diff --git "a/eleventh_doctor_beta2.ipynb" "b/eleventh_doctor_beta2.ipynb" new file mode 100644--- /dev/null +++ "b/eleventh_doctor_beta2.ipynb" @@ -0,0 +1,4176 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "eleventh-doctor-beta.ipynb", + "provenance": [], + "collapsed_sections": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU", + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "a49e5fd0d85444a3aa9f786455ca8770": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "HBoxView", + "_dom_classes": [], + "_model_name": "HBoxModel", + "_view_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_view_count": null, + "_view_module_version": "1.5.0", + "box_style": "", + "layout": "IPY_MODEL_73e8d052a86647919649a367aa773c8e", + "_model_module": "@jupyter-widgets/controls", + "children": [ + "IPY_MODEL_40760124752846209e61177280a005bd", + "IPY_MODEL_eae3f41495884830818311e51920c956", + "IPY_MODEL_5d1a116b987549d780ee25723f83d45a" + ] + } + }, + "73e8d052a86647919649a367aa773c8e": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_view_name": "LayoutView", + "grid_template_rows": null, + "right": null, + "justify_content": null, + "_view_module": "@jupyter-widgets/base", + "overflow": null, + "_model_module_version": "1.2.0", + "_view_count": null, + "flex_flow": null, + "width": null, + "min_width": null, + "border": null, + "align_items": null, + "bottom": null, + "_model_module": "@jupyter-widgets/base", + "top": null, + "grid_column": null, + "overflow_y": null, + "overflow_x": null, + "grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + }, + "40760124752846209e61177280a005bd": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "HTMLView", + "style": "IPY_MODEL_46f7e33281354ef488945f5f1cfe4c06", + "_dom_classes": [], + "description": "", + "_model_name": "HTMLModel", + "placeholder": "​", + "_view_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "value": "Epoch: 100%", + "_view_count": null, + "_view_module_version": "1.5.0", + "description_tooltip": null, + "_model_module": "@jupyter-widgets/controls", + "layout": "IPY_MODEL_e987dfed8c624717b5ae2054cce74f05" + } + }, + "eae3f41495884830818311e51920c956": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "ProgressView", + "style": "IPY_MODEL_c49e73bdfe544de0ae62034cef7eb0da", + "_dom_classes": [], + "description": "", + "_model_name": "FloatProgressModel", + "bar_style": "success", + "max": 4, + "_view_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "value": 4, + "_view_count": null, + "_view_module_version": "1.5.0", + "orientation": "horizontal", + "min": 0, + "description_tooltip": null, + "_model_module": "@jupyter-widgets/controls", + "layout": "IPY_MODEL_52e599d90ccd44938d982310fb7e4341" + } + }, + "5d1a116b987549d780ee25723f83d45a": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "HTMLView", + "style": "IPY_MODEL_c01768976adf465ebfad5c3eedfe1d58", + "_dom_classes": [], + "description": "", + "_model_name": "HTMLModel", + "placeholder": "​", + "_view_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "value": " 4/4 [00:11<00:00, 2.87s/it]", + "_view_count": null, + "_view_module_version": "1.5.0", + "description_tooltip": null, + "_model_module": "@jupyter-widgets/controls", + "layout": "IPY_MODEL_27fb7c7e261b4a3b9656a37b1fcde71a" + } + }, + "46f7e33281354ef488945f5f1cfe4c06": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "StyleView", + "_model_name": "DescriptionStyleModel", + "description_width": "", + "_view_module": "@jupyter-widgets/base", + "_model_module_version": "1.5.0", + "_view_count": null, + "_view_module_version": "1.2.0", + "_model_module": "@jupyter-widgets/controls" + } + }, + "e987dfed8c624717b5ae2054cce74f05": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_view_name": "LayoutView", + "grid_template_rows": null, + "right": null, + "justify_content": null, + "_view_module": "@jupyter-widgets/base", + "overflow": null, + "_model_module_version": "1.2.0", + "_view_count": null, + "flex_flow": null, + "width": null, + "min_width": null, + "border": null, + "align_items": null, + "bottom": null, + "_model_module": "@jupyter-widgets/base", + "top": null, + "grid_column": null, + "overflow_y": null, + "overflow_x": null, + "grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + }, + "c49e73bdfe544de0ae62034cef7eb0da": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "StyleView", + "_model_name": "ProgressStyleModel", + "description_width": "", + "_view_module": "@jupyter-widgets/base", + "_model_module_version": "1.5.0", + "_view_count": null, + "_view_module_version": "1.2.0", + "bar_color": null, + "_model_module": "@jupyter-widgets/controls" + } + }, + "52e599d90ccd44938d982310fb7e4341": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_view_name": "LayoutView", + "grid_template_rows": null, + "right": null, + "justify_content": null, + "_view_module": "@jupyter-widgets/base", + "overflow": null, + "_model_module_version": "1.2.0", + "_view_count": null, + "flex_flow": null, + "width": null, + "min_width": null, + "border": null, + "align_items": null, + "bottom": null, + "_model_module": "@jupyter-widgets/base", + "top": null, + "grid_column": null, + "overflow_y": null, + "overflow_x": null, + "grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + }, + "c01768976adf465ebfad5c3eedfe1d58": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "StyleView", + "_model_name": "DescriptionStyleModel", + "description_width": "", + "_view_module": "@jupyter-widgets/base", + "_model_module_version": "1.5.0", + "_view_count": null, + "_view_module_version": "1.2.0", + "_model_module": "@jupyter-widgets/controls" + } + }, + "27fb7c7e261b4a3b9656a37b1fcde71a": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_view_name": "LayoutView", + "grid_template_rows": null, + "right": null, + "justify_content": null, + "_view_module": "@jupyter-widgets/base", + "overflow": null, + "_model_module_version": "1.2.0", + "_view_count": null, + "flex_flow": null, + "width": null, + "min_width": null, + "border": null, + "align_items": null, + "bottom": null, + "_model_module": "@jupyter-widgets/base", + "top": null, + "grid_column": null, + "overflow_y": null, + "overflow_x": null, + "grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + }, + "1c82670ef31346eb97dff63429fd522f": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "HBoxView", + "_dom_classes": [], + "_model_name": "HBoxModel", + "_view_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_view_count": null, + "_view_module_version": "1.5.0", + "box_style": "", + "layout": "IPY_MODEL_a8c2fda5e0be4c638919b4ca1007dea3", + "_model_module": "@jupyter-widgets/controls", + "children": [ + "IPY_MODEL_5e60bbde81ed452fa0c8d7094d98b052", + "IPY_MODEL_3ac055433ca94c2ebe9f8b44e38be5e0", + "IPY_MODEL_67e70ffbe152488fb036968be105a368" + ] + } + }, + "a8c2fda5e0be4c638919b4ca1007dea3": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_view_name": "LayoutView", + "grid_template_rows": null, + "right": null, + "justify_content": null, + "_view_module": "@jupyter-widgets/base", + "overflow": null, + "_model_module_version": "1.2.0", + "_view_count": null, + "flex_flow": null, + "width": null, + "min_width": null, + "border": null, + "align_items": null, + "bottom": null, + "_model_module": "@jupyter-widgets/base", + "top": null, + "grid_column": null, + "overflow_y": null, + "overflow_x": null, + "grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + }, + "5e60bbde81ed452fa0c8d7094d98b052": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "HTMLView", + "style": "IPY_MODEL_bbeb9a01f5bb4aebba239db555f4b16b", + "_dom_classes": [], + "description": "", + "_model_name": "HTMLModel", + "placeholder": "​", + "_view_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "value": "Iteration: 100%", + "_view_count": null, + "_view_module_version": "1.5.0", + "description_tooltip": null, + "_model_module": "@jupyter-widgets/controls", + "layout": "IPY_MODEL_771602cc4d9444e7ab0d20438639cddd" + } + }, + "3ac055433ca94c2ebe9f8b44e38be5e0": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "ProgressView", + "style": "IPY_MODEL_f154ee2be8a044b3aeeb0e904411ffbd", + "_dom_classes": [], + "description": "", + "_model_name": "FloatProgressModel", + "bar_style": "success", + "max": 5, + "_view_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "value": 5, + "_view_count": null, + "_view_module_version": "1.5.0", + "orientation": "horizontal", + "min": 0, + "description_tooltip": null, + "_model_module": "@jupyter-widgets/controls", + "layout": "IPY_MODEL_a3a6841089054f1cbc31f638424674b3" + } + }, + "67e70ffbe152488fb036968be105a368": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "HTMLView", + "style": "IPY_MODEL_41061818a9c94956a7d1cd129028d805", + "_dom_classes": [], + "description": "", + "_model_name": "HTMLModel", + "placeholder": "​", + "_view_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "value": " 5/5 [00:02<00:00, 1.89it/s]", + "_view_count": null, + "_view_module_version": "1.5.0", + "description_tooltip": null, + "_model_module": "@jupyter-widgets/controls", + "layout": "IPY_MODEL_bab64ef3864248018e9476bc8c4018f4" + } + }, + "bbeb9a01f5bb4aebba239db555f4b16b": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "StyleView", + "_model_name": "DescriptionStyleModel", + "description_width": "", + "_view_module": "@jupyter-widgets/base", + "_model_module_version": "1.5.0", + "_view_count": null, + "_view_module_version": "1.2.0", + "_model_module": "@jupyter-widgets/controls" + } + }, + "771602cc4d9444e7ab0d20438639cddd": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_view_name": "LayoutView", + "grid_template_rows": null, + "right": null, + "justify_content": null, + "_view_module": "@jupyter-widgets/base", + "overflow": null, + "_model_module_version": "1.2.0", + "_view_count": null, + "flex_flow": null, + "width": null, + "min_width": null, + "border": null, + "align_items": null, + "bottom": null, + "_model_module": "@jupyter-widgets/base", + "top": null, + "grid_column": null, + "overflow_y": null, + "overflow_x": null, + "grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + }, + "f154ee2be8a044b3aeeb0e904411ffbd": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "StyleView", + "_model_name": "ProgressStyleModel", + "description_width": "", + "_view_module": "@jupyter-widgets/base", + "_model_module_version": "1.5.0", + "_view_count": null, + "_view_module_version": "1.2.0", + "bar_color": null, + "_model_module": "@jupyter-widgets/controls" + } + }, + "a3a6841089054f1cbc31f638424674b3": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_view_name": "LayoutView", + "grid_template_rows": null, + "right": null, + "justify_content": null, + "_view_module": "@jupyter-widgets/base", + "overflow": null, + "_model_module_version": "1.2.0", + "_view_count": null, + "flex_flow": null, + "width": null, + "min_width": null, + "border": null, + "align_items": null, + "bottom": null, + "_model_module": "@jupyter-widgets/base", + "top": null, + "grid_column": null, + "overflow_y": null, + "overflow_x": null, + "grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + }, + "41061818a9c94956a7d1cd129028d805": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "StyleView", + "_model_name": "DescriptionStyleModel", + "description_width": "", + "_view_module": "@jupyter-widgets/base", + "_model_module_version": "1.5.0", + "_view_count": null, + "_view_module_version": "1.2.0", + "_model_module": "@jupyter-widgets/controls" + } + }, + "bab64ef3864248018e9476bc8c4018f4": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_view_name": "LayoutView", + "grid_template_rows": null, + "right": null, + "justify_content": null, + "_view_module": "@jupyter-widgets/base", + "overflow": null, + "_model_module_version": "1.2.0", + "_view_count": null, + "flex_flow": null, + "width": null, + "min_width": null, + "border": null, + "align_items": null, + "bottom": null, + "_model_module": "@jupyter-widgets/base", + "top": null, + "grid_column": null, + "overflow_y": null, + "overflow_x": null, + "grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + }, + "f3fa20cd1c40453bb17b2f109607e1bf": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "HBoxView", + "_dom_classes": [], + "_model_name": "HBoxModel", + "_view_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_view_count": null, + "_view_module_version": "1.5.0", + "box_style": "", + "layout": "IPY_MODEL_97b3a5270a014515bbc712b44dba38a0", + "_model_module": "@jupyter-widgets/controls", + "children": [ + "IPY_MODEL_afe3a3438fb145d4a015fdb0709e3156", + "IPY_MODEL_6d6078316fe54c9e83a3c3a35a1169fc", + "IPY_MODEL_4700a281e7d347db8a58c6f181706b54" + ] + } + }, + "97b3a5270a014515bbc712b44dba38a0": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_view_name": "LayoutView", + "grid_template_rows": null, + "right": null, + "justify_content": null, + "_view_module": "@jupyter-widgets/base", + "overflow": null, + "_model_module_version": "1.2.0", + "_view_count": null, + "flex_flow": null, + "width": null, + "min_width": null, + "border": null, + "align_items": null, + "bottom": null, + "_model_module": "@jupyter-widgets/base", + "top": null, + "grid_column": null, + "overflow_y": null, + "overflow_x": null, + "grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + }, + "afe3a3438fb145d4a015fdb0709e3156": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "HTMLView", + "style": "IPY_MODEL_84f8bdfeb6bf4bb7ba4585eba47a7092", + "_dom_classes": [], + "description": "", + "_model_name": "HTMLModel", + "placeholder": "​", + "_view_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "value": "Iteration: 100%", + "_view_count": null, + "_view_module_version": "1.5.0", + "description_tooltip": null, + "_model_module": "@jupyter-widgets/controls", + "layout": "IPY_MODEL_9262f880b64a4abb80013f6997901bcb" + } + }, + "6d6078316fe54c9e83a3c3a35a1169fc": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "ProgressView", + "style": "IPY_MODEL_386289f0bf56453484a6637d3263da4c", + "_dom_classes": [], + "description": "", + "_model_name": "FloatProgressModel", + "bar_style": "success", + "max": 5, + "_view_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "value": 5, + "_view_count": null, + "_view_module_version": "1.5.0", + "orientation": "horizontal", + "min": 0, + "description_tooltip": null, + "_model_module": "@jupyter-widgets/controls", + "layout": "IPY_MODEL_8366904443f649928aa9cfd915cd938a" + } + }, + "4700a281e7d347db8a58c6f181706b54": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "HTMLView", + "style": "IPY_MODEL_80f977590ae94733a8a8552241c12e3b", + "_dom_classes": [], + "description": "", + "_model_name": "HTMLModel", + "placeholder": "​", + "_view_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "value": " 5/5 [00:02<00:00, 1.79it/s]", + "_view_count": null, + "_view_module_version": "1.5.0", + "description_tooltip": null, + "_model_module": "@jupyter-widgets/controls", + "layout": "IPY_MODEL_3ff5971c055144a3b81190d199ffe3de" + } + }, + "84f8bdfeb6bf4bb7ba4585eba47a7092": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "StyleView", + "_model_name": "DescriptionStyleModel", + "description_width": "", + "_view_module": "@jupyter-widgets/base", + "_model_module_version": "1.5.0", + "_view_count": null, + "_view_module_version": "1.2.0", + "_model_module": "@jupyter-widgets/controls" + } + }, + "9262f880b64a4abb80013f6997901bcb": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_view_name": "LayoutView", + "grid_template_rows": null, + "right": null, + "justify_content": null, + "_view_module": "@jupyter-widgets/base", + "overflow": null, + "_model_module_version": "1.2.0", + "_view_count": null, + "flex_flow": null, + "width": null, + "min_width": null, + "border": null, + "align_items": null, + "bottom": null, + "_model_module": "@jupyter-widgets/base", + "top": null, + "grid_column": null, + "overflow_y": null, + "overflow_x": null, + "grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + }, + "386289f0bf56453484a6637d3263da4c": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "StyleView", + "_model_name": "ProgressStyleModel", + "description_width": "", + "_view_module": "@jupyter-widgets/base", + "_model_module_version": "1.5.0", + "_view_count": null, + "_view_module_version": "1.2.0", + "bar_color": null, + "_model_module": "@jupyter-widgets/controls" + } + }, + "8366904443f649928aa9cfd915cd938a": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_view_name": "LayoutView", + "grid_template_rows": null, + "right": null, + "justify_content": null, + "_view_module": "@jupyter-widgets/base", + "overflow": null, + "_model_module_version": "1.2.0", + "_view_count": null, + "flex_flow": null, + "width": null, + "min_width": null, + "border": null, + "align_items": null, + "bottom": null, + "_model_module": "@jupyter-widgets/base", + "top": null, + "grid_column": null, + "overflow_y": null, + "overflow_x": null, + "grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + }, + "80f977590ae94733a8a8552241c12e3b": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "StyleView", + "_model_name": "DescriptionStyleModel", + "description_width": "", + "_view_module": "@jupyter-widgets/base", + "_model_module_version": "1.5.0", + "_view_count": null, + "_view_module_version": "1.2.0", + "_model_module": "@jupyter-widgets/controls" + } + }, + "3ff5971c055144a3b81190d199ffe3de": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_view_name": "LayoutView", + "grid_template_rows": null, + "right": null, + "justify_content": null, + "_view_module": "@jupyter-widgets/base", + "overflow": null, + "_model_module_version": "1.2.0", + "_view_count": null, + "flex_flow": null, + "width": null, + "min_width": null, + "border": null, + "align_items": null, + "bottom": null, + "_model_module": "@jupyter-widgets/base", + "top": null, + "grid_column": null, + "overflow_y": null, + "overflow_x": null, + "grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + }, + "be5e0fa21fea43e8bf003ae954c29d03": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "HBoxView", + "_dom_classes": [], + "_model_name": "HBoxModel", + "_view_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_view_count": null, + "_view_module_version": "1.5.0", + "box_style": "", + "layout": "IPY_MODEL_7535306bf05847629946e333021e0ef5", + "_model_module": "@jupyter-widgets/controls", + "children": [ + "IPY_MODEL_671f7a7556b1412cbd48237293431c0d", + "IPY_MODEL_8957849d6dbf44cfafa965d71de78255", + "IPY_MODEL_a55488797f71453e917032008f198b9c" + ] + } + }, + "7535306bf05847629946e333021e0ef5": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_view_name": "LayoutView", + "grid_template_rows": null, + "right": null, + "justify_content": null, + "_view_module": "@jupyter-widgets/base", + "overflow": null, + "_model_module_version": "1.2.0", + "_view_count": null, + "flex_flow": null, + "width": null, + "min_width": null, + "border": null, + "align_items": null, + "bottom": null, + "_model_module": "@jupyter-widgets/base", + "top": null, + "grid_column": null, + "overflow_y": null, + "overflow_x": null, + "grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + }, + "671f7a7556b1412cbd48237293431c0d": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "HTMLView", + "style": "IPY_MODEL_38ccc42c4ea14f0e83fca1cb9452bfad", + "_dom_classes": [], + "description": "", + "_model_name": "HTMLModel", + "placeholder": "​", + "_view_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "value": "Iteration: 100%", + "_view_count": null, + "_view_module_version": "1.5.0", + "description_tooltip": null, + "_model_module": "@jupyter-widgets/controls", + "layout": "IPY_MODEL_ef2ddbabd7b042c0821cf999a9265867" + } + }, + "8957849d6dbf44cfafa965d71de78255": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "ProgressView", + "style": "IPY_MODEL_0222497184b2446e90f801d84af22b82", + "_dom_classes": [], + "description": "", + "_model_name": "FloatProgressModel", + "bar_style": "success", + "max": 5, + "_view_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "value": 5, + "_view_count": null, + "_view_module_version": "1.5.0", + "orientation": "horizontal", + "min": 0, + "description_tooltip": null, + "_model_module": "@jupyter-widgets/controls", + "layout": "IPY_MODEL_6a70d59d1c834989a54deda9e776bf41" + } + }, + "a55488797f71453e917032008f198b9c": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "HTMLView", + "style": "IPY_MODEL_62e05dc21f5e438cb5e94e400071a39b", + "_dom_classes": [], + "description": "", + "_model_name": "HTMLModel", + "placeholder": "​", + "_view_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "value": " 5/5 [00:02<00:00, 1.86it/s]", + "_view_count": null, + "_view_module_version": "1.5.0", + "description_tooltip": null, + "_model_module": "@jupyter-widgets/controls", + "layout": "IPY_MODEL_803884fef29b433db14e61df7fae1ee7" + } + }, + "38ccc42c4ea14f0e83fca1cb9452bfad": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "StyleView", + "_model_name": "DescriptionStyleModel", + "description_width": "", + "_view_module": "@jupyter-widgets/base", + "_model_module_version": "1.5.0", + "_view_count": null, + "_view_module_version": "1.2.0", + "_model_module": "@jupyter-widgets/controls" + } + }, + "ef2ddbabd7b042c0821cf999a9265867": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_view_name": "LayoutView", + "grid_template_rows": null, + "right": null, + "justify_content": null, + "_view_module": "@jupyter-widgets/base", + "overflow": null, + "_model_module_version": "1.2.0", + "_view_count": null, + "flex_flow": null, + "width": null, + "min_width": null, + "border": null, + "align_items": null, + "bottom": null, + "_model_module": "@jupyter-widgets/base", + "top": null, + "grid_column": null, + "overflow_y": null, + "overflow_x": null, + "grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + }, + "0222497184b2446e90f801d84af22b82": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "StyleView", + "_model_name": "ProgressStyleModel", + "description_width": "", + "_view_module": "@jupyter-widgets/base", + "_model_module_version": "1.5.0", + "_view_count": null, + "_view_module_version": "1.2.0", + "bar_color": null, + "_model_module": "@jupyter-widgets/controls" + } + }, + "6a70d59d1c834989a54deda9e776bf41": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_view_name": "LayoutView", + "grid_template_rows": null, + "right": null, + "justify_content": null, + "_view_module": "@jupyter-widgets/base", + "overflow": null, + "_model_module_version": "1.2.0", + "_view_count": null, + "flex_flow": null, + "width": null, + "min_width": null, + "border": null, + "align_items": null, + "bottom": null, + "_model_module": "@jupyter-widgets/base", + "top": null, + "grid_column": null, + "overflow_y": null, + "overflow_x": null, + "grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + }, + "62e05dc21f5e438cb5e94e400071a39b": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "StyleView", + "_model_name": "DescriptionStyleModel", + "description_width": "", + "_view_module": "@jupyter-widgets/base", + "_model_module_version": "1.5.0", + "_view_count": null, + "_view_module_version": "1.2.0", + "_model_module": "@jupyter-widgets/controls" + } + }, + "803884fef29b433db14e61df7fae1ee7": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_view_name": "LayoutView", + "grid_template_rows": null, + "right": null, + "justify_content": null, + "_view_module": "@jupyter-widgets/base", + "overflow": null, + "_model_module_version": "1.2.0", + "_view_count": null, + "flex_flow": null, + "width": null, + "min_width": null, + "border": null, + "align_items": null, + "bottom": null, + "_model_module": "@jupyter-widgets/base", + "top": null, + "grid_column": null, + "overflow_y": null, + "overflow_x": null, + "grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + }, + "83414a06fd504f71aa212d9fce15ffb5": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "HBoxView", + "_dom_classes": [], + "_model_name": "HBoxModel", + "_view_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_view_count": null, + "_view_module_version": "1.5.0", + "box_style": "", + "layout": "IPY_MODEL_e2c8785b7c51448296c8cf54331f4a68", + "_model_module": "@jupyter-widgets/controls", + "children": [ + "IPY_MODEL_3f9881738b644024aea6371982320d97", + "IPY_MODEL_143666ff0c7b4e6491779b64f6212818", + "IPY_MODEL_bf699df8e3d844f68a68491c00e8f0bc" + ] + } + }, + "e2c8785b7c51448296c8cf54331f4a68": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_view_name": "LayoutView", + "grid_template_rows": null, + "right": null, + "justify_content": null, + "_view_module": "@jupyter-widgets/base", + "overflow": null, + "_model_module_version": "1.2.0", + "_view_count": null, + "flex_flow": null, + "width": null, + "min_width": null, + "border": null, + "align_items": null, + "bottom": null, + "_model_module": "@jupyter-widgets/base", + "top": null, + "grid_column": null, + "overflow_y": null, + "overflow_x": null, + "grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + }, + "3f9881738b644024aea6371982320d97": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "HTMLView", + "style": "IPY_MODEL_9e955ce77097447a8c085ea592ae8a5e", + "_dom_classes": [], + "description": "", + "_model_name": "HTMLModel", + "placeholder": "​", + "_view_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "value": "Iteration: 100%", + "_view_count": null, + "_view_module_version": "1.5.0", + "description_tooltip": null, + "_model_module": "@jupyter-widgets/controls", + "layout": "IPY_MODEL_2f2aecb73861473ba553b4ebebd52e0b" + } + }, + "143666ff0c7b4e6491779b64f6212818": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "ProgressView", + "style": "IPY_MODEL_db21f601fd6342a5815cea18c417aa99", + "_dom_classes": [], + "description": "", + "_model_name": "FloatProgressModel", + "bar_style": "success", + "max": 5, + "_view_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "value": 5, + "_view_count": null, + "_view_module_version": "1.5.0", + "orientation": "horizontal", + "min": 0, + "description_tooltip": null, + "_model_module": "@jupyter-widgets/controls", + "layout": "IPY_MODEL_67bf9d4f1a8e431bb580337be0e67f82" + } + }, + "bf699df8e3d844f68a68491c00e8f0bc": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "HTMLView", + "style": "IPY_MODEL_ddcba8098de6433a8584045d52cb1f3b", + "_dom_classes": [], + "description": "", + "_model_name": "HTMLModel", + "placeholder": "​", + "_view_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "value": " 5/5 [00:02<00:00, 1.89it/s]", + "_view_count": null, + "_view_module_version": "1.5.0", + "description_tooltip": null, + "_model_module": "@jupyter-widgets/controls", + "layout": "IPY_MODEL_60062f7860944d0d85c4ef1773c151c3" + } + }, + "9e955ce77097447a8c085ea592ae8a5e": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "StyleView", + "_model_name": "DescriptionStyleModel", + "description_width": "", + "_view_module": "@jupyter-widgets/base", + "_model_module_version": "1.5.0", + "_view_count": null, + "_view_module_version": "1.2.0", + "_model_module": "@jupyter-widgets/controls" + } + }, + "2f2aecb73861473ba553b4ebebd52e0b": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_view_name": "LayoutView", + "grid_template_rows": null, + "right": null, + "justify_content": null, + "_view_module": "@jupyter-widgets/base", + "overflow": null, + "_model_module_version": "1.2.0", + "_view_count": null, + "flex_flow": null, + "width": null, + "min_width": null, + "border": null, + "align_items": null, + "bottom": null, + "_model_module": "@jupyter-widgets/base", + "top": null, + "grid_column": null, + "overflow_y": null, + "overflow_x": null, + "grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + }, + "db21f601fd6342a5815cea18c417aa99": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "StyleView", + "_model_name": "ProgressStyleModel", + "description_width": "", + "_view_module": "@jupyter-widgets/base", + "_model_module_version": "1.5.0", + "_view_count": null, + "_view_module_version": "1.2.0", + "bar_color": null, + "_model_module": "@jupyter-widgets/controls" + } + }, + "67bf9d4f1a8e431bb580337be0e67f82": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_view_name": "LayoutView", + "grid_template_rows": null, + "right": null, + "justify_content": null, + "_view_module": "@jupyter-widgets/base", + "overflow": null, + "_model_module_version": "1.2.0", + "_view_count": null, + "flex_flow": null, + "width": null, + "min_width": null, + "border": null, + "align_items": null, + "bottom": null, + "_model_module": "@jupyter-widgets/base", + "top": null, + "grid_column": null, + "overflow_y": null, + "overflow_x": null, + "grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + }, + "ddcba8098de6433a8584045d52cb1f3b": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "StyleView", + "_model_name": "DescriptionStyleModel", + "description_width": "", + "_view_module": "@jupyter-widgets/base", + "_model_module_version": "1.5.0", + "_view_count": null, + "_view_module_version": "1.2.0", + "_model_module": "@jupyter-widgets/controls" + } + }, + "60062f7860944d0d85c4ef1773c151c3": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_view_name": "LayoutView", + "grid_template_rows": null, + "right": null, + "justify_content": null, + "_view_module": "@jupyter-widgets/base", + "overflow": null, + "_model_module_version": "1.2.0", + "_view_count": null, + "flex_flow": null, + "width": null, + "min_width": null, + "border": null, + "align_items": null, + "bottom": null, + "_model_module": "@jupyter-widgets/base", + "top": null, + "grid_column": null, + "overflow_y": null, + "overflow_x": null, + "grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + }, + "cc13e655b33d4fa390960d1fa40a0e1f": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "HBoxView", + "_dom_classes": [], + "_model_name": "HBoxModel", + "_view_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_view_count": null, + "_view_module_version": "1.5.0", + "box_style": "", + "layout": "IPY_MODEL_2e418d21ae4f4123a9d7b13cbc368605", + "_model_module": "@jupyter-widgets/controls", + "children": [ + "IPY_MODEL_ba1f476b8bdc4fce8a703a0220bd4770", + "IPY_MODEL_26560e743afa4a3fad0eb1e0ed567a64", + "IPY_MODEL_27e7a38811a94548b8dc1980e1c83acd" + ] + } + }, + "2e418d21ae4f4123a9d7b13cbc368605": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_view_name": "LayoutView", + "grid_template_rows": null, + "right": null, + "justify_content": null, + "_view_module": "@jupyter-widgets/base", + "overflow": null, + "_model_module_version": "1.2.0", + "_view_count": null, + "flex_flow": null, + "width": null, + "min_width": null, + "border": null, + "align_items": null, + "bottom": null, + "_model_module": "@jupyter-widgets/base", + "top": null, + "grid_column": null, + "overflow_y": null, + "overflow_x": null, + "grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + }, + "ba1f476b8bdc4fce8a703a0220bd4770": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "HTMLView", + "style": "IPY_MODEL_c5ceabb016a74435be6659f1116c9945", + "_dom_classes": [], + "description": "", + "_model_name": "HTMLModel", + "placeholder": "​", + "_view_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "value": "Evaluating: ", + "_view_count": null, + "_view_module_version": "1.5.0", + "description_tooltip": null, + "_model_module": "@jupyter-widgets/controls", + "layout": "IPY_MODEL_4ca6ee080dad41aa8abbbea5a96e3922" + } + }, + "26560e743afa4a3fad0eb1e0ed567a64": { + "model_module": "@jupyter-widgets/controls", + "model_name": "FloatProgressModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "ProgressView", + "style": "IPY_MODEL_c0fd4025a3e84d0ca8c360887d7126ba", + "_dom_classes": [], + "description": "", + "_model_name": "FloatProgressModel", + "bar_style": "success", + "max": 1, + "_view_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "value": 0, + "_view_count": null, + "_view_module_version": "1.5.0", + "orientation": "horizontal", + "min": 0, + "description_tooltip": null, + "_model_module": "@jupyter-widgets/controls", + "layout": "IPY_MODEL_4a374856a56c4dc6a163ee5779d6b666" + } + }, + "27e7a38811a94548b8dc1980e1c83acd": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HTMLModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "HTMLView", + "style": "IPY_MODEL_ef067c6b95ac48c58428f62ecef22e33", + "_dom_classes": [], + "description": "", + "_model_name": "HTMLModel", + "placeholder": "​", + "_view_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "value": " 0/0 [00:00<?, ?it/s]", + "_view_count": null, + "_view_module_version": "1.5.0", + "description_tooltip": null, + "_model_module": "@jupyter-widgets/controls", + "layout": "IPY_MODEL_88521418122646b0b6c7d41be73e747a" + } + }, + "c5ceabb016a74435be6659f1116c9945": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "StyleView", + "_model_name": "DescriptionStyleModel", + "description_width": "", + "_view_module": "@jupyter-widgets/base", + "_model_module_version": "1.5.0", + "_view_count": null, + "_view_module_version": "1.2.0", + "_model_module": "@jupyter-widgets/controls" + } + }, + "4ca6ee080dad41aa8abbbea5a96e3922": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_view_name": "LayoutView", + "grid_template_rows": null, + "right": null, + "justify_content": null, + "_view_module": "@jupyter-widgets/base", + "overflow": null, + "_model_module_version": "1.2.0", + "_view_count": null, + "flex_flow": null, + "width": null, + "min_width": null, + "border": null, + "align_items": null, + "bottom": null, + "_model_module": "@jupyter-widgets/base", + "top": null, + "grid_column": null, + "overflow_y": null, + "overflow_x": null, + "grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + }, + "c0fd4025a3e84d0ca8c360887d7126ba": { + "model_module": "@jupyter-widgets/controls", + "model_name": "ProgressStyleModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "StyleView", + "_model_name": "ProgressStyleModel", + "description_width": "", + "_view_module": "@jupyter-widgets/base", + "_model_module_version": "1.5.0", + "_view_count": null, + "_view_module_version": "1.2.0", + "bar_color": null, + "_model_module": "@jupyter-widgets/controls" + } + }, + "4a374856a56c4dc6a163ee5779d6b666": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_view_name": "LayoutView", + "grid_template_rows": null, + "right": null, + "justify_content": null, + "_view_module": "@jupyter-widgets/base", + "overflow": null, + "_model_module_version": "1.2.0", + "_view_count": null, + "flex_flow": null, + "width": "20px", + "min_width": null, + "border": null, + "align_items": null, + "bottom": null, + "_model_module": "@jupyter-widgets/base", + "top": null, + "grid_column": null, + "overflow_y": null, + "overflow_x": null, + "grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + }, + "ef067c6b95ac48c58428f62ecef22e33": { + "model_module": "@jupyter-widgets/controls", + "model_name": "DescriptionStyleModel", + "model_module_version": "1.5.0", + "state": { + "_view_name": "StyleView", + "_model_name": "DescriptionStyleModel", + "description_width": "", + "_view_module": "@jupyter-widgets/base", + "_model_module_version": "1.5.0", + "_view_count": null, + "_view_module_version": "1.2.0", + "_model_module": "@jupyter-widgets/controls" + } + }, + "88521418122646b0b6c7d41be73e747a": { + "model_module": "@jupyter-widgets/base", + "model_name": "LayoutModel", + "model_module_version": "1.2.0", + "state": { + "_view_name": "LayoutView", + "grid_template_rows": null, + "right": null, + "justify_content": null, + "_view_module": "@jupyter-widgets/base", + "overflow": null, + "_model_module_version": "1.2.0", + "_view_count": null, + "flex_flow": null, + "width": null, + "min_width": null, + "border": null, + "align_items": null, + "bottom": null, + "_model_module": "@jupyter-widgets/base", + "top": null, + "grid_column": null, + "overflow_y": null, + "overflow_x": null, + "grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + } + } + } + }, + "cells": [ + { + "cell_type": "code", + "execution_count": 57, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tSIO1yDEJbxI", + "outputId": "43bc1501-529c-48bc-d825-08c242d5de04" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" + ] + } + ], + "source": [ + "from google.colab import drive\n", + "drive.mount(\"/content/drive\")" + ] + }, + { + "cell_type": "code", + "source": [ + "!pip -q install transformers" + ], + "metadata": { + "id": "LwrtmgMvMSey" + }, + "execution_count": 58, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "os.chdir(\"/content/drive/My Drive/Colab Notebooks\")" + ], + "metadata": { + "id": "Mp864lxgIbJE" + }, + "execution_count": 59, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# libraries\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from __future__ import division\n", + "\n", + "import random\n", + "import glob\n", + "import logging\n", + "import os\n", + "import pickle\n", + "import re\n", + "import shutil\n", + "from typing import List, Dict, Tuple\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "from torch.nn.utils.rnn import pad_sequence\n", + "from torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler\n", + "from torch.utils.data.distributed import DistributedSampler\n", + "from tqdm.notebook import tqdm, trange\n", + "\n", + "from pathlib import Path\n", + "\n", + "from transformers import (\n", + " MODEL_WITH_LM_HEAD_MAPPING,\n", + " WEIGHTS_NAME,\n", + " AdamW,\n", + " AutoConfig,\n", + " PreTrainedModel,\n", + " PreTrainedTokenizer,\n", + " get_linear_schedule_with_warmup,\n", + ")\n", + "\n", + "try:\n", + " from torch.utils.tensorboard import SummaryWriter\n", + "except ImportError:\n", + " from tensorboardX import SummaryWriter" + ], + "metadata": { + "id": "ujmUewQ5NVoO" + }, + "execution_count": 60, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# visualize raw data\n", + "d = pd.read_csv(\"/content/drive/MyDrive/final-all-scripts.csv\", sep=\"delimiter\", header=None)\n", + "d.head()" + ], + "metadata": { + "id": "8gMOER_tVuIr", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 261 + }, + "outputId": "f8275436-770a-424d-fb41-a296ddc45045" + }, + "execution_count": 61, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support regex separators (separators > 1 char and different from '\\s+' are interpreted as regex); you can avoid this warning by specifying engine='python'.\n", + " \n" + ] + }, + { + "output_type": "execute_result", + "data": { + "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", + "
0
0[Class room]
1(A city is flying through space, stuck on the ...
2COMPUTER: Well done, Mabel. Well done, Alfie. ...
3(It is the little boy's turn.)
4COMPUTER: Bad boy, Timmy.
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ], + "text/plain": [ + " 0\n", + "0 [Class room]\n", + "1 (A city is flying through space, stuck on the ...\n", + "2 COMPUTER: Well done, Mabel. Well done, Alfie. ...\n", + "3 (It is the little boy's turn.)\n", + "4 COMPUTER: Bad boy, Timmy." + ] + }, + "metadata": {}, + "execution_count": 61 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Data Preprocessing" + ], + "metadata": { + "id": "Vr2Y_QbooJUM" + } + }, + { + "cell_type": "code", + "source": [ + "print(f\"Data type of file: {type(d)}\",\n", + " f\"\\nShape of file: {d.shape}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_mKAYMdxj2-l", + "outputId": "5bb10015-4046-41c8-e774-ed42e87ccfc7" + }, + "execution_count": 62, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Data type of file: \n", + "Shape of file: (27597, 1)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "print(f\"Type of first element: {type(d.iloc[0])}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "YiVX_L8P0P1K", + "outputId": "e280fb35-b11d-42d8-8ba3-50843120cea9" + }, + "execution_count": 63, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Type of first element: \n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "dd = []\n", + "\n", + "for i in d[0]:\n", + " if not (i.startswith(\"(\") or i.startswith(\"[\")):\n", + " dd.append(i)\n", + "\n", + "dd[:10]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "y019y7pT02GL", + "outputId": "30c9a13b-3a42-4ac2-871b-9f830d6aa5a4" + }, + "execution_count": 64, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "['COMPUTER: Well done, Mabel. Well done, Alfie. Good girl, Tabitha. Very well done, Ranjit. Good girl, Chloe. Well done, Ben. Well done, Mandy.',\n", + " 'COMPUTER: Bad boy, Timmy.',\n", + " 'COMPUTER: Zero.',\n", + " \"MANDY: You got a zero, didn't you?\",\n", + " 'TIMMY: Yeah? So?',\n", + " \"MANDY: You'll have to walk home then.\",\n", + " \"TIMMY: Walk to London? That's twenty decks!\",\n", + " \"MANDY: You can't ride a Vator with a zero. You know what happens. You'll get sent below.\",\n", + " \"MANDY: I'll wait for you.\",\n", + " \"SMILER: Welcome to Vator Verse, sponsored by McLintock's Candy Burgers. TIMMY: London, please.\"]" + ] + }, + "metadata": {}, + "execution_count": 64 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# person-text split\n", + "#dd[1].split(\":\")\n", + "\n", + "# each dialogue\n", + "#dialogues[0][1]" + ], + "metadata": { + "id": "fsT-q0762yJ8" + }, + "execution_count": 65, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "dialogues = [l.split(\":\") for l in dd]\n", + "len(dialogues)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "lcA-TTbR64_q", + "outputId": "9b59a4f0-b3fb-4ff5-c2a3-059183309548" + }, + "execution_count": 66, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "20594" + ] + }, + "metadata": {}, + "execution_count": 66 + } + ] + }, + { + "cell_type": "code", + "source": [ + "chars= []\n", + "txts = []\n", + "\n", + "for i in range(len(dialogues)):\n", + " chars.append(dialogues[i][0])\n", + " txts.append(dialogues[i][1])" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 200 + }, + "id": "FtSYUoGO7XLk", + "outputId": "78760c97-5c54-4b9b-b263-a9ee0809ca1f" + }, + "execution_count": 67, + "outputs": [ + { + "output_type": "error", + "ename": "IndexError", + "evalue": "ignored", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdialogues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mchars\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdialogues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mtxts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdialogues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mIndexError\u001b[0m: list index out of range" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "len(chars)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "TWxj098VzBXF", + "outputId": "198df600-0cbf-431b-eede-58a40b62f108" + }, + "execution_count": 68, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "104" + ] + }, + "metadata": {}, + "execution_count": 68 + } + ] + }, + { + "cell_type": "code", + "source": [ + "dialogues[len(dialogues)-1][1]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "id": "l0PCKQI5LkuR", + "outputId": "fb6f3000-7378-4f30-bd3a-e193bce34ba8" + }, + "execution_count": 69, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + }, + "text/plain": [ + "' So, dresses, then.'" + ] + }, + "metadata": {}, + "execution_count": 69 + } + ] + }, + { + "cell_type": "code", + "source": [ + "#dialogues[len(dialogues)-1][1] == dialogues[-1][1]" + ], + "metadata": { + "id": "kFVPluE3-ojX" + }, + "execution_count": 70, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "#pd.isnull(dialogues).sum()" + ], + "metadata": { + "id": "oemthiCWInCq" + }, + "execution_count": 71, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# seperate person-text and convert dataframe\n", + "df = pd.DataFrame(list(zip(chars, txts)), columns=[\"Character\", \"Text\"])\n", + "df.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "UqgM35hBrQp6", + "outputId": "346eab19-132c-433b-9a99-3e1012b83eac" + }, + "execution_count": 72, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "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", + "
CharacterText
0COMPUTERWell done, Mabel. Well done, Alfie. Good girl...
