{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "provenance": [], "gpuType": "T4", "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" }, "accelerator": "GPU", "widgets": { "application/vnd.jupyter.widget-state+json": { "c18b4e07a5634929bb1cf1869b5bd3cd": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_e16d47fc272444c5804ed52924092a54", "IPY_MODEL_d95bf93709a54bd0a4003eb7d0c54063", "IPY_MODEL_b90ba9ba331f4fea9915183527425152" ], "layout": "IPY_MODEL_ce33d4e563e443b99ebb9b70f508b2f7" } }, "e16d47fc272444c5804ed52924092a54": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_7f0872cc54304ff7839fd52c390ab23a", "placeholder": "", "style": "IPY_MODEL_cd54d91069034b1ab8d54277242e1212", "value": "tokenizer_config.json: 100%" } }, "d95bf93709a54bd0a4003eb7d0c54063": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_65d197e11c77469d9d05a054e63fba77", "max": 25, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_36c979ba9b374a64870941b6c269c70f", "value": 25 } }, "b90ba9ba331f4fea9915183527425152": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_ed4f92c51cbe4416b27d5b2e06a61ff8", "placeholder": "", "style": "IPY_MODEL_4498ec5e15814967be17503536acd8fa", "value": " 25.0/25.0 [00:00<00:00, 1.70kB/s]" } }, "ce33d4e563e443b99ebb9b70f508b2f7": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "7f0872cc54304ff7839fd52c390ab23a": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "cd54d91069034b1ab8d54277242e1212": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "65d197e11c77469d9d05a054e63fba77": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "36c979ba9b374a64870941b6c269c70f": { "model_module": "@jupyter-widgets/controls", "model_name": "ProgressStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "ed4f92c51cbe4416b27d5b2e06a61ff8": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "4498ec5e15814967be17503536acd8fa": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "ba981d27be0a434fb215169036ca4d47": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_f837a0b50aa84bc58d256ef0434c101d", "IPY_MODEL_222adb2c788e4dc5964d005b6752dab8", "IPY_MODEL_b4908d98da594c1e8932cdcc63d3fa53" ], "layout": "IPY_MODEL_c9a7bba8cef94fc3ac38fededc4206b6" } }, "f837a0b50aa84bc58d256ef0434c101d": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_52bcf8dfa4964e0aa3bc882c3b01babf", "placeholder": "", "style": "IPY_MODEL_52a28fd53a984c829f24883f612e9264", "value": "config.json: 100%" } }, "222adb2c788e4dc5964d005b6752dab8": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_1c4207338d5c41af91975f4eba907c06", "max": 616, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_54d17297d0054f2f97c866896fc275f0", "value": 616 } }, "b4908d98da594c1e8932cdcc63d3fa53": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_9cba19df4b044452bf585d2ef1066a5f", "placeholder": "", "style": "IPY_MODEL_78b7a1ee87f340439a6c8f53c217ba17", "value": " 616/616 [00:00<00:00, 30.3kB/s]" } }, "c9a7bba8cef94fc3ac38fededc4206b6": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "52bcf8dfa4964e0aa3bc882c3b01babf": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "52a28fd53a984c829f24883f612e9264": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "1c4207338d5c41af91975f4eba907c06": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "54d17297d0054f2f97c866896fc275f0": { "model_module": "@jupyter-widgets/controls", "model_name": "ProgressStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "9cba19df4b044452bf585d2ef1066a5f": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "78b7a1ee87f340439a6c8f53c217ba17": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "f19c8a98bc494fb294a27b5d180877f1": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_dec6e5de29f744aeadf716d91af52bf0", "IPY_MODEL_7e639de76dc94ac68b3f1ead715c0215", "IPY_MODEL_482a10e359f848039a796e2ed4fad03b" ], "layout": "IPY_MODEL_e851d46ff72d4843a974f6e3e4962542" } }, "dec6e5de29f744aeadf716d91af52bf0": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_6d499c9ca96349f6a78a55069af586af", "placeholder": "", "style": "IPY_MODEL_2ed8f78c1a414b93b4293ef3a2e368f9", "value": "sentencepiece.bpe.model: 100%" } }, "7e639de76dc94ac68b3f1ead715c0215": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_f57eee3bcf924f77a553c792431608ad", "max": 5069051, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_7e390da8f4134b58b3ac0351bdf0729c", "value": 5069051 } }, "482a10e359f848039a796e2ed4fad03b": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_7f3d8132f798405c8359ddfb886e448b", "placeholder": "", "style": "IPY_MODEL_49fee89388804ee79d0574f5aa7ac928", "value": " 5.07M/5.07M [00:00<00:00, 29.1MB/s]" } }, "e851d46ff72d4843a974f6e3e4962542": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "6d499c9ca96349f6a78a55069af586af": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "2ed8f78c1a414b93b4293ef3a2e368f9": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "f57eee3bcf924f77a553c792431608ad": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "7e390da8f4134b58b3ac0351bdf0729c": { "model_module": "@jupyter-widgets/controls", "model_name": "ProgressStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "7f3d8132f798405c8359ddfb886e448b": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "49fee89388804ee79d0574f5aa7ac928": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "3dafad27f524422c848165dcd0baa9eb": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_4aec17e7b4f34c77a016202dc32027a6", "IPY_MODEL_d3b0c0b11c3845289e976d635352c712", "IPY_MODEL_47b4097d57c8466193037605b7c9a8ab" ], "layout": "IPY_MODEL_714eec46a29f4896aa83924f0c200fac" } }, "4aec17e7b4f34c77a016202dc32027a6": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_a494c37e39df4f91b09507f79eea7a6a", "placeholder": "", "style": "IPY_MODEL_855be793ef9e41a99b99ade4b95c1fe3", "value": "tokenizer.json: 100%" } }, "d3b0c0b11c3845289e976d635352c712": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_b51c0a2e93864935bdbd2308e478a47e", "max": 9096718, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_2917b399c7bf4599a614a5314cb3f9b2", "value": 9096718 } }, "47b4097d57c8466193037605b7c9a8ab": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_fae1dc5f82044cc5bbe863285dd87da6", "placeholder": "", "style": "IPY_MODEL_a2e32d2b66604c95a5b4c219e892b675", "value": " 9.10M/9.10M [00:00<00:00, 27.9MB/s]" } }, "714eec46a29f4896aa83924f0c200fac": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "a494c37e39df4f91b09507f79eea7a6a": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "855be793ef9e41a99b99ade4b95c1fe3": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "b51c0a2e93864935bdbd2308e478a47e": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "2917b399c7bf4599a614a5314cb3f9b2": { "model_module": "@jupyter-widgets/controls", "model_name": "ProgressStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "fae1dc5f82044cc5bbe863285dd87da6": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "a2e32d2b66604c95a5b4c219e892b675": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "60ee88fb52de47cf8c661e723a3f7f05": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_1a7a5a9eded149bcb4170da34c47bdee", "IPY_MODEL_99b8f7cd76ce4d8cb3690e795bf896eb", "IPY_MODEL_af18a8d8e70940169d839c6a857884c4" ], "layout": "IPY_MODEL_817978a5c92b4371a5a85cab84c67034" } }, "1a7a5a9eded149bcb4170da34c47bdee": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_2a374450c9954701aa101a3d15bedfdf", "placeholder": "", "style": "IPY_MODEL_6fa7487cc8c0458a87f7e522c7489205", "value": "model.safetensors: 100%" } }, "99b8f7cd76ce4d8cb3690e795bf896eb": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_7080ddf19d544cd68a1d5beac78ccb07", "max": 260815972, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_0e2eeba08b34428ba8c6cec0f5061997", "value": 260815972 } }, "af18a8d8e70940169d839c6a857884c4": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_5b795044243f4f2ba562b76993e5e9a5", "placeholder": "", "style": "IPY_MODEL_6ad7a9f47709470fbce3ebc6b6df4415", "value": " 261M/261M [00:04<00:00, 56.6MB/s]" } }, "817978a5c92b4371a5a85cab84c67034": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "2a374450c9954701aa101a3d15bedfdf": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "6fa7487cc8c0458a87f7e522c7489205": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "7080ddf19d544cd68a1d5beac78ccb07": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "0e2eeba08b34428ba8c6cec0f5061997": { "model_module": "@jupyter-widgets/controls", "model_name": "ProgressStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "5b795044243f4f2ba562b76993e5e9a5": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "6ad7a9f47709470fbce3ebc6b6df4415": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "d7cd5a9e10634439a1232945e5cdcc19": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_72a63f5d04ca41b19d5c933e668f3e5e", "IPY_MODEL_c3010269ce8a45509b900945448e7a59", "IPY_MODEL_eb0a2a2a916447b9b4526182e8e0ee6c" ], "layout": "IPY_MODEL_f10d55952ced44ad99b9344e7882c733" } }, "72a63f5d04ca41b19d5c933e668f3e5e": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_24db282d2bec4178a41870f98cd77978", "placeholder": "", "style": "IPY_MODEL_3e8c746979f84b578fa43e8d4bedfa7a", "value": "tokenizer_config.json: 100%" } }, "c3010269ce8a45509b900945448e7a59": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_6cc96ac0676d4d2b88854bc319162192", "max": 48, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_1c663101871e44cbb2b3b51435afcebd", "value": 48 } }, "eb0a2a2a916447b9b4526182e8e0ee6c": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_3d74f6fdfcc6450e8152edb342944617", "placeholder": "", "style": "IPY_MODEL_d37cdbb0ba4243e2b6ac50e082d569c0", "value": " 48.0/48.0 [00:00<00:00, 3.15kB/s]" } }, "f10d55952ced44ad99b9344e7882c733": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "24db282d2bec4178a41870f98cd77978": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "3e8c746979f84b578fa43e8d4bedfa7a": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "6cc96ac0676d4d2b88854bc319162192": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "1c663101871e44cbb2b3b51435afcebd": { "model_module": "@jupyter-widgets/controls", "model_name": "ProgressStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "3d74f6fdfcc6450e8152edb342944617": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "d37cdbb0ba4243e2b6ac50e082d569c0": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "81c1865eb4854617868dc5367b336f37": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_8bc184cd03594e959c7bfee39dc06311", "IPY_MODEL_af035cc9ff3d450e8277870e732cfd84", "IPY_MODEL_807beb95b9614f05ad71ea75ab4ebe8a" ], "layout": "IPY_MODEL_d95b2befc2174930b3e26b4b44d0d19b" } }, "8bc184cd03594e959c7bfee39dc06311": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_68c2ae33c39c4a22b2d943c0fa3d3578", "placeholder": "", "style": "IPY_MODEL_d370aaecd4594d87b9f5bd56819f43f0", "value": "config.json: 100%" } }, "af035cc9ff3d450e8277870e732cfd84": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_54aef43e55a74556ba031b9ca0703f4f", "max": 570, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_1b1bd825c40e4d1886fd0d933f7b85dc", "value": 570 } }, "807beb95b9614f05ad71ea75ab4ebe8a": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_c827bcbf83fd4b72a151e7f2e236ee2c", "placeholder": "", "style": "IPY_MODEL_c7479edadb16409892b8d40eae8c5ae7", "value": " 570/570 [00:00<00:00, 38.7kB/s]" } }, "d95b2befc2174930b3e26b4b44d0d19b": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "68c2ae33c39c4a22b2d943c0fa3d3578": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "d370aaecd4594d87b9f5bd56819f43f0": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "54aef43e55a74556ba031b9ca0703f4f": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "1b1bd825c40e4d1886fd0d933f7b85dc": { "model_module": "@jupyter-widgets/controls", "model_name": "ProgressStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "c827bcbf83fd4b72a151e7f2e236ee2c": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "c7479edadb16409892b8d40eae8c5ae7": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "ecdc5018b8564413b61e4a144de70789": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_41ea8f1dd0d743818a5d7984ff7e93ab", "IPY_MODEL_76bb200c0e364a9fa6ab6cd8cae8122f", "IPY_MODEL_39c59b5cda744b59859e4c16c74d5e4b" ], "layout": "IPY_MODEL_d2ed6dd95b4c4dafa60748b7f6a41cfa" } }, "41ea8f1dd0d743818a5d7984ff7e93ab": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_e0d80b1df93f44e8bdcf4cbc4afe0fb3", "placeholder": "", "style": "IPY_MODEL_e2717ee254c44d6e9e50b2b9c7eb1880", "value": "vocab.txt: 100%" } }, "76bb200c0e364a9fa6ab6cd8cae8122f": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_0814066fe77844119158291d7e3cb400", "max": 231508, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_00c6e8d7e7394ddf9e5be62e89ce8bd7", "value": 231508 } }, "39c59b5cda744b59859e4c16c74d5e4b": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_88687d46d89946e8961854f42d437fee", "placeholder": "", "style": "IPY_MODEL_9e9e9863e3c04fac9f2abbd37d02e3ad", "value": " 232k/232k [00:00<00:00, 1.43MB/s]" } }, "d2ed6dd95b4c4dafa60748b7f6a41cfa": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "e0d80b1df93f44e8bdcf4cbc4afe0fb3": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "e2717ee254c44d6e9e50b2b9c7eb1880": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "0814066fe77844119158291d7e3cb400": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "00c6e8d7e7394ddf9e5be62e89ce8bd7": { "model_module": "@jupyter-widgets/controls", "model_name": "ProgressStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "88687d46d89946e8961854f42d437fee": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "9e9e9863e3c04fac9f2abbd37d02e3ad": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "5a6d2aeee5734dfea0f5d79437337401": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_a5ecacc31d4c439dbd1a6d29897cbe5a", "IPY_MODEL_42e8205fa3224e27b183d18d02d4963e", "IPY_MODEL_e74622c87a2d433f932c557c296e7372" ], "layout": "IPY_MODEL_1f03fe99e2054c49af372637c5049876" } }, "a5ecacc31d4c439dbd1a6d29897cbe5a": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_885892016e394018a2899bd5beccc451", "placeholder": "", "style": "IPY_MODEL_02ec11f904cc4b83bc2d3741514647cc", "value": "tokenizer.json: 100%" } }, "42e8205fa3224e27b183d18d02d4963e": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_0876516a3c714edf86d07db200afc3c0", "max": 466062, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_3bb772d4c616468dad906282e66f704a", "value": 466062 } }, "e74622c87a2d433f932c557c296e7372": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_a60c123492864a93b86ec270171a4cc9", "placeholder": "", "style": "IPY_MODEL_32012291680e4563831007e133fd320d", "value": " 466k/466k [00:00<00:00, 