb162852e4" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Training history saved to training_history_gcn.csv\n" ] } ] }, { "cell_type": "markdown", "source": [ "# 6. Pre-processing Transformer Model" ], "metadata": { "id": "ozQbNk4CDWlA" } }, { "cell_type": "markdown", "source": [ "## 6.1 Split Data" ], "metadata": { "id": "CoDZhWgkAHbx" } }, { "cell_type": "code", "source": [ "# ── CELL: Split train/dev untuk transformer ───────────────────────────────────\n", "\n", "train_df = df[df['split'] == 'train'].copy().reset_index(drop=True)\n", "dev_df = df[df['split'] == 'dev'].copy().reset_index(drop=True)\n", "\n", "# Pastiin kolom extra ada (dibuat di section baseline, carry over ke sini)\n", "LENGTH_COLS = ['word_count', 'utterance_count', 'avg_utt_len', 'response_brevity']\n", "LEXICAL_COLS = ['neg_ratio', 'pos_ratio', 'sentiment_gap']\n", "\n", "# Kalau kolom ini belum ada (misal baseline section di-skip), bikin ulang\n", "if 'avg_utt_len' not in train_df.columns:\n", " for d in [train_df, dev_df]:\n", " d['avg_utt_len'] = d['word_count'] / (d['utterance_count'] + 1)\n", " d['response_brevity'] = (d['utterance_count'] < 50).astype(int)\n", "\n", "if 'neg_ratio' not in train_df.columns:\n", " NEGATIVE_WORDS = {'hopeless','worthless','empty','tired','sad','lonely',\n", " 'fail','useless','burden','numb','anxious','hate'}\n", " POSITIVE_WORDS = {'happy','good','fine','great','better','enjoy',\n", " 'love','hope','okay','well','improving'}\n", " def lexical_features(text):\n", " words = set(str(text).lower().split())\n", " neg = len(words & NEGATIVE_WORDS)\n", " pos = len(words & POSITIVE_WORDS)\n", " total = len(words) + 1\n", " return {'neg_ratio': neg/total, 'pos_ratio': pos/total, 'sentiment_gap': (neg-pos)/total}\n", " for d in [train_df, dev_df]:\n", " lex = d['text'].apply(lexical_features).apply(pd.Series)\n", " d[LEXICAL_COLS] = lex.values\n", "\n", "NV_FEATURE_COLS = [f'nv_{tag}' for tag in NONVERBAL_TAGS] + ['nv_total', 'filler_count']\n", "ALL_EXTRA_COLS = NV_FEATURE_COLS + LENGTH_COLS + LEXICAL_COLS\n", "\n", "from sklearn.preprocessing import StandardScaler\n", "extra_scaler = StandardScaler()\n", "nv_train = extra_scaler.fit_transform(train_df[ALL_EXTRA_COLS].fillna(0)).astype(np.float32)\n", "nv_dev = extra_scaler.transform(dev_df[ALL_EXTRA_COLS].fillna(0)).astype(np.float32)\n", "\n", "y_train = train_df['phq8_target'].values\n", "y_dev = dev_df['phq8_target'].values\n", "\n", "NV_SIZE = len(ALL_EXTRA_COLS)\n", "print(f\"Train: {len(train_df)} | Dev: {len(dev_df)}\")\n", "print(f\"NV feature size: {NV_SIZE}\")\n", "print(f\"Columns check: {ALL_EXTRA_COLS}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "1Y5WYY34AFpc", "outputId": "9860fcc4-8939-472a-c509-583e50097e7f" }, "execution_count": 50, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Train: 107 | Dev: 35\n", "NV feature size: 18\n", "Columns check: ['nv_laugh', 'nv_sigh', 'nv_cough', 'nv_breath', 'nv_sniff', 'nv_groan', 'nv_pause', 'nv_um', 'nv_uh', 'nv_total', 'filler_count', 'word_count', 'utterance_count', 'avg_utt_len', 'response_brevity', 'neg_ratio', 'pos_ratio', 'sentiment_gap']\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 6.2 Text Cleaning" ], "metadata": { "id": "HJNBwNFPAJCY" } }, { "cell_type": "code", "source": [ "# ── CELL: clean_text_transformer ─────────────────────────────────────────────\n", "\n", "def normalize_slang(text, slang_dict):\n", " pattern = re.compile(\n", " r'\\b(' + '|'.join(map(re.escape, slang_dict.keys())) + r')\\b',\n", " re.IGNORECASE\n", " )\n", " return pattern.sub(lambda m: slang_dict.get(m.group().lower(), m.group()), text)\n", "\n", "def clean_text_transformer(text, slang_dict=None):\n", " text = str(text).lower()\n", " text = re.sub(r'<[^>]+>', ' ', text)\n", " text = re.sub(r'\\[.*?\\]', ' ', text)\n", " text = re.sub(r'\\(.*?\\)', ' ', text)\n", " text = re.sub(r'scrubbed_entry', ' ', text)\n", " if slang_dict:\n", " text = normalize_slang(text, slang_dict)\n", " text = re.sub(r\"[^a-z0-9\\s']\", ' ', text)\n", " text = re.sub(r\"(? inner:\n", " h = min(head, inner // 2)\n", " t = inner - h\n", " ids = ids[:h] + ids[-t:]\n", "\n", " ids = [bos] + ids + [eos]\n", " pad = max_len - len(ids)\n", " mask = [1] * len(ids) + [0] * pad\n", " ids = ids + [tokenizer.pad_token_id] * pad\n", "\n", " return ids, mask\n", "\n", "# Quick check kedua tokenizer\n", "for name, tok in [(\"BERT\", tokenizer_bert), (\"MPNet\", tokenizer_mpnet)]:\n", " ids, mask = tokenize_head_tail(train_df['text_bert'].iloc[0], tok)\n", " print(f\"[{name}] ids len: {len(ids)} | non-pad tokens: {sum(mask)}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "vY-jcEqfAg9l", "outputId": "1239652c-ed5b-4b28-ddf2-f4e254494979" }, "execution_count": 55, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "[transformers] Token indices sequence length is longer than the specified maximum sequence length for this model (928 > 512). Running this sequence through the model will result in indexing errors\n", "[transformers] Token indices sequence length is longer than the specified maximum sequence length for this model (928 > 512). Running this sequence through the model will result in indexing errors\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[BERT] ids len: 512 | non-pad tokens: 512\n", "[MPNet] ids len: 512 | non-pad tokens: 512\n" ] } ] }, { "cell_type": "markdown", "source": [ "### MiniLM" ], "metadata": { "id": "b95y9x4gFRkp" } }, { "cell_type": "code", "source": [ "# ── CELL: Tokenizer MiniLM ────────────────────────────────────────────────────\n", "\n", "from transformers import AutoTokenizer\n", "\n", "MINILM_MODEL_NAME = \"sentence-transformers/all-MiniLM-L6-v2\"\n", "tokenizer_minilm = AutoTokenizer.from_pretrained(MINILM_MODEL_NAME)\n", "\n", "sample = train_df['text_bert'].iloc[0]\n", "tokens = tokenizer_minilm(\n", " sample,\n", " truncation=True,\n", " padding=\"max_length\",\n", " max_length=512,\n", " return_tensors=\"pt\"\n", ")\n", "print(\"[MiniLM] input_ids shape :\", tokens['input_ids'].shape)\n", "print(\"[MiniLM] attention_mask shape:\", tokens['attention_mask'].shape)\n", "print(\"[MiniLM] CLS token id :\", tokens['input_ids'][0][0].item())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 231, "referenced_widgets": [ "3dc7b6d8d4334771b912f3cc4ac2f24b", "4abbed3718cc4dec9f7d154025db1245", "4ab959c3e3f741be8b6078bab217115c", "805c260860da493daa27b2f3fcfb384c", "58f9a0e7e5b14d328f18d9cab5d156b4", "8f577de3d5424034a5b175c4b1459ae6", "f3c3983867e64f67b34fb7beee83b764", "eb0418f6f19a4c6ea04e5fb15cc73242", "1a5950617a134f83ab50a34494ea799f", "29af3687441a4eca977a49cb8d820d39", "21454dc2e95d4d3084818701bb388ee3", "088c579a23304e67b80bc4532caaa918", "8e6a029cd02b40b4b42b65c5ff81f0c9", "1819f8db365943c7b165c505a85f9059", "ef043a8ecea54da0bf9fbd5533414189", "0cab4876eae64f5dbadc9407c19645bb", "42015f941c4d4595a24273fa7a573168", "74ce1eeb3333418dac5d47fae2b13459", "e570847f0d4246df90266c02b94812b7", "fab34b84b65f45188c2dab3f190d9427", "483ac012db0d4f0bb4ce6d2318cd3f48", "df6bcb73aae5472393289ae329c5da63", "127580b2218c4622b1df09bc2dece359", "f473dba0b5c1449cb9863dfa47bf4884", "aa5fd41275f64eea8e27879eca9eb566", "cad46f22d1004c09bbfa2c3f5601f25a", "934567710e70481498b630d04f3028e1", "46befe6ebe3f41c59be09fbdcb7cf0a4", "d255e29011f4486e9f4618868321c034", "6d153212b999440587c7380d5027fdf5", "a4accceb01a6475495f9e097c7da05e2", "b13aff45ec424043a2751149ab14b07b", "1badd1ba063e4a468fdf80adfc16d585", "089d9b1283f14a8f9ec35906ca251b66", "777cf0361d2041a28a9ad97cc82dca41", "1efe9e40981e4421bca9282277af981e", "791830aa732d46bdae414c52a8f06b0d", "5fe15dd7bc854adba1fe32ce030ccd1c", "34ffec0a789143ef921c0e65eb39b2df", "cc81b2d1c9084876b375fa780cbbf077", "273e64a7fdaa41ddbf87cf4797dd9639", "066989efe995420491df8cfb563af3a4", "d409977a86194c3585734453d64f2686", "b875f56a5d3b4e27877c98c2e0db5151", "4a533c99a3724a3382fc0565f7cba05e", "2039b497b8144f16a8e7b6429898c0b1", "8d4d033be4b04efdb03cf69463440fca", "2aa472f432d94379a85af45af02c98b1", "110828b1523241369b5f9ee342482a63", "375c9b4ff9814a6886d5052e038ccba9", "00d3227c953645249747a5d7576702b3", "56c095db3441460b80d712c3604b4828", "c33ed0cc05ff4d1a9e7e63ea60dce780", "79df65241e9e4ed4ab9371e495496ddd", "a4ed9199cae247b69d90b668cd1304da" ] }, "id": "yo9Tgiw7FT4X", "outputId": "ba1aaabd-fd38-4082-bd39-06d7b06bc05a" }, "execution_count": 56, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/612 [00:00 512). Running this sequence through the model will result in indexing errors\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "[MiniLM] Train batches: 14 | Dev batches: 5\n", " input_ids: torch.Size([8, 512])\n", " attention_mask: torch.Size([8, 512])\n", " nv_features: torch.Size([8, 18])\n", " labels: torch.Size([8])\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 7.4 Model Definition" ], "metadata": { "id": "J_aMTTHMBvsU" } }, { "cell_type": "code", "source": [ "# ── CELL: Model Definition ────────────────────────────────────────────────────\n", "\n", "import torch.nn as nn\n", "from transformers import AutoModel\n", "\n", "class TransformerRegressor(nn.Module):\n", " \"\"\"\n", " Transformer encoder + regression head.\n", " head_type:\n", " 'linear' — CLS → Linear(1)\n", " 'mlp' — CLS → MLP → scalar\n", " 'attn' — weighted mean pool semua token → MLP → scalar\n", " nv_features digabung ke head input.\n", " \"\"\"\n", " def __init__(self, model_name, head_type='mlp', nv_size=NV_SIZE, dropout=0.1):\n", " super().