1COMPUTERBad boy, Timmy.
2COMPUTERZero.
3MANDYYou got a zero, didn't you?
4TIMMYYeah? So?
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ], + "text/plain": [ + " Character Text\n", + "0 COMPUTER Well done, Mabel. Well done, Alfie. Good girl...\n", + "1 COMPUTER Bad boy, Timmy.\n", + "2 COMPUTER Zero.\n", + "3 MANDY You got a zero, didn't you?\n", + "4 TIMMY Yeah? So?" + ] + }, + "metadata": {}, + "execution_count": 72 + } + ] + }, + { + "cell_type": "code", + "source": [ + "CHARACTER_NAME = \"DOCTOR\"" + ], + "metadata": { + "id": "FY639-Fi7WfF" + }, + "execution_count": 73, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "contexted = []\n", + "\n", + "# context window of size 7\n", + "n = 7\n", + "\n", + "for i in df[df.Character == CHARACTER_NAME].index:\n", + " if i < n:\n", + " continue\n", + " row = []\n", + " prev = i - 1 - n # we additionally substract 1, so row will contain current responce and 7 previous responces \n", + " for j in range(i, prev, -1):\n", + " row.append(df.Text[j])\n", + " contexted.append(row)\n", + "\n", + "columns = ['response', 'context'] \n", + "columns = columns + ['context/' + str(i) for i in range(n - 1)]\n", + "\n", + "df = pd.DataFrame.from_records(contexted, columns=columns)" + ], + "metadata": { + "id": "vSqHrtAOz_1j" + }, + "execution_count": 74, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df.sample(6)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 490 + }, + "id": "GEVvAnKN0xad", + "outputId": "cdd5a2d2-f465-4d2a-947a-7269447ede2e" + }, + "execution_count": 75, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "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", + "
responsecontextcontext/0context/1context/2context/3context/4context/5
8An important thing. In fact, Thing One. We ar...A thing?Course we can. But first, there's a thing.Can we go out and see?Well, come on. I've found us a spaceship. Thi...Doctor!Isn't that amazing?Doctor?
16Don't know. I think a lot. It's hard to keep ...Why did you just do that with the water?Sorry. Checking all the water in this area. T...What are you doing?Life on a giant starship. Back to basics. Bic...London Market is a crime-free zone.Now, come on, look around you. Actually look.Oh my God, I'm in my nightie.
0Come on, Pond.My name is Amy Pond. When I was seven, I had ...Help! Help me!Though the man above might say hello, expect ...A horse and a man, above, below. One has a pl...Welcome to Vator Verse, sponsored by McLintoc...I'll wait for you.You can't ride a Vator with a zero. You know ...
23What I always do. Stay out of trouble. Badly.What are you going to do?It's this or Leadworth. What do you think? Le...No, hang on. What do I do? I don't know what ...They're clean. Everything else here is all ba...But they're just things.Deck two oh seven. Apple Sesame block, dwelli...Where'd she go?
11Come on, use your eyes. Notice everything. Wh...What's wrong?Oh, lovely. You're a cheery one. Never mind d...I'm in the future. Like hundreds of years in ...Welcome to London Market. You are being monit...Doctor?So we're like a wildlife documentary, yeah? B...Ooo, that's interesting.
9Ooo, that's interesting.An important thing. In fact, Thing One. We ar...A thing?Course we can. But first, there's a thing.Can we go out and see?Well, come on. I've found us a spaceship. Thi...Doctor!Isn't that amazing?
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ], + "text/plain": [ + " response ... context/5\n", + "8 An important thing. In fact, Thing One. We ar... ... Doctor?\n", + "16 Don't know. I think a lot. It's hard to keep ... ... Oh my God, I'm in my nightie.\n", + "0 Come on, Pond. ... You can't ride a Vator with a zero. You know ...\n", + "23 What I always do. Stay out of trouble. Badly. ... Where'd she go?\n", + "11 Come on, use your eyes. Notice everything. Wh... ... Ooo, that's interesting.\n", + "9 Ooo, that's interesting. ... Isn't that amazing?\n", + "\n", + "[6 rows x 8 columns]" + ] + }, + "metadata": {}, + "execution_count": 75 + } + ] + }, + { + "cell_type": "code", + "source": [ + "trn_df, val_df = train_test_split(df, test_size=0.1)" + ], + "metadata": { + "id": "nYM_4zKirQ5A" + }, + "execution_count": 76, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "trn_df.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 424 + }, + "id": "RKF8dGVxS61X", + "outputId": "46d99699-9659-4d69-fd54-16b290ec2491" + }, + "execution_count": 77, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "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", + "
responsecontextcontext/0context/1context/2context/3context/4context/5
7Course we can. But first, there's a thing.Can we go out and see?Well, come on. I've found us a spaceship. Thi...Doctor!Isn't that amazing?Doctor?Migrating to the stars.Doctor?
17There.Where?Don't know. I think a lot. It's hard to keep ...Why did you just do that with the water?Sorry. Checking all the water in this area. T...What are you doing?Life on a giant starship. Back to basics. Bic...London Market is a crime-free zone.
1Now do you believe me?And my imaginary friend came back.Come on, Pond.My name is Amy Pond. When I was seven, I had ...Help! Help me!Though the man above might say hello, expect ...A horse and a man, above, below. One has a pl...Welcome to Vator Verse, sponsored by McLintoc...
20Deck two oh seven. Apple Sesame block, dwelli...Where'd she go?Hundreds of parents walking past who spot her...Are you a parent?Crying silently. I mean, children cry because...One little girl crying. So?I'll have a look on the monitors.Apparently.
0Come on, Pond.My name is Amy Pond. When I was seven, I had ...Help! Help me!Though the man above might say hello, expect ...A horse and a man, above, below. One has a pl...Welcome to Vator Verse, sponsored by McLintoc...I'll wait for you.You can't ride a Vator with a zero. You know ...
\n", + "
\n", + " \n", + " \n", + " \n", + "\n", + " \n", + "
\n", + "
\n", + " " + ], + "text/plain": [ + " response ... context/5\n", + "7 Course we can. But first, there's a thing. ... Doctor?\n", + "17 There. ... London Market is a crime-free zone.\n", + "1 Now do you believe me? ... Welcome to Vator Verse, sponsored by McLintoc...\n", + "20 Deck two oh seven. Apple Sesame block, dwelli... ... Apparently.\n", + "0 Come on, Pond. ... You can't ride a Vator with a zero. You know ...\n", + "\n", + "[5 rows x 8 columns]" + ] + }, + "metadata": {}, + "execution_count": 77 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# create dataset suitable for our model\n", + "def construct_conv(row, tokenizer, eos = True):\n", + " flatten = lambda l: [item for sublist in l for item in sublist]\n", + " conv = list(reversed([tokenizer.encode(x) + [tokenizer.eos_token_id] for x in row]))\n", + " conv = flatten(conv)\n", + " return conv\n", + "\n", + "class ConversationDataset(Dataset):\n", + " def __init__(self, tokenizer: PreTrainedTokenizer, args, df, block_size=512):\n", + "\n", + " block_size = block_size - (tokenizer.model_max_length - tokenizer.max_len_single_sentence)\n", + "\n", + " directory = args.cache_dir\n", + " cached_features_file = os.path.join(\n", + " directory, args.model_type + \"_cached_lm_\" + str(block_size)\n", + " )\n", + "\n", + " if os.path.exists(cached_features_file) and not args.overwrite_cache:\n", + " logger.info(\"Loading features from cached file %s\", cached_features_file)\n", + " with open(cached_features_file, \"rb\") as handle:\n", + " self.examples = pickle.load(handle)\n", + " else:\n", + " logger.info(\"Creating features from dataset file at %s\", directory)\n", + "\n", + " self.examples = []\n", + " for _, row in df.iterrows():\n", + " conv = construct_conv(row, tokenizer)\n", + " self.examples.append(conv)\n", + "\n", + " logger.info(\"Saving features into cached file %s\", cached_features_file)\n", + " with open(cached_features_file, \"wb\") as handle:\n", + " pickle.dump(self.examples, handle, protocol=pickle.HIGHEST_PROTOCOL)\n", + "\n", + " def __len__(self):\n", + " return len(self.examples)\n", + "\n", + " def __getitem__(self, item):\n", + " return torch.tensor(self.examples[item], dtype=torch.long)" + ], + "metadata": { + "id": "va9Olm-DoR9w" + }, + "execution_count": 78, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Cacheing and storing of data/checkpoints\n", + "\n", + "def load_and_cache_examples(args, tokenizer, df_trn, df_val, evaluate=False):\n", + " return ConversationDataset(tokenizer, args, df_val if evaluate else df_trn)\n", + "\n", + "\n", + "def set_seed(args):\n", + " random.seed(args.seed)\n", + " np.random.seed(args.seed)\n", + " torch.manual_seed(args.seed)\n", + " if args.n_gpu > 0:\n", + " torch.cuda.manual_seed_all(args.seed)\n", + "\n", + "\n", + "def _sorted_checkpoints(args, checkpoint_prefix=\"checkpoint\", use_mtime=False) -> List[str]:\n", + " ordering_and_checkpoint_path = []\n", + "\n", + " glob_checkpoints = glob.glob(os.path.join(args.output_dir, \"{}-*\".format(checkpoint_prefix)))\n", + "\n", + " for path in glob_checkpoints:\n", + " if use_mtime:\n", + " ordering_and_checkpoint_path.append((os.path.getmtime(path), path))\n", + " else:\n", + " regex_match = re.match(\".