1.93MB/s]" } }, "1f03fe99e2054c49af372637c5049876": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "885892016e394018a2899bd5beccc451": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "02ec11f904cc4b83bc2d3741514647cc": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "0876516a3c714edf86d07db200afc3c0": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "3bb772d4c616468dad906282e66f704a": { "model_module": "@jupyter-widgets/controls", "model_name": "ProgressStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "a60c123492864a93b86ec270171a4cc9": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "32012291680e4563831007e133fd320d": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "d09bf4b2b00645ae801039fe86eec1be": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_53dae5f5782446b79a54dfba95950859", "IPY_MODEL_9c0f82538b15420f8810dd3c14b942f6", "IPY_MODEL_bd7e5e72d86a4f3290a5c92b61fc8b3d" ], "layout": "IPY_MODEL_4aef3db3496c4117b72884967266e992" } }, "53dae5f5782446b79a54dfba95950859": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_f12403295a384081bfa2748147d7b03d", "placeholder": "", "style": "IPY_MODEL_3bfef039f33245e795ce431dd20b58be", "value": "model.safetensors: 100%" } }, "9c0f82538b15420f8810dd3c14b942f6": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_1178a785cf1c468dbfcf4b4a8c622249", "max": 440449768, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_3186a33bf7b04dd7a8b7368acee94c47", "value": 440449768 } }, "bd7e5e72d86a4f3290a5c92b61fc8b3d": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_397ea04a9a7b40ec8e7edf03a66b4fa0", "placeholder": "", "style": "IPY_MODEL_fdbeb8f01a1c46cba428648be4c1dd13", "value": " 440M/440M [00:03<00:00, 151MB/s]" } }, "4aef3db3496c4117b72884967266e992": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "f12403295a384081bfa2748147d7b03d": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "3bfef039f33245e795ce431dd20b58be": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "1178a785cf1c468dbfcf4b4a8c622249": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "3186a33bf7b04dd7a8b7368acee94c47": { "model_module": "@jupyter-widgets/controls", "model_name": "ProgressStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "397ea04a9a7b40ec8e7edf03a66b4fa0": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "fdbeb8f01a1c46cba428648be4c1dd13": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "fba645b2f67747e6b6567ec0a5fdea20": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_79547ecdb2684c93b670c9b654429402", "IPY_MODEL_fd6624bacc6d4c47acae7b4ede8c1f3b", "IPY_MODEL_6cd6ee458afb48d398022267f39b0fc4" ], "layout": "IPY_MODEL_9b66439e9e7e422a97995cfb5d8f33b8" } }, "79547ecdb2684c93b670c9b654429402": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_9858a3b7c3944e4fae6b3fadc9642125", "placeholder": "", "style": "IPY_MODEL_775a0752d94a40138acdee0db8739031", "value": "model.safetensors: 100%" } }, "fd6624bacc6d4c47acae7b4ede8c1f3b": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_445f2113b9f7485c868570d49eb52568", "max": 260815972, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_e709ba7a504b4efe934d3ee7774ffc5f", "value": 260815972 } }, "6cd6ee458afb48d398022267f39b0fc4": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_42ebf1faa74b46d5b1b868f83f44a657", "placeholder": "", "style": "IPY_MODEL_b8992ac697014624a58a541efca24655", "value": " 261M/261M [00:06<00:00, 53.7MB/s]" } }, "9b66439e9e7e422a97995cfb5d8f33b8": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "9858a3b7c3944e4fae6b3fadc9642125": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "775a0752d94a40138acdee0db8739031": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "445f2113b9f7485c868570d49eb52568": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "e709ba7a504b4efe934d3ee7774ffc5f": { "model_module": "@jupyter-widgets/controls", "model_name": "ProgressStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "42ebf1faa74b46d5b1b868f83f44a657": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "b8992ac697014624a58a541efca24655": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "be4b170b77c2428a83104f56c2774eff": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_ad4b06c06e894c58956c8708c0ccfd67", "IPY_MODEL_f3efe96ce3d14872b1b62ef0b0f6122f", "IPY_MODEL_1a6cb5fe580141c6bdeb1dc0a321d380" ], "layout": "IPY_MODEL_1f2a427df9604930b915441f2e89ddd3" } }, "ad4b06c06e894c58956c8708c0ccfd67": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_2ac0d35389794c3faceb679a43c7ceea", "placeholder": "", "style": "IPY_MODEL_b31ac4e6bd36460987d448b9e7b1f08c", "value": "Upload 2 LFS files: 100%" } }, "f3efe96ce3d14872b1b62ef0b0f6122f": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_492b9b0c9f594ef6ae0d238b694ca181", "max": 2, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_97f064e2b05f44778b84d5d5d9cad20e", "value": 2 } }, "1a6cb5fe580141c6bdeb1dc0a321d380": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_43e09ad9787244f8bf20c01c7da81991", "placeholder": "", "style": "IPY_MODEL_46b4241483ea4dc984cc92cce41514ee", "value": " 2/2 [00:06<00:00, 6.45s/it]" } }, "1f2a427df9604930b915441f2e89ddd3": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "2ac0d35389794c3faceb679a43c7ceea": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "b31ac4e6bd36460987d448b9e7b1f08c": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "492b9b0c9f594ef6ae0d238b694ca181": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "97f064e2b05f44778b84d5d5d9cad20e": { "model_module": "@jupyter-widgets/controls", "model_name": "ProgressStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "43e09ad9787244f8bf20c01c7da81991": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "46b4241483ea4dc984cc92cce41514ee": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "3afc5d399625437196c6a52d36373f61": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HBoxView", "box_style": "", "children": [ "IPY_MODEL_62f221b1ab7a4c608be46ab9fab43af1", "IPY_MODEL_cbca91cd984d42fd9ad90fcb58331d6e", "IPY_MODEL_63931ac1a75241dfb7519da2194969d2" ], "layout": "IPY_MODEL_a576f7800c0740a1a7b70c55e2c0f071" } }, "62f221b1ab7a4c608be46ab9fab43af1": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_5cd48e3b8d1c42edadc2413f1baac426", "placeholder": "", "style": "IPY_MODEL_51efa950d7a44737b9e7dbdf177be9a3", "value": "events.out.tfevents.1711602729.7cf60fa1c92f.682.0: 100%" } }, "cbca91cd984d42fd9ad90fcb58331d6e": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "FloatProgressModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "ProgressView", "bar_style": "success", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_59878cd245324664bbeb4aefc0fc36e4", "max": 19400, "min": 0, "orientation": "horizontal", "style": "IPY_MODEL_bac939ffbe8f4edd9d4bf7f4281c0707", "value": 19400 } }, "63931ac1a75241dfb7519da2194969d2": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_71c6509f4497436fb9bd2c3e141990f4", "placeholder": "", "style": "IPY_MODEL_20c2b2fd5aa24683ad3cbe18a485bcb1", "value": " 19.4k/19.4k [00:00<00:00, 42.2kB/s]" } }, "a576f7800c0740a1a7b70c55e2c0f071": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "5cd48e3b8d1c42edadc2413f1baac426": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "51efa950d7a44737b9e7dbdf177be9a3": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "59878cd245324664bbeb4aefc0fc36e4": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "bac939ffbe8f4edd9d4bf7f4281c0707": { "model_module": "@jupyter-widgets/controls", "model_name": "ProgressStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "ProgressStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "bar_color": null, "description_width": "" } }, "71c6509f4497436fb9bd2c3e141990f4": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "LayoutView", "align_content": null, "align_items": null, "align_self": null, "border": null, "bottom": null, "display": null, "flex": null, "flex_flow": null, "grid_area": null, "grid_auto_columns": null, "grid_auto_flow": null, "grid_auto_rows": null, "grid_column": null, "grid_gap": null, "grid_row": null, "grid_template_areas": null, "grid_template_columns": null, "grid_template_rows": null, "height": null, "justify_content": null, "justify_items": null, "left": null, "margin": null, "max_height": null, "max_width": null, "min_height": null, "min_width": null, "object_fit": null, "object_position": null, "order": null, "overflow": null, "overflow_x": null, "overflow_y": null, "padding": null, "right": null, "top": null, "visibility": null, "width": null } }, "20c2b2fd5aa24683ad3cbe18a485bcb1": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", "state": { "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "DescriptionStyleModel", "_view_count": null, "_view_module": "@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } }, "014c9bc0a96c4d84b591680bc0ea9cd1": { "model_module": "@jupyter-widgets/controls", "model_name": "VBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "VBoxModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "VBoxView", "box_style": "", "children": [ "IPY_MODEL_c5e868801cee452996340508fbb75d7c", "IPY_MODEL_1399178c2ff14e3a9ba8bf346edd6aff", "IPY_MODEL_3a8d6eb73f4248e2a959bc55a2df77df", "IPY_MODEL_daef153e621e40a2a87e03bf68fe68bc" ], "layout": "IPY_MODEL_0280f5e1b3f54bf08ab3fe4f589d6cf0" } }, "e01913638285433a8a88f0897c5cdfb7": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", "_model_module_version": "1.5.0", "_model_name": "HTMLModel", "_view_count": null, "_view_module": "@jupyter-widgets/controls", "_view_module_version": "1.5.0", "_view_name": "HTMLView", "description": "", "description_tooltip": null, "layout": "IPY_MODEL_120995429b8f4bb8a5d6d4c8eaab72e9", "placeholder": "", "style": "IPY_MODEL_d486b8c62a354b17a757080a11785083", "value": "
Epoch | \n", "Training Loss | \n", "Validation Loss | \n", "Precision | \n", "Recall | \n", "F1 | \n", "Accuracy | \n", "
---|---|---|---|---|---|---|
1 | \n", "No log | \n", "1.982627 | \n", "0.000000 | \n", "0.000000 | \n", "0.000000 | \n", "0.429379 | \n", "
2 | \n", "No log | \n", "1.752454 | \n", "0.307692 | \n", "0.053333 | \n", "0.090909 | \n", "0.491525 | \n", "
3 | \n", "No log | \n", "1.570154 | \n", "0.384615 | \n", "0.133333 | \n", "0.198020 | \n", "0.514124 | \n", "
4 | \n", "No log | \n", "1.439291 | \n", "0.366667 | \n", "0.146667 | \n", "0.209524 | \n", "0.525424 | \n", "
5 | \n", "No log | \n", "1.316981 | \n", "0.393939 | \n", "0.173333 | \n", "0.240741 | \n", "0.553672 | \n", "
6 | \n", "No log | \n", "1.204373 | \n", "0.583333 | \n", "0.280000 | \n", "0.378378 | \n", "0.604520 | \n", "
7 | \n", "No log | \n", "1.084689 | \n", "0.596154 | \n", "0.413333 | \n", "0.488189 | \n", "0.661017 | \n", "
8 | \n", "No log | \n", "0.979839 | \n", "0.650000 | \n", "0.520000 | \n", "0.577778 | \n", "0.711864 | \n", "
9 | \n", "No log | \n", "0.889085 | \n", "0.685714 | \n", "0.640000 | \n", "0.662069 | \n", "0.757062 | \n", "
10 | \n", "No log | \n", "0.804649 | \n", "0.718310 | \n", "0.680000 | \n", "0.698630 | \n", "0.774011 | \n", "
11 | \n", "No log | \n", "0.731301 | \n", "0.729730 | \n", "0.720000 | \n", "0.724832 | \n", "0.802260 | \n", "
12 | \n", "No log | \n", "0.666918 | \n", "0.815789 | \n", "0.826667 | \n", "0.821192 | \n", "0.853107 | \n", "
13 | \n", "No log | \n", "0.645252 | \n", "0.794872 | \n", "0.826667 | \n", "0.810458 | \n", "0.853107 | \n", "
14 | \n", "No log | \n", "0.599339 | \n", "0.807692 | \n", "0.840000 | \n", "0.823529 | \n", "0.864407 | \n", "
15 | \n", "No log | \n", "0.559112 | \n", "0.820513 | \n", "0.853333 | \n", "0.836601 | \n", "0.870056 | \n", "
16 | \n", "No log | \n", "0.549788 | \n", "0.780488 | \n", "0.853333 | \n", "0.815287 | \n", "0.870056 | \n", "
17 | \n", "No log | \n", "0.529846 | \n", "0.800000 | \n", "0.853333 | \n", "0.825806 | \n", "0.875706 | \n", "
18 | \n", "No log | \n", "0.521326 | \n", "0.777778 | \n", "0.840000 | \n", "0.807692 | \n", "0.864407 | \n", "
19 | \n", "No log | \n", "0.523422 | \n", "0.825000 | \n", "0.880000 | \n", "0.851613 | \n", "0.887006 | \n", "
20 | \n", "No log | \n", "0.523808 | \n", "0.822785 | \n", "0.866667 | \n", "0.844156 | \n", "0.881356 | \n", "
21 | \n", "No log | \n", "0.535967 | \n", "0.817073 | \n", "0.893333 | \n", "0.853503 | \n", "0.887006 | \n", "
22 | \n", "No log | \n", "0.515644 | \n", "0.825000 | \n", "0.880000 | \n", "0.851613 | \n", "0.887006 | \n", "
23 | \n", "No log | \n", "0.511107 | \n", "0.825000 | \n", "0.880000 | \n", "0.851613 | \n", "0.887006 | \n", "
24 | \n", "No log | \n", "0.519790 | \n", "0.825000 | \n", "0.880000 | \n", "0.851613 | \n", "0.887006 | \n", "
25 | \n", "No log | \n", "0.518594 | \n", "0.825000 | \n", "0.880000 | \n", "0.851613 | \n", "0.887006 | \n", "
26 | \n", "No log | \n", "0.517694 | \n", "0.825000 | \n", "0.880000 | \n", "0.851613 | \n", "0.887006 | \n", "
27 | \n", "No log | \n", "0.514700 | \n", "0.825000 | \n", "0.880000 | \n", "0.851613 | \n", "0.887006 | \n", "
28 | \n", "No log | \n", "0.515272 | \n", "0.825000 | \n", "0.880000 | \n", "0.851613 | \n", "0.887006 | \n", "
29 | \n", "No log | \n", "0.515349 | \n", "0.825000 | \n", "0.880000 | \n", "0.851613 | \n", "0.887006 | \n", "
30 | \n", "No log | \n", "0.515360 | \n", "0.825000 | \n", "0.880000 | \n", "0.851613 | \n", "0.887006 | \n", "
"
]
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"/usr/local/lib/python3.10/dist-packages/seqeval/metrics/v1.py:57: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"TrainOutput(global_step=210, training_loss=0.47932579403831843, metrics={'train_runtime': 508.6205, 'train_samples_per_second': 2.949, 'train_steps_per_second': 0.413, 'total_flos': 52770861370944.0, 'train_loss': 0.47932579403831843, 'epoch': 30.0})"
]
},
"metadata": {},
"execution_count": 29
}
]
},
{
"cell_type": "markdown",
"source": [
"#### push to hub the final model"
],
"metadata": {
"id": "ZWWdzstTQvVl"
}
},
{
"cell_type": "code",
"source": [
"trainer.push_to_hub()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 149,
"referenced_widgets": [
"fba645b2f67747e6b6567ec0a5fdea20",
"79547ecdb2684c93b670c9b654429402",
"fd6624bacc6d4c47acae7b4ede8c1f3b",
"6cd6ee458afb48d398022267f39b0fc4",
"9b66439e9e7e422a97995cfb5d8f33b8",
"9858a3b7c3944e4fae6b3fadc9642125",
"775a0752d94a40138acdee0db8739031",
"445f2113b9f7485c868570d49eb52568",
"e709ba7a504b4efe934d3ee7774ffc5f",
"42ebf1faa74b46d5b1b868f83f44a657",
"b8992ac697014624a58a541efca24655",
"be4b170b77c2428a83104f56c2774eff",
"ad4b06c06e894c58956c8708c0ccfd67",
"f3efe96ce3d14872b1b62ef0b0f6122f",
"1a6cb5fe580141c6bdeb1dc0a321d380",
"1f2a427df9604930b915441f2e89ddd3",
"2ac0d35389794c3faceb679a43c7ceea",
"b31ac4e6bd36460987d448b9e7b1f08c",
"492b9b0c9f594ef6ae0d238b694ca181",
"97f064e2b05f44778b84d5d5d9cad20e",
"43e09ad9787244f8bf20c01c7da81991",
"46b4241483ea4dc984cc92cce41514ee",
"3afc5d399625437196c6a52d36373f61",
"62f221b1ab7a4c608be46ab9fab43af1",
"cbca91cd984d42fd9ad90fcb58331d6e",
"63931ac1a75241dfb7519da2194969d2",
"a576f7800c0740a1a7b70c55e2c0f071",
"5cd48e3b8d1c42edadc2413f1baac426",
"51efa950d7a44737b9e7dbdf177be9a3",
"59878cd245324664bbeb4aefc0fc36e4",
"bac939ffbe8f4edd9d4bf7f4281c0707",
"71c6509f4497436fb9bd2c3e141990f4",
"20c2b2fd5aa24683ad3cbe18a485bcb1"
]
},
"id": "rsUb3czVK8ub",