__init__()\n", " self.encoder = AutoModel.from_pretrained(model_name)\n", " hidden = self.encoder.config.hidden_size\n", " self.head_type = head_type\n", " self.dropout = nn.Dropout(dropout)\n", "\n", " in_features = hidden + nv_size\n", "\n", " if head_type == 'linear':\n", " self.head = nn.Linear(in_features, 1)\n", "\n", " elif head_type == 'mlp':\n", " self.head = nn.Sequential(\n", " nn.Linear(in_features, 256),\n", " nn.LayerNorm(256),\n", " nn.GELU(),\n", " nn.Dropout(dropout),\n", " nn.Linear(256, 64),\n", " nn.GELU(),\n", " nn.Linear(64, 1),\n", " )\n", "\n", " elif head_type == 'attn':\n", " # Belajar bobot tiap token sebelum pooling\n", " self.token_attn = nn.Linear(hidden, 1)\n", " self.head = nn.Sequential(\n", " nn.Linear(in_features, 256),\n", " nn.LayerNorm(256),\n", " nn.GELU(),\n", " nn.Dropout(dropout),\n", " nn.Linear(256, 1),\n", " )\n", "\n", " def forward(self, input_ids, attention_mask, nv_features):\n", " out = self.encoder(input_ids=input_ids, attention_mask=attention_mask)\n", " hidden = out.last_hidden_state # (B, L, H)\n", "\n", " if self.head_type == 'attn':\n", " # Attention pool atas semua token (bukan cuma CLS)\n", " scores = self.token_attn(hidden).squeeze(-1) # (B, L)\n", " scores = scores.masked_fill(attention_mask == 0, -1e9)\n", " weights = torch.softmax(scores, dim=1).unsqueeze(-1) # (B, L, 1)\n", " pooled = (hidden * weights).sum(dim=1) # (B, H)\n", " else:\n", " pooled = hidden[:, 0, :] # CLS token\n", "\n", " pooled = self.dropout(pooled)\n", " x = torch.cat([pooled, nv_features], dim=-1)\n", " return self.head(x).squeeze(-1)" ], "metadata": { "id": "Ogb08joAB0vg" }, "execution_count": 64, "outputs": [] }, { "cell_type": "markdown", "source": [ "## 7.5 Inisiate BERT" ], "metadata": { "id": "7C5NrOS6B2tf" } }, { "cell_type": "code", "source": [ "# ── CELL: Instantiate BERT Models ────────────────────────────────────────────\n", "\n", "model_bert_mlp = TransformerRegressor(\n", " model_name = \"bert-base-uncased\",\n", " head_type = 'mlp',\n", " nv_size = NV_SIZE,\n", " dropout = 0.1,\n", ").to(DEVICE)\n", "\n", "model_bert_attn = TransformerRegressor(\n", " model_name = \"bert-base-uncased\",\n", " head_type = 'attn',\n", " nv_size = NV_SIZE,\n", " dropout = 0.1,\n", ").to(DEVICE)\n", "\n", "model_bert_linear = TransformerRegressor(\n", " model_name = \"bert-base-uncased\",\n", " head_type = 'linear',\n", " nv_size = NV_SIZE,\n", " dropout = 0.1,\n", ").to(DEVICE)\n", "\n", "def count_params(m):\n", " return sum(p.numel() for p in m.parameters() if p.requires_grad)\n", "\n", "print(f\"[BERT MLP] trainable params: {count_params(model_bert_mlp):,}\")\n", "print(f\"[BERT Attn] trainable params: {count_params(model_bert_attn):,}\")\n", "print(f\"[BERT Linear] trainable params: {count_params(model_bert_linear):,}\")\n", "print(f\"\\nEncoder hidden size: {model_bert_mlp.encoder.config.hidden_size}\")\n", "print(model_bert_mlp)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "0a2459c8a7de4294a1ade237676c96ab", "6934f03c261f4ce89cec74025add01bb", "675d00a8eb5d406391f384f23d1f12b0", "205bf6184a9640e69121ef97338be1f0", "0f17531695ba4a91a228659a24b4cea5", "1abbfdd8144c4545bd5858677d32ca73", "9f5c4c9922f24ca68de36427ac06ea5e", "aa5d032a46734c6f86a558d92e799f32", "d85fff2a40aa43078811dd69f64add3d", "7ec3e909ebdb4070b0edb1cd6c53f5ca", "3ccf81d28773416fb3d54a06021bf85d", "0a81951dcc464eefb1177cb38a2f73a3", "de64b156784b425a8ca67545da3a8ac4", "d4da9324dd354becb0f38b8221037ad6", "c87d01a2893841a8908e3b316994911b", "06b8f31470ea4d989140aeb88abc7fc5", "3c71a0d4ebb74eb1a17a1a5b0c5957f1", "faf347c958b54bfca10cc4df65151e89", "d8dcad34c5ff449992bf30e4e8a92d48", "b5aac165360b47a2b7fae1d8871a908b", "ad2958c3a71f477c977b768dbb3c4f1a", "1c5c7539f57145888d310d060e4c7074", "ceb65dd460ef4082ad02f44e8a1d3598", "6fb076778c9040689bea2689e7cccacc", "9d34c573558f4b61b2aa448dec8b763b", "0e36a1f93ebd45368150b1c5fcd79d0e", "d4974e0196804bafa6fb8bd875dbe6d2", "404569a966034d66b67d3868f16d355e", "fc390f5f4a3b4ffabeef9b86bd766b86", "4f1142afcfc9405cb1e841343cba97e2", "d4ff423744ad440db7addb1f6f1f6079", "7345ff3eadb14881a97b8afe739c211a", "909a41d45cdb4b1f9a108779cb820e3f", "ca947aba033d4389b5610c35871069e3", "959ab929add9432db6918499f6c24a1f", "973930ba65884315877004531fda4b36", "54e8ad1ad3a846c5a2d18558473146ec", "026a353a30d647ea940ab998d00f86a8", "6198c3916fe54eb19bc6876e62e2b241", "818ff352151c42fabe2b2e74b8b0260c", "8f826e39660c4507a71569a7bc1de69c", "87424abee7d94bbe85d7022470c10de8", "5d64709161a44ffc86917502917037e3", "49e13101eb3b4d5fae55de4c87a948cb" ] }, "id": "qQCc5h9pB6XH", "outputId": "c993f4f1-f5c1-403d-a124-dd036acd26c1" }, "execution_count": 65, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "model.safetensors: 0%| | 0.00/440M [00:00= patience:\n", " print(f\" Early stopping epoch {epoch}\"); break\n", "\n", "print(f\"Best epoch: {best_epoch} | Best MSE: {best_val_loss:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "a2G4nkXICP1T", "outputId": "647a8373-337e-4462-ebdb-2809230e7bd1" }, "execution_count": 77, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[BERT-MLP] Ep 01/40 | Loss: 0.5047 | MAE: 4.6422 | RMSE: 5.5312 | R²: 0.2748\n", " ✅ Saved (epoch 1)\n", "[BERT-MLP] Ep 02/40 | Loss: 0.3939 | MAE: 4.6809 | RMSE: 5.5075 | R²: 0.2810\n", " ✅ Saved (epoch 2)\n", "[BERT-MLP] Ep 03/40 | Loss: 0.4544 | MAE: 4.5749 | RMSE: 5.6233 | R²: 0.2504\n", "[BERT-MLP] Ep 04/40 | Loss: 0.3438 | MAE: 4.6105 | RMSE: 5.7594 | R²: 0.2137\n", "[BERT-MLP] Ep 05/40 | Loss: 0.4768 | MAE: 4.6478 | RMSE: 5.5328 | R²: 0.2744\n", "[BERT-MLP] Ep 06/40 | Loss: 0.4634 | MAE: 4.5605 | RMSE: 5.8181 | R²: 0.1976\n", "[BERT-MLP] Ep 07/40 | Loss: 0.5885 | MAE: 4.5850 | RMSE: 5.7610 | R²: 0.2133\n", "[BERT-MLP] Ep 08/40 | Loss: 0.4503 | MAE: 4.5920 | RMSE: 5.8749 | R²: 0.1819\n", "[BERT-MLP] Ep 09/40 | Loss: 0.6050 | MAE: 4.5874 | RMSE: 5.8443 | R²: 0.1904\n", " Early stopping epoch 9\n", "Best epoch: 2 | Best MSE: 30.3321\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 8.2 BERT Attn" ], "metadata": { "id": "LjZK_A7PCSqQ" } }, { "cell_type": "code", "source": [ "# ── CELL: Train BERT + Attn ───────────────────────────────────────────────────\n", "\n", "EPOCHS_TF = 20\n", "LR_TF = 1e-5\n", "SAVE_NAME = \"best_bert_attn.pt\"\n", "\n", "model = model_bert_attn\n", "optimizer = optim.AdamW(\n", " [{'params': model.encoder.parameters(), 'lr': LR_TF},\n", " {'params': model.token_attn.parameters(), 'lr': LR_TF * 10},\n", " {'params': model.head.parameters(), 'lr': LR_TF * 10}],\n", " weight_decay=1e-2\n", ")\n", "total_steps = len(train_loader_bert) * EPOCHS_TF\n", "scheduler = get_cosine_schedule_with_warmup(optimizer, int(total_steps * 0.1), total_steps)\n", "loss_fn = nn.HuberLoss(delta=3.0)\n", "\n", "history_bert_attn = {'train_loss': [], 'val_mae': [], 'val_rmse': [], 'val_r2': []}\n", "best_val_loss = float('inf')\n", "best_epoch = -1\n", "patience, no_impr = 7, 0\n", "\n", "for epoch in range(1, EPOCHS_TF + 1):\n", " tr_loss = train_one_epoch(model, train_loader_bert, optimizer, scheduler, DEVICE, loss_fn)\n", " metrics = evaluate(model, dev_loader_bert, DEVICE)\n", "\n", " history_bert_attn['train_loss'].append(tr_loss)\n", " history_bert_attn['val_mae'].append(metrics['mae'])\n", " history_bert_attn['val_rmse'].append(metrics['rmse'])\n", " history_bert_attn['val_r2'].append(metrics['r2'])\n", "\n", " print(f\"[BERT-Attn] Ep {epoch:02d}/{EPOCHS_TF} | Loss: {tr_loss:.4f} | MAE: {metrics['mae']:.4f} | RMSE: {metrics['rmse']:.4f} | R²: {metrics['r2']:.4f}\")\n", "\n", " if metrics['mse'] < best_val_loss:\n", " best_val_loss = metrics['mse']; best_epoch = epoch; no_impr = 0\n", " torch.save(model.state_dict(), SAVE_NAME)\n", " print(f\" ✅ Saved (epoch {epoch})\")\n", " else:\n", " no_impr += 1\n", " if no_impr >= patience:\n", " print(f\" Early stopping epoch {epoch}\"); break\n", "\n", "print(f\"Best epoch: {best_epoch} | Best MSE: {best_val_loss:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "LRr5VRWWCUkp", "outputId": "ed64bd3e-274c-4020-e77c-6e77d563b31f" }, "execution_count": 79, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[BERT-Attn] Ep 01/20 | Loss: 3.2805 | MAE: 5.2000 | RMSE: 6.3001 | R²: 0.0592\n", " ✅ Saved (epoch 1)\n", "[BERT-Attn] Ep 02/20 | Loss: 3.0686 | MAE: 5.0571 | RMSE: 6.4366 | R²: 0.0180\n", "[BERT-Attn] Ep 03/20 | Loss: 2.9033 | MAE: 5.2369 | RMSE: 6.2620 | R²: 0.0705\n", " ✅ Saved (epoch 3)\n", "[BERT-Attn] Ep 04/20 | Loss: 3.0525 | MAE: 5.0459 | RMSE: 6.3365 | R²: 0.0483\n", "[BERT-Attn] Ep 05/20 | Loss: 2.8548 | MAE: 5.1150 | RMSE: 6.2376 | R²: 0.0777\n", " ✅ Saved (epoch 5)\n", "[BERT-Attn] Ep 06/20 | Loss: 2.7821 | MAE: 5.1655 | RMSE: 6.2763 | R²: 0.0663\n", "[BERT-Attn] Ep 07/20 | Loss: 2.5095 | MAE: 5.0286 | RMSE: 6.3116 | R²: 0.0557\n", "[BERT-Attn] Ep 08/20 | Loss: 2.9203 | MAE: 5.3084 | RMSE: 6.1921 | R²: 0.0912\n", " ✅ Saved (epoch 8)\n", "[BERT-Attn] Ep 09/20 | Loss: 2.6084 | MAE: 5.0944 | RMSE: 6.1763 | R²: 0.0958\n", " ✅ Saved (epoch 9)\n", "[BERT-Attn] Ep 10/20 | Loss: 2.4585 | MAE: 5.0878 | RMSE: 6.1092 | R²: 0.1153\n", " ✅ Saved (epoch 10)\n", "[BERT-Attn] Ep 11/20 | Loss: 2.3433 | MAE: 5.0524 | RMSE: 6.1997 | R²: 0.0889\n", "[BERT-Attn] Ep 12/20 | Loss: 2.2121 | MAE: 5.0968 | RMSE: 6.1553 | R²: 0.1019\n", "[BERT-Attn] Ep 13/20 | Loss: 2.2950 | MAE: 5.0523 | RMSE: 6.1633 | R²: 0.0996\n", "[BERT-Attn] Ep 14/20 | Loss: 2.2144 | MAE: 5.0150 | RMSE: 6.1439 | R²: 0.1052\n", "[BERT-Attn] Ep 15/20 | Loss: 2.1830 | MAE: 5.0785 | RMSE: 6.0786 | R²: 0.1242\n", " ✅ Saved (epoch 15)\n", "[BERT-Attn] Ep 16/20 | Loss: 2.2196 | MAE: 5.1148 | RMSE: 6.0745 | R²: 0.1254\n", " ✅ Saved (epoch 16)\n", "[BERT-Attn] Ep 17/20 | Loss: 2.2464 | MAE: 5.0249 | RMSE: 6.1505 | R²: 0.1033\n", "[BERT-Attn] Ep 18/20 | Loss: 2.0616 | MAE: 5.0699 | RMSE: 6.1040 | R²: 0.1168\n", "[BERT-Attn] Ep 