*{}-([0-9]+)\".format(checkpoint_prefix), path)\n", + " if regex_match and regex_match.groups():\n", + " ordering_and_checkpoint_path.append((int(regex_match.groups()[0]), path))\n", + "\n", + " checkpoints_sorted = sorted(ordering_and_checkpoint_path)\n", + " checkpoints_sorted = [checkpoint[1] for checkpoint in checkpoints_sorted]\n", + " return checkpoints_sorted\n", + "\n", + "\n", + "def _rotate_checkpoints(args, checkpoint_prefix=\"checkpoint\", use_mtime=False) -> None:\n", + " if not args.save_total_limit:\n", + " return\n", + " if args.save_total_limit <= 0:\n", + " return\n", + "\n", + " # Check if we should delete older checkpoint(s)\n", + " checkpoints_sorted = _sorted_checkpoints(args, checkpoint_prefix, use_mtime)\n", + " if len(checkpoints_sorted) <= args.save_total_limit:\n", + " return\n", + "\n", + " number_of_checkpoints_to_delete = max(0, len(checkpoints_sorted) - args.save_total_limit)\n", + " checkpoints_to_be_deleted = checkpoints_sorted[:number_of_checkpoints_to_delete]\n", + " for checkpoint in checkpoints_to_be_deleted:\n", + " logger.info(\"Deleting older checkpoint [{}] due to args.save_total_limit\".format(checkpoint))\n", + " shutil.rmtree(checkpoint)" + ], + "metadata": { + "id": "wj1yakcqTCNx" + }, + "execution_count": 79, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Build Model" + ], + "metadata": { + "id": "PNcEUFjSoML0" + } + }, + { + "cell_type": "code", + "source": [ + "from transformers import AutoModelWithLMHead, AutoModelForCausalLM, AutoTokenizer\n", + "import torch\n", + "\n", + "tokenizer = AutoTokenizer.from_pretrained(\"microsoft/DialoGPT-small\")\n", + "model = AutoModelWithLMHead.from_pretrained(\"microsoft/DialoGPT-small\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "UTI0g8PPg628", + "outputId": "8b8c6e19-fa17-42be-c9e4-25e5ae4c1819" + }, + "execution_count": 80, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/transformers/models/auto/modeling_auto.py:787: FutureWarning: The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use `AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and `AutoModelForSeq2SeqLM` for encoder-decoder models.\n", + " FutureWarning,\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# configs\n", + "\n", + "logger = logging.getLogger(__name__)\n", + "\n", + "MODEL_CONFIG_CLASSES = list(MODEL_WITH_LM_HEAD_MAPPING.keys())\n", + "MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)" + ], + "metadata": { + "id": "lzDAg6-eg7Fj" + }, + "execution_count": 81, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Args to allow for easy convertion of python script to notebook\n", + "class Args():\n", + " def __init__(self):\n", + " self.output_dir = 'output-small'\n", + " self.model_type = 'gpt2'\n", + " self.model_name_or_path = 'microsoft/DialoGPT-small'\n", + " self.config_name = 'microsoft/DialoGPT-small'\n", + " self.tokenizer_name = 'microsoft/DialoGPT-small'\n", + " self.cache_dir = 'cached'\n", + " self.block_size = 512\n", + " self.do_train = True\n", + " self.do_eval = True\n", + " self.evaluate_during_training = False\n", + " self.per_gpu_train_batch_size = 4\n", + " self.per_gpu_eval_batch_size = 4\n", + " self.gradient_accumulation_steps = 1\n", + " self.learning_rate = 5e-5\n", + " self.weight_decay = 0.0\n", + " self.adam_epsilon = 1e-8\n", + " self.max_grad_norm = 1.0\n", + " self.num_train_epochs = 4\n", + " self.max_steps = -1\n", + " self.warmup_steps = 0\n", + " self.logging_steps = 1000\n", + " self.save_steps = 3500\n", + " self.save_total_limit = None\n", + " self.eval_all_checkpoints = False\n", + " self.no_cuda = False\n", + " self.overwrite_output_dir = True\n", + " self.overwrite_cache = True\n", + " self.should_continue = False\n", + " self.seed = 42\n", + " self.local_rank = -1\n", + " self.fp16 = False\n", + " self.fp16_opt_level = 'O1'\n", + "\n", + "args = Args()" + ], + "metadata": { + "id": "8b20p10Xg7M-" + }, + "execution_count": 82, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Train and Evaluate" + ], + "metadata": { + "id": "9QaybLujoTg-" + } + }, + { + "cell_type": "code", + "source": [ + "def train(args, train_dataset, model: PreTrainedModel, tokenizer: PreTrainedTokenizer) -> Tuple[int, float]:\n", + " \"\"\" Train the model \"\"\"\n", + " if args.local_rank in [-1, 0]:\n", + " tb_writer = SummaryWriter()\n", + "\n", + " args.train_batch_size = args.per_gpu_train_batch_size * max(1, args.n_gpu)\n", + "\n", + " def collate(examples: List[torch.Tensor]):\n", + " if tokenizer._pad_token is None:\n", + " return pad_sequence(examples, batch_first=True)\n", + " return pad_sequence(examples, batch_first=True, padding_value=tokenizer.pad_token_id)\n", + "\n", + " train_sampler = RandomSampler(train_dataset) if args.local_rank == -1 else DistributedSampler(train_dataset)\n", + " train_dataloader = DataLoader(\n", + " train_dataset, sampler=train_sampler, batch_size=args.train_batch_size, collate_fn=collate, drop_last = True\n", + " )\n", + "\n", + " if args.max_steps > 0:\n", + " t_total = args.max_steps\n", + " args.num_train_epochs = args.max_steps // (len(train_dataloader) // args.gradient_accumulation_steps) + 1\n", + " else:\n", + " t_total = len(train_dataloader) // args.gradient_accumulation_steps * args.num_train_epochs\n", + "\n", + " model = model.module if hasattr(model, \"module\") else model # Take care of distributed/parallel training\n", + " model.resize_token_embeddings(len(tokenizer))\n", + " # add_special_tokens_(model, tokenizer)\n", + "\n", + "\n", + " # Prepare optimizer and schedule (linear warmup and decay)\n", + " no_decay = [\"bias\", \"LayerNorm.weight\"]\n", + " optimizer_grouped_parameters = [\n", + " {\n", + " \"params\": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],\n", + " \"weight_decay\": args.weight_decay,\n", + " },\n", + " {\"params\": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)], \"weight_decay\": 0.0},\n", + " ]\n", + " optimizer = AdamW(optimizer_grouped_parameters, lr=args.learning_rate, eps=args.adam_epsilon)\n", + " scheduler = get_linear_schedule_with_warmup(\n", + " optimizer, num_warmup_steps=args.warmup_steps, num_training_steps=t_total\n", + " )\n", + "\n", + " # Check if saved optimizer or scheduler states exist\n", + " if (\n", + " args.model_name_or_path\n", + " and os.path.isfile(os.path.join(args.model_name_or_path, \"optimizer.pt\"))\n", + " and os.path.isfile(os.path.join(args.model_name_or_path, \"scheduler.pt\"))\n", + " ):\n", + " # Load in optimizer and scheduler states\n", + " optimizer.load_state_dict(torch.load(os.path.join(args.model_name_or_path, \"optimizer.pt\")))\n", + " scheduler.load_state_dict(torch.load(os.path.join(args.model_name_or_path, \"scheduler.pt\")))\n", + "\n", + " if args.fp16:\n", + " try:\n", + " from apex import amp\n", + " except ImportError:\n", + " raise ImportError(\"Please install apex from https://www.github.com/nvidia/apex to use fp16 training.\")\n", + " model, optimizer = amp.initialize(model, optimizer, opt_level=args.fp16_opt_level)\n", + "\n", + " # multi-gpu training (should be after apex fp16 initialization)\n", + " if args.n_gpu > 1:\n", + " model = torch.nn.DataParallel(model)\n", + "\n", + " # Distributed training (should be after apex fp16 initialization)\n", + " if args.local_rank != -1:\n", + " model = torch.nn.parallel.DistributedDataParallel(\n", + " model, device_ids=[args.local_rank], output_device=args.local_rank, find_unused_parameters=True\n", + " )\n", + "\n", + " # Train!\n", + " logger.info(\"***** Running training *****\")\n", + " logger.info(\" Num examples = %d\", len(train_dataset))\n", + " logger.info(\" Num Epochs = %d\", args.num_train_epochs)\n", + " logger.info(\" Instantaneous batch size per GPU = %d\", args.per_gpu_train_batch_size)\n", + " logger.info(\n", + " \" Total train batch size (w. parallel, distributed & accumulation) = %d\",\n", + " args.train_batch_size\n", + " * args.gradient_accumulation_steps\n", + " * (torch.distributed.get_world_size() if args.local_rank != -1 else 1),\n", + " )\n", + " logger.info(\" Gradient Accumulation steps = %d\", args.gradient_accumulation_steps)\n", + " logger.info(\" Total optimization steps = %d\", t_total)\n", + "\n", + " global_step = 0\n", + " epochs_trained = 0\n", + " steps_trained_in_current_epoch = 0\n", + " # Check if continuing training from a checkpoint\n", + " if args.model_name_or_path and os.path.exists(args.model_name_or_path):\n", + " try:\n", + " # set global_step to gobal_step of last saved checkpoint from model path\n", + " checkpoint_suffix = args.model_name_or_path.split(\"-\")[-1].split(\"/\")[0]\n", + " global_step = int(checkpoint_suffix)\n", + " epochs_trained = global_step // (len(train_dataloader) // args.gradient_accumulation_steps)\n", + " steps_trained_in_current_epoch = global_step % (len(train_dataloader) // args.gradient_accumulation_steps)\n", + "\n", + " logger.info(\" Continuing training from checkpoint, will skip to saved global_step\")\n", + " logger.info(\" Continuing training from epoch %d\", epochs_trained)\n", + " logger.info(\" Continuing training from global step %d\", global_step)\n", + " logger.info(\" Will skip the first %d steps in the first epoch\", steps_trained_in_current_epoch)\n", + " except ValueError:\n", + " logger.info(\" Starting fine-tuning.