"outputId": "f00733ec-7ddb-4664-c778-1555853a4e7e"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"model.safetensors: 0%| | 0.00/261M [00:00, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "fba645b2f67747e6b6567ec0a5fdea20"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Upload 2 LFS files: 0%| | 0/2 [00:00, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "be4b170b77c2428a83104f56c2774eff"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"events.out.tfevents.1711602729.7cf60fa1c92f.682.0: 0%| | 0.00/19.4k [00:00, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "3afc5d399625437196c6a52d36373f61"
}
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"CommitInfo(commit_url='https://huggingface.co/harsh13333/ner_bert_model/commit/c9b064cf83491ebaf0871839b906851c94c26a6b', commit_message='End of training', commit_description='', oid='c9b064cf83491ebaf0871839b906851c94c26a6b', pr_url=None, pr_revision=None, pr_num=None)"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 20
}
]
},
{
"cell_type": "code",
"source": [
"from transformers import pipeline\n",
"\n",
"# Load the pipeline\n",
"nlp = pipeline(\"ner\", model=\"harsh13333/ner_bert_model\",aggregation_strategy=\"simple\")\n",
"\n",
"\n",
"# Pass a text to the pipeline\n",
"text = \"LEX2 43Lbs 03/14 OMI6 Jullen Cohen GC11909 A654SW75THAVE 33155FL UnitedStates T8A305725101973 DMI6 CYCLE BHM1 MGES MGES B040 MIA5 DMI6\""
],
"metadata": {
"id": "-kZvv2hbTADJ",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
"60ee88fb52de47cf8c661e723a3f7f05",
"1a7a5a9eded149bcb4170da34c47bdee",
"99b8f7cd76ce4d8cb3690e795bf896eb",
"af18a8d8e70940169d839c6a857884c4",
"817978a5c92b4371a5a85cab84c67034",
"2a374450c9954701aa101a3d15bedfdf",
"6fa7487cc8c0458a87f7e522c7489205",
"7080ddf19d544cd68a1d5beac78ccb07",
"0e2eeba08b34428ba8c6cec0f5061997",
"5b795044243f4f2ba562b76993e5e9a5",
"6ad7a9f47709470fbce3ebc6b6df4415"
]
},
"outputId": "1993ce24-5f5c-45cb-cf53-bc3b3276f142"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"model.safetensors: 0%| | 0.00/261M [00:00, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "60ee88fb52de47cf8c661e723a3f7f05"
}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"nlp(text)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "W_qPlwCLbrv4",
"outputId": "4fd0df60-3e32-4e06-ac0d-e776f3002426"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"[{'entity': 'B-NAME',\n",
" 'score': 0.9495337,\n",
" 'index': 13,\n",
" 'word': 'Jul',\n",
" 'start': 22,\n",
" 'end': 25},\n",
" {'entity': 'I-NAME',\n",
" 'score': 0.9651188,\n",
" 'index': 15,\n",
" 'word': 'Cohen',\n",
" 'start': 29,\n",
" 'end': 34},\n",
" {'entity': 'B-GCNUMBER',\n",
" 'score': 0.96900594,\n",
" 'index': 16,\n",
" 'word': 'G',\n",
" 'start': 35,\n",
" 'end': 36},\n",
" {'entity': 'B-LOCATION',\n",
" 'score': 0.7842003,\n",
" 'index': 30,\n",
" 'word': '33',\n",
" 'start': 57,\n",
" 'end': 59},\n",
" {'entity': 'B-COUNTRY',\n",
" 'score': 0.7859303,\n",
" 'index': 34,\n",
" 'word': 'United',\n",
" 'start': 65,\n",
" 'end': 71},\n",
" {'entity': 'B-ORG',\n",
" 'score': 0.5347484,\n",
" 'index': 49,\n",
" 'word': 'D',\n",
" 'start': 94,\n",
" 'end': 95},\n",
" {'entity': 'B-ORG',\n",
" 'score': 0.4627055,\n",
" 'index': 70,\n",
" 'word': 'D',\n",
" 'start': 130,\n",
" 'end': 131}]"
]
},
"metadata": {},
"execution_count": 42
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "phjdNmZQcIrE"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"### flair"
],
"metadata": {
"id": "Is_xhvyhoi3j"
}
},
{
"cell_type": "code",
"source": [
"!pip install flair"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "v9rcr9eWoqPH",
"outputId": "5ff78f37-f61c-4b90-a8b1-6c225d1b92a8"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Collecting flair\n",
" Downloading flair-0.13.1-py3-none-any.whl (388 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m388.3/388.3 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting boto3>=1.20.27 (from flair)\n",
" Downloading boto3-1.34.75-py3-none-any.whl (139 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.3/139.3 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting bpemb>=0.3.2 (from flair)\n",
" Downloading bpemb-0.3.5-py3-none-any.whl (19 kB)\n",
"Collecting conllu>=4.0 (from flair)\n",
" Downloading conllu-4.5.3-py2.py3-none-any.whl (16 kB)\n",
"Collecting deprecated>=1.2.13 (from flair)\n",
" Downloading Deprecated-1.2.14-py2.py3-none-any.whl (9.6 kB)\n",
"Collecting ftfy>=6.1.0 (from flair)\n",
" Downloading ftfy-6.2.0-py3-none-any.whl (54 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.4/54.4 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: gdown>=4.4.0 in /usr/local/lib/python3.10/dist-packages (from flair) (4.7.3)\n",
"Requirement already satisfied: gensim>=4.2.0 in /usr/local/lib/python3.10/dist-packages (from flair) (4.3.2)\n",
"Requirement already satisfied: huggingface-hub>=0.10.0 in /usr/local/lib/python3.10/dist-packages (from flair) (0.20.3)\n",
"Collecting janome>=0.4.2 (from flair)\n",
" Downloading Janome-0.5.0-py2.py3-none-any.whl (19.7 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19.7/19.7 MB\u001b[0m \u001b[31m49.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting langdetect>=1.0.9 (from flair)\n",
" Downloading langdetect-1.0.9.tar.gz (981 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m981.5/981.5 kB\u001b[0m \u001b[31m57.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"Requirement already satisfied: lxml>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from flair) (4.9.4)\n",
"Requirement already satisfied: matplotlib>=2.2.3 in /usr/local/lib/python3.10/dist-packages (from flair) (3.7.1)\n",
"Requirement already satisfied: more-itertools>=8.13.0 in /usr/local/lib/python3.10/dist-packages (from flair) (10.1.0)\n",
"Collecting mpld3>=0.3 (from flair)\n",
" Downloading mpld3-0.5.10-py3-none-any.whl (202 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m202.6/202.6 kB\u001b[0m \u001b[31m22.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting pptree>=3.1 (from flair)\n",
" Downloading pptree-3.1.tar.gz (3.0 kB)\n",
" Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from flair) (2.8.2)\n",
"Collecting pytorch-revgrad>=0.2.0 (from flair)\n",
" Downloading pytorch_revgrad-0.2.0-py3-none-any.whl (4.6 kB)\n",
"Requirement already satisfied: regex>=2022.1.18 in /usr/local/lib/python3.10/dist-packages (from flair) (2023.12.25)\n",
"Requirement already satisfied: scikit-learn>=1.0.2 in /usr/local/lib/python3.10/dist-packages (from flair) (1.2.2)\n",
"Collecting segtok>=1.5.11 (from flair)\n",
" Downloading segtok-1.5.11-py3-none-any.whl (24 kB)\n",
"Collecting sqlitedict>=2.0.0 (from flair)\n",
" Downloading sqlitedict-2.1.0.tar.gz (21 kB)\n",
" Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
"Requirement already satisfied: tabulate>=0.8.10 in /usr/local/lib/python3.10/dist-packages (from flair) (0.9.0)\n",
"Requirement already satisfied: torch!=1.8,>=1.5.0 in /usr/local/lib/python3.10/dist-packages (from flair) (2.2.1+cu121)\n",
"Requirement already satisfied: tqdm>=4.63.0 in /usr/local/lib/python3.10/dist-packages (from flair) (4.66.2)\n",
"Collecting transformer-smaller-training-vocab>=0.2.3 (from flair)\n",
" Downloading transformer_smaller_training_vocab-0.3.3-py3-none-any.whl (14 kB)\n",
"Requirement already satisfied: transformers[sentencepiece]<5.0.0,>=4.18.0 in /usr/local/lib/python3.10/dist-packages (from flair) (4.38.2)\n",
"Collecting urllib3<2.0.0,>=1.0.0 (from flair)\n",
" Downloading urllib3-1.26.18-py2.py3-none-any.whl (143 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.8/143.8 kB\u001b[0m \u001b[31m19.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting wikipedia-api>=0.5.7 (from flair)\n",
" Downloading Wikipedia_API-0.6.0-py3-none-any.whl (14 kB)\n",
"Collecting semver<4.0.0,>=3.0.0 (from flair)\n",
" Downloading semver-3.0.2-py3-none-any.whl (17 kB)\n",
"Collecting botocore<1.35.0,>=1.34.75 (from boto3>=1.20.27->flair)\n",
" Downloading botocore-1.34.75-py3-none-any.whl (12.1 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.1/12.1 MB\u001b[0m \u001b[31m94.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting jmespath<2.0.0,>=0.7.1 (from boto3>=1.20.27->flair)\n",
" Downloading jmespath-1.0.1-py3-none-any.whl (20 kB)\n",
"Collecting s3transfer<0.11.0,>=0.10.0 (from boto3>=1.20.27->flair)\n",
" Downloading s3transfer-0.10.1-py3-none-any.whl (82 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m82.2/82.2 kB\u001b[0m \u001b[31m12.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from bpemb>=0.3.2->flair) (1.25.2)\n",
"Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from bpemb>=0.3.2->flair) (2.31.0)\n",
"Requirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (from bpemb>=0.3.2->flair) (0.1.99)\n",
"Requirement already satisfied: wrapt<2,>=1.10 in /usr/local/lib/python3.10/dist-packages (from deprecated>=1.2.13->flair) (1.14.1)\n",
"Requirement already satisfied: wcwidth<0.3.0,>=0.2.12 in /usr/local/lib/python3.10/dist-packages (from ftfy>=6.1.0->flair) (0.2.13)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown>=4.4.0->flair) (3.13.3)\n",
"Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from gdown>=4.4.0->flair) (1.16.0)\n",
"Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown>=4.4.0->flair) (4.12.3)\n",
"Requirement already satisfied: scipy>=1.7.0 in /usr/local/lib/python3.10/dist-packages (from gensim>=4.2.0->flair) (1.11.4)\n",
"Requirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.10/dist-packages (from gensim>=4.2.0->flair) (6.4.0)\n",
"Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.10.0->flair) (2023.6.0)\n",
"Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.10.0->flair) (6.0.1)\n",
"Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.10.0->flair) (4.10.0)\n",
"Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.10.0->flair) (24.0)\n",
"Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.2.3->flair) (1.2.0)\n",
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.2.3->flair) (0.12.1)\n",
"Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.2.3->flair) (4.50.0)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.2.3->flair) (1.4.5)\n",
"Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.2.3->flair) (9.4.0)\n",
"Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=2.2.3->flair) (3.1.2)\n",
"Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from mpld3>=0.3->flair) (3.1.3)\n",
"Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0.2->flair) (1.3.2)\n",
"Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=1.0.2->flair) (3.4.0)\n",
"Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch!=1.8,>=1.5.0->flair) (1.12)\n",
"Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch!=1.8,>=1.5.0->flair) (3.2.1)\n",
"Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch!=1.8,>=1.5.0->flair)\n",
" Downloading nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m23.7/23.7 MB\u001b[0m \u001b[31m54.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting nvidia-cuda-runtime-cu12==12.1.105 (from torch!=1.8,>=1.5.0->flair)\n",
" Downloading nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m823.6/823.6 kB\u001b[0m \u001b[31m66.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting nvidia-cuda-cupti-cu12==12.1.105 (from torch!=1.8,>=1.5.0->flair)\n",
" Downloading nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.1/14.1 MB\u001b[0m \u001b[31m103.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting nvidia-cudnn-cu12==8.9.2.26 (from torch!=1.8,>=1.5.0->flair)\n",
" Downloading nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m731.7/731.7 MB\u001b[0m \u001b[31m1.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting nvidia-cublas-cu12==12.1.3.1 (from torch!=1.8,>=1.5.0->flair)\n",
" Downloading nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m410.6/410.6 MB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting nvidia-cufft-cu12==11.0.2.54 (from torch!=1.8,>=1.5.0->flair)\n",
" Downloading nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m121.6/121.6 MB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting nvidia-curand-cu12==10.3.2.106 (from torch!=1.8,>=1.5.0->flair)\n",
" Downloading nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m56.5/56.5 MB\u001b[0m \u001b[31m15.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting nvidia-cusolver-cu12==11.4.5.107 (from torch!=1.8,>=1.5.0->flair)\n",
" Downloading nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m124.2/124.2 MB\u001b[0m \u001b[31m8.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting nvidia-cusparse-cu12==12.1.0.106 (from torch!=1.8,>=1.5.0->flair)\n",
" Downloading nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m196.0/196.0 MB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting nvidia-nccl-cu12==2.19.3 (from torch!=1.8,>=1.5.0->flair)\n",
" Downloading nvidia_nccl_cu12-2.19.3-py3-none-manylinux1_x86_64.whl (166.0 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m166.0/166.0 MB\u001b[0m \u001b[31m7.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hCollecting nvidia-nvtx-cu12==12.1.105 (from torch!=1.8,>=1.5.0->flair)\n",
" Downloading nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m99.1/99.1 kB\u001b[0m \u001b[31m13.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch!=1.8,>=1.5.0->flair) (2.2.0)\n",
"Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch!=1.8,>=1.5.0->flair)\n",
" Downloading nvidia_nvjitlink_cu12-12.4.99-py3-none-manylinux2014_x86_64.whl (21.1 MB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.1/21.1 MB\u001b[0m \u001b[31m72.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]<5.0.0,>=4.18.0->flair) (0.15.2)\n",
"Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]<5.0.0,>=4.18.0->flair) (0.4.2)\n",
"Requirement already satisfied: protobuf in /usr/local/lib/python3.10/dist-packages (from transformers[sentencepiece]<5.0.0,>=4.18.0->flair) (3.20.3)\n",
"Collecting accelerate>=0.21.0 (from transformers[sentencepiece]<5.0.0,>=4.18.0->flair)\n",
" Downloading accelerate-0.28.0-py3-none-any.whl (290 kB)\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m290.1/290.1 kB\u001b[0m \u001b[31m36.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown>=4.4.0->flair) (2.5)\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->mpld3>=0.3->flair) (2.1.5)\n",
"Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->bpemb>=0.3.2->flair) (3.3.2)\n",
"Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->bpemb>=0.3.2->flair) (3.6)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->bpemb>=0.3.2->flair) (2024.2.2)\n",
"Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests->bpemb>=0.3.2->flair) (1.7.1)\n",
"Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch!=1.8,>=1.5.0->flair) (1.3.0)\n",
"Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate>=0.21.0->transformers[sentencepiece]<5.0.0,>=4.18.0->flair) (5.9.5)\n",
"Building wheels for collected packages: langdetect, pptree, sqlitedict\n",
" Building wheel for langdetect (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for langdetect: filename=langdetect-1.0.9-py3-none-any.whl size=993227 sha256=e01c539a9b90d599d2fe35adb08c91677e0990b60f131cf84b977d00693652b5\n",
" Stored in directory: /root/.cache/pip/wheels/95/03/7d/59ea870c70ce4e5a370638b5462a7711ab78fba2f655d05106\n",
" Building wheel for pptree (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for pptree: filename=pptree-3.1-py3-none-any.whl size=4609 sha256=1abdab5cf109ed1043f5d6e0f525dd465a853b61e8a0fb8ef0eea4386141c870\n",