19/20 | Loss: 2.2888 | MAE: 5.0703 | RMSE: 6.1052 | R²: 0.1165\n", "[BERT-Attn] Ep 20/20 | Loss: 2.0837 | MAE: 5.0706 | RMSE: 6.1051 | R²: 0.1165\n", "Best epoch: 16 | Best MSE: 36.8995\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 8.3 BERT Linear" ], "metadata": { "id": "Uup02-DKCXG7" } }, { "cell_type": "code", "source": [ "# ── CELL: Train BERT + Linear ─────────────────────────────────────────────────\n", "\n", "EPOCHS_TF = 20\n", "LR_TF = 2e-5\n", "SAVE_NAME = \"best_bert_linear.pt\"\n", "\n", "model = model_bert_linear\n", "optimizer = optim.AdamW(\n", " [{'params': model.encoder.parameters(), 'lr': LR_TF},\n", " {'params': model.head.parameters(), 'lr': LR_TF * 10}],\n", " weight_decay=1e-2\n", ")\n", "total_steps = len(train_loader_bert) * EPOCHS_TF\n", "scheduler = get_cosine_schedule_with_warmup(optimizer, int(total_steps * 0.1), total_steps)\n", "loss_fn = nn.HuberLoss(delta=3.0)\n", "\n", "history_bert_linear = {'train_loss': [], 'val_mae': [], 'val_rmse': [], 'val_r2': []}\n", "best_val_loss = float('inf')\n", "best_epoch = -1\n", "patience, no_impr = 7, 0\n", "\n", "for epoch in range(1, EPOCHS_TF + 1):\n", " tr_loss = train_one_epoch(model, train_loader_bert, optimizer, scheduler, DEVICE, loss_fn)\n", " metrics = evaluate(model, dev_loader_bert, DEVICE)\n", "\n", " history_bert_linear['train_loss'].append(tr_loss)\n", " history_bert_linear['val_mae'].append(metrics['mae'])\n", " history_bert_linear['val_rmse'].append(metrics['rmse'])\n", " history_bert_linear['val_r2'].append(metrics['r2'])\n", "\n", " print(f\"[BERT-Lin] Ep {epoch:02d}/{EPOCHS_TF} | Loss: {tr_loss:.4f} | MAE: {metrics['mae']:.4f} | RMSE: {metrics['rmse']:.4f} | R²: {metrics['r2']:.4f}\")\n", "\n", " if metrics['mse'] < best_val_loss:\n", " best_val_loss = metrics['mse']; best_epoch = epoch; no_impr = 0\n", " torch.save(model.state_dict(), SAVE_NAME)\n", " print(f\" ✅ Saved (epoch {epoch})\")\n", " else:\n", " no_impr += 1\n", " if no_impr >= patience:\n", " print(f\" Early stopping epoch {epoch}\"); break\n", "\n", "print(f\"Best epoch: {best_epoch} | Best MSE: {best_val_loss:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6qzkAC7qCddc", "outputId": "fbe6540a-37ba-4492-ebad-e472c31d72be" }, "execution_count": 80, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[BERT-Lin] Ep 01/20 | Loss: 0.6739 | MAE: 4.9965 | RMSE: 6.3573 | R²: 0.0420\n", " ✅ Saved (epoch 1)\n", "[BERT-Lin] Ep 02/20 | Loss: 0.7223 | MAE: 4.9379 | RMSE: 6.3833 | R²: 0.0342\n", "[BERT-Lin] Ep 03/20 | Loss: 0.8918 | MAE: 5.0576 | RMSE: 6.2024 | R²: 0.0881\n", " ✅ Saved (epoch 3)\n", "[BERT-Lin] Ep 04/20 | Loss: 1.2597 | MAE: 5.0562 | RMSE: 6.5495 | R²: -0.0168\n", "[BERT-Lin] Ep 05/20 | Loss: 0.8090 | MAE: 5.0766 | RMSE: 6.7298 | R²: -0.0736\n", "[BERT-Lin] Ep 06/20 | Loss: 0.8845 | MAE: 4.9981 | RMSE: 6.5831 | R²: -0.0273\n", "[BERT-Lin] Ep 07/20 | Loss: 0.8674 | MAE: 5.3318 | RMSE: 6.2062 | R²: 0.0870\n", "[BERT-Lin] Ep 08/20 | Loss: 1.0645 | MAE: 5.0373 | RMSE: 6.4313 | R²: 0.0196\n", "[BERT-Lin] Ep 09/20 | Loss: 0.5107 | MAE: 4.9971 | RMSE: 6.4660 | R²: 0.0090\n", "[BERT-Lin] Ep 10/20 | Loss: 0.5402 | MAE: 5.0851 | RMSE: 6.1935 | R²: 0.0908\n", " ✅ Saved (epoch 10)\n", "[BERT-Lin] Ep 11/20 | Loss: 0.4678 | MAE: 5.0390 | RMSE: 6.5411 | R²: -0.0142\n", "[BERT-Lin] Ep 12/20 | Loss: 0.4877 | MAE: 5.0422 | RMSE: 6.5378 | R²: -0.0132\n", "[BERT-Lin] Ep 13/20 | Loss: 0.4242 | MAE: 5.1006 | RMSE: 6.5082 | R²: -0.0040\n", "[BERT-Lin] Ep 14/20 | Loss: 0.3307 | MAE: 5.0924 | RMSE: 6.4312 | R²: 0.0196\n", "[BERT-Lin] Ep 15/20 | Loss: 0.3430 | MAE: 5.0472 | RMSE: 6.5358 | R²: -0.0125\n", "[BERT-Lin] Ep 16/20 | Loss: 0.2893 | MAE: 5.0609 | RMSE: 6.3042 | R²: 0.0580\n", "[BERT-Lin] Ep 17/20 | Loss: 0.2042 | MAE: 5.0406 | RMSE: 6.4362 | R²: 0.0181\n", " Early stopping epoch 17\n", "Best epoch: 10 | Best MSE: 38.3590\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 8.4 MPNet MLP" ], "metadata": { "id": "6WbScWcICexB" } }, { "cell_type": "code", "source": [ "# ── CELL: Train MPNet + MLP ───────────────────────────────────────────────────\n", "\n", "EPOCHS_TF = 20\n", "LR_TF = 1e-5\n", "SAVE_NAME = \"best_mpnet_mlp.pt\"\n", "\n", "model = model_mpnet_mlp\n", "optimizer = optim.AdamW(\n", " [{'params': model.encoder.parameters(), 'lr': LR_TF},\n", " {'params': model.head.parameters(), 'lr': LR_TF * 10}],\n", " weight_decay=1e-2\n", ")\n", "total_steps = len(train_loader_mpnet) * EPOCHS_TF\n", "scheduler = get_cosine_schedule_with_warmup(optimizer, int(total_steps * 0.1), total_steps)\n", "loss_fn = nn.HuberLoss(delta=3.0)\n", "\n", "history_mpnet_mlp = {'train_loss': [], 'val_mae': [], 'val_rmse': [], 'val_r2': []}\n", "best_val_loss = float('inf')\n", "best_epoch = -1\n", "patience, no_impr = 7, 0\n", "\n", "for epoch in range(1, EPOCHS_TF + 1):\n", " tr_loss = train_one_epoch(model, train_loader_mpnet, optimizer, scheduler, DEVICE, loss_fn)\n", " metrics = evaluate(model, dev_loader_mpnet, DEVICE)\n", "\n", " history_mpnet_mlp['train_loss'].append(tr_loss)\n", " history_mpnet_mlp['val_mae'].append(metrics['mae'])\n", " history_mpnet_mlp['val_rmse'].append(metrics['rmse'])\n", " history_mpnet_mlp['val_r2'].append(metrics['r2'])\n", "\n", " print(f\"[MPNet-MLP] Ep {epoch:02d}/{EPOCHS_TF} | Loss: {tr_loss:.4f} | MAE: {metrics['mae']:.4f} | RMSE: {metrics['rmse']:.4f} | R²: {metrics['r2']:.4f}\")\n", "\n", " if metrics['mse'] < best_val_loss:\n", " best_val_loss = metrics['mse']; best_epoch = epoch; no_impr = 0\n", " torch.save(model.state_dict(), SAVE_NAME)\n", " print(f\" ✅ Saved (epoch {epoch})\")\n", " else:\n", " no_impr += 1\n", " if no_impr >= patience:\n", " print(f\" Early stopping epoch {epoch}\"); break\n", "\n", "print(f\"Best epoch: {best_epoch} | Best MSE: {best_val_loss:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "hyLh5M93CjeC", "outputId": "5d60aa1a-e3a4-48f1-b4cd-27cc89891fbf" }, "execution_count": 81, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[MPNet-MLP] Ep 01/20 | Loss: 6.5622 | MAE: 5.0175 | RMSE: 6.4276 | R²: 0.0207\n", " ✅ Saved (epoch 1)\n", "[MPNet-MLP] Ep 02/20 | Loss: 6.2723 | MAE: 4.6785 | RMSE: 6.2600 | R²: 0.0711\n", " ✅ Saved (epoch 2)\n", "[MPNet-MLP] Ep 03/20 | Loss: 5.8545 | MAE: 5.1941 | RMSE: 6.3708 | R²: 0.0380\n", "[MPNet-MLP] Ep 04/20 | Loss: 5.7563 | MAE: 5.2309 | RMSE: 6.3331 | R²: 0.0493\n", "[MPNet-MLP] Ep 05/20 | Loss: 5.5651 | MAE: 4.7042 | RMSE: 6.0145 | R²: 0.1426\n", " ✅ Saved (epoch 5)\n", "[MPNet-MLP] Ep 06/20 | Loss: 5.3928 | MAE: 4.5098 | RMSE: 5.9300 | R²: 0.1665\n", " ✅ Saved (epoch 6)\n", "[MPNet-MLP] Ep 07/20 | Loss: 5.0226 | MAE: 4.5863 | RMSE: 5.8715 | R²: 0.1828\n", " ✅ Saved (epoch 7)\n", "[MPNet-MLP] Ep 08/20 | Loss: 4.4632 | MAE: 4.1344 | RMSE: 5.6117 | R²: 0.2535\n", " ✅ Saved (epoch 8)\n", "[MPNet-MLP] Ep 09/20 | Loss: 4.4963 | MAE: 4.1493 | RMSE: 5.5329 | R²: 0.2744\n", " ✅ Saved (epoch 9)\n", "[MPNet-MLP] Ep 10/20 | Loss: 4.0044 | MAE: 4.1624 | RMSE: 5.4563 | R²: 0.2943\n", " ✅ Saved (epoch 10)\n", "[MPNet-MLP] Ep 11/20 | Loss: 4.5001 | MAE: 4.5883 | RMSE: 5.7058 | R²: 0.2283\n", "[MPNet-MLP] Ep 12/20 | Loss: 3.9877 | MAE: 4.8452 | RMSE: 5.8994 | R²: 0.1751\n", "[MPNet-MLP] Ep 13/20 | Loss: 3.9717 | MAE: 4.2491 | RMSE: 5.5177 | R²: 0.2783\n", "[MPNet-MLP] Ep 14/20 | Loss: 3.8199 | MAE: 4.2671 | RMSE: 5.4852 | R²: 0.2868\n", "[MPNet-MLP] Ep 15/20 | Loss: 4.1102 | MAE: 4.6385 | RMSE: 5.7153 | R²: 0.2257\n", "[MPNet-MLP] Ep 16/20 | Loss: 3.4509 | MAE: 4.2580 | RMSE: 5.4560 | R²: 0.2944\n", " ✅ Saved (epoch 16)\n", "[MPNet-MLP] Ep 17/20 | Loss: 3.6169 | MAE: 4.3484 | RMSE: 5.5007 | R²: 0.2828\n", "[MPNet-MLP] Ep 18/20 | Loss: 3.2956 | MAE: 4.4131 | RMSE: 5.5385 | R²: 0.2729\n", "[MPNet-MLP] Ep 19/20 | Loss: 3.2332 | MAE: 4.4124 | RMSE: 5.5370 | R²: 0.2733\n", "[MPNet-MLP] Ep 20/20 | Loss: 3.3961 | MAE: 4.3968 | RMSE: 5.5270 | R²: 0.2759\n", "Best epoch: 16 | Best MSE: 29.7675\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 8.5 MPNet Attn" ], "metadata": { "id": "2Oc36f9hCm7q" } }, { "cell_type": "code", "source": [ "# ── CELL: Train MPNet + Attn ──────────────────────────────────────────────────\n", "\n", "EPOCHS_TF = 20\n", "LR_TF = 1e-5\n", "SAVE_NAME = \"best_mpnet_attn.pt\"\n", "\n", "model = model_mpnet_attn\n", "optimizer = optim.AdamW(\n", " [{'params': model.encoder.parameters(), 'lr': LR_TF},\n", " {'params': model.token_attn.parameters(), 'lr': LR_TF * 10},\n", " {'params': model.head.parameters(), 'lr': LR_TF * 10}],\n", " weight_decay=1e-2\n", ")\n", "total_steps = len(train_loader_mpnet) * EPOCHS_TF\n", "scheduler = get_cosine_schedule_with_warmup(optimizer, int(total_steps * 0.1), total_steps)\n", "loss_fn = nn.HuberLoss(delta=3.0)\n", "\n", "history_mpnet_attn = {'train_loss': [], 'val_mae': [], 'val_rmse': [], 'val_r2': []}\n", "best_val_loss = float('inf')\n", "best_epoch = -1\n", "patience, no_impr = 7, 0\n", "\n", "for epoch in range(1, EPOCHS_TF + 1):\n", " tr_loss = train_one_epoch(model, train_loader_mpnet, optimizer, scheduler, DEVICE, loss_fn)\n", " metrics = evaluate(model, dev_loader_mpnet, DEVICE)\n", "\n", " history_mpnet_attn['train_loss'].append(tr_loss)\n", " history_mpnet_attn['val_mae'].append(metrics['mae'])\n", " history_mpnet_attn['val_rmse'].append(metrics['rmse'])\n", " history_mpnet_attn['val_r2'].append(metrics['r2'])\n", "\n", " print(f\"[MPNet-Attn] Ep {epoch:02d}/{EPOCHS_TF} | Loss: {tr_loss:.4f} | MAE: {metrics['mae']:.4f} | RMSE: {metrics['rmse']:.4f} | R²: {metrics['r2']:.4f}\")\n", "\n", " if metrics['mse'] < best_val_loss:\n", " best_val_loss = metrics['mse']; best_epoch = epoch; no_impr = 0\n", " torch.save(model.state_dict(), SAVE_NAME)\n", " print(f\" ✅ Saved (epoch {epoch})\")\n", " else:\n", " no_impr += 1\n", " if no_impr >= patience:\n", " print(f\" Early stopping epoch {epoch}\"); break\n", "\n", "print(f\"Best epoch: {best_epoch} | Best MSE: {best_val_loss:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 216 }, "id": "xOpqqDElCptk", "outputId": "0118b013-495f-4705-e796-9668f5e529e1" }, "execution_count": 2, "outputs": [ { "output_type": "error", "ename": "NameError", "evalue": "name 'model_mpnet_attn' is not defined", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m/tmp/ipykernel_2176/2122877788.py\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mSAVE_NAME\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"best_mpnet_attn.pt\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel_mpnet_attn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 8\u001b[0m optimizer = optim.AdamW(\n\u001b[1;32m 9\u001b[0m [{'params': model.encoder.parameters(), 'lr': LR_TF},\n", "\u001b[0;31mNameError\u001b[0m: name 'model_mpnet_attn' is not defined" ] } ] }, { "cell_type": "code", "source": [ "# ── CELL: Train MPNet + Attn ──────────────────────────────────────────────────\n", "\n", "EPOCHS_TF = 35\n", "LR_TF = 2e-5\n", "SAVE_NAME = \"best_mpnet_attn.pt\"\n", "\n", "model = model_mpnet_attn\n", "optimizer = optim.AdamW(\n", " [{'params': model.encoder.parameters(), 'lr': LR_TF},\n", " {'params': model.token_attn.parameters(), 'lr': LR_TF * 10},\n", " {'params': model.head.parameters(), 'lr': LR_TF * 10}],\n", " weight_decay=1e-2\n", ")\n", "total_steps = len(train_loader_mpnet) * EPOCHS_TF\n", "scheduler = get_cosine_schedule_with_warmup(optimizer, int(total_steps * 0.1), total_steps)\n", "loss_fn = nn.HuberLoss(delta=3.0)\n", "\n", "history_mpnet_attn = {'train_loss': [], 'val_mae': [], 'val_rmse': [], 'val_r2': []}\n", "best_val_loss = float('inf')\n", "best_epoch = -1\n", "patience, no_impr = 7, 0\n", "\n", "for epoch in range(1, EPOCHS_TF + 1):\n", " tr_loss = train_one_epoch(model, train_loader_mpnet, optimizer, scheduler, DEVICE, loss_fn)\n", " metrics = evaluate(model, dev_loader_mpnet, DEVICE)\n", "\n", " history_mpnet_attn['train_loss'].append(tr_loss)\n", " history_mpnet_attn['val_mae'].append(metrics['mae'])\n", " history_mpnet_attn['val_rmse'].append(metrics['rmse'])\n", " history_mpnet_attn['val_r2'].append(metrics['r2'])\n", "\n", " print(f\"[MPNet-Attn] Ep {epoch:02d}/{EPOCHS_TF} | Loss: {tr_loss:.4f} | MAE: {metrics['mae']:.4f} | RMSE: {metrics['rmse']:.4f} | R²: {metrics['r2']:.4f}\")\n", "\n", " if metrics['mse'] < best_val_loss:\n", " best_val_loss = metrics['mse']; best_epoch = epoch; no_impr = 0\n", " torch.save(model.state_dict(), SAVE_NAME)\n", " print(f\" ✅ Saved (epoch {epoch})\")\n", " else:\n", " no_impr += 1\n", " if no_impr >= patience:\n", " print(f\" Early stopping epoch {epoch}\"); break\n", "\n", "print(f\"Best epoch: {best_epoch} | Best MSE: {best_val_loss:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "t7_InX1YRcCq", "outputId": "d3033615-b378-4c3b-e86b-972bda8b2b86" }, "execution_count": 59, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[MPNet-Attn] Ep 01/35 | Loss: 4.0665 | MAE: 4.5709 | RMSE: 6.1834 | R²: 0.0937\n", " ✅ Saved (epoch 1)\n", "[MPNet-Attn] Ep 02/35 | Loss: 3.9874 | MAE: 4.6293 | RMSE: 6.0513 | R²: 0.1320\n", " ✅ Saved (epoch 2)\n", "[MPNet-Attn] Ep 03/35 | Loss: 4.0860 | MAE: 4.6451 | RMSE: 5.9864 | R²: 0.1505\n", " ✅ Saved (epoch 3)\n", "[MPNet-Attn] Ep 04/35 | Loss: 3.7560 | MAE: 4.4960 | RMSE: 6.3831 | R²: 0.0342\n", "[MPNet-Attn] Ep 05/35 | Loss: 4.3299 | MAE: 4.7105 | RMSE: 6.1615 | R²: 0.1001\n", "[MPNet-Attn] Ep 06/35 | Loss: 3.7349 | MAE: 4.4975 | RMSE: 5.8318 | R²: 0.1938\n", " ✅ Saved (epoch 6)\n", "[MPNet-Attn] Ep 07/35 | Loss: 3.6111 | MAE: 4.4156 | RMSE: 6.1222 | R²: 0.1116\n", "[MPNet-Attn] Ep 08/35 | Loss: 3.3290 | MAE: 4.6306 | RMSE: 6.5524 | R²: -0.0177\n", "[MPNet-Attn] Ep 09/35 | Loss: 3.2196 | MAE: 4.2107 | RMSE: 5.6247 | R²: 0.2501\n", " ✅ Saved (epoch 9)\n", "[MPNet-Attn] Ep 10/35 | Loss: 3.0814 | MAE: 4.2159 | RMSE: 5.8189 | R²: 0.1974\n", "[MPNet-Attn] Ep 11/35 | Loss: 3.2135 | MAE: 4.1161 | RMSE: 5.6859 | R²: 0.2337\n", "[MPNet-Attn] Ep 12/35 | Loss: 2.9465 | MAE: 4.1310 | RMSE: 5.4887 | R²: 0.2859\n", " ✅ Saved (epoch 12)\n", "[MPNet-Attn] Ep 13/35 | Loss: 2.4240 | MAE: 4.2650 | RMSE: 5.5337 | R²: 0.2742\n", "[MPNet-Attn] Ep 14/35 | Loss: 2.3282 | MAE: 4.3893 | RMSE: 5.5736 | R²: 0.2637\n", "[MPNet-Attn] Ep 15/35 | Loss: 2.0639 | MAE: 4.2656 | RMSE: 5.6752 | R²: 0.2366\n", "[MPNet-Attn] Ep 16/35 | Loss: 1.9674 | MAE: 4.3419 | RMSE: 5.5516 | R²: 0.2694\n", "[MPNet-Attn] Ep 17/35 | Loss: 2.2346 | MAE: 4.1913 | RMSE: 5.6050 | R²: 0.2553\n", "[MPNet-Attn] Ep 18/35 | Loss: 2.0205 | MAE: 4.1673 | RMSE: 5.6637 | R²: 0.2397\n", "[MPNet-Attn] Ep 19/35 | Loss: 1.9343 | MAE: 4.2988 | RMSE: 5.6059 | R²: 0.2551\n", " Early stopping epoch 19\n", "Best epoch: 12 | Best MSE: 30.1253\n" ] } ] }, { "cell_type": "code", "source": [ "# ── CELL: Train MPNet + Attn ──────────────────────────────────────────────────\n", "\n", "EPOCHS_TF = 35\n", "LR_TF = 1e-5\n", "SAVE_NAME = \"best_mpnet_attn.pt\"\n", "\n", "model = model_mpnet_attn\n", "optimizer = optim.AdamW(\n", " [{'params': model.encoder.parameters(), 'lr': LR_TF},\n", " {'params': model.token_attn.parameters(), 'lr': LR_TF * 10},\n", " {'params': model.head.parameters(), 'lr': LR_TF * 10}],\n", " weight_decay=1e-2\n", ")\n", "total_steps = len(train_loader_mpnet) * EPOCHS_TF\n", "scheduler = get_cosine_schedule_with_warmup(optimizer, int(total_steps * 0.1), total_steps)\n", "loss_fn = nn.HuberLoss(delta=3.0)\n", "\n", "history_mpnet_attn = {'train_loss': [], 'val_mae': [], 'val_rmse': [], 'val_r2': []}\n", "best_val_loss = float('inf')\n", "best_epoch = -1\n", "patience, no_impr = 7, 0\n", "\n", "for epoch in range(1, EPOCHS_TF + 1):\n", " tr_loss = train_one_epoch(model, train_loader_mpnet, optimizer, scheduler, DEVICE, loss_fn)\n", " metrics = evaluate(model, dev_loader_mpnet, DEVICE)\n", "\n", " history_mpnet_attn['train_loss'].append(tr_loss)\n", " history_mpnet_attn['val_mae'].append(metrics['mae'])\n", " history_mpnet_attn['val_rmse'].append(metrics['rmse'])\n", " history_mpnet_attn['val_r2'].append(metrics['r2'])\n", "\n", " print(f\"[MPNet-Attn] Ep {epoch:02d}/{EPOCHS_TF} | Loss: {tr_loss:.4f} | MAE: {metrics['mae']:.4f} | RMSE: {metrics['rmse']:.4f} | R²: {metrics['r2']:.4f}\")\n", "\n", " if metrics['mse'] < best_val_loss:\n", " best_val_loss = metrics['mse']; best_epoch = epoch; no_impr = 0\n", " torch.save(model.state_dict(), SAVE_NAME)\n", " print(f\" ✅ Saved (epoch {epoch})\")\n", " else:\n", " no_impr += 1\n", " if no_impr >= patience:\n", " print(f\" Early stopping epoch {epoch}\"); break\n", "\n", "print(f\"Best epoch: {best_epoch} | Best MSE: {best_val_loss:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "JNxck9LcTRT-", "outputId": "eaeffffa-2192-46d1-aef0-b412f3103666" }, "execution_count": 61, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[MPNet-Attn] Ep 01/35 | Loss: 1.9564 | MAE: 4.1842 | RMSE: 5.5263 | R²: 0.2761\n", " ✅ Saved (epoch 1)\n", "[MPNet-Attn] Ep 02/35 | Loss: 1.6960 | MAE: 4.2419 | RMSE: 5.5978 | R²: 0.2572\n", "[MPNet-Attn] Ep 03/35 | Loss: 1.6080 | MAE: 4.2845 | RMSE: 5.6463 | R²: 0.2443\n", "[MPNet-Attn] Ep 04/35 | Loss: 1.6767 | MAE: 4.2495 | RMSE: 5.6054 | R²: 0.2552\n", "[MPNet-Attn] Ep 05/35 | Loss: 1.5503 | MAE: 4.3124 | RMSE: 5.6889 | R²: 0.2329\n", "[MPNet-Attn] Ep 06/35 | Loss: 1.6376 | MAE: 4.0964 | RMSE: 5.3180 | R²: 0.3296\n", " ✅ Saved (epoch 6)\n", "[MPNet-Attn] Ep 07/35 | Loss: 1.7667 | MAE: 4.3226 | RMSE: 5.5254 | R²: 0.2763\n", "[MPNet-Attn] Ep 08/35 | Loss: 1.6526 | MAE: 4.1940 | RMSE: 5.4926 | R²: 0.2849\n", "[MPNet-Attn] Ep 09/35 | Loss: 1.3705 | MAE: 4.3609 | RMSE: 5.6432 | R²: 0.2452\n", "[MPNet-Attn] Ep 10/35 | Loss: 1.3278 | MAE: 4.4806 | RMSE: 5.8994 | R²: 0.1750\n", "[MPNet-Attn] Ep 11/35 | Loss: 1.2774 | MAE: 4.3279 | RMSE: 5.4689 | R²: 0.2910\n", "[MPNet-Attn] Ep 12/35 | Loss: 1.2606 | MAE: 4.4014 | RMSE: 5.5643 | R²: 0.2661\n", "[MPNet-Attn] Ep 13/35 | Loss: 1.3467 | MAE: 4.3065 | RMSE: 5.5024 | R²: 0.2823\n", " Early stopping epoch 13\n", "Best epoch: 6 | Best MSE: 28.2812\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 8.6 MPNet Linear" ], "metadata": { "id": "PE5flHWDCpGO" } }, { "cell_type": "code", "source": [ "# ── CELL: Train MPNet + Linear ────────────────────────────────────────────────\n", "\n", "EPOCHS_TF = 20\n", "LR_TF = 1e-5\n", "SAVE_NAME = \"best_mpnet_linear.pt\"\n", "\n", "model = model_mpnet_linear\n", "optimizer = optim.AdamW(\n", " [{'params': model.encoder.parameters(), 'lr': LR_TF},\n", " {'params': model.head.parameters(), 'lr': LR_TF * 10}],\n", " weight_decay=1e-2\n", ")\n", "total_steps = len(train_loader_mpnet) * EPOCHS_TF\n", "scheduler = get_cosine_schedule_with_warmup(optimizer, int(total_steps * 0.1), total_steps)\n", "loss_fn = nn.HuberLoss(delta=3.0)\n", "\n", "history_mpnet_linear = {'train_loss': [], 'val_mae': [], 'val_rmse': [], 'val_r2': []}\n", "best_val_loss = float('inf')\n", "best_epoch = -1\n", "patience, no_impr = 7, 0\n", "\n", "for epoch in range(1, EPOCHS_TF + 1):\n", " tr_loss = train_one_epoch(model, train_loader_mpnet, optimizer, scheduler, DEVICE, loss_fn)\n", " metrics = evaluate(model, dev_loader_mpnet, DEVICE)\n", "\n", " history_mpnet_linear['train_loss'].append(tr_loss)\n", " history_mpnet_linear['val_mae'].append(metrics['mae'])\n", " history_mpnet_linear['val_rmse'].append(metrics['rmse'])\n", " history_mpnet_linear['val_r2'].append(metrics['r2'])\n", "\n", " print(f\"[MPNet-Lin] Ep {epoch:02d}/{EPOCHS_TF} | Loss: {tr_loss:.4f} | MAE: {metrics['mae']:.4f} | RMSE: {metrics['rmse']:.4f} | R²: {metrics['r2']:.4f}\")\n", "\n", " if metrics['mse'] < best_val_loss:\n", " best_val_loss = metrics['mse']; best_epoch = epoch; no_impr = 0\n", " torch.save(model.state_dict(), SAVE_NAME)\n", " print(f\" ✅ Saved (epoch {epoch})\")\n", " else:\n", " no_impr += 1\n", " if no_impr >= patience:\n", " print(f\" Early stopping epoch {epoch}\"); break\n", "\n", "print(f\"Best epoch: {best_epoch} | Best MSE: {best_val_loss:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ApHFuSQ1Cvvx", "outputId": "af3a8b5c-a0ec-41e8-c73e-ce388e27c66f" }, "execution_count": 84, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[MPNet-Lin] Ep 01/20 | Loss: 2.6555 | MAE: 4.9363 | RMSE: 6.5339 | R²: -0.0120\n", " ✅ Saved (epoch 1)\n", "[MPNet-Lin] Ep 02/20 | Loss: 2.4177 | MAE: 4.8184 | RMSE: 6.3037 | R²: 0.0581\n", " ✅ Saved (epoch 2)\n", "[MPNet-Lin] Ep 03/20 | Loss: 2.6401 | MAE: 4.8466 | RMSE: 6.1179 | R²: 0.1128\n", " ✅ Saved (epoch 3)\n", "[MPNet-Lin] Ep 04/20 | Loss: 2.5222 | MAE: 4.8013 | RMSE: 6.3047 | R²: 0.0578\n", "[MPNet-Lin] Ep 05/20 | Loss: 2.3130 | MAE: 4.7635 | RMSE: 6.1122 | R²: 0.1144\n", " ✅ Saved (epoch 5)\n", "[MPNet-Lin] Ep 06/20 | Loss: 2.2656 | MAE: 4.8012 | RMSE: 6.3601 | R²: 0.0412\n", "[MPNet-Lin] Ep 07/20 | Loss: 2.1714 | MAE: 4.8049 | RMSE: 6.3668 | R²: 0.0391\n", "[MPNet-Lin] Ep 08/20 | Loss: 2.1144 | MAE: 4.7977 | RMSE: 6.2124 | R²: 0.0852\n", "[MPNet-Lin] Ep 09/20 | Loss: 2.1097 | MAE: 4.8280 | RMSE: 6.3939 | R²: 0.0309\n", "[MPNet-Lin] Ep 10/20 | Loss: 2.1265 | MAE: 4.7990 | RMSE: 6.3704 | R²: 0.0381\n", "[MPNet-Lin] Ep 11/20 | Loss: 1.9333 | MAE: 4.7960 | RMSE: 6.1902 | R²: 0.0917\n", "[MPNet-Lin] Ep 12/20 | Loss: 2.3311 | MAE: 4.7805 | RMSE: 6.4588 | R²: 0.0112\n", " Early stopping epoch 12\n", "Best epoch: 5 | Best MSE: 37.3594\n" ] } ] }, { "cell_type": "code", "source": [ "# ── CELL: Train MPNet + Linear ────────────────────────────────────────────────\n", "\n", "EPOCHS_TF = 30\n", "LR_TF = 2e-5\n", "SAVE_NAME = \"best_mpnet_linear.pt\"\n", "\n", "model = model_mpnet_linear\n", "optimizer = optim.AdamW(\n", " [{'params': model.encoder.parameters(), 'lr': LR_TF},\n", " {'params': model.head.parameters(), 'lr': LR_TF * 10}],\n", " weight_decay=1e-2\n", ")\n", "total_steps = len(train_loader_mpnet) * EPOCHS_TF\n", "scheduler = get_cosine_schedule_with_warmup(optimizer, int(total_steps * 0.1), total_steps)\n", "loss_fn = nn.HuberLoss(delta=3.0)\n", "\n", "history_mpnet_linear = {'train_loss': [], 'val_mae': [], 'val_rmse': [], 'val_r2': []}\n", "best_val_loss = float('inf')\n", "best_epoch = -1\n", "patience, no_impr = 7, 0\n", "\n", "for epoch in range(1, EPOCHS_TF + 1):\n", " tr_loss = train_one_epoch(model, train_loader_mpnet, optimizer, scheduler, DEVICE, loss_fn)\n", " metrics = evaluate(model, dev_loader_mpnet, DEVICE)\n", "\n", " history_mpnet_linear['train_loss'].append(tr_loss)\n", " history_mpnet_linear['val_mae'].append(metrics['mae'])\n", " history_mpnet_linear['val_rmse'].append(metrics['rmse'])\n", " history_mpnet_linear['val_r2'].append(metrics['r2'])\n", "\n", " print(f\"[MPNet-Lin] Ep {epoch:02d}/{EPOCHS_TF} | Loss: {tr_loss:.4f} | MAE: {metrics['mae']:.4f} | RMSE: {metrics['rmse']:.4f} | R²: {metrics['r2']:.4f}\")\n", "\n", " if metrics['mse'] < best_val_loss:\n", " best_val_loss = metrics['mse']; best_epoch = epoch; no_impr = 0\n", " torch.save(model.state_dict(), SAVE_NAME)\n", " print(f\" ✅ Saved (epoch {epoch})\")\n", " else:\n", " no_impr += 1\n", " if no_impr >= patience:\n", " print(f\" Early stopping epoch {epoch}\"); break\n", "\n", "print(f\"Best epoch: {best_epoch} | Best MSE: {best_val_loss:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "frpTg7T6VWtF", "outputId": "39eb944a-0a50-406e-bd2d-c36bd13f0ff4" }, "execution_count": 85, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[MPNet-Lin] Ep 01/30 | Loss: 1.9438 | MAE: 4.7323 | RMSE: 6.2258 | R²: 0.0812\n", " ✅ Saved (epoch 1)\n", "[MPNet-Lin] Ep 02/30 | Loss: 1.9345 | MAE: 4.6950 | RMSE: 6.2229 | R²: 0.0821\n", " ✅ Saved (epoch 2)\n", "[MPNet-Lin] Ep 03/30 | Loss: 2.0515 | MAE: 4.8064 | RMSE: 6.3821 | R²: 0.0345\n", "[MPNet-Lin] Ep 04/30 | Loss: 1.8942 | MAE: 4.8182 | RMSE: 6.3300 | R²: 0.0502\n", "[MPNet-Lin] Ep 05/30 | Loss: 1.7820 | MAE: 4.9324 | RMSE: 6.3116 | R²: 0.0557\n", "[MPNet-Lin] Ep 06/30 | Loss: 1.7937 | MAE: 4.6891 | RMSE: 6.2141 | R²: 0.0847\n", " ✅ Saved (epoch 6)\n", "[MPNet-Lin] Ep 07/30 | Loss: 1.7167 | MAE: 4.6596 | RMSE: 6.1262 | R²: 0.1104\n", " ✅ Saved (epoch 7)\n", "[MPNet-Lin] Ep 08/30 | Loss: 1.8588 | MAE: 4.5379 | RMSE: 5.7716 | R²: 0.2104\n", " ✅ Saved (epoch 8)\n", "[MPNet-Lin] Ep 09/30 | Loss: 1.7872 | MAE: 4.7604 | RMSE: 5.9560 | R²: 0.1591\n", "[MPNet-Lin] Ep 10/30 | Loss: 1.9186 | MAE: 4.6536 | RMSE: 6.1815 | R²: 0.0943\n", "[MPNet-Lin] Ep 11/30 | Loss: 1.4922 | MAE: 4.5979 | RMSE: 6.1087 | R²: 0.1155\n", "[MPNet-Lin] Ep 12/30 | Loss: 1.4333 | MAE: 4.5390 | RMSE: 6.0708 | R²: 0.1264\n", "[MPNet-Lin] Ep 13/30 | Loss: 1.3928 | MAE: 4.5033 | RMSE: 6.0101 | R²: 0.1438\n", "[MPNet-Lin] Ep 14/30 | Loss: 1.3916 | MAE: 4.5722 | RMSE: 5.9214 | R²: 0.1689\n", "[MPNet-Lin] Ep 15/30 | Loss: 1.2290 | MAE: 4.8035 | RMSE: 6.3142 | R²: 0.0550\n", " Early stopping epoch 15\n", "Best epoch: 8 | Best MSE: 33.3110\n" ] } ] }, { "cell_type": "code", "source": [ "# ── CELL: Train MPNet + Linear ────────────────────────────────────────────────\n", "\n", "EPOCHS_TF = 40\n", "LR_TF = 2e-5\n", "SAVE_NAME = \"best_mpnet_linear.pt\"\n", "\n", "model = model_mpnet_linear\n", "optimizer = optim.AdamW(\n", " [{'params': model.encoder.parameters(), 'lr': LR_TF},\n", " {'params': model.head.parameters(), 'lr': LR_TF * 10}],\n", " weight_decay=1e-2\n", ")\n", "total_steps = len(train_loader_mpnet) * EPOCHS_TF\n", "scheduler = get_cosine_schedule_with_warmup(optimizer, int(total_steps * 0.1), total_steps)\n", "loss_fn = nn.HuberLoss(delta=3.0)\n", "\n", "history_mpnet_linear = {'train_loss': [], 'val_mae': [], 'val_rmse': [], 'val_r2': []}\n", "best_val_loss = float('inf')\n", "best_epoch = -1\n", "patience, no_impr = 7, 0\n", "\n", "for epoch in range(1, EPOCHS_TF + 1):\n", " tr_loss = train_one_epoch(model, train_loader_mpnet, optimizer, scheduler, DEVICE, loss_fn)\n", " metrics = evaluate(model, dev_loader_mpnet, DEVICE)\n", "\n", " history_mpnet_linear['train_loss'].append(tr_loss)\n", " history_mpnet_linear['val_mae'].append(metrics['mae'])\n", " history_mpnet_linear['val_rmse'].append(metrics['rmse'])\n", " history_mpnet_linear['val_r2'].append(metrics['r2'])\n", "\n", " print(f\"[MPNet-Lin] Ep {epoch:02d}/{EPOCHS_TF} | Loss: {tr_loss:.4f} | MAE: {metrics['mae']:.4f} | RMSE: {metrics['rmse']:.4f} | R²: {metrics['r2']:.4f}\")\n", "\n", " if metrics['mse'] < best_val_loss:\n", " best_val_loss = metrics['mse']; best_epoch = epoch; no_impr = 0\n", " torch.save(model.state_dict(), SAVE_NAME)\n", " print(f\" ✅ Saved (epoch {epoch})\")\n", " else:\n", " no_impr += 1\n", " if no_impr >= patience:\n", " print(f\" Early stopping epoch {epoch}\"); break\n", "\n", "print(f\"Best epoch: {best_epoch} | Best MSE: {best_val_loss:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "dT9BTADtTqHb", "outputId": "9c3e22ad-c99d-47f1-a81b-bd7e35d0a60d" }, "execution_count": 86, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[MPNet-Lin] Ep 01/40 | Loss: 1.2613 | MAE: 4.7742 | RMSE: 6.2880 | R²: 0.0628\n", " ✅ Saved (epoch 1)\n", "[MPNet-Lin] Ep 02/40 | Loss: 1.4488 | MAE: 4.8075 | RMSE: 6.3872 | R²: 0.0330\n", "[MPNet-Lin] Ep 03/40 | Loss: 1.1697 | MAE: 4.8342 | RMSE: 6.4770 | R²: 0.0056\n", "[MPNet-Lin] Ep 04/40 | Loss: 1.1944 | MAE: 4.7421 | RMSE: 6.3878 | R²: 0.0328\n", "[MPNet-Lin] Ep 05/40 | Loss: 1.2698 | MAE: 4.9037 | RMSE: 6.4201 | R²: 0.0230\n", "[MPNet-Lin] Ep 06/40 | Loss: 1.1224 | MAE: 5.0895 | RMSE: 6.7856 | R²: -0.0914\n", "[MPNet-Lin] Ep 07/40 | Loss: 1.2608 | MAE: 4.8319 | RMSE: 6.1493 | R²: 0.1037\n", " ✅ Saved (epoch 7)\n", "[MPNet-Lin] Ep 08/40 | Loss: 1.1213 | MAE: 4.7829 | RMSE: 6.0634 | R²: 0.1286\n", " ✅ Saved (epoch 8)\n", "[MPNet-Lin] Ep 09/40 | Loss: 0.9512 | MAE: 4.8724 | RMSE: 6.6055 | R²: -0.0343\n", "[MPNet-Lin] Ep 10/40 | Loss: 1.1094 | MAE: 4.6772 | RMSE: 6.1897 | R²: 0.0919\n", "[MPNet-Lin] Ep 11/40 | Loss: 1.0801 | MAE: 5.2744 | RMSE: 6.5285 | R²: -0.0103\n", "[MPNet-Lin] Ep 12/40 | Loss: 0.9344 | MAE: 4.9823 | RMSE: 6.3542 | R²: 0.0429\n", "[MPNet-Lin] Ep 13/40 | Loss: 1.0978 | MAE: 5.2325 | RMSE: 6.4042 | R²: 0.0278\n", "[MPNet-Lin] Ep 14/40 | Loss: 1.0275 | MAE: 5.2184 | RMSE: 6.4400 | R²: 0.0169\n", "[MPNet-Lin] Ep 15/40 | Loss: 0.8018 | MAE: 4.9775 | RMSE: 6.3570 | R²: 0.0421\n", " Early stopping epoch 15\n", "Best epoch: 8 | Best MSE: 36.7644\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 8.7 XGBOOST with BERT" ], "metadata": { "id": "HJflsvpYCweW" } }, { "cell_type": "code", "source": [ "# ── CELL: XGBoost on Frozen BERT Embeddings ───────────────────────────────────\n", "\n", "import joblib\n", "from xgboost import XGBRegressor\n", "from transformers import AutoModel\n", "\n", "@torch.no_grad()\n", "def extract_cls_embeddings(loader, model_name, device):\n", " encoder = AutoModel.from_pretrained(model_name).to(device)\n", " encoder.eval()\n", " embs, labels = [], []\n", " for batch in loader:\n", " ids = batch['input_ids'].to(device)\n", " mask = batch['attention_mask'].to(device)\n", " out = encoder(input_ids=ids, attention_mask=mask)\n", " cls = out.last_hidden_state[:, 0, :].cpu().numpy()\n", " nv = batch['nv_features'].numpy()\n", " embs.append(np.hstack([cls, nv]))\n", " labels.append(batch['labels'].numpy())\n", " del encoder\n", " return np.concatenate(embs), np.concatenate(labels)\n", "\n", "print(\"Extracting BERT embeddings...