\")\n", + "\n", + " tr_loss, logging_loss = 0.0, 0.0\n", + "\n", + " model.zero_grad()\n", + " train_iterator = trange(\n", + " epochs_trained, int(args.num_train_epochs), desc=\"Epoch\", disable=args.local_rank not in [-1, 0]\n", + " )\n", + " set_seed(args) # Added here for reproducibility\n", + " for _ in train_iterator:\n", + " epoch_iterator = tqdm(train_dataloader, desc=\"Iteration\", disable=args.local_rank not in [-1, 0])\n", + " for step, batch in enumerate(epoch_iterator):\n", + "\n", + " # Skip past any already trained steps if resuming training\n", + " if steps_trained_in_current_epoch > 0:\n", + " steps_trained_in_current_epoch -= 1\n", + " continue\n", + "\n", + " inputs, labels = (batch, batch)\n", + " if inputs.shape[1] > 1024: continue\n", + " inputs = inputs.to(args.device)\n", + " labels = labels.to(args.device)\n", + " model.train()\n", + " outputs = model(inputs, labels=labels)\n", + " loss = outputs[0] # model outputs are always tuple in transformers (see doc)\n", + "\n", + " if args.n_gpu > 1:\n", + " loss = loss.mean() # mean() to average on multi-gpu parallel training\n", + " if args.gradient_accumulation_steps > 1:\n", + " loss = loss / args.gradient_accumulation_steps\n", + "\n", + " if args.fp16:\n", + " with amp.scale_loss(loss, optimizer) as scaled_loss:\n", + " scaled_loss.backward()\n", + " else:\n", + " loss.backward()\n", + "\n", + " tr_loss += loss.item()\n", + " if (step + 1) % args.gradient_accumulation_steps == 0:\n", + " if args.fp16:\n", + " torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), args.max_grad_norm)\n", + " else:\n", + " torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_grad_norm)\n", + " optimizer.step()\n", + " scheduler.step() # Update learning rate schedule\n", + " model.zero_grad()\n", + " global_step += 1\n", + "\n", + " if args.local_rank in [-1, 0] and args.logging_steps > 0 and global_step % args.logging_steps == 0:\n", + " # Log metrics\n", + " if (\n", + " args.local_rank == -1 and args.evaluate_during_training\n", + " ): # Only evaluate when single GPU otherwise metrics may not average well\n", + " results = evaluate(args, model, tokenizer)\n", + " for key, value in results.items():\n", + " tb_writer.add_scalar(\"eval_{}\".format(key), value, global_step)\n", + " tb_writer.add_scalar(\"lr\", scheduler.get_lr()[0], global_step)\n", + " tb_writer.add_scalar(\"loss\", (tr_loss - logging_loss) / args.logging_steps, global_step)\n", + " logging_loss = tr_loss\n", + "\n", + " if args.local_rank in [-1, 0] and args.save_steps > 0 and global_step % args.save_steps == 0:\n", + " checkpoint_prefix = \"checkpoint\"\n", + " # Save model checkpoint\n", + " output_dir = os.path.join(args.output_dir, \"{}-{}\".format(checkpoint_prefix, global_step))\n", + " os.makedirs(output_dir, exist_ok=True)\n", + " model_to_save = (\n", + " model.module if hasattr(model, \"module\") else model\n", + " ) # Take care of distributed/parallel training\n", + " model_to_save.save_pretrained(output_dir)\n", + " tokenizer.save_pretrained(output_dir)\n", + "\n", + " torch.save(args, os.path.join(output_dir, \"training_args.bin\"))\n", + " logger.info(\"Saving model checkpoint to %s\", output_dir)\n", + "\n", + " _rotate_checkpoints(args, checkpoint_prefix)\n", + "\n", + " torch.save(optimizer.state_dict(), os.path.join(output_dir, \"optimizer.pt\"))\n", + " torch.save(scheduler.state_dict(), os.path.join(output_dir, \"scheduler.pt\"))\n", + " logger.info(\"Saving optimizer and scheduler states to %s\", output_dir)\n", + "\n", + " if args.max_steps > 0 and global_step > args.max_steps:\n", + " epoch_iterator.close()\n", + " break\n", + " if args.max_steps > 0 and global_step > args.max_steps:\n", + " train_iterator.close()\n", + " break\n", + "\n", + " if args.local_rank in [-1, 0]:\n", + " tb_writer.close()\n", + "\n", + " return global_step, tr_loss / global_step\n", + "\n", + "# Evaluation of some model\n", + "\n", + "def evaluate(args, model: PreTrainedModel, tokenizer: PreTrainedTokenizer, df_trn, df_val, prefix=\"\") -> Dict:\n", + " # Loop to handle MNLI double evaluation (matched, mis-matched)\n", + " eval_output_dir = args.output_dir\n", + "\n", + " eval_dataset = load_and_cache_examples(args, tokenizer, df_trn, df_val, evaluate=True)\n", + " os.makedirs(eval_output_dir, exist_ok=True)\n", + " args.eval_batch_size = args.per_gpu_eval_batch_size * max(1, args.n_gpu)\n", + " # Note that DistributedSampler samples randomly\n", + "\n", + " def collate(examples: List[torch.Tensor]):\n", + " if tokenizer._pad_token is None:\n", + " return pad_sequence(examples, batch_first=True)\n", + " return pad_sequence(examples, batch_first=True, padding_value=tokenizer.pad_token_id)\n", + "\n", + " eval_sampler = SequentialSampler(eval_dataset)\n", + " eval_dataloader = DataLoader(\n", + " eval_dataset, sampler=eval_sampler, batch_size=args.eval_batch_size, collate_fn=collate, drop_last = True\n", + " )\n", + "\n", + " # multi-gpu evaluate\n", + " if args.n_gpu > 1:\n", + " model = torch.nn.DataParallel(model)\n", + "\n", + " # Eval!\n", + " logger.info(\"***** Running evaluation {} *****\".format(prefix))\n", + " logger.info(\" Num examples = %d\", len(eval_dataset))\n", + " logger.info(\" Batch size = %d\", args.eval_batch_size)\n", + " eval_loss = 0.0\n", + " nb_eval_steps = 0\n", + " model.eval()\n", + "\n", + " for batch in tqdm(eval_dataloader, desc=\"Evaluating\"):\n", + " inputs, labels = (batch, batch)\n", + " inputs = inputs.to(args.device)\n", + " labels = labels.to(args.device)\n", + "\n", + " with torch.no_grad():\n", + " outputs = model(inputs, labels=labels)\n", + " lm_loss = outputs[0]\n", + " eval_loss += lm_loss.mean().item()\n", + " nb_eval_steps += 1\n", + "\n", + " eval_loss = eval_loss / (nb_eval_steps + 0.00001)\n", + " perplexity = torch.exp(torch.tensor(eval_loss))\n", + "\n", + " result = {\"perplexity\": perplexity}\n", + "\n", + " output_eval_file = os.path.join(eval_output_dir, prefix, \"eval_results.txt\")\n", + " with open(output_eval_file, \"w\") as writer:\n", + " logger.info(\"***** Eval results {} *****\".format(prefix))\n", + " for key in sorted(result.keys()):\n", + " logger.info(\" %s = %s\", key, str(result[key]))\n", + " writer.write(\"%s = %s\\n\" % (key, str(result[key])))\n", + "\n", + " return result" + ], + "metadata": { + "id": "Yd7cAl8-oVSR" + }, + "execution_count": 83, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Main runner\n", + "\n", + "def main(df_trn, df_val):\n", + " args = Args()\n", + " \n", + " if args.should_continue:\n", + " sorted_checkpoints = _sorted_checkpoints(args)\n", + " if len(sorted_checkpoints) == 0:\n", + " raise ValueError(\"Used --should_continue but no checkpoint was found in --output_dir.\")\n", + " else:\n", + " args.model_name_or_path = sorted_checkpoints[-1]\n", + "\n", + " if (\n", + " os.path.exists(args.output_dir)\n", + " and os.listdir(args.output_dir)\n", + " and args.do_train\n", + " and not args.overwrite_output_dir\n", + " and not args.should_continue\n", + " ):\n", + " raise ValueError(\n", + " \"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome.\".format(\n", + " args.output_dir\n", + " )\n", + " )\n", + "\n", + " # Setup CUDA, GPU & distributed training\n", + " device = torch.device(\"cuda\")\n", + " args.n_gpu = torch.cuda.device_count()\n", + " args.device = device\n", + "\n", + " # Setup logging\n", + " logging.basicConfig(\n", + " format=\"%(asctime)s - %(levelname)s - %(name)s - %(message)s\",\n", + " datefmt=\"%m/%d/%Y %H:%M:%S\",\n", + " level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,\n", + " )\n", + " logger.warning(\n", + " \"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s\",\n", + " args.local_rank,\n", + " device,\n", + " args.n_gpu,\n", + " bool(args.local_rank != -1),\n", + " args.fp16,\n", + " )\n", + "\n", + " # Set seed\n", + " set_seed(args)\n", + "\n", + " config = AutoConfig.from_pretrained(args.config_name, cache_dir=args.cache_dir)\n", + " tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name, cache_dir=args.cache_dir)\n", + " model = AutoModelWithLMHead.from_pretrained(\n", + " args.model_name_or_path,\n", + " from_tf=False,\n", + " config=config,\n", + " cache_dir=args.cache_dir,\n", + " )\n", + " model.to(args.device)\n", + " \n", + " logger.info(\"Training/evaluation parameters %s\", args)\n", + "\n", + " # Training\n", + " if args.do_train:\n", + " train_dataset = load_and_cache_examples(args, tokenizer, df_trn, df_val, evaluate=False)\n", + "\n", + " global_step, tr_loss = train(args, train_dataset, model, tokenizer)\n", + " logger.info(\" global_step = %s, average loss = %s\", global_step, tr_loss)\n", + "\n", + " # Saving best-practices: if you use save_pretrained for the model and tokenizer, you can reload them using from_pretrained()\n", + " if args.do_train:\n", + " # Create output directory if needed\n", + " os.makedirs(args.output_dir, exist_ok=True)\n", + "\n", + " logger.info(\"Saving model checkpoint to %s\", args.output_dir)\n", + " # Save a trained model, configuration and tokenizer using `save_pretrained()`.