" Stored in directory: /root/.cache/pip/wheels/9f/b6/0e/6f26eb9e6eb53ff2107a7888d72b5a6a597593956113037828\n",
" Building wheel for sqlitedict (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Created wheel for sqlitedict: filename=sqlitedict-2.1.0-py3-none-any.whl size=16862 sha256=f599976600ef120eaf537e850d09be842667ef488860734f697009d1faa9c664\n",
" Stored in directory: /root/.cache/pip/wheels/79/d6/e7/304e0e6cb2221022c26d8161f7c23cd4f259a9e41e8bbcfabd\n",
"Successfully built langdetect pptree sqlitedict\n",
"Installing collected packages: sqlitedict, pptree, janome, urllib3, semver, segtok, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, langdetect, jmespath, ftfy, deprecated, conllu, nvidia-cusparse-cu12, nvidia-cudnn-cu12, botocore, wikipedia-api, s3transfer, nvidia-cusolver-cu12, mpld3, bpemb, boto3, pytorch-revgrad, accelerate, transformer-smaller-training-vocab, flair\n",
" Attempting uninstall: urllib3\n",
" Found existing installation: urllib3 2.0.7\n",
" Uninstalling urllib3-2.0.7:\n",
" Successfully uninstalled urllib3-2.0.7\n",
"Successfully installed accelerate-0.28.0 boto3-1.34.75 botocore-1.34.75 bpemb-0.3.5 conllu-4.5.3 deprecated-1.2.14 flair-0.13.1 ftfy-6.2.0 janome-0.5.0 jmespath-1.0.1 langdetect-1.0.9 mpld3-0.5.10 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.99 nvidia-nvtx-cu12-12.1.105 pptree-3.1 pytorch-revgrad-0.2.0 s3transfer-0.10.1 segtok-1.5.11 semver-3.0.2 sqlitedict-2.1.0 transformer-smaller-training-vocab-0.3.3 urllib3-1.26.18 wikipedia-api-0.6.0\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!pip install torch"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xZ9OVcoGJ5HI",
"outputId": "3ba88ce4-6389-4bde-8b45-cd19b49c2adc"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.2.1+cu121)\n",
"Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.13.3)\n",
"Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch) (4.10.0)\n",
"Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.12)\n",
"Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.2.1)\n",
"Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.3)\n",
"Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2023.6.0)\n",
"Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.105)\n",
"Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.105)\n",
"Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.105)\n",
"Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch) (8.9.2.26)\n",
"Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.3.1)\n",
"Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch) (11.0.2.54)\n",
"Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch) (10.3.2.106)\n",
"Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch) (11.4.5.107)\n",
"Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.0.106)\n",
"Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /usr/local/lib/python3.10/dist-packages (from torch) (2.19.3)\n",
"Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.105)\n",
"Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch) (2.2.0)\n",
"Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch) (12.4.99)\n",
"Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.5)\n",
"Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# directory where the data resides\n",
"from flair.datasets import ColumnCorpus\n",
"from flair.data import Corpus\n",
"data_folder = '/content'\n",
"# initializing the corpus\n",
"columns = {0 : 'text', 1 : 'ner'}\n",
"corpus:Corpus = ColumnCorpus(data_folder, columns,\n",
" train_file = 'train.txt',\n",
" test_file = 'test.txt',\n",
" dev_file = 'val.txt')"
],
"metadata": {
"id": "Kcdec6ATIWWO",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "38387105-26f7-49b3-84ae-1c838893eece"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"2024-04-02 12:38:16,830 Reading data from /content\n",
"2024-04-02 12:38:16,832 Train: /content/train.txt\n",
"2024-04-02 12:38:16,833 Dev: /content/val.txt\n",
"2024-04-02 12:38:16,834 Test: /content/test.txt\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"# prompt: hstack merge 2 numpy array\n",
"\n",
"import numpy as np\n",
"\n",
"# Create two numpy arrays\n",
"arr1 = np.array([1, 2, 3])\n",
"arr2 = np.array([4, 5, 6])\n",
"\n",
"# Concatenate the arrays horizontally (side-by-side)\n",
"arr_hstack = np.hstack((arr1, arr2))\n",
"\n",
"# Print the resulting array\n",
"print(arr_hstack)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "poqwQIWP2p3R",
"outputId": "9e3d838a-4ddd-4647-d0c3-531634922f38"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"[1 2 3 4 5 6]\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"from flair.data import Corpus\n",
"from flair.datasets import ColumnCorpus\n",
"from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings\n",
"from flair.embeddings import TransformerWordEmbeddings\n",
"\n",
"# embeddings = TransformerWordEmbeddings(\n",
"# model='distilbert-base-cased',\n",
"# layers=\"-1\",\n",
"# subtoken_pooling=\"first\",\n",
"# fine_tune=True,\n",
"# use_context=True,\n",
"# )\n",
"\n",
"# 1. get the corpus\n",
"columns = {0 : 'text', 1 : 'ner'}\n",
"corpus:Corpus = ColumnCorpus(data_folder, columns,\n",
" train_file = 'train.txt',\n",
" test_file = 'test.txt',\n",
" dev_file = 'val.txt')\n",
"\n",
"# 2. what tag do we want to predict?\n",
"tag_type = 'ner'\n",
"\n",
"# 3. make the tag dictionary from the corpus\n",
"tag_dictionary = corpus.make_label_dictionary(label_type = 'ner')\n",
"print(tag_dictionary)\n",
"# 4. initialize each embedding we use\n",
"embedding_types = [\n",
" WordEmbeddings('en-glove'),\n",
" FlairEmbeddings('news-forward'),\n",
" FlairEmbeddings('news-backward'),\n",
"]\n",
"\n",
"# embedding stack consists of Flair and GloVe embeddings\n",
"embeddings = StackedEmbeddings(embeddings=embedding_types)\n",
"\n",
"# 5. initialize sequence tagger\n",
"from flair.models import SequenceTagger\n",
"\n",
"tagger = SequenceTagger(hidden_size=256,\n",
" embeddings=embeddings,\n",
" tag_dictionary=tag_dictionary,\n",
" tag_type=tag_type)\n",
"\n",
"# 6. initialize trainer\n",
"from flair.trainers import ModelTrainer\n",
"\n",
"trainer = ModelTrainer(tagger, corpus)\n",
"\n",
"# 7. run training\n",
"trainer.train('/content/drive/MyDrive/resources/taggers/ner-english',\n",
" train_with_dev=True,\n",
" max_epochs=150)\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "wA0xkTtZoiBY",
"outputId": "abcb9503-849f-40aa-9cbf-2cc67ee1f258"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"2024-04-02 12:38:21,457 Reading data from /content\n",
"2024-04-02 12:38:21,460 Train: /content/train.txt\n",
"2024-04-02 12:38:21,463 Dev: /content/val.txt\n",
"2024-04-02 12:38:21,468 Test: /content/test.txt\n",
"2024-04-02 12:38:21,549 Computing label dictionary. Progress:\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"0it [00:00, ?it/s]\n",
"45it [00:00, 15121.27it/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"2024-04-02 12:38:21,619 Dictionary created for label 'ner' with 2 values: GCNUM (seen 43 times), TRACK-ID (seen 42 times)\n",
"Dictionary with 2 tags: GCNUM, TRACK-ID\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"2024-04-02 12:38:21,903 https://flair.informatik.hu-berlin.de/resources/embeddings/token/glove.gensim.vectors.npy not found in cache, downloading to /tmp/tmpnvmwlbpa\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 153M/153M [00:04<00:00, 38.2MB/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"2024-04-02 12:38:26,387 copying /tmp/tmpnvmwlbpa to cache at /root/.flair/embeddings/glove.gensim.vectors.npy\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"2024-04-02 12:38:27,524 removing temp file /tmp/tmpnvmwlbpa\n",
"2024-04-02 12:38:27,835 https://flair.informatik.hu-berlin.de/resources/embeddings/token/glove.gensim not found in cache, downloading to /tmp/tmpydg9ahnl\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 20.5M/20.5M [00:00<00:00, 30.1MB/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"2024-04-02 12:38:28,743 copying /tmp/tmpydg9ahnl to cache at /root/.flair/embeddings/glove.gensim\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"2024-04-02 12:38:28,820 removing temp file /tmp/tmpydg9ahnl\n",
"2024-04-02 12:38:37,031 https://flair.informatik.hu-berlin.de/resources/embeddings/flair/news-forward-0.4.1.pt not found in cache, downloading to /tmp/tmpeed_9gtd\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 69.7M/69.7M [00:01<00:00, 36.9MB/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"2024-04-02 12:38:39,131 copying /tmp/tmpeed_9gtd to cache at /root/.flair/embeddings/news-forward-0.4.1.pt\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"2024-04-02 12:38:39,229 removing temp file /tmp/tmpeed_9gtd\n",
"2024-04-02 12:38:40,492 https://flair.informatik.hu-berlin.de/resources/embeddings/flair/news-backward-0.4.1.pt not found in cache, downloading to /tmp/tmphmv3s30g\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 69.7M/69.7M [00:01<00:00, 36.9MB/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"2024-04-02 12:38:42,596 copying /tmp/tmphmv3s30g to cache at /root/.flair/embeddings/news-backward-0.4.1.pt\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"2024-04-02 12:38:42,755 removing temp file /tmp/tmphmv3s30g\n",
"2024-04-02 12:38:43,154 SequenceTagger predicts: Dictionary with 9 tags: O, S-GCNUM, B-GCNUM, E-GCNUM, I-GCNUM, S-TRACK-ID, B-TRACK-ID, E-TRACK-ID, I-TRACK-ID\n",
"2024-04-02 12:38:43,551 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:43,553 Model: \"SequenceTagger(\n",
" (embeddings): StackedEmbeddings(\n",
" (list_embedding_0): WordEmbeddings(\n",
" 'en-glove'\n",
" (embedding): Embedding(400001, 100)\n",
" )\n",
" (list_embedding_1): FlairEmbeddings(\n",
" (lm): LanguageModel(\n",
" (drop): Dropout(p=0.05, inplace=False)\n",
" (encoder): Embedding(300, 100)\n",
" (rnn): LSTM(100, 2048)\n",
" )\n",
" )\n",
" (list_embedding_2): FlairEmbeddings(\n",
" (lm): LanguageModel(\n",
" (drop): Dropout(p=0.05, inplace=False)\n",
" (encoder): Embedding(300, 100)\n",
" (rnn): LSTM(100, 2048)\n",
" )\n",
" )\n",
" )\n",
" (word_dropout): WordDropout(p=0.05)\n",
" (locked_dropout): LockedDropout(p=0.5)\n",
" (embedding2nn): Linear(in_features=4196, out_features=4196, bias=True)\n",
" (rnn): LSTM(4196, 256, batch_first=True, bidirectional=True)\n",
" (linear): Linear(in_features=512, out_features=11, bias=True)\n",
" (loss_function): ViterbiLoss()\n",
" (crf): CRF()\n",
")\"\n",
"2024-04-02 12:38:43,555 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:43,557 Corpus: 45 train + 16 dev + 3 test sentences\n",
"2024-04-02 12:38:43,558 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:43,560 Train: 61 sentences\n",
"2024-04-02 12:38:43,562 (train_with_dev=True, train_with_test=False)\n",
"2024-04-02 12:38:43,563 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:43,564 Training Params:\n",
"2024-04-02 12:38:43,565 - learning_rate: \"0.1\" \n",
"2024-04-02 12:38:43,566 - mini_batch_size: \"32\"\n",
"2024-04-02 12:38:43,567 - max_epochs: \"150\"\n",
"2024-04-02 12:38:43,568 - shuffle: \"True\"\n",
"2024-04-02 12:38:43,569 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:43,570 Plugins:\n",
"2024-04-02 12:38:43,571 - AnnealOnPlateau | patience: '3', anneal_factor: '0.5', min_learning_rate: '0.0001'\n",
"2024-04-02 12:38:43,572 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:43,573 Final evaluation on model from best epoch (best-model.pt)\n",
"2024-04-02 12:38:43,574 - metric: \"('micro avg', 'f1-score')\"\n",
"2024-04-02 12:38:43,575 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:43,576 Computation:\n",
"2024-04-02 12:38:43,577 - compute on device: cuda:0\n",
"2024-04-02 12:38:43,578 - embedding storage: cpu\n",
"2024-04-02 12:38:43,578 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:43,579 Model training base path: \"/content/drive/MyDrive/resources/taggers/ner-english\"\n",
"2024-04-02 12:38:43,580 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:43,581 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:44,976 epoch 1 - iter 1/2 - loss 3.44404515 - time (sec): 1.39 - samples/sec: 402.00 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:45,490 epoch 1 - iter 2/2 - loss 2.92139873 - time (sec): 1.91 - samples/sec: 569.80 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:45,492 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:45,494 EPOCH 1 done: loss 2.9214 - lr: 0.100000\n",
"2024-04-02 12:38:45,496 - 0 epochs without improvement\n",
"2024-04-02 12:38:45,498 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:45,597 epoch 2 - iter 1/2 - loss 1.28463274 - time (sec): 0.10 - samples/sec: 6158.11 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:45,672 epoch 2 - iter 2/2 - loss 1.07516005 - time (sec): 0.17 - samples/sec: 6349.58 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:45,673 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:45,676 EPOCH 2 done: loss 1.0752 - lr: 0.100000\n",
"2024-04-02 12:38:45,677 - 0 epochs without improvement\n",
"2024-04-02 12:38:45,679 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:45,753 epoch 3 - iter 1/2 - loss 0.69531815 - time (sec): 0.07 - samples/sec: 7510.14 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:45,845 epoch 3 - iter 2/2 - loss 0.70569185 - time (sec): 0.16 - samples/sec: 6627.54 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:45,847 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:45,850 EPOCH 3 done: loss 0.7057 - lr: 0.100000\n",
"2024-04-02 12:38:45,852 - 0 epochs without improvement\n",
"2024-04-02 12:38:45,854 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:45,948 epoch 4 - iter 1/2 - loss 0.65479236 - time (sec): 0.09 - samples/sec: 6255.63 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:46,022 epoch 4 - iter 2/2 - loss 0.67509532 - time (sec): 0.17 - samples/sec: 6535.21 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:46,024 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:46,027 EPOCH 4 done: loss 0.6751 - lr: 0.100000\n",
"2024-04-02 12:38:46,030 - 0 epochs without improvement\n",
"2024-04-02 12:38:46,032 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:46,131 epoch 5 - iter 1/2 - loss 0.64115141 - time (sec): 0.10 - samples/sec: 5984.08 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:46,206 epoch 5 - iter 2/2 - loss 0.64161874 - time (sec): 0.17 - samples/sec: 6370.44 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:46,207 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:46,210 EPOCH 5 done: loss 0.6416 - lr: 0.100000\n",
"2024-04-02 12:38:46,212 - 0 epochs without improvement\n",
"2024-04-02 12:38:46,215 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:46,310 epoch 6 - iter 1/2 - loss 0.58738084 - time (sec): 0.09 - samples/sec: 6258.22 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:46,385 epoch 6 - iter 2/2 - loss 0.59091217 - time (sec): 0.17 - samples/sec: 6477.29 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:46,387 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:46,389 EPOCH 6 done: loss 0.5909 - lr: 0.100000\n",