\")\n", "X_train_xgb_bert, y_train_xgb = extract_cls_embeddings(train_loader_bert, \"bert-base-uncased\", DEVICE)\n", "X_dev_xgb_bert, y_dev_xgb = extract_cls_embeddings(dev_loader_bert, \"bert-base-uncased\", DEVICE)\n", "print(f\"Train: {X_train_xgb_bert.shape} | Dev: {X_dev_xgb_bert.shape}\")\n", "\n", "xgb_bert = XGBRegressor(\n", " n_estimators=300, max_depth=4, learning_rate=0.05,\n", " subsample=0.8, colsample_bytree=0.8,\n", " reg_alpha=1.0, reg_lambda=2.0,\n", " random_state=SEED, n_jobs=-1\n", ")\n", "xgb_bert.fit(X_train_xgb_bert, y_train_xgb,\n", " eval_set=[(X_dev_xgb_bert, y_dev_xgb)], verbose=50)\n", "\n", "preds = np.clip(xgb_bert.predict(X_dev_xgb_bert), 0, 27)\n", "print(f\"\\n[XGB-BERT] MAE: {mean_absolute_error(y_dev_xgb, preds):.4f} | \"\n", " f\"RMSE: {mean_squared_error(y_dev_xgb, preds)**0.5:.4f} | \"\n", " f\"R²: {r2_score(y_dev_xgb, preds):.4f}\")\n", "\n", "joblib.dump(xgb_bert, \"best_xgb_bert.pkl\")\n", "print(\"Saved: best_xgb_bert.pkl\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 780, "referenced_widgets": [ "e535fa1057044bdca9a902e03c36f1aa", "370b091db53744b19d6ed87e7031d8a4", "0bca46a07e124ca588294052dfd09e9a", "e4acc782baef42af8b707030d4a7f9d6", "280aff8760f24e058a5f91720ff56b4d", "47510542985a4c029fd476bb22c2e4c3", "716672a27e774c34baefc98750b3dd85", "abad2c605c37441e9a62a1b53249fc3c", "df1c5460a60f4ac2b70a851d64322e3f", "edd8ece504454be6a194b65c008d3283", "0b3831a4f78b4c90b4f4cffb45eb0a4e", "824b572d37284b96bebe7ba679af9f68", "845222cc81724a888967af20af091cb6", "22448ee8bcf14a2d9fc04bf93b19edd3", "8040e587fd444b23a0ffc74023c39cfe", "1cb05a936e3441ab8f71edfc180a7ab6", "ca5e66967e564c63bc88d07b6d8b1d63", "a722985e957243d9b1a76ded8823530c", "2e908da1909440e9ba74c0b01c79f30e", "ca9e791240654d75b2e72e7440c9cef4", "2275b145a55c4a859d0cf142d5328da9", "546171091aea43b0873e0dc890396dff" ] }, "id": "85FcqX1DC0Li", "outputId": "fd49db7c-0861-4620-a4fb-b1538cc4e655" }, "execution_count": 48, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Extracting BERT embeddings...\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/199 [00:00= patience:\n", " print(f\" Early stopping epoch {epoch}\"); break\n", "\n", "print(f\"Best epoch: {best_epoch} | Best MSE: {best_val_loss:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "fT7RJbxYGFJl", "outputId": "20ed792d-8184-4e3b-925b-0595b5654f50" }, "execution_count": 78, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[MiniLM-MLP] Ep 01/20 | Loss: 9.3144 | MAE: 5.4281 | RMSE: 7.1206 | R²: -0.2018\n", " ✅ Saved (epoch 1)\n", "[MiniLM-MLP] Ep 02/20 | Loss: 8.8974 | MAE: 5.5203 | RMSE: 6.8093 | R²: -0.0991\n", " ✅ Saved (epoch 2)\n", "[MiniLM-MLP] Ep 03/20 | Loss: 7.2373 | MAE: 5.1225 | RMSE: 7.1324 | R²: -0.2058\n", "[MiniLM-MLP] Ep 04/20 | Loss: 6.8551 | MAE: 5.2871 | RMSE: 6.6860 | R²: -0.0596\n", " ✅ Saved (epoch 4)\n", "[MiniLM-MLP] Ep 05/20 | Loss: 6.1517 | MAE: 4.8724 | RMSE: 6.9655 | R²: -0.1500\n", "[MiniLM-MLP] Ep 06/20 | Loss: 5.2113 | MAE: 4.8560 | RMSE: 6.4930 | R²: 0.0007\n", " ✅ Saved (epoch 6)\n", "[MiniLM-MLP] Ep 07/20 | Loss: 4.6240 | MAE: 4.6891 | RMSE: 6.4718 | R²: 0.0072\n", " ✅ Saved (epoch 7)\n", "[MiniLM-MLP] Ep 08/20 | Loss: 4.2991 | MAE: 4.8793 | RMSE: 6.3348 | R²: 0.0488\n", " ✅ Saved (epoch 8)\n", "[MiniLM-MLP] Ep 09/20 | Loss: 4.0210 | MAE: 4.7464 | RMSE: 6.3203 | R²: 0.0531\n", " ✅ Saved (epoch 9)\n", "[MiniLM-MLP] Ep 10/20 | Loss: 3.7007 | MAE: 4.9249 | RMSE: 7.0111 | R²: -0.1652\n", "[MiniLM-MLP] Ep 11/20 | Loss: 4.5679 | MAE: 4.9001 | RMSE: 6.2600 | R²: 0.0711\n", " ✅ Saved (epoch 11)\n", "[MiniLM-MLP] Ep 12/20 | Loss: 3.2671 | MAE: 4.7220 | RMSE: 6.4136 | R²: 0.0250\n", "[MiniLM-MLP] Ep 13/20 | Loss: 3.2168 | MAE: 4.9030 | RMSE: 6.9087 | R²: -0.1314\n", "[MiniLM-MLP] Ep 14/20 | Loss: 3.2649 | MAE: 4.8862 | RMSE: 6.8378 | R²: -0.1083\n", "[MiniLM-MLP] Ep 15/20 | Loss: 3.5401 | MAE: 4.7288 | RMSE: 6.5377 | R²: -0.0131\n", "[MiniLM-MLP] Ep 16/20 | Loss: 2.8919 | MAE: 4.7403 | RMSE: 6.4394 | R²: 0.0171\n", "[MiniLM-MLP] Ep 17/20 | Loss: 2.9151 | MAE: 4.7481 | RMSE: 6.5474 | R²: -0.0161\n", "[MiniLM-MLP] Ep 18/20 | Loss: 2.6586 | MAE: 4.7590 | RMSE: 6.6198 | R²: -0.0387\n", " Early stopping epoch 18\n", "Best epoch: 11 | Best MSE: 39.1881\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 8.10 MiniLM Attn" ], "metadata": { "id": "9HC3ZjrLGJiC" } }, { "cell_type": "code", "source": [ "# ── CELL: Train MiniLM + Attn ─────────────────────────────────────────────────\n", "\n", "EPOCHS_TF = 20\n", "LR_TF = 2e-5\n", "SAVE_NAME = \"best_minilm_attn.pt\"\n", "\n", "model = model_minilm_attn\n", "optimizer = optim.AdamW(\n", " [{'params': model.encoder.parameters(), 'lr': LR_TF},\n", " {'params': model.token_attn.parameters(), 'lr': LR_TF * 10},\n", " {'params': model.head.parameters(), 'lr': LR_TF * 10}],\n", " weight_decay=1e-2\n", ")\n", "total_steps = len(train_loader_minilm) * EPOCHS_TF\n", "scheduler = get_cosine_schedule_with_warmup(optimizer, int(total_steps * 0.1), total_steps)\n", "loss_fn = nn.HuberLoss(delta=3.0)\n", "\n", "history_minilm_attn = {'train_loss': [], 'val_mae': [], 'val_rmse': [], 'val_r2': []}\n", "best_val_loss = float('inf')\n", "best_epoch = -1\n", "patience, no_impr = 7, 0\n", "\n", "for epoch in range(1, EPOCHS_TF + 1):\n", " tr_loss = train_one_epoch(model, train_loader_minilm, optimizer, scheduler, DEVICE, loss_fn)\n", " metrics = evaluate(model, dev_loader_minilm, DEVICE)\n", "\n", " history_minilm_attn['train_loss'].append(tr_loss)\n", " history_minilm_attn['val_mae'].append(metrics['mae'])\n", " history_minilm_attn['val_rmse'].append(metrics['rmse'])\n", " history_minilm_attn['val_r2'].append(metrics['r2'])\n", "\n", " print(f\"[MiniLM-Attn] Ep {epoch:02d}/{EPOCHS_TF} | Loss: {tr_loss:.4f} | MAE: {metrics['mae']:.4f} | RMSE: {metrics['rmse']:.4f} | R²: {metrics['r2']:.4f}\")\n", "\n", " if metrics['mse'] < best_val_loss:\n", " best_val_loss = metrics['mse']; best_epoch = epoch; no_impr = 0\n", " torch.save(model.state_dict(), SAVE_NAME)\n", " print(f\" ✅ Saved (epoch {epoch})\")\n", " else:\n", " no_impr += 1\n", " if no_impr >= patience:\n", " print(f\" Early stopping epoch {epoch}\"); break\n", "\n", "print(f\"Best epoch: {best_epoch} | Best MSE: {best_val_loss:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "cvdusdJjGMb-", "outputId": "1b2ee085-09c7-4d8d-a9a8-e14dd51cdf5b" }, "execution_count": 56, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[MiniLM-Attn] Ep 01/20 | Loss: 14.8206 | MAE: 6.3989 | RMSE: 8.7779 | R²: -0.8264\n", " ✅ Saved (epoch 1)\n", "[MiniLM-Attn] Ep 02/20 | Loss: 10.9587 | MAE: 5.5899 | RMSE: 7.3555 | R²: -0.2824\n", " ✅ Saved (epoch 2)\n", "[MiniLM-Attn] Ep 03/20 | Loss: 9.4370 | MAE: 5.5238 | RMSE: 7.0515 | R²: -0.1786\n", " ✅ Saved (epoch 3)\n", "[MiniLM-Attn] Ep 04/20 | Loss: 9.5072 | MAE: 5.5312 | RMSE: 6.9232 | R²: -0.1361\n", " ✅ Saved (epoch 4)\n", "[MiniLM-Attn] Ep 05/20 | Loss: 9.1637 | MAE: 5.5030 | RMSE: 7.0136 | R²: -0.1660\n", "[MiniLM-Attn] Ep 06/20 | Loss: 9.4833 | MAE: 5.4885 | RMSE: 6.9550 | R²: -0.1466\n", "[MiniLM-Attn] Ep 07/20 | Loss: 8.3836 | MAE: 5.3564 | RMSE: 7.1746 | R²: -0.2201\n", "[MiniLM-Attn] Ep 08/20 | Loss: 7.6389 | MAE: 5.4079 | RMSE: 7.1252 | R²: -0.2034\n", "[MiniLM-Attn] Ep 09/20 | Loss: 7.3527 | MAE: 5.4707 | RMSE: 6.9549 | R²: -0.1466\n", "[MiniLM-Attn] Ep 10/20 | Loss: 6.2460 | MAE: 5.4801 | RMSE: 6.8353 | R²: -0.1075\n", " ✅ Saved (epoch 10)\n", "[MiniLM-Attn] Ep 11/20 | Loss: 6.1733 | MAE: 5.2910 | RMSE: 7.1320 | R²: -0.2057\n", "[MiniLM-Attn] Ep 12/20 | Loss: 5.5764 | MAE: 5.2919 | RMSE: 7.1588 | R²: -0.2148\n", "[MiniLM-Attn] Ep 13/20 | Loss: 5.5141 | MAE: 5.3176 | RMSE: 7.0464 | R²: -0.1769\n", "[MiniLM-Attn] Ep 14/20 | Loss: 5.3164 | MAE: 5.3504 | RMSE: 6.9135 | R²: -0.1329\n", "[MiniLM-Attn] Ep 15/20 | Loss: 5.2534 | MAE: 5.3158 | RMSE: 6.9331 | R²: -0.1394\n", "[MiniLM-Attn] Ep 16/20 | Loss: 5.1622 | MAE: 5.2478 | RMSE: 7.2375 | R²: -0.2416\n", "[MiniLM-Attn] Ep 17/20 | Loss: 4.8994 | MAE: 5.3054 | RMSE: 6.9523 | R²: -0.1457\n", " Early stopping epoch 17\n", "Best epoch: 10 | Best MSE: 46.7211\n" ] } ] }, { "cell_type": "markdown", "source": [ "## 8.11 MiniLM Linear" ], "metadata": { "id": "JeNNffIrGPfD" } }, { "cell_type": "code", "source": [ "# ── CELL: Train MiniLM + Linear ───────────────────────────────────────────────\n", "\n", "EPOCHS_TF = 20\n", "LR_TF = 2e-5\n", "SAVE_NAME = \"best_minilm_linear.pt\"\n", "\n", "model = model_minilm_linear\n", "optimizer = optim.AdamW(\n", " [{'params': model.encoder.parameters(), 'lr': LR_TF},\n", " {'params': model.head.parameters(), 'lr': LR_TF * 10}],\n", " weight_decay=1e-2\n", ")\n", "total_steps = len(train_loader_minilm) * EPOCHS_TF\n", "scheduler = get_cosine_schedule_with_warmup(optimizer, int(total_steps * 0.1), total_steps)\n", "loss_fn = nn.HuberLoss(delta=3.0)\n", "\n", "history_minilm_linear = {'train_loss': [], 'val_mae': [], 'val_rmse': [], 'val_r2': []}\n", "best_val_loss = float('inf')\n", "best_epoch = -1\n", "patience, no_impr = 7, 0\n", "\n", "for epoch in range(1, EPOCHS_TF + 1):\n", " tr_loss = train_one_epoch(model, train_loader_minilm, optimizer, scheduler, DEVICE, loss_fn)\n", " metrics = evaluate(model, dev_loader_minilm, DEVICE)\n", "\n", " history_minilm_linear['train_loss'].append(tr_loss)\n", " history_minilm_linear['val_mae'].append(metrics['mae'])\n", " history_minilm_linear['val_rmse'].append(metrics['rmse'])\n", " history_minilm_linear['val_r2'].append(metrics['r2'])\n", "\n", " print(f\"[MiniLM-Lin] Ep {epoch:02d}/{EPOCHS_TF} | Loss: {tr_loss:.4f} | MAE: {metrics['mae']:.4f} | RMSE: {metrics['rmse']:.4f} | R²: {metrics['r2']:.4f}\")\n", "\n", " if metrics['mse'] < best_val_loss:\n", " best_val_loss = metrics['mse']; best_epoch = epoch; no_impr = 0\n", " torch.save(model.state_dict(), SAVE_NAME)\n", " print(f\" ✅ Saved (epoch {epoch})\")\n", " else:\n", " no_impr += 1\n", " if no_impr >= patience:\n", " print(f\" Early stopping epoch {epoch}\"); break\n", "\n", "print(f\"Best epoch: {best_epoch} | Best MSE: {best_val_loss:.4f}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "t-ZiqPdOGS4P", "outputId": "870ea74d-fe5b-4872-dd37-de4468cea609" }, "execution_count": 57, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[MiniLM-Lin] Ep 01/20 | Loss: 16.2683 | MAE: 6.9767 | RMSE: 9.3879 | R²: -1.0890\n", " ✅ Saved (epoch 1)\n", "[MiniLM-Lin] Ep 02/20 | Loss: 12.6676 | MAE: 5.6913 | RMSE: 7.8359 | R²: -0.4554\n", " ✅ Saved (epoch 2)\n", "[MiniLM-Lin] Ep 03/20 | Loss: 10.0399 | MAE: 5.4390 | RMSE: 6.8797 | R²: -0.1219\n", " ✅ Saved (epoch 3)\n", "[MiniLM-Lin] Ep 04/20 | Loss: 9.6464 | MAE: 5.4394 | RMSE: 6.6904 | R²: -0.0610\n", " ✅ Saved (epoch 4)\n", "[MiniLM-Lin] Ep 05/20 | Loss: 9.6974 | MAE: 5.4832 | RMSE: 6.6409 | R²: -0.0454\n", " ✅ Saved (epoch 5)\n", "[MiniLM-Lin] Ep 06/20 | Loss: 9.3710 | MAE: 5.4361 | RMSE: 6.6631 | R²: -0.0524\n", "[MiniLM-Lin] Ep 07/20 | Loss: 9.2467 | MAE: 5.4162 | RMSE: 6.6792 | R²: -0.0575\n", "[MiniLM-Lin] Ep 08/20 | Loss: 9.0975 | MAE: 5.3998 | RMSE: 6.6838 | R²: -0.0589\n", "[MiniLM-Lin] Ep 09/20 | Loss: 9.0357 | MAE: 5.3475 | RMSE: 6.7639 | R²: -0.0845\n", "[MiniLM-Lin] Ep 10/20 | Loss: 8.9361 | MAE: 5.4385 | RMSE: 6.6210 | R²: -0.0391\n", " ✅ Saved (epoch 10)\n", "[MiniLM-Lin] Ep 11/20 | Loss: 8.8315 | MAE: 5.1866 | RMSE: 7.0776 | R²: -0.1874\n", "[MiniLM-Lin] Ep 12/20 | Loss: 8.0381 | MAE: 5.1700 | RMSE: 6.5457 | R²: -0.0156\n", " ✅ Saved (epoch 12)\n", "[MiniLM-Lin] Ep 13/20 | Loss: 7.6144 | MAE: 5.0521 | RMSE: 6.6836 | R²: -0.0589\n", "[MiniLM-Lin] Ep 14/20 | Loss: 6.9453 | MAE: 5.0581 | RMSE: 6.5916 | R²: -0.0299\n", "[MiniLM-Lin] Ep 15/20 | Loss: 6.7577 | MAE: 5.0361 | RMSE: 6.5188 | R²: -0.0073\n", " ✅ Saved (epoch 15)\n", "[MiniLM-Lin] Ep 16/20 | Loss: 6.5294 | MAE: 4.8992 | RMSE: 6.6068 | R²: -0.0347\n", "[MiniLM-Lin] Ep 17/20 | Loss: 6.3750 | MAE: 4.9193 | RMSE: 6.5964 | R²: -0.0314\n", "[MiniLM-Lin] Ep 18/20 | Loss: 5.8879 | MAE: 4.9695 | RMSE: 6.5243 | R²: -0.0090\n", "[MiniLM-Lin] Ep 19/20 | Loss: 6.2652 | MAE: 4.9904 | RMSE: 6.5042 | R²: -0.0028\n", " ✅ Saved (epoch 19)\n", "[MiniLM-Lin] Ep 20/20 | Loss: 6.4879 | MAE: 4.9878 | RMSE: 6.5059 | R²: -0.0033\n", "Best epoch: 19 | Best MSE: 42.3042\n" ] } ] }, { "cell_type": "markdown", "source": [ "# 9. Eval" ], "metadata": { "id": "SIanU7A1C5xY" } }, { "cell_type": "code", "source": [ "# ── CELL: Plot & Save All Histories ──────────────────────────────────────────\n", "\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "\n", "HISTORIES = {\n", " 'BERT-MLP': history_bert_mlp,\n", " 'BERT-Attn': history_bert_attn,\n", " 'BERT-Linear': history_bert_linear,\n", " 'MPNet-MLP': history_mpnet_mlp,\n", " 'MPNet-Attn': history_mpnet_attn,\n", " 'MPNet-Linear': history_mpnet_linear,\n", " 'MiniLM-MLP': history_minilm_mlp,\n", " 'MiniLM-Attn': history_minilm_attn,\n", " 'MiniLM-Linear': history_minilm_linear,\n", "}\n", "\n", "for name, h in HISTORIES.items():\n", " fname = f\"history_{name.lower().replace('-','_')}.csv\"\n", " pd.DataFrame(h).assign(epoch=range(1, len(h['train_loss']) + 1)).to_csv(fname, index=False)\n", " print(f\"Saved: {fname}\")\n", "\n", "metrics_keys = ['train_loss', 'val_mae', 'val_rmse', 'val_r2']\n", "titles = ['Train Loss (Huber)', 'Val MAE', 'Val RMSE', 'Val R²']\n", "colors = ['steelblue','cornflowerblue','lightblue',\n", " 'darkorange','sandybrown','moccasin',\n", " 'green','mediumseagreen','lightgreen']\n", "\n", "fig, axes = plt.subplots(2, 2, figsize=(20, 12))\n", "for ax, mkey, title in zip(axes.flatten(), metrics_keys, titles):\n", " for (name, h), color in zip(HISTORIES.items(), colors):\n", " if len(h[mkey]) > 0:\n", " ax.plot(h[mkey], label=name, color=color, marker='o', markersize=3, alpha=0.85)\n", " ax.set_title(title, fontsize=13)\n", " ax.set_xlabel('Epoch')\n", " ax.legend(fontsize=7)\n", " ax.grid(True, linestyle='--', alpha=0.5)\n", "\n", "plt.suptitle('BERT & MPNet & MiniLM — Training History Comparison', fontsize=15)\n", "plt.tight_layout()\n", "plt.savefig('history_all_transformer.png', dpi=150, bbox_inches='tight')\n", "plt.show()\n", "print(\"Saved: history_all_transformer.png\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 760 }, "id": "srI4cZECGeKZ", "outputId": "0c67e99a-7008-465b-8439-f3f2f38e2b59" }, "execution_count": 70, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Saved: history_bert_mlp.csv\n", "Saved: history_bert_attn.csv\n", "Saved: history_bert_linear.csv\n", "Saved: history_mpnet_mlp.csv\n", "Saved: history_mpnet_attn.csv\n", "Saved: history_mpnet_linear.csv\n", "Saved: history_minilm_mlp.csv\n", "Saved: history_minilm_attn.csv\n", "Saved: history_minilm_linear.csv\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": "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\n" }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Saved: history_all_transformer.png\n" ] } ] }, { "cell_type": "code", "source": [ "# ── CELL: Download ────────────────────────────────────────────────────────────\n", "\n", "from google.colab import files\n", "import os\n", "\n", "to_download = [\n", " \"best_bert_mlp.pt\", \"best_bert_attn.pt\", \"best_bert_linear.pt\",\n", " \"best_mpnet_mlp.pt\", \"best_mpnet_attn.pt\", \"best_mpnet_linear.pt\",\n", " \"best_minilm_mlp.pt\", \"best_minilm_attn.pt\", \"best_minilm_linear.pt\",\n", " \"best_xgb_bert.pkl\", \"best_xgb_mpnet.pkl\",\n", " \"history_bert_mlp.csv\", \"history_bert_attn.csv\", \"history_bert_linear.csv\",\n", " \"history_mpnet_mlp.csv\", \"history_mpnet_attn.csv\", \"history_mpnet_linear.csv\",\n", " \"history_minilm_mlp.csv\", \"history_minilm_attn.csv\", \"history_minilm_linear.csv\",\n", " \"history_all_transformer.png\",\n", "]\n", "\n", "for f in to_download:\n", " if os.path.exists(f):\n", " files.download(f)\n", " print(f\"⬇️ {f}\")\n", " else:\n", " print(f\"Not found: {f}\")" ], "metadata": { "id": "2hBpDKy1Ghdj" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "# 10. Final Model" ], "metadata": { "id": "3Q-b27XdWndi" } }, { "cell_type": "markdown", "source": [ "## Setup Multi Output" ], "metadata": { "id": "F2KN4rMa5EEN" } }, { "cell_type": "code", "source": [ "# ── CELL: Setup Multi-Output PHQ8 Targets ────────────────────────────────────\n", "\n", "PHQ8_COLS = ['PHQ8_1_NoInterest', 'PHQ8_2_Depressed', 'PHQ8_3_Sleep',\n", " 'PHQ8_4_Tired', 'PHQ8_5_Appetite', 'PHQ8_6_Failure',\n", " 'PHQ8_7_Concentration', 'PHQ8_8_Psychomotor', 'PHQ8_Score']\n", "N_TARGETS = len(PHQ8_COLS) # 9\n", "\n", "# Merge PHQ8 item columns dari label_df ke df\n", "df_final = df.merge(\n", " label_df[['Participant_ID'] + PHQ8_COLS],\n", " on='Participant_ID',\n", " how='inner',\n", " suffixes=('', '_lbldf')\n", ")\n", "\n", "# Drop duplikat PHQ8_Score kalau ada (karena sudah ada di df)\n", "dup_cols = [c for c in df_final.columns if c.endswith('_lbldf')]\n", "df_final = df_final.drop(columns=dup_cols)\n", "\n", "train_df_final = df_final[df_final['split'] == 'train'].copy().reset_index(drop=True)\n", "dev_df_final = df_final[df_final['split'] == 'dev'].copy().reset_index(drop=True)\n", "\n", "# Add text_bert to train_df_final and dev_df_final\n", "# Assuming train_df and dev_df (from earlier cells) already have 'text_bert'\n", "train_df_final['text_bert'] = train_df['text_bert']\n", "dev_df_final['text_bert'] = dev_df['text_bert']\n", "\n", "y_train_multi = train_df_final[PHQ8_COLS].values.astype(np.float32) # (N_train, 9)\n", "y_dev_multi = dev_df_final[PHQ8_COLS].values.astype(np.float32) # (N_dev, 9)\n", "\n", "nv_train_final = extra_scaler.transform(train_df_final[ALL_EXTRA_COLS].fillna(0)).astype(np.float32)\n", "nv_dev_final = extra_scaler.transform(dev_df_final[ALL_EXTRA_COLS].fillna(0)).astype(np.float32)\n", "\n", "print(f\"Train: {len(train_df_final)} | Dev: {len(dev_df_final)}\")\n", "print(f\"y_train_multi shape: {y_train_multi.shape}\")\n", "print(f\"y_dev_multi shape: {y_dev_multi.shape}\")\n", "print(f\"PHQ8 columns: {PHQ8_COLS}\")\n", "\n", "# Sanity check nilai\n", "print(f\"\\nRange tiap target:\")\n", "for col in PHQ8_COLS:\n", " mn, mx = df_final[col].min(), df_final[col].max()\n", " print(f\" {col:<30} min={mn} max={mx}\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ncamFg1i49YF", "outputId": "659f1c67-8be0-4a79-be87-4d51ce98919e" }, "execution_count": 74, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Train: 107 | Dev: 35\n", "y_train_multi shape: (107, 9)\n", "y_dev_multi shape: (35, 9)\n", "PHQ8 columns: ['PHQ8_1_NoInterest', 'PHQ8_2_Depressed', 'PHQ8_3_Sleep', 'PHQ8_4_Tired', 'PHQ8_5_Appetite', 'PHQ8_6_Failure', 'PHQ8_7_Concentration', 'PHQ8_8_Psychomotor', 'PHQ8_Score']\n", "\n", "Range tiap target:\n", " PHQ8_1_NoInterest min=0.0 max=3.0\n", " PHQ8_2_Depressed min=0.0 max=3.0\n", " PHQ8_3_Sleep min=0.0 max=3.0\n", " PHQ8_4_Tired min=0.0 max=3.0\n", " PHQ8_5_Appetite min=0.0 max=3.0\n", " PHQ8_6_Failure min=0.0 max=3.0\n", " PHQ8_7_Concentration min=0.0 max=3.0\n", " PHQ8_8_Psychomotor min=0.0 max=3.0\n", " PHQ8_Score min=0 max=23\n" ] } ] }, { "cell_type": "code", "source": [ "print(df.columns.tolist())\n", "print(label_df.columns.tolist())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "enT0s9TZ5-rY", "outputId": "1dfecccf-7ad1-45cb-a510-5731dded9244" }, "execution_count": 69, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "['Participant_ID', 'text', 'utterance_count', 'word_count', 'nv_laugh', 'nv_sigh', 'nv_cough', 'nv_breath', 'nv_sniff', 'nv_groan', 'nv_pause', 'nv_um', 'nv_uh', 'nv_total', 'filler_count', 'PHQ8_Score', 'phq8_target', 'label', 'gender', 'split', 'text_clean', 'word_count_raw', 'word_count_clean_temp', 'word_count_clean', 'avg_utt_len', 'response_brevity', 'neg_ratio', 'pos_ratio', 'sentiment_gap']\n", "['Participant_ID', 'PHQ8_1_NoInterest', 'PHQ8_2_Depressed', 'PHQ8_3_Sleep', 'PHQ8_4_Tired', 'PHQ8_5_Appetite', 'PHQ8_6_Failure', 'PHQ8_7_Concentration', 'PHQ8_8_Psychomotor', 'PHQ8_Score', 'gender', 'label', 'split', 'phq8_target']\n" ] } ] }, { "cell_type": "code", "source": [ "# ── CELL: Dataset Class Multi-Output ─────────────────────────────────────────\n", "\n", "from torch.utils.data import Dataset, DataLoader\n", "\n", "class MultiOutputDataset(Dataset):\n", " def __init__(self, texts, labels, nv_features, tokenizer, max_len=MAX_LEN):\n", " self.labels = labels # (N, 9)\n", " self.nv = nv_features # (N, NV_SIZE)\n", " self.data = []\n", " for text in texts:\n", " ids, mask = tokenize_head_tail(text, tokenizer, max_len)\n", " self.data.append({\n", " 'input_ids': torch.tensor(ids, dtype=torch.long),\n", " 'attention_mask': torch.tensor(mask, dtype=torch.long),\n", " })\n", "\n", " def __len__(self):\n", " return len(self.labels)\n", "\n", " def __getitem__(self, idx):\n", " return {\n", " 'input_ids': self.data[idx]['input_ids'],\n", " 'attention_mask': self.data[idx]['attention_mask'],\n", " 'nv_features': torch.tensor(self.nv[idx], dtype=torch.float),\n", " 'labels': torch.tensor(self.labels[idx], dtype=torch.float), # (9,)\n", " }\n", "\n", "print(\"MultiOutputDataset ready.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "P3c4nlnJ5BRi", "outputId": "6d1cfafb-b18b-4aa0-cf19-e49154396542" }, "execution_count": 75, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "MultiOutputDataset ready.\n" ] } ] }, { "cell_type": "markdown", "source": [ "## Model Definition Multi Output" ], "metadata": { "id": "o18Biy9Z5I9P" } }, { "cell_type": "code", "source": [ "# ── CELL: Model Multi-Output MLPNet ──────────────────────────────────────────\n", "\n", "import torch.nn as nn\n", "from transformers import AutoModel\n", "\n", "class MultiOutputTransformer(nn.Module):\n", " \"\"\"\n", " MPNet encoder + MLP head → 9 output:\n", " PHQ8_1..PHQ8_8 (0-3) + PHQ8_Score (0-24)\n", " \"\"\"\n", " def __init__(self, model_name, n_targets=9, nv_size=NV_SIZE, dropout=0.1):\n", " super().__init__()\n", " self.encoder = AutoModel.from_pretrained(model_name)\n", " hidden = self.encoder.config.hidden_size # 768\n", " self.dropout = nn.Dropout(dropout)\n", " in_features = hidden + nv_size\n", "\n", " self.head = nn.Sequential(\n", " nn.Linear(in_features, 256),\n", " nn.LayerNorm(256),\n", " nn.GELU(),\n", " nn.Dropout(dropout),\n", " nn.Linear(256, 64),\n", " nn.GELU(),\n", " nn.Linear(64, n_targets), # (B, 9)\n", " )\n", "\n", " def forward(self, input_ids, attention_mask, nv_features):\n", " out = self.encoder(input_ids=input_ids, attention_mask=attention_mask)\n", " pooled = out.last_hidden_state[:, 0, :] # CLS\n", " pooled = self.dropout(pooled)\n", " x = torch.cat([pooled, nv_features], dim=-1)\n", " return self.head(x) # (B, 9)\n", "\n", "MPNET_NAME = \"sentence-transformers/all-mpnet-base-v2\"\n", "\n", "# Cek\n", "_dummy = MultiOutputTransformer(MPNET_NAME, N_TARGETS, NV_SIZE).to(DEVICE)\n", "print(f\"Trainable params: {sum(p.numel() for p in _dummy.parameters() if p.requires_grad):,}\")\n", "print(f\"Output: {N_TARGETS} targets → {PHQ8_COLS}\")\n", "del _dummy" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 105, "referenced_widgets": [ "a6635fb873bb42da9e782af28814ef13", "ec33e406e1ee4c0486bcf03a701c2819", "fe7e42aa3e784b0e9eb56a1080194c4a", "7325f0bad69a4df38faf20722bbd3d67", "ced18e6e03204f4186657eb0be4216a5", "9c2e20be3a5e42ecb5ed018a34dba314", "e9f57987bc4b486f8c0a5f0fe49f0ce5", "1ba4835a0fb843f88c7fd5ed6c9731ba", "0388c6cebe4f4f5193db1eaab0752565", "18753db1c8d6423aa54990312cd597bd", "c2b0a5873d644fbbbe4b18947bc9b7cc" ] }, "id": "MTvPvrfD5K4Z", "outputId": "b4492f5b-c686-4e47-dc81-805e2a297319" }, "execution_count": 76, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/199 [00:00= PATIENCE:\n", "\n", " print(\n", " f\"Early stopping \"\n", " f\"(epoch {epoch+1})\"\n", " )\n", "\n", " break\n", "\n", " model.load_state_dict(best_fold_state)\n", "\n", " model.eval()\n", "\n", " fold_preds = []\n", "\n", " with torch.no_grad():\n", "\n", " for batch in val_loader:\n", "\n", " ids = batch[\"input_ids\"].to(DEVICE)\n", " mask = batch[\"attention_mask\"].to(DEVICE)\n", " nv = batch[\"nv_features\"].to(DEVICE)\n", "\n", " pred = model(\n", " ids,\n", " mask,\n", " nv\n", " ).cpu().numpy()\n", "\n", " fold_preds.append(pred)\n", "\n", " fold_preds = np.clip(\n", " np.concatenate(fold_preds),\n", " 0,\n", " 27\n", " )\n", "\n", " oof_preds[val_idx] = fold_preds\n", "\n", " fold_val_maes.append(best_fold_mae)\n", "\n", " print(\n", " f\"Fold {fold+1} Best MAE: \"\n", " f\"{best_fold_mae:.4f}\"\n", " )\n", "\n", "print(\"\\n\" + \"=\" * 60)\n", "print(\"KFold Finished\")\n", "print(\"=\" * 60)\n", "\n", "print(\n", " f\"OOF MAE = \"\n", " f\"{np.mean(fold_val_maes):.4f} ± \"\n", " f\"{np.std(fold_val_maes):.4f}\"\n", ")\n", "\n", "print(\n", " f\"Global Best MAE : \"\n", " f\"{global_best_mae:.4f}\"\n", ")\n", "\n", "print(\n", " f\"Global Best Fold: \"\n", " f\"{global_best_fold}\"\n", ")\n", "\n", "print(\n", " f\"Global Best Epoch: \"\n", " f\"{global_best_epoch}\"\n", ")\n", "\n", "print(\n", " f\"Saved Model: \"\n", " f\"{GLOBAL_MODEL_PATH}\"\n", ")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "65f54fc10c41402b8fa9bb5d36f7e5e5", "1112196e941c4d4ab1dd1260cae19c09", "accc25dc5efb43a99ef4e5ef4acbd9c7", "a077870634f84a88b957e9c5baa8f067", "f93320e7c250436f9cebdd2d58345048", "81fbfc3e510c46f0b5cd2a02bbdcaf09", "707439897b3d4d12b89b765a0d3f0530", "6f8efa8cbb30447b8dec5015553ba7b2", "a4ec015060c948fe868df77b45dea664", "fc70f34b68964e6ca8084171133914d3", "2969238ef13c47f78e92752d2965c460", "568d02dd3230470782ccd07601901030", "f2645974758e48c8a9fcc8b7af019f6a", "f1c38312f074469c8ffd979c170a9b77", "62cbcb6b5d81441d80054adaf1bbdd94", "7dd4abccc0664ed48e025749f2b036d8", "2c718501d9644f918ec5a339b650cc75", "bbafbe1bee764fbfaca60f9708c06b2a", "e39c47a6564d4a73b126bc87ad3b46a9", "749c923302da46fd840165f14b58ce2b", "d494f3ed335443e49d178de99093bac5", "1a72f7a912ff4f4c9b64c7d63ed7ea34", "38bdd92592934db5b25ba0580cec5fda", "9ab319d71fbc40baa086dc6a60add71a", "ea50a117323546af9b73e4009ee1ce2c", "9b2361422eee430dbeaebee00f85a92c", "57c66f9e291d4dc2a42b6e0e9772d0cb", "3937ece8b29144a385cab475ee5a5995", "7d4b79d7ee71470eb7d8bcee704762ed", "ebcdd6b311764b01933f494fbbce4f9a", "17936403b8d84d22aede55c1ebee0428", "d377708450c94aedbe52a540a0adf4e1", "2c5a458d317b408b8e6acf002b1247cf", "17cf09f81b794da7801f7d5c11b2d3f2", "2bbd4dff6a47437e8d91455b2b61237f", "ca2fcf8403884589b0a76d2d254f5fc2", "49212e5215914e328dc033f58580b854", "4af979031150487782f7c922d5876458", "297dc3cfd1cc44c88df5b86505209729", "cdb85b6e499a4973afa5aeb93a2506d4", "ca8cebbe6b1840fab983f35852a106a4", "9b1e53da0f7448348c8024891ddccf37", "75cb898336d44c75ae6314e5be4db9f9", "55c9c2302dac41d7ad03b7840226f26e", "7c21b560ab5d47468c6c5e90ff1e0d76" ] }, "id": "1XfdU0fb5PhJ", "outputId": "ad73ce0a-55cb-45f9-b149-2f9a07fb8235" }, "execution_count": 78, "outputs": [ { "metadata": { "tags": null }, "name": "stdout", "output_type": "stream", "text": [ "\n", "============================================================\n", "FOLD 1/5 | train=113 | val=29\n", "============================================================\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "65f54fc10c41402b8fa9bb5d36f7e5e5", "version_major": 2, "version_minor": 0 }, "text/plain": [ "Loading weights: 0%| | 0/199 [00:008} {'RMSE':>8} {'R²':>8}\")\n", "print(\"-\" * 42)\n", "\n", "results_per_target = []\n", "for i, col in enumerate(PHQ8_COLS):\n", " preds = oof_preds[:, i]\n", " labels = oof_labels[:, i]\n", " mae = mean_absolute_error(labels, preds)\n", " rmse = mean_squared_error(labels, preds) ** 0.5\n", " r2 = r2_score(labels, preds)\n", " results_per_target.append({'target': col, 'mae': mae, 'rmse': rmse, 'r2': r2})\n", " print(f\"{col:<15} {mae:>8.4f} {rmse:>8.4f} {r2:>8.4f}\")\n", "\n", "print(\"-\" * 42)\n", "overall_mae = mean_absolute_error(oof_labels, oof_preds)\n", "overall_rmse = mean_squared_error(oof_labels, oof_preds) ** 0.5\n", "print(f\"{'OVERALL':<15} {overall_mae:>8.4f} {overall_rmse:>8.4f}\")\n", "\n", "# Save results\n", "import pandas as pd\n", "pd.DataFrame(results_per_target).to_csv(\"final_model_results.csv\", index=False)\n", "print(\"\\nSaved: final_model_results.csv\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "aghOpKw65UTm", "outputId": "968c6603-efed-4ac3-8a08-56c17eae8966" }, "execution_count": 79, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Target MAE RMSE R²\n", "------------------------------------------\n", "PHQ8_1_NoInterest 0.5936 0.7550 0.1593\n", "PHQ8_2_Depressed 0.6403 0.8222 0.1373\n", "PHQ8_3_Sleep 0.8817 1.0578 0.0150\n", "PHQ8_4_Tired 0.7481 0.9386 0.0334\n", "PHQ8_5_Appetite 0.8079 0.9673 0.1245\n", "PHQ8_6_Failure 0.7714 0.9608 0.1411\n", "PHQ8_7_Concentration 0.7189 0.9564 0.0147\n", "PHQ8_8_Psychomotor 0.4791 0.6734 0.0498\n", "PHQ8_Score 4.1250 5.5418 0.0656\n", "------------------------------------------\n", "OVERALL 1.0851 2.0326\n", "\n", "Saved: final_model_results.csv\n" ] } ] } ] }