\n", + " # They can then be reloaded using `from_pretrained()`\n", + " model_to_save = (\n", + " model.module if hasattr(model, \"module\") else model\n", + " ) # Take care of distributed/parallel training\n", + " model_to_save.save_pretrained(args.output_dir)\n", + " tokenizer.save_pretrained(args.output_dir)\n", + "\n", + " # Good practice: save your training arguments together with the trained model\n", + " torch.save(args, os.path.join(args.output_dir, \"training_args.bin\"))\n", + "\n", + " # Load a trained model and vocabulary that you have fine-tuned\n", + " model = AutoModelWithLMHead.from_pretrained(args.output_dir)\n", + " tokenizer = AutoTokenizer.from_pretrained(args.output_dir)\n", + " model.to(args.device)\n", + "\n", + " # Evaluation\n", + " results = {}\n", + " if args.do_eval and args.local_rank in [-1, 0]:\n", + " checkpoints = [args.output_dir]\n", + " if args.eval_all_checkpoints:\n", + " checkpoints = list(\n", + " os.path.dirname(c) for c in sorted(glob.glob(args.output_dir + \"/**/\" + WEIGHTS_NAME, recursive=True))\n", + " )\n", + " logging.getLogger(\"transformers.modeling_utils\").setLevel(logging.WARN) # Reduce logging\n", + " logger.info(\"Evaluate the following checkpoints: %s\", checkpoints)\n", + " for checkpoint in checkpoints:\n", + " global_step = checkpoint.split(\"-\")[-1] if len(checkpoints) > 1 else \"\"\n", + " prefix = checkpoint.split(\"/\")[-1] if checkpoint.find(\"checkpoint\") != -1 else \"\"\n", + "\n", + " model = AutoModelWithLMHead.from_pretrained(checkpoint)\n", + " model.to(args.device)\n", + " result = evaluate(args, model, tokenizer, df_trn, df_val, prefix=prefix)\n", + " result = dict((k + \"_{}\".format(global_step), v) for k, v in result.items())\n", + " results.update(result)\n", + "\n", + " return results" + ], + "metadata": { + "id": "M93fjuFwiu-T" + }, + "execution_count": 84, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Run The Main Function" + ], + "metadata": { + "id": "jdBULkbmoX6E" + } + }, + { + "cell_type": "code", + "source": [ + "main(trn_df, val_df)" + ], + "metadata": { + "id": "IhQ-I1_Vobx0", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 646, + "referenced_widgets": [ + "a49e5fd0d85444a3aa9f786455ca8770", + "73e8d052a86647919649a367aa773c8e", + "40760124752846209e61177280a005bd", + "eae3f41495884830818311e51920c956", + "5d1a116b987549d780ee25723f83d45a", + "46f7e33281354ef488945f5f1cfe4c06", + "e987dfed8c624717b5ae2054cce74f05", + "c49e73bdfe544de0ae62034cef7eb0da", + "52e599d90ccd44938d982310fb7e4341", + "c01768976adf465ebfad5c3eedfe1d58", + "27fb7c7e261b4a3b9656a37b1fcde71a", + "1c82670ef31346eb97dff63429fd522f", + "a8c2fda5e0be4c638919b4ca1007dea3", + "5e60bbde81ed452fa0c8d7094d98b052", + "3ac055433ca94c2ebe9f8b44e38be5e0", + "67e70ffbe152488fb036968be105a368", + "bbeb9a01f5bb4aebba239db555f4b16b", + "771602cc4d9444e7ab0d20438639cddd", + "f154ee2be8a044b3aeeb0e904411ffbd", + "a3a6841089054f1cbc31f638424674b3", + "41061818a9c94956a7d1cd129028d805", + "bab64ef3864248018e9476bc8c4018f4", + "f3fa20cd1c40453bb17b2f109607e1bf", + "97b3a5270a014515bbc712b44dba38a0", + "afe3a3438fb145d4a015fdb0709e3156", + "6d6078316fe54c9e83a3c3a35a1169fc", + "4700a281e7d347db8a58c6f181706b54", + "84f8bdfeb6bf4bb7ba4585eba47a7092", + "9262f880b64a4abb80013f6997901bcb", + "386289f0bf56453484a6637d3263da4c", + "8366904443f649928aa9cfd915cd938a", + "80f977590ae94733a8a8552241c12e3b", + "3ff5971c055144a3b81190d199ffe3de", + "be5e0fa21fea43e8bf003ae954c29d03", + "7535306bf05847629946e333021e0ef5", + "671f7a7556b1412cbd48237293431c0d", + "8957849d6dbf44cfafa965d71de78255", + "a55488797f71453e917032008f198b9c", + "38ccc42c4ea14f0e83fca1cb9452bfad", + "ef2ddbabd7b042c0821cf999a9265867", + "0222497184b2446e90f801d84af22b82", + "6a70d59d1c834989a54deda9e776bf41", + "62e05dc21f5e438cb5e94e400071a39b", + "803884fef29b433db14e61df7fae1ee7", + "83414a06fd504f71aa212d9fce15ffb5", + "e2c8785b7c51448296c8cf54331f4a68", + "3f9881738b644024aea6371982320d97", + "143666ff0c7b4e6491779b64f6212818", + "bf699df8e3d844f68a68491c00e8f0bc", + "9e955ce77097447a8c085ea592ae8a5e", + "2f2aecb73861473ba553b4ebebd52e0b", + "db21f601fd6342a5815cea18c417aa99", + "67bf9d4f1a8e431bb580337be0e67f82", + "ddcba8098de6433a8584045d52cb1f3b", + "60062f7860944d0d85c4ef1773c151c3", + "cc13e655b33d4fa390960d1fa40a0e1f", + "2e418d21ae4f4123a9d7b13cbc368605", + "ba1f476b8bdc4fce8a703a0220bd4770", + "26560e743afa4a3fad0eb1e0ed567a64", + "27e7a38811a94548b8dc1980e1c83acd", + "c5ceabb016a74435be6659f1116c9945", + "4ca6ee080dad41aa8abbbea5a96e3922", + "c0fd4025a3e84d0ca8c360887d7126ba", + "4a374856a56c4dc6a163ee5779d6b666", + "ef067c6b95ac48c58428f62ecef22e33", + "88521418122646b0b6c7d41be73e747a" + ] + }, + "outputId": "612cca73-0fa4-41c0-f2a3-ba9df82d4b4c" + }, + "execution_count": 85, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "01/24/2022 12:02:55 - WARNING - __main__ - Process rank: -1, device: cuda, n_gpu: 1, distributed training: False, 16-bits training: False\n", + "/usr/local/lib/python3.7/dist-packages/transformers/models/auto/modeling_auto.py:787: FutureWarning: The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use `AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and `AutoModelForSeq2SeqLM` for encoder-decoder models.\n", + " FutureWarning,\n", + "01/24/2022 12:03:00 - INFO - __main__ - Training/evaluation parameters <__main__.Args object at 0x7f0555d60550>\n", + "01/24/2022 12:03:00 - INFO - __main__ - Creating features from dataset file at cached\n", + "01/24/2022 12:03:00 - INFO - __main__ - Saving features into cached file cached/gpt2_cached_lm_512\n", + "01/24/2022 12:03:00 - INFO - __main__ - ***** Running training *****\n", + "01/24/2022 12:03:00 - INFO - __main__ - Num examples = 22\n", + "01/24/2022 12:03:00 - INFO - __main__ - Num Epochs = 4\n", + "01/24/2022 12:03:00 - INFO - __main__ - Instantaneous batch size per GPU = 4\n", + "01/24/2022 12:03:00 - INFO - __main__ - Total train batch size (w. parallel, distributed & accumulation) = 4\n", + "01/24/2022 12:03:00 - INFO - __main__ - Gradient Accumulation steps = 1\n", + "01/24/2022 12:03:00 - INFO - __main__ - Total optimization steps = 20\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a49e5fd0d85444a3aa9f786455ca8770", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "Epoch: 0%| | 0/4 [00:00> User:\") + tokenizer.eos_token, return_tensors='pt')\n", + " # print(new_user_input_ids)\n", + "\n", + " # append the new user input tokens to the chat history\n", + " bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids\n", + "\n", + " # generated a response while limiting the total chat history to 1000 tokens, \n", + " chat_history_ids = model.generate(\n", + " bot_input_ids, max_length=200,\n", + " pad_token_id=tokenizer.eos_token_id, \n", + " no_repeat_ngram_size=3, \n", + " do_sample=True, \n", + " top_k=100, \n", + " top_p=0.7,\n", + " temperature=0.8\n", + " )\n", + " \n", + " # pretty print last ouput tokens from bot\n", + " print(\"EleventhDoctorBot: {}\".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))" + ], + "metadata": { + "id": "2-CQOEWPrmE7", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "fb711e82-492c-45a3-8aab-7e888bff12cb" + }, + "execution_count": 90, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + ">> User:why are you not working\n", + "EleventhDoctorBot: I'm not a lawyer, but I'm going to go out on a limb and say that the defendant's name was already on the back of the police's desk.\n", + ">> User:i wish you were here\n", + "EleventhDoctorBot: I'll be back in a few hours to talk about this case and the police report.\n", + ">> User:what police\n", + "EleventhDoctorBot: I just got a new phone and it's my new phone so I'm not going to talk to anyone on it\n", + ">> User:haha\n", + "EleventhDoctorBot: I know, I just had a phone call and it was a phonecall that was being sent to the police and they were on it when they came back to\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Push Model to HuggingFace" + ], + "metadata": { + "id": "KQBRSKDcoiJ4" + } + }, + { + "cell_type": "code", + "source": [ + "#model.push_to_hub(MY_MODEL_NAME, use_auth_token=HUGGINGFACE_API_KEY)\n", + "#tokenizer.push_to_hub(MY_MODEL_NAME, use_auth_token=HUGGINGFACE_API_KEY)" + ], + "metadata": { + "id": "E_IH5n-P2u3N" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "os.chdir(\"/content/\")" + ], + "metadata": { + "id": "tQtHvpnXA2fC" + }, + "execution_count": 88, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "" + ], + "metadata": { + "id": "SJa0EMUZ-gYI" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file