"2024-04-02 12:38:46,391 - 0 epochs without improvement\n",
"2024-04-02 12:38:46,393 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:46,480 epoch 7 - iter 1/2 - loss 0.55795262 - time (sec): 0.08 - samples/sec: 6800.72 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:46,571 epoch 7 - iter 2/2 - loss 0.58513366 - time (sec): 0.18 - samples/sec: 6187.30 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:46,573 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:46,575 EPOCH 7 done: loss 0.5851 - lr: 0.100000\n",
"2024-04-02 12:38:46,578 - 0 epochs without improvement\n",
"2024-04-02 12:38:46,580 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:46,664 epoch 8 - iter 1/2 - loss 0.50878422 - time (sec): 0.08 - samples/sec: 6799.65 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:46,754 epoch 8 - iter 2/2 - loss 0.54670745 - time (sec): 0.17 - samples/sec: 6304.28 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:46,757 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:46,759 EPOCH 8 done: loss 0.5467 - lr: 0.100000\n",
"2024-04-02 12:38:46,762 - 0 epochs without improvement\n",
"2024-04-02 12:38:46,764 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:46,862 epoch 9 - iter 1/2 - loss 0.42821300 - time (sec): 0.10 - samples/sec: 6373.38 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:46,933 epoch 9 - iter 2/2 - loss 0.54806942 - time (sec): 0.17 - samples/sec: 6544.42 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:46,935 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:46,937 EPOCH 9 done: loss 0.5481 - lr: 0.100000\n",
"2024-04-02 12:38:46,940 - 1 epochs without improvement\n",
"2024-04-02 12:38:46,942 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:47,021 epoch 10 - iter 1/2 - loss 0.47240739 - time (sec): 0.08 - samples/sec: 7317.90 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:47,110 epoch 10 - iter 2/2 - loss 0.50139522 - time (sec): 0.17 - samples/sec: 6507.94 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:47,112 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:47,115 EPOCH 10 done: loss 0.5014 - lr: 0.100000\n",
"2024-04-02 12:38:47,117 - 0 epochs without improvement\n",
"2024-04-02 12:38:47,119 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:47,219 epoch 11 - iter 1/2 - loss 0.44289096 - time (sec): 0.10 - samples/sec: 6106.63 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:47,297 epoch 11 - iter 2/2 - loss 0.47118983 - time (sec): 0.17 - samples/sec: 6227.87 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:47,299 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:47,301 EPOCH 11 done: loss 0.4712 - lr: 0.100000\n",
"2024-04-02 12:38:47,303 - 0 epochs without improvement\n",
"2024-04-02 12:38:47,306 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:47,405 epoch 12 - iter 1/2 - loss 0.42449305 - time (sec): 0.10 - samples/sec: 6091.96 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:47,510 epoch 12 - iter 2/2 - loss 0.45789551 - time (sec): 0.20 - samples/sec: 5407.34 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:47,512 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:47,514 EPOCH 12 done: loss 0.4579 - lr: 0.100000\n",
"2024-04-02 12:38:47,516 - 0 epochs without improvement\n",
"2024-04-02 12:38:47,518 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:47,596 epoch 13 - iter 1/2 - loss 0.44945066 - time (sec): 0.08 - samples/sec: 7236.94 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:47,692 epoch 13 - iter 2/2 - loss 0.44613633 - time (sec): 0.17 - samples/sec: 6291.01 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:47,694 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:47,696 EPOCH 13 done: loss 0.4461 - lr: 0.100000\n",
"2024-04-02 12:38:47,698 - 0 epochs without improvement\n",
"2024-04-02 12:38:47,700 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:47,779 epoch 14 - iter 1/2 - loss 0.38720482 - time (sec): 0.08 - samples/sec: 7137.44 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:47,873 epoch 14 - iter 2/2 - loss 0.42500368 - time (sec): 0.17 - samples/sec: 6353.72 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:47,874 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:47,877 EPOCH 14 done: loss 0.4250 - lr: 0.100000\n",
"2024-04-02 12:38:47,879 - 0 epochs without improvement\n",
"2024-04-02 12:38:47,881 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:47,979 epoch 15 - iter 1/2 - loss 0.41062502 - time (sec): 0.10 - samples/sec: 6093.96 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:48,056 epoch 15 - iter 2/2 - loss 0.42484871 - time (sec): 0.17 - samples/sec: 6298.04 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:48,057 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:48,059 EPOCH 15 done: loss 0.4248 - lr: 0.100000\n",
"2024-04-02 12:38:48,061 - 0 epochs without improvement\n",
"2024-04-02 12:38:48,063 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:48,164 epoch 16 - iter 1/2 - loss 0.42647916 - time (sec): 0.10 - samples/sec: 5824.25 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:48,243 epoch 16 - iter 2/2 - loss 0.41399557 - time (sec): 0.18 - samples/sec: 6146.45 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:48,245 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:48,247 EPOCH 16 done: loss 0.4140 - lr: 0.100000\n",
"2024-04-02 12:38:48,250 - 0 epochs without improvement\n",
"2024-04-02 12:38:48,252 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:48,334 epoch 17 - iter 1/2 - loss 0.39168397 - time (sec): 0.08 - samples/sec: 6976.05 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:48,427 epoch 17 - iter 2/2 - loss 0.39061826 - time (sec): 0.17 - samples/sec: 6309.59 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:48,428 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:48,431 EPOCH 17 done: loss 0.3906 - lr: 0.100000\n",
"2024-04-02 12:38:48,434 - 0 epochs without improvement\n",
"2024-04-02 12:38:48,436 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:48,527 epoch 18 - iter 1/2 - loss 0.40167976 - time (sec): 0.09 - samples/sec: 6566.29 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:48,619 epoch 18 - iter 2/2 - loss 0.37354796 - time (sec): 0.18 - samples/sec: 6027.18 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:48,621 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:48,623 EPOCH 18 done: loss 0.3735 - lr: 0.100000\n",
"2024-04-02 12:38:48,626 - 0 epochs without improvement\n",
"2024-04-02 12:38:48,628 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:48,729 epoch 19 - iter 1/2 - loss 0.35808818 - time (sec): 0.10 - samples/sec: 5792.09 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:48,810 epoch 19 - iter 2/2 - loss 0.36616759 - time (sec): 0.18 - samples/sec: 6064.52 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:48,812 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:48,815 EPOCH 19 done: loss 0.3662 - lr: 0.100000\n",
"2024-04-02 12:38:48,817 - 0 epochs without improvement\n",
"2024-04-02 12:38:48,820 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:48,921 epoch 20 - iter 1/2 - loss 0.34479054 - time (sec): 0.10 - samples/sec: 5643.94 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:49,003 epoch 20 - iter 2/2 - loss 0.34221306 - time (sec): 0.18 - samples/sec: 6013.25 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:49,004 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:49,008 EPOCH 20 done: loss 0.3422 - lr: 0.100000\n",
"2024-04-02 12:38:49,012 - 0 epochs without improvement\n",
"2024-04-02 12:38:49,014 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:49,112 epoch 21 - iter 1/2 - loss 0.32889740 - time (sec): 0.09 - samples/sec: 5996.51 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:49,192 epoch 21 - iter 2/2 - loss 0.33733788 - time (sec): 0.17 - samples/sec: 6216.19 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:49,195 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:49,198 EPOCH 21 done: loss 0.3373 - lr: 0.100000\n",
"2024-04-02 12:38:49,203 - 0 epochs without improvement\n",
"2024-04-02 12:38:49,208 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:49,306 epoch 22 - iter 1/2 - loss 0.27969826 - time (sec): 0.10 - samples/sec: 6196.75 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:49,386 epoch 22 - iter 2/2 - loss 0.32045580 - time (sec): 0.18 - samples/sec: 6186.06 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:49,388 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:49,390 EPOCH 22 done: loss 0.3205 - lr: 0.100000\n",
"2024-04-02 12:38:49,392 - 0 epochs without improvement\n",
"2024-04-02 12:38:49,395 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:49,504 epoch 23 - iter 1/2 - loss 0.30802871 - time (sec): 0.11 - samples/sec: 5234.59 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:49,586 epoch 23 - iter 2/2 - loss 0.32645577 - time (sec): 0.19 - samples/sec: 5728.11 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:49,589 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:49,591 EPOCH 23 done: loss 0.3265 - lr: 0.100000\n",
"2024-04-02 12:38:49,593 - 1 epochs without improvement\n",
"2024-04-02 12:38:49,595 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:49,678 epoch 24 - iter 1/2 - loss 0.29259292 - time (sec): 0.08 - samples/sec: 7146.77 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:49,772 epoch 24 - iter 2/2 - loss 0.28968734 - time (sec): 0.17 - samples/sec: 6245.59 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:49,773 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:49,776 EPOCH 24 done: loss 0.2897 - lr: 0.100000\n",
"2024-04-02 12:38:49,778 - 0 epochs without improvement\n",
"2024-04-02 12:38:49,781 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:49,882 epoch 25 - iter 1/2 - loss 0.26763737 - time (sec): 0.10 - samples/sec: 5888.06 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:49,964 epoch 25 - iter 2/2 - loss 0.30177488 - time (sec): 0.18 - samples/sec: 6028.73 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:49,965 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:49,968 EPOCH 25 done: loss 0.3018 - lr: 0.100000\n",
"2024-04-02 12:38:49,971 - 1 epochs without improvement\n",
"2024-04-02 12:38:49,973 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:50,070 epoch 26 - iter 1/2 - loss 0.26146290 - time (sec): 0.09 - samples/sec: 5978.86 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:50,149 epoch 26 - iter 2/2 - loss 0.27233112 - time (sec): 0.17 - samples/sec: 6300.31 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:50,150 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:50,154 EPOCH 26 done: loss 0.2723 - lr: 0.100000\n",
"2024-04-02 12:38:50,156 - 0 epochs without improvement\n",
"2024-04-02 12:38:50,158 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:50,255 epoch 27 - iter 1/2 - loss 0.24458568 - time (sec): 0.09 - samples/sec: 6267.43 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:50,341 epoch 27 - iter 2/2 - loss 0.26482273 - time (sec): 0.18 - samples/sec: 6030.94 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:50,343 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:50,345 EPOCH 27 done: loss 0.2648 - lr: 0.100000\n",
"2024-04-02 12:38:50,347 - 0 epochs without improvement\n",
"2024-04-02 12:38:50,350 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:50,432 epoch 28 - iter 1/2 - loss 0.25307725 - time (sec): 0.08 - samples/sec: 7351.31 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:50,538 epoch 28 - iter 2/2 - loss 0.25461486 - time (sec): 0.19 - samples/sec: 5841.93 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:50,540 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:50,542 EPOCH 28 done: loss 0.2546 - lr: 0.100000\n",
"2024-04-02 12:38:50,544 - 0 epochs without improvement\n",
"2024-04-02 12:38:50,546 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:50,626 epoch 29 - iter 1/2 - loss 0.26980714 - time (sec): 0.08 - samples/sec: 7002.56 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:50,725 epoch 29 - iter 2/2 - loss 0.24249902 - time (sec): 0.18 - samples/sec: 6163.12 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:50,727 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:50,729 EPOCH 29 done: loss 0.2425 - lr: 0.100000\n",
"2024-04-02 12:38:50,731 - 0 epochs without improvement\n",
"2024-04-02 12:38:50,733 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:50,814 epoch 30 - iter 1/2 - loss 0.19740648 - time (sec): 0.08 - samples/sec: 7302.75 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:50,909 epoch 30 - iter 2/2 - loss 0.23087483 - time (sec): 0.17 - samples/sec: 6274.14 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:50,910 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:50,913 EPOCH 30 done: loss 0.2309 - lr: 0.100000\n",
"2024-04-02 12:38:50,915 - 0 epochs without improvement\n",
"2024-04-02 12:38:50,917 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:51,013 epoch 31 - iter 1/2 - loss 0.23110126 - time (sec): 0.09 - samples/sec: 5975.11 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:51,100 epoch 31 - iter 2/2 - loss 0.22741171 - time (sec): 0.18 - samples/sec: 6028.79 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:51,102 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:51,104 EPOCH 31 done: loss 0.2274 - lr: 0.100000\n",
"2024-04-02 12:38:51,106 - 0 epochs without improvement\n",
"2024-04-02 12:38:51,108 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:51,211 epoch 32 - iter 1/2 - loss 0.24045745 - time (sec): 0.10 - samples/sec: 5865.59 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:51,292 epoch 32 - iter 2/2 - loss 0.25111941 - time (sec): 0.18 - samples/sec: 5985.53 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:51,293 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:51,295 EPOCH 32 done: loss 0.2511 - lr: 0.100000\n",
"2024-04-02 12:38:51,297 - 1 epochs without improvement\n",
"2024-04-02 12:38:51,299 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:51,400 epoch 33 - iter 1/2 - loss 0.21789198 - time (sec): 0.10 - samples/sec: 5989.39 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:51,480 epoch 33 - iter 2/2 - loss 0.22149147 - time (sec): 0.18 - samples/sec: 6080.23 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:51,482 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:51,484 EPOCH 33 done: loss 0.2215 - lr: 0.100000\n",
"2024-04-02 12:38:51,486 - 0 epochs without improvement\n",
"2024-04-02 12:38:51,488 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:51,595 epoch 34 - iter 1/2 - loss 0.23112546 - time (sec): 0.10 - samples/sec: 5508.79 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:51,682 epoch 34 - iter 2/2 - loss 0.22252127 - time (sec): 0.19 - samples/sec: 5682.85 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:51,684 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:51,687 EPOCH 34 done: loss 0.2225 - lr: 0.100000\n",
"2024-04-02 12:38:51,689 - 1 epochs without improvement\n",
"2024-04-02 12:38:51,691 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:51,770 epoch 35 - iter 1/2 - loss 0.21186577 - time (sec): 0.08 - samples/sec: 7131.70 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:51,871 epoch 35 - iter 2/2 - loss 0.21989569 - time (sec): 0.18 - samples/sec: 6117.52 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:51,873 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:51,876 EPOCH 35 done: loss 0.2199 - lr: 0.100000\n",
"2024-04-02 12:38:51,878 - 0 epochs without improvement\n",
"2024-04-02 12:38:51,880 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:51,962 epoch 36 - iter 1/2 - loss 0.24179878 - time (sec): 0.08 - samples/sec: 7033.68 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:52,059 epoch 36 - iter 2/2 - loss 0.20401711 - time (sec): 0.18 - samples/sec: 6140.66 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:52,061 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:52,063 EPOCH 36 done: loss 0.2040 - lr: 0.100000\n",
"2024-04-02 12:38:52,065 - 0 epochs without improvement\n",
"2024-04-02 12:38:52,068 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:52,150 epoch 37 - iter 1/2 - loss 0.20632705 - time (sec): 0.08 - samples/sec: 6811.88 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:52,248 epoch 37 - iter 2/2 - loss 0.21099051 - time (sec): 0.18 - samples/sec: 6095.13 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:52,250 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:52,253 EPOCH 37 done: loss 0.2110 - lr: 0.100000\n",
"2024-04-02 12:38:52,255 - 1 epochs without improvement\n",
"2024-04-02 12:38:52,257 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:52,358 epoch 38 - iter 1/2 - loss 0.18219516 - time (sec): 0.10 - samples/sec: 6054.16 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:52,444 epoch 38 - iter 2/2 - loss 0.20445531 - time (sec): 0.18 - samples/sec: 5895.79 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:52,447 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:52,449 EPOCH 38 done: loss 0.2045 - lr: 0.100000\n",
"2024-04-02 12:38:52,451 - 2 epochs without improvement\n",
"2024-04-02 12:38:52,453 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:52,561 epoch 39 - iter 1/2 - loss 0.22816141 - time (sec): 0.11 - samples/sec: 5549.30 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:52,651 epoch 39 - iter 2/2 - loss 0.19591870 - time (sec): 0.20 - samples/sec: 5542.28 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:52,653 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:52,655 EPOCH 39 done: loss 0.1959 - lr: 0.100000\n",
"2024-04-02 12:38:52,658 - 0 epochs without improvement\n",
"2024-04-02 12:38:52,664 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:52,739 epoch 40 - iter 1/2 - loss 0.20606261 - time (sec): 0.07 - samples/sec: 7483.91 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:52,836 epoch 40 - iter 2/2 - loss 0.19719533 - time (sec): 0.17 - samples/sec: 6357.67 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:52,838 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:52,840 EPOCH 40 done: loss 0.1972 - lr: 0.100000\n",
"2024-04-02 12:38:52,842 - 1 epochs without improvement\n",
"2024-04-02 12:38:52,844 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:52,946 epoch 41 - iter 1/2 - loss 0.19984477 - time (sec): 0.10 - samples/sec: 5879.62 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:53,025 epoch 41 - iter 2/2 - loss 0.20566837 - time (sec): 0.18 - samples/sec: 6056.58 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:53,027 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:53,031 EPOCH 41 done: loss 0.2057 - lr: 0.100000\n",
"2024-04-02 12:38:53,034 - 2 epochs without improvement\n",
"2024-04-02 12:38:53,036 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:53,136 epoch 42 - iter 1/2 - loss 0.18924362 - time (sec): 0.10 - samples/sec: 5850.44 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:53,215 epoch 42 - iter 2/2 - loss 0.20212508 - time (sec): 0.18 - samples/sec: 6141.99 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:53,217 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:53,219 EPOCH 42 done: loss 0.2021 - lr: 0.100000\n",
"2024-04-02 12:38:53,222 - 3 epochs without improvement\n",
"2024-04-02 12:38:53,224 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:53,306 epoch 43 - iter 1/2 - loss 0.20068359 - time (sec): 0.08 - samples/sec: 7153.91 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:53,401 epoch 43 - iter 2/2 - loss 0.17652243 - time (sec): 0.18 - samples/sec: 6209.30 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:53,403 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:53,405 EPOCH 43 done: loss 0.1765 - lr: 0.100000\n",
"2024-04-02 12:38:53,407 - 0 epochs without improvement\n",
"2024-04-02 12:38:53,410 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:53,488 epoch 44 - iter 1/2 - loss 0.15478516 - time (sec): 0.08 - samples/sec: 7239.23 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:53,595 epoch 44 - iter 2/2 - loss 0.17919922 - time (sec): 0.18 - samples/sec: 5938.84 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:53,597 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:53,599 EPOCH 44 done: loss 0.1792 - lr: 0.100000\n",
"2024-04-02 12:38:53,600 - 1 epochs without improvement\n",
"2024-04-02 12:38:53,602 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:53,685 epoch 45 - iter 1/2 - loss 0.17908520 - time (sec): 0.08 - samples/sec: 6931.02 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:53,785 epoch 45 - iter 2/2 - loss 0.18064003 - time (sec): 0.18 - samples/sec: 6005.46 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:53,788 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:53,790 EPOCH 45 done: loss 0.1806 - lr: 0.100000\n",
"2024-04-02 12:38:53,795 - 2 epochs without improvement\n",
"2024-04-02 12:38:53,797 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:53,902 epoch 46 - iter 1/2 - loss 0.15840863 - time (sec): 0.10 - samples/sec: 6008.01 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:53,980 epoch 46 - iter 2/2 - loss 0.18372312 - time (sec): 0.18 - samples/sec: 6019.85 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:53,982 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:53,984 EPOCH 46 done: loss 0.1837 - lr: 0.100000\n",
"2024-04-02 12:38:53,986 - 3 epochs without improvement\n",
"2024-04-02 12:38:53,988 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:54,088 epoch 47 - iter 1/2 - loss 0.17463030 - time (sec): 0.10 - samples/sec: 6164.60 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:54,167 epoch 47 - iter 2/2 - loss 0.17576261 - time (sec): 0.18 - samples/sec: 6179.81 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:54,168 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:54,170 EPOCH 47 done: loss 0.1758 - lr: 0.100000\n",
"2024-04-02 12:38:54,173 - 0 epochs without improvement\n",
"2024-04-02 12:38:54,175 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:54,258 epoch 48 - iter 1/2 - loss 0.13366355 - time (sec): 0.08 - samples/sec: 7025.57 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:54,353 epoch 48 - iter 2/2 - loss 0.17624550 - time (sec): 0.18 - samples/sec: 6159.30 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:54,355 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:54,357 EPOCH 48 done: loss 0.1762 - lr: 0.100000\n",
"2024-04-02 12:38:54,359 - 1 epochs without improvement\n",
"2024-04-02 12:38:54,361 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:54,466 epoch 49 - iter 1/2 - loss 0.15727480 - time (sec): 0.10 - samples/sec: 5638.24 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:54,574 epoch 49 - iter 2/2 - loss 0.17193385 - time (sec): 0.21 - samples/sec: 5155.73 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:54,579 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:54,580 EPOCH 49 done: loss 0.1719 - lr: 0.100000\n",
"2024-04-02 12:38:54,581 - 0 epochs without improvement\n",
"2024-04-02 12:38:54,583 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:54,730 epoch 50 - iter 1/2 - loss 0.14977512 - time (sec): 0.15 - samples/sec: 3910.86 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:54,844 epoch 50 - iter 2/2 - loss 0.15545896 - time (sec): 0.26 - samples/sec: 4200.34 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:54,849 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:54,853 EPOCH 50 done: loss 0.1555 - lr: 0.100000\n",
"2024-04-02 12:38:54,854 - 0 epochs without improvement\n",
"2024-04-02 12:38:54,859 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:55,000 epoch 51 - iter 1/2 - loss 0.15834969 - time (sec): 0.14 - samples/sec: 4268.16 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:55,096 epoch 51 - iter 2/2 - loss 0.15354020 - time (sec): 0.23 - samples/sec: 4634.39 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:55,098 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:55,099 EPOCH 51 done: loss 0.1535 - lr: 0.100000\n",
"2024-04-02 12:38:55,102 - 0 epochs without improvement\n",
"2024-04-02 12:38:55,104 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:55,235 epoch 52 - iter 1/2 - loss 0.14571885 - time (sec): 0.13 - samples/sec: 4605.82 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:55,340 epoch 52 - iter 2/2 - loss 0.15986135 - time (sec): 0.23 - samples/sec: 4671.90 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:55,342 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:55,344 EPOCH 52 done: loss 0.1599 - lr: 0.100000\n",
"2024-04-02 12:38:55,346 - 1 epochs without improvement\n",
"2024-04-02 12:38:55,348 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:55,480 epoch 53 - iter 1/2 - loss 0.10458194 - time (sec): 0.13 - samples/sec: 4550.62 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:55,582 epoch 53 - iter 2/2 - loss 0.12304710 - time (sec): 0.23 - samples/sec: 4678.70 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:55,585 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:55,586 EPOCH 53 done: loss 0.1230 - lr: 0.100000\n",
"2024-04-02 12:38:55,588 - 0 epochs without improvement\n",
"2024-04-02 12:38:55,590 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:55,713 epoch 54 - iter 1/2 - loss 0.16139668 - time (sec): 0.12 - samples/sec: 4894.67 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:55,851 epoch 54 - iter 2/2 - loss 0.14513430 - time (sec): 0.26 - samples/sec: 4198.29 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:55,853 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:55,855 EPOCH 54 done: loss 0.1451 - lr: 0.100000\n",
"2024-04-02 12:38:55,858 - 1 epochs without improvement\n",
"2024-04-02 12:38:55,860 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:55,965 epoch 55 - iter 1/2 - loss 0.18373649 - time (sec): 0.10 - samples/sec: 5480.08 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:56,107 epoch 55 - iter 2/2 - loss 0.16968290 - time (sec): 0.24 - samples/sec: 4449.06 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:56,109 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:56,111 EPOCH 55 done: loss 0.1697 - lr: 0.100000\n",
"2024-04-02 12:38:56,114 - 2 epochs without improvement\n",
"2024-04-02 12:38:56,116 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:56,269 epoch 56 - iter 1/2 - loss 0.14895640 - time (sec): 0.15 - samples/sec: 3905.18 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:56,380 epoch 56 - iter 2/2 - loss 0.14475585 - time (sec): 0.26 - samples/sec: 4139.29 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:56,383 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:56,385 EPOCH 56 done: loss 0.1448 - lr: 0.100000\n",
"2024-04-02 12:38:56,387 - 3 epochs without improvement\n",
"2024-04-02 12:38:56,389 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:56,504 epoch 57 - iter 1/2 - loss 0.15683315 - time (sec): 0.11 - samples/sec: 4974.73 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:56,609 epoch 57 - iter 2/2 - loss 0.14270435 - time (sec): 0.22 - samples/sec: 4993.55 - lr: 0.100000 - momentum: 0.000000\n",
"2024-04-02 12:38:56,610 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:56,613 EPOCH 57 done: loss 0.1427 - lr: 0.100000\n",
"2024-04-02 12:38:56,616 - 4 epochs without improvement (above 'patience')-> annealing learning_rate to [0.05]\n",
"2024-04-02 12:38:56,617 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:56,734 epoch 58 - iter 1/2 - loss 0.14036764 - time (sec): 0.11 - samples/sec: 4821.27 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:56,824 epoch 58 - iter 2/2 - loss 0.12033280 - time (sec): 0.21 - samples/sec: 5295.05 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:56,826 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:56,828 EPOCH 58 done: loss 0.1203 - lr: 0.050000\n",
"2024-04-02 12:38:56,830 - 0 epochs without improvement\n",
"2024-04-02 12:38:56,832 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:56,917 epoch 59 - iter 1/2 - loss 0.11941228 - time (sec): 0.08 - samples/sec: 6737.79 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:57,016 epoch 59 - iter 2/2 - loss 0.11664262 - time (sec): 0.18 - samples/sec: 5949.76 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:57,018 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:57,020 EPOCH 59 done: loss 0.1166 - lr: 0.050000\n",
"2024-04-02 12:38:57,022 - 0 epochs without improvement\n",
"2024-04-02 12:38:57,024 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:57,109 epoch 60 - iter 1/2 - loss 0.09714617 - time (sec): 0.08 - samples/sec: 6770.29 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:57,211 epoch 60 - iter 2/2 - loss 0.11093283 - time (sec): 0.18 - samples/sec: 5897.83 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:57,213 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:57,215 EPOCH 60 done: loss 0.1109 - lr: 0.050000\n",
"2024-04-02 12:38:57,217 - 0 epochs without improvement\n",
"2024-04-02 12:38:57,218 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:57,325 epoch 61 - iter 1/2 - loss 0.11121512 - time (sec): 0.11 - samples/sec: 5487.88 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:57,409 epoch 61 - iter 2/2 - loss 0.11645126 - time (sec): 0.19 - samples/sec: 5730.14 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:57,411 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:57,413 EPOCH 61 done: loss 0.1165 - lr: 0.050000\n",
"2024-04-02 12:38:57,415 - 1 epochs without improvement\n",
"2024-04-02 12:38:57,417 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:57,500 epoch 62 - iter 1/2 - loss 0.13044009 - time (sec): 0.08 - samples/sec: 6848.41 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:57,598 epoch 62 - iter 2/2 - loss 0.11779617 - time (sec): 0.18 - samples/sec: 6077.83 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:57,600 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:57,602 EPOCH 62 done: loss 0.1178 - lr: 0.050000\n",
"2024-04-02 12:38:57,603 - 2 epochs without improvement\n",
"2024-04-02 12:38:57,605 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:57,707 epoch 63 - iter 1/2 - loss 0.10047390 - time (sec): 0.10 - samples/sec: 5865.93 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:57,794 epoch 63 - iter 2/2 - loss 0.11731148 - time (sec): 0.19 - samples/sec: 5841.52 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:57,795 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:57,798 EPOCH 63 done: loss 0.1173 - lr: 0.050000\n",
"2024-04-02 12:38:57,801 - 3 epochs without improvement\n",
"2024-04-02 12:38:57,805 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:57,880 epoch 64 - iter 1/2 - loss 0.10613811 - time (sec): 0.07 - samples/sec: 7459.84 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:57,978 epoch 64 - iter 2/2 - loss 0.10944687 - time (sec): 0.17 - samples/sec: 6387.11 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:57,979 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:57,982 EPOCH 64 done: loss 0.1094 - lr: 0.050000\n",
"2024-04-02 12:38:57,984 - 0 epochs without improvement\n",
"2024-04-02 12:38:57,986 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:58,069 epoch 65 - iter 1/2 - loss 0.13350388 - time (sec): 0.08 - samples/sec: 7159.80 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:58,167 epoch 65 - iter 2/2 - loss 0.10426040 - time (sec): 0.18 - samples/sec: 6064.69 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:58,169 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:58,171 EPOCH 65 done: loss 0.1043 - lr: 0.050000\n",
"2024-04-02 12:38:58,173 - 0 epochs without improvement\n",
"2024-04-02 12:38:58,176 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:58,254 epoch 66 - iter 1/2 - loss 0.07423303 - time (sec): 0.08 - samples/sec: 7207.75 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:58,350 epoch 66 - iter 2/2 - loss 0.09263823 - time (sec): 0.17 - samples/sec: 6322.38 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:58,351 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:58,354 EPOCH 66 done: loss 0.0926 - lr: 0.050000\n",
"2024-04-02 12:38:58,356 - 0 epochs without improvement\n",
"2024-04-02 12:38:58,358 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:58,462 epoch 67 - iter 1/2 - loss 0.08090147 - time (sec): 0.10 - samples/sec: 5731.48 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:58,545 epoch 67 - iter 2/2 - loss 0.09237679 - time (sec): 0.18 - samples/sec: 5896.55 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:58,546 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:58,549 EPOCH 67 done: loss 0.0924 - lr: 0.050000\n",
"2024-04-02 12:38:58,551 - 0 epochs without improvement\n",
"2024-04-02 12:38:58,553 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:58,653 epoch 68 - iter 1/2 - loss 0.09407286 - time (sec): 0.10 - samples/sec: 6222.91 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:58,731 epoch 68 - iter 2/2 - loss 0.10339344 - time (sec): 0.18 - samples/sec: 6188.13 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:58,733 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:58,735 EPOCH 68 done: loss 0.1034 - lr: 0.050000\n",
"2024-04-02 12:38:58,737 - 1 epochs without improvement\n",
"2024-04-02 12:38:58,740 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:58,850 epoch 69 - iter 1/2 - loss 0.09276591 - time (sec): 0.11 - samples/sec: 5410.58 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:58,929 epoch 69 - iter 2/2 - loss 0.08267720 - time (sec): 0.19 - samples/sec: 5808.62 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:58,930 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:58,933 EPOCH 69 done: loss 0.0827 - lr: 0.050000\n",
"2024-04-02 12:38:58,936 - 0 epochs without improvement\n",
"2024-04-02 12:38:58,938 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:59,040 epoch 70 - iter 1/2 - loss 0.10548705 - time (sec): 0.10 - samples/sec: 5882.08 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:59,121 epoch 70 - iter 2/2 - loss 0.09925675 - time (sec): 0.18 - samples/sec: 6022.49 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:59,123 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:59,125 EPOCH 70 done: loss 0.0993 - lr: 0.050000\n",
"2024-04-02 12:38:59,128 - 1 epochs without improvement\n",
"2024-04-02 12:38:59,130 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:59,210 epoch 71 - iter 1/2 - loss 0.11336365 - time (sec): 0.08 - samples/sec: 7135.39 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:59,318 epoch 71 - iter 2/2 - loss 0.09933357 - time (sec): 0.19 - samples/sec: 5869.40 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:59,320 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:59,321 EPOCH 71 done: loss 0.0993 - lr: 0.050000\n",
"2024-04-02 12:38:59,323 - 2 epochs without improvement\n",
"2024-04-02 12:38:59,325 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:59,430 epoch 72 - iter 1/2 - loss 0.10677456 - time (sec): 0.10 - samples/sec: 5976.21 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:59,504 epoch 72 - iter 2/2 - loss 0.10004914 - time (sec): 0.18 - samples/sec: 6152.94 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:59,506 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:59,508 EPOCH 72 done: loss 0.1000 - lr: 0.050000\n",
"2024-04-02 12:38:59,510 - 3 epochs without improvement\n",
"2024-04-02 12:38:59,512 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:59,615 epoch 73 - iter 1/2 - loss 0.08836593 - time (sec): 0.10 - samples/sec: 6013.71 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:59,702 epoch 73 - iter 2/2 - loss 0.08963128 - time (sec): 0.19 - samples/sec: 5798.26 - lr: 0.050000 - momentum: 0.000000\n",
"2024-04-02 12:38:59,704 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:59,706 EPOCH 73 done: loss 0.0896 - lr: 0.050000\n",
"2024-04-02 12:38:59,708 - 4 epochs without improvement (above 'patience')-> annealing learning_rate to [0.025]\n",
"2024-04-02 12:38:59,710 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:59,800 epoch 74 - iter 1/2 - loss 0.07189532 - time (sec): 0.09 - samples/sec: 6479.90 - lr: 0.025000 - momentum: 0.000000\n",
"2024-04-02 12:38:59,897 epoch 74 - iter 2/2 - loss 0.08220644 - time (sec): 0.18 - samples/sec: 5903.60 - lr: 0.025000 - momentum: 0.000000\n",
"2024-04-02 12:38:59,899 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:38:59,901 EPOCH 74 done: loss 0.0822 - lr: 0.025000\n",
"2024-04-02 12:38:59,903 - 0 epochs without improvement\n",
"2024-04-02 12:38:59,905 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:00,007 epoch 75 - iter 1/2 - loss 0.08889680 - time (sec): 0.10 - samples/sec: 6085.51 - lr: 0.025000 - momentum: 0.000000\n",
"2024-04-02 12:39:00,087 epoch 75 - iter 2/2 - loss 0.07784245 - time (sec): 0.18 - samples/sec: 6033.48 - lr: 0.025000 - momentum: 0.000000\n",
"2024-04-02 12:39:00,089 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:00,091 EPOCH 75 done: loss 0.0778 - lr: 0.025000\n",
"2024-04-02 12:39:00,093 - 0 epochs without improvement\n",
"2024-04-02 12:39:00,095 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:00,178 epoch 76 - iter 1/2 - loss 0.08413783 - time (sec): 0.08 - samples/sec: 6942.49 - lr: 0.025000 - momentum: 0.000000\n",
"2024-04-02 12:39:00,277 epoch 76 - iter 2/2 - loss 0.07547427 - time (sec): 0.18 - samples/sec: 6040.45 - lr: 0.025000 - momentum: 0.000000\n",
"2024-04-02 12:39:00,278 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:00,281 EPOCH 76 done: loss 0.0755 - lr: 0.025000\n",
"2024-04-02 12:39:00,283 - 0 epochs without improvement\n",
"2024-04-02 12:39:00,285 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:00,369 epoch 77 - iter 1/2 - loss 0.08878959 - time (sec): 0.08 - samples/sec: 6850.04 - lr: 0.025000 - momentum: 0.000000\n",
"2024-04-02 12:39:00,467 epoch 77 - iter 2/2 - loss 0.08625149 - time (sec): 0.18 - samples/sec: 6048.16 - lr: 0.025000 - momentum: 0.000000\n",
"2024-04-02 12:39:00,469 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:00,471 EPOCH 77 done: loss 0.0863 - lr: 0.025000\n",
"2024-04-02 12:39:00,474 - 1 epochs without improvement\n",
"2024-04-02 12:39:00,476 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:00,587 epoch 78 - iter 1/2 - loss 0.07817116 - time (sec): 0.11 - samples/sec: 5550.93 - lr: 0.025000 - momentum: 0.000000\n",
"2024-04-02 12:39:00,670 epoch 78 - iter 2/2 - loss 0.08518015 - time (sec): 0.19 - samples/sec: 5658.80 - lr: 0.025000 - momentum: 0.000000\n",
"2024-04-02 12:39:00,672 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:00,674 EPOCH 78 done: loss 0.0852 - lr: 0.025000\n",
"2024-04-02 12:39:00,676 - 2 epochs without improvement\n",
"2024-04-02 12:39:00,678 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:00,777 epoch 79 - iter 1/2 - loss 0.07011843 - time (sec): 0.10 - samples/sec: 5666.35 - lr: 0.025000 - momentum: 0.000000\n",
"2024-04-02 12:39:00,871 epoch 79 - iter 2/2 - loss 0.08749443 - time (sec): 0.19 - samples/sec: 5668.71 - lr: 0.025000 - momentum: 0.000000\n",
"2024-04-02 12:39:00,873 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:00,875 EPOCH 79 done: loss 0.0875 - lr: 0.025000\n",
"2024-04-02 12:39:00,878 - 3 epochs without improvement\n",
"2024-04-02 12:39:00,880 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:00,982 epoch 80 - iter 1/2 - loss 0.06268984 - time (sec): 0.10 - samples/sec: 5884.95 - lr: 0.025000 - momentum: 0.000000\n",
"2024-04-02 12:39:01,062 epoch 80 - iter 2/2 - loss 0.08230347 - time (sec): 0.18 - samples/sec: 6049.08 - lr: 0.025000 - momentum: 0.000000\n",
"2024-04-02 12:39:01,063 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:01,066 EPOCH 80 done: loss 0.0823 - lr: 0.025000\n",
"2024-04-02 12:39:01,068 - 4 epochs without improvement (above 'patience')-> annealing learning_rate to [0.0125]\n",
"2024-04-02 12:39:01,070 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:01,174 epoch 81 - iter 1/2 - loss 0.07875583 - time (sec): 0.10 - samples/sec: 5871.13 - lr: 0.012500 - momentum: 0.000000\n",
"2024-04-02 12:39:01,254 epoch 81 - iter 2/2 - loss 0.06995134 - time (sec): 0.18 - samples/sec: 5988.31 - lr: 0.012500 - momentum: 0.000000\n",
"2024-04-02 12:39:01,255 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:01,258 EPOCH 81 done: loss 0.0700 - lr: 0.012500\n",
"2024-04-02 12:39:01,260 - 0 epochs without improvement\n",
"2024-04-02 12:39:01,262 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:01,345 epoch 82 - iter 1/2 - loss 0.07585760 - time (sec): 0.08 - samples/sec: 6868.62 - lr: 0.012500 - momentum: 0.000000\n",
"2024-04-02 12:39:01,443 epoch 82 - iter 2/2 - loss 0.06974426 - time (sec): 0.18 - samples/sec: 6097.87 - lr: 0.012500 - momentum: 0.000000\n",
"2024-04-02 12:39:01,445 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:01,448 EPOCH 82 done: loss 0.0697 - lr: 0.012500\n",
"2024-04-02 12:39:01,450 - 0 epochs without improvement\n",
"2024-04-02 12:39:01,452 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:01,554 epoch 83 - iter 1/2 - loss 0.07174847 - time (sec): 0.10 - samples/sec: 5756.80 - lr: 0.012500 - momentum: 0.000000\n",
"2024-04-02 12:39:01,637 epoch 83 - iter 2/2 - loss 0.07034619 - time (sec): 0.18 - samples/sec: 5967.24 - lr: 0.012500 - momentum: 0.000000\n",
"2024-04-02 12:39:01,639 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:01,641 EPOCH 83 done: loss 0.0703 - lr: 0.012500\n",
"2024-04-02 12:39:01,644 - 1 epochs without improvement\n",
"2024-04-02 12:39:01,646 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:01,747 epoch 84 - iter 1/2 - loss 0.06419448 - time (sec): 0.10 - samples/sec: 5921.64 - lr: 0.012500 - momentum: 0.000000\n",
"2024-04-02 12:39:01,836 epoch 84 - iter 2/2 - loss 0.07776609 - time (sec): 0.19 - samples/sec: 5767.26 - lr: 0.012500 - momentum: 0.000000\n",
"2024-04-02 12:39:01,839 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:01,841 EPOCH 84 done: loss 0.0778 - lr: 0.012500\n",
"2024-04-02 12:39:01,843 - 2 epochs without improvement\n",
"2024-04-02 12:39:01,846 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:01,952 epoch 85 - iter 1/2 - loss 0.09023521 - time (sec): 0.10 - samples/sec: 5571.57 - lr: 0.012500 - momentum: 0.000000\n",
"2024-04-02 12:39:02,034 epoch 85 - iter 2/2 - loss 0.08321849 - time (sec): 0.19 - samples/sec: 5813.13 - lr: 0.012500 - momentum: 0.000000\n",
"2024-04-02 12:39:02,036 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:02,038 EPOCH 85 done: loss 0.0832 - lr: 0.012500\n",
"2024-04-02 12:39:02,040 - 3 epochs without improvement\n",
"2024-04-02 12:39:02,042 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:02,122 epoch 86 - iter 1/2 - loss 0.05939736 - time (sec): 0.08 - samples/sec: 7163.40 - lr: 0.012500 - momentum: 0.000000\n",
"2024-04-02 12:39:02,219 epoch 86 - iter 2/2 - loss 0.07049263 - time (sec): 0.17 - samples/sec: 6216.52 - lr: 0.012500 - momentum: 0.000000\n",
"2024-04-02 12:39:02,221 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:02,223 EPOCH 86 done: loss 0.0705 - lr: 0.012500\n",
"2024-04-02 12:39:02,225 - 4 epochs without improvement (above 'patience')-> annealing learning_rate to [0.00625]\n",
"2024-04-02 12:39:02,227 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:02,313 epoch 87 - iter 1/2 - loss 0.09161505 - time (sec): 0.08 - samples/sec: 6758.79 - lr: 0.006250 - momentum: 0.000000\n",
"2024-04-02 12:39:02,411 epoch 87 - iter 2/2 - loss 0.07844079 - time (sec): 0.18 - samples/sec: 5980.73 - lr: 0.006250 - momentum: 0.000000\n",
"2024-04-02 12:39:02,412 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:02,414 EPOCH 87 done: loss 0.0784 - lr: 0.006250\n",
"2024-04-02 12:39:02,416 - 1 epochs without improvement\n",
"2024-04-02 12:39:02,419 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:02,503 epoch 88 - iter 1/2 - loss 0.06252489 - time (sec): 0.08 - samples/sec: 6884.54 - lr: 0.006250 - momentum: 0.000000\n",
"2024-04-02 12:39:02,599 epoch 88 - iter 2/2 - loss 0.05931741 - time (sec): 0.18 - samples/sec: 6098.92 - lr: 0.006250 - momentum: 0.000000\n",
"2024-04-02 12:39:02,600 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:02,603 EPOCH 88 done: loss 0.0593 - lr: 0.006250\n",
"2024-04-02 12:39:02,605 - 0 epochs without improvement\n",
"2024-04-02 12:39:02,610 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:02,717 epoch 89 - iter 1/2 - loss 0.06447771 - time (sec): 0.10 - samples/sec: 5676.90 - lr: 0.006250 - momentum: 0.000000\n",
"2024-04-02 12:39:02,799 epoch 89 - iter 2/2 - loss 0.07432611 - time (sec): 0.18 - samples/sec: 5928.24 - lr: 0.006250 - momentum: 0.000000\n",
"2024-04-02 12:39:02,801 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:02,806 EPOCH 89 done: loss 0.0743 - lr: 0.006250\n",
"2024-04-02 12:39:02,809 - 1 epochs without improvement\n",
"2024-04-02 12:39:02,811 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:02,917 epoch 90 - iter 1/2 - loss 0.06415097 - time (sec): 0.10 - samples/sec: 5681.39 - lr: 0.006250 - momentum: 0.000000\n",
"2024-04-02 12:39:02,998 epoch 90 - iter 2/2 - loss 0.06999941 - time (sec): 0.18 - samples/sec: 5895.46 - lr: 0.006250 - momentum: 0.000000\n",
"2024-04-02 12:39:03,000 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:03,002 EPOCH 90 done: loss 0.0700 - lr: 0.006250\n",
"2024-04-02 12:39:03,004 - 2 epochs without improvement\n",
"2024-04-02 12:39:03,006 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:03,107 epoch 91 - iter 1/2 - loss 0.07426291 - time (sec): 0.10 - samples/sec: 5820.25 - lr: 0.006250 - momentum: 0.000000\n",
"2024-04-02 12:39:03,191 epoch 91 - iter 2/2 - loss 0.08922745 - time (sec): 0.18 - samples/sec: 5930.01 - lr: 0.006250 - momentum: 0.000000\n",
"2024-04-02 12:39:03,194 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:03,196 EPOCH 91 done: loss 0.0892 - lr: 0.006250\n",
"2024-04-02 12:39:03,198 - 3 epochs without improvement\n",
"2024-04-02 12:39:03,199 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:03,294 epoch 92 - iter 1/2 - loss 0.08202867 - time (sec): 0.09 - samples/sec: 6106.90 - lr: 0.006250 - momentum: 0.000000\n",
"2024-04-02 12:39:03,391 epoch 92 - iter 2/2 - loss 0.06541127 - time (sec): 0.19 - samples/sec: 5713.71 - lr: 0.006250 - momentum: 0.000000\n",
"2024-04-02 12:39:03,393 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:03,397 EPOCH 92 done: loss 0.0654 - lr: 0.006250\n",
"2024-04-02 12:39:03,399 - 4 epochs without improvement (above 'patience')-> annealing learning_rate to [0.003125]\n",
"2024-04-02 12:39:03,400 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:03,484 epoch 93 - iter 1/2 - loss 0.07317067 - time (sec): 0.08 - samples/sec: 6754.21 - lr: 0.003125 - momentum: 0.000000\n",
"2024-04-02 12:39:03,581 epoch 93 - iter 2/2 - loss 0.07066782 - time (sec): 0.18 - samples/sec: 6072.35 - lr: 0.003125 - momentum: 0.000000\n",
"2024-04-02 12:39:03,583 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:03,585 EPOCH 93 done: loss 0.0707 - lr: 0.003125\n",
"2024-04-02 12:39:03,587 - 1 epochs without improvement\n",
"2024-04-02 12:39:03,589 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:03,673 epoch 94 - iter 1/2 - loss 0.09488721 - time (sec): 0.08 - samples/sec: 6672.02 - lr: 0.003125 - momentum: 0.000000\n",
"2024-04-02 12:39:03,773 epoch 94 - iter 2/2 - loss 0.07745569 - time (sec): 0.18 - samples/sec: 6034.86 - lr: 0.003125 - momentum: 0.000000\n",
"2024-04-02 12:39:03,775 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:03,778 EPOCH 94 done: loss 0.0775 - lr: 0.003125\n",
"2024-04-02 12:39:03,780 - 2 epochs without improvement\n",
"2024-04-02 12:39:03,783 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:03,902 epoch 95 - iter 1/2 - loss 0.06722196 - time (sec): 0.12 - samples/sec: 5138.63 - lr: 0.003125 - momentum: 0.000000\n",
"2024-04-02 12:39:03,984 epoch 95 - iter 2/2 - loss 0.06787020 - time (sec): 0.20 - samples/sec: 5488.23 - lr: 0.003125 - momentum: 0.000000\n",
"2024-04-02 12:39:03,985 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:03,988 EPOCH 95 done: loss 0.0679 - lr: 0.003125\n",
"2024-04-02 12:39:03,990 - 3 epochs without improvement\n",
"2024-04-02 12:39:03,992 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:04,077 epoch 96 - iter 1/2 - loss 0.07972961 - time (sec): 0.08 - samples/sec: 6759.32 - lr: 0.003125 - momentum: 0.000000\n",
"2024-04-02 12:39:04,177 epoch 96 - iter 2/2 - loss 0.08482842 - time (sec): 0.18 - samples/sec: 5979.22 - lr: 0.003125 - momentum: 0.000000\n",
"2024-04-02 12:39:04,179 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:04,182 EPOCH 96 done: loss 0.0848 - lr: 0.003125\n",
"2024-04-02 12:39:04,184 - 4 epochs without improvement (above 'patience')-> annealing learning_rate to [0.0015625]\n",
"2024-04-02 12:39:04,187 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:04,274 epoch 97 - iter 1/2 - loss 0.07143414 - time (sec): 0.08 - samples/sec: 7129.24 - lr: 0.001563 - momentum: 0.000000\n",
"2024-04-02 12:39:04,369 epoch 97 - iter 2/2 - loss 0.06959602 - time (sec): 0.18 - samples/sec: 6103.03 - lr: 0.001563 - momentum: 0.000000\n",
"2024-04-02 12:39:04,371 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:04,373 EPOCH 97 done: loss 0.0696 - lr: 0.001563\n",
"2024-04-02 12:39:04,375 - 1 epochs without improvement\n",
"2024-04-02 12:39:04,378 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:04,461 epoch 98 - iter 1/2 - loss 0.05324973 - time (sec): 0.08 - samples/sec: 6916.43 - lr: 0.001563 - momentum: 0.000000\n",
"2024-04-02 12:39:04,558 epoch 98 - iter 2/2 - loss 0.06146819 - time (sec): 0.18 - samples/sec: 6102.94 - lr: 0.001563 - momentum: 0.000000\n",
"2024-04-02 12:39:04,560 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:04,562 EPOCH 98 done: loss 0.0615 - lr: 0.001563\n",
"2024-04-02 12:39:04,565 - 2 epochs without improvement\n",
"2024-04-02 12:39:04,567 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:04,670 epoch 99 - iter 1/2 - loss 0.05931310 - time (sec): 0.10 - samples/sec: 5753.78 - lr: 0.001563 - momentum: 0.000000\n",
"2024-04-02 12:39:04,754 epoch 99 - iter 2/2 - loss 0.07862271 - time (sec): 0.18 - samples/sec: 5893.28 - lr: 0.001563 - momentum: 0.000000\n",
"2024-04-02 12:39:04,755 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:04,758 EPOCH 99 done: loss 0.0786 - lr: 0.001563\n",
"2024-04-02 12:39:04,761 - 3 epochs without improvement\n",
"2024-04-02 12:39:04,763 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:04,865 epoch 100 - iter 1/2 - loss 0.05068619 - time (sec): 0.10 - samples/sec: 5671.71 - lr: 0.001563 - momentum: 0.000000\n",
"2024-04-02 12:39:04,955 epoch 100 - iter 2/2 - loss 0.06862395 - time (sec): 0.19 - samples/sec: 5727.15 - lr: 0.001563 - momentum: 0.000000\n",
"2024-04-02 12:39:04,957 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:04,959 EPOCH 100 done: loss 0.0686 - lr: 0.001563\n",
"2024-04-02 12:39:04,961 - 4 epochs without improvement (above 'patience')-> annealing learning_rate to [0.00078125]\n",
"2024-04-02 12:39:04,963 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:05,065 epoch 101 - iter 1/2 - loss 0.04959086 - time (sec): 0.10 - samples/sec: 5946.08 - lr: 0.000781 - momentum: 0.000000\n",
"2024-04-02 12:39:05,149 epoch 101 - iter 2/2 - loss 0.05854479 - time (sec): 0.18 - samples/sec: 5913.49 - lr: 0.000781 - momentum: 0.000000\n",
"2024-04-02 12:39:05,150 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:05,153 EPOCH 101 done: loss 0.0585 - lr: 0.000781\n",
"2024-04-02 12:39:05,156 - 0 epochs without improvement\n",
"2024-04-02 12:39:05,158 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:05,260 epoch 102 - iter 1/2 - loss 0.07184639 - time (sec): 0.10 - samples/sec: 5999.68 - lr: 0.000781 - momentum: 0.000000\n",
"2024-04-02 12:39:05,343 epoch 102 - iter 2/2 - loss 0.07709004 - time (sec): 0.18 - samples/sec: 5958.24 - lr: 0.000781 - momentum: 0.000000\n",
"2024-04-02 12:39:05,345 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:05,347 EPOCH 102 done: loss 0.0771 - lr: 0.000781\n",
"2024-04-02 12:39:05,349 - 1 epochs without improvement\n",
"2024-04-02 12:39:05,351 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:05,430 epoch 103 - iter 1/2 - loss 0.06619445 - time (sec): 0.08 - samples/sec: 7203.90 - lr: 0.000781 - momentum: 0.000000\n",
"2024-04-02 12:39:05,527 epoch 103 - iter 2/2 - loss 0.07354270 - time (sec): 0.17 - samples/sec: 6257.03 - lr: 0.000781 - momentum: 0.000000\n",
"2024-04-02 12:39:05,529 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:05,534 EPOCH 103 done: loss 0.0735 - lr: 0.000781\n",
"2024-04-02 12:39:05,535 - 2 epochs without improvement\n",
"2024-04-02 12:39:05,537 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:05,622 epoch 104 - iter 1/2 - loss 0.06166407 - time (sec): 0.08 - samples/sec: 6742.59 - lr: 0.000781 - momentum: 0.000000\n",
"2024-04-02 12:39:05,720 epoch 104 - iter 2/2 - loss 0.06257367 - time (sec): 0.18 - samples/sec: 6019.34 - lr: 0.000781 - momentum: 0.000000\n",
"2024-04-02 12:39:05,721 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:05,724 EPOCH 104 done: loss 0.0626 - lr: 0.000781\n",
"2024-04-02 12:39:05,726 - 3 epochs without improvement\n",
"2024-04-02 12:39:05,728 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:05,834 epoch 105 - iter 1/2 - loss 0.07319657 - time (sec): 0.10 - samples/sec: 5565.30 - lr: 0.000781 - momentum: 0.000000\n",
"2024-04-02 12:39:05,915 epoch 105 - iter 2/2 - loss 0.06395451 - time (sec): 0.19 - samples/sec: 5856.23 - lr: 0.000781 - momentum: 0.000000\n",
"2024-04-02 12:39:05,922 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:05,924 EPOCH 105 done: loss 0.0640 - lr: 0.000781\n",
"2024-04-02 12:39:05,926 - 4 epochs without improvement (above 'patience')-> annealing learning_rate to [0.000390625]\n",
"2024-04-02 12:39:05,929 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:06,018 epoch 106 - iter 1/2 - loss 0.05404112 - time (sec): 0.09 - samples/sec: 6505.52 - lr: 0.000391 - momentum: 0.000000\n",
"2024-04-02 12:39:06,114 epoch 106 - iter 2/2 - loss 0.07654067 - time (sec): 0.18 - samples/sec: 5959.38 - lr: 0.000391 - momentum: 0.000000\n",
"2024-04-02 12:39:06,116 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:06,118 EPOCH 106 done: loss 0.0765 - lr: 0.000391\n",
"2024-04-02 12:39:06,120 - 1 epochs without improvement\n",
"2024-04-02 12:39:06,122 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:06,228 epoch 107 - iter 1/2 - loss 0.06587247 - time (sec): 0.10 - samples/sec: 5804.27 - lr: 0.000391 - momentum: 0.000000\n",
"2024-04-02 12:39:06,326 epoch 107 - iter 2/2 - loss 0.07110983 - time (sec): 0.20 - samples/sec: 5400.98 - lr: 0.000391 - momentum: 0.000000\n",
"2024-04-02 12:39:06,327 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:06,329 EPOCH 107 done: loss 0.0711 - lr: 0.000391\n",
"2024-04-02 12:39:06,331 - 2 epochs without improvement\n",
"2024-04-02 12:39:06,333 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:06,432 epoch 108 - iter 1/2 - loss 0.07758797 - time (sec): 0.10 - samples/sec: 6020.48 - lr: 0.000391 - momentum: 0.000000\n",
"2024-04-02 12:39:06,532 epoch 108 - iter 2/2 - loss 0.07718707 - time (sec): 0.20 - samples/sec: 5554.28 - lr: 0.000391 - momentum: 0.000000\n",
"2024-04-02 12:39:06,535 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:06,536 EPOCH 108 done: loss 0.0772 - lr: 0.000391\n",
"2024-04-02 12:39:06,538 - 3 epochs without improvement\n",
"2024-04-02 12:39:06,540 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:06,664 epoch 109 - iter 1/2 - loss 0.05915363 - time (sec): 0.12 - samples/sec: 4673.94 - lr: 0.000391 - momentum: 0.000000\n",
"2024-04-02 12:39:06,794 epoch 109 - iter 2/2 - loss 0.05913773 - time (sec): 0.25 - samples/sec: 4323.62 - lr: 0.000391 - momentum: 0.000000\n",
"2024-04-02 12:39:06,796 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:06,798 EPOCH 109 done: loss 0.0591 - lr: 0.000391\n",
"2024-04-02 12:39:06,800 - 4 epochs without improvement (above 'patience')-> annealing learning_rate to [0.0001953125]\n",
"2024-04-02 12:39:06,801 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:06,938 epoch 110 - iter 1/2 - loss 0.06734865 - time (sec): 0.13 - samples/sec: 4363.30 - lr: 0.000195 - momentum: 0.000000\n",
"2024-04-02 12:39:07,048 epoch 110 - iter 2/2 - loss 0.06403043 - time (sec): 0.24 - samples/sec: 4442.16 - lr: 0.000195 - momentum: 0.000000\n",
"2024-04-02 12:39:07,051 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:07,053 EPOCH 110 done: loss 0.0640 - lr: 0.000195\n",
"2024-04-02 12:39:07,055 - 1 epochs without improvement\n",
"2024-04-02 12:39:07,057 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:07,165 epoch 111 - iter 1/2 - loss 0.08403663 - time (sec): 0.11 - samples/sec: 5375.66 - lr: 0.000195 - momentum: 0.000000\n",
"2024-04-02 12:39:07,295 epoch 111 - iter 2/2 - loss 0.08189290 - time (sec): 0.24 - samples/sec: 4599.19 - lr: 0.000195 - momentum: 0.000000\n",
"2024-04-02 12:39:07,302 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:07,304 EPOCH 111 done: loss 0.0819 - lr: 0.000195\n",
"2024-04-02 12:39:07,306 - 2 epochs without improvement\n",
"2024-04-02 12:39:07,310 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:07,447 epoch 112 - iter 1/2 - loss 0.06873646 - time (sec): 0.13 - samples/sec: 4332.50 - lr: 0.000195 - momentum: 0.000000\n",
"2024-04-02 12:39:07,550 epoch 112 - iter 2/2 - loss 0.07077024 - time (sec): 0.24 - samples/sec: 4566.74 - lr: 0.000195 - momentum: 0.000000\n",
"2024-04-02 12:39:07,553 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:07,554 EPOCH 112 done: loss 0.0708 - lr: 0.000195\n",
"2024-04-02 12:39:07,556 - 3 epochs without improvement\n",
"2024-04-02 12:39:07,558 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:07,691 epoch 113 - iter 1/2 - loss 0.06367003 - time (sec): 0.13 - samples/sec: 4449.81 - lr: 0.000195 - momentum: 0.000000\n",
"2024-04-02 12:39:07,802 epoch 113 - iter 2/2 - loss 0.07219690 - time (sec): 0.24 - samples/sec: 4490.01 - lr: 0.000195 - momentum: 0.000000\n",
"2024-04-02 12:39:07,806 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:07,809 EPOCH 113 done: loss 0.0722 - lr: 0.000195\n",
"2024-04-02 12:39:07,811 - 4 epochs without improvement (above 'patience')-> annealing learning_rate to [9.765625e-05]\n",
"2024-04-02 12:39:07,813 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:07,815 learning rate too small - quitting training!\n",
"2024-04-02 12:39:07,817 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:07,819 Saving model ...\n",
"2024-04-02 12:39:09,605 Done.\n",
"2024-04-02 12:39:09,607 ----------------------------------------------------------------------------------------------------\n",
"2024-04-02 12:39:09,611 Testing using last state of model ...\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"100%|██████████| 1/1 [00:00<00:00, 3.71it/s]"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"2024-04-02 12:39:09,914 \n",
"Results:\n",
"- F-score (micro) 0.9091\n",
"- F-score (macro) 0.9\n",
"- Accuracy 0.8333\n",
"\n",
"By class:\n",
" precision recall f1-score support\n",
"\n",
" GCNUM 1.0000 1.0000 1.0000 3\n",
" TRACK-ID 1.0000 0.6667 0.8000 3\n",
"\n",
" micro avg 1.0000 0.8333 0.9091 6\n",
" macro avg 1.0000 0.8333 0.9000 6\n",
"weighted avg 1.0000 0.8333 0.9000 6\n",
"\n",
"2024-04-02 12:39:09,916 ----------------------------------------------------------------------------------------------------\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"{'test_score': 0.9090909090909091}"
]
},
"metadata": {},
"execution_count": 3
}
]
},
{
"cell_type": "code",
"source": [
"import torch\n",
"torch.cuda.empty_cache()"
],
"metadata": {
"id": "Wp195IvjvS-d"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from flair.data import Sentence\n",
"from flair.models import SequenceTagger\n",
"# load the trained model\n",
"model = SequenceTagger.load('/content/resources/taggers/ner-english/final-model.pt')\n",
"# create example sentence\n",
"sentence = Sentence('DOOMDAY WHITESTOWN INADS dipak manavr dipak manvar GC14024 INDIA FL55 BG2303140267275 GROUND OnTrac')\n",
"# predict the tags\n",
"model.predict(sentence)\n",
"print(sentence.to_tagged_string())"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "tkBblzFUrFdT",
"outputId": "93fa8370-cbae-48dc-bdaf-fd57f3dc5129"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"2024-04-02 06:50:57,549 SequenceTagger predicts: Dictionary with 11 tags: O, S-GCNUM, B-GCNUM, E-GCNUM, I-GCNUM, S-TRACK-ID, B-TRACK-ID, E-TRACK-ID, I-TRACK-ID, \n",
" \n",
"
\n",
"\n",
" \n",
" \n",
" \n",
" \n",
" s1 \n",
" s2 \n",
" \n",
" \n",
" 0 \n",
" 1 \n",
" 3 \n",
" \n",
" \n",
" 1 \n",
" 2 \n",
" 4 \n",
" \n",
" \n",
" 2 \n",
" 3 \n",
" 5 \n",
" \n",
" \n",
" \n",
"3 \n",
" 4 \n",
" 6 \n",
"