2023-02-10 22:26:16 +01:00
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
},
"accelerator": "GPU",
2023-02-12 20:03:40 +01:00
"gpuClass": "standard",
2023-02-10 22:26:16 +01:00
"widgets": {
"application/vnd.jupyter.widget-state+json": {
2023-02-12 23:36:28 +01:00
"16f1b324020d48c3a8fd4487c42bbd6b": {
2023-02-10 22:26:16 +01:00
"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": [
2023-02-12 23:36:28 +01:00
"IPY_MODEL_df036cb95c56454998cb7c788d341584",
"IPY_MODEL_4a0387dee622459498ddc9d7bf201187",
"IPY_MODEL_604840d710474c71a676c8368c9b3f2f"
2023-02-10 22:26:16 +01:00
],
2023-02-12 23:36:28 +01:00
"layout": "IPY_MODEL_7e3d18de5d554030bd6aa801ac7f3192"
2023-02-10 22:26:16 +01:00
}
},
2023-02-12 23:36:28 +01:00
"df036cb95c56454998cb7c788d341584": {
2023-02-10 22:26:16 +01:00
"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,
2023-02-12 23:36:28 +01:00
"layout": "IPY_MODEL_419e5a2aab8147d79490748f633675cd",
2023-02-10 22:26:16 +01:00
"placeholder": "
2023-02-12 23:36:28 +01:00
"style": "IPY_MODEL_41452374c2a64afd82d30744b36dd801",
2023-02-12 20:03:40 +01:00
"value": "100%"
2023-02-10 22:26:16 +01:00
}
},
2023-02-12 23:36:28 +01:00
"4a0387dee622459498ddc9d7bf201187": {
2023-02-10 22:26:16 +01:00
"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,
2023-02-12 23:36:28 +01:00
"layout": "IPY_MODEL_ea94ef66672d4b69bf0d5eac6f7dada3",
2023-02-10 22:26:16 +01:00
"max": 3,
"min": 0,
"orientation": "horizontal",
2023-02-12 23:36:28 +01:00
"style": "IPY_MODEL_a7e7dd3a259a4a878cfcd6a66ed35c7c",
2023-02-10 22:26:16 +01:00
"value": 3
}
},
2023-02-12 23:36:28 +01:00
"604840d710474c71a676c8368c9b3f2f": {
2023-02-10 22:26:16 +01:00
"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,
2023-02-12 23:36:28 +01:00
"layout": "IPY_MODEL_275a9313a7e14c57b66cbe484499c8ec",
2023-02-10 22:26:16 +01:00
"placeholder": "
2023-02-12 23:36:28 +01:00
"style": "IPY_MODEL_c01651c9c010429bbf5507770ce6b6ce",
"value": " 3/3 [00:00<00:00, 136.35it/s]"
2023-02-10 22:26:16 +01:00
}
},
2023-02-12 23:36:28 +01:00
"7e3d18de5d554030bd6aa801ac7f3192": {
2023-02-10 22:26:16 +01:00
"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
}
},
2023-02-12 23:36:28 +01:00
"419e5a2aab8147d79490748f633675cd": {
2023-02-10 22:26:16 +01:00
"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
}
},
2023-02-12 23:36:28 +01:00
"41452374c2a64afd82d30744b36dd801": {
2023-02-10 22:26:16 +01:00
"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": ""
}
},
2023-02-12 23:36:28 +01:00
"ea94ef66672d4b69bf0d5eac6f7dada3": {
2023-02-10 22:26:16 +01:00
"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
}
},
2023-02-12 23:36:28 +01:00
"a7e7dd3a259a4a878cfcd6a66ed35c7c": {
2023-02-10 22:26:16 +01:00
"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": ""
}
},
2023-02-12 23:36:28 +01:00
"275a9313a7e14c57b66cbe484499c8ec": {
2023-02-10 22:26:16 +01:00
"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
}
},
2023-02-12 23:36:28 +01:00
"c01651c9c010429bbf5507770ce6b6ce": {
2023-02-10 22:26:16 +01:00
"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": ""
}
2023-02-12 20:03:40 +01:00
}
}
}
},
"cells": [
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "JErLYXsaYy8-"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"! pip install datasets transformers torch scikit-learn evaluate"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
2023-02-10 22:26:16 +01:00
},
2023-02-12 20:03:40 +01:00
"id": "u29i-U30zRjY",
2023-02-12 23:36:28 +01:00
"outputId": "55534ca2-097f-4e7a-a517-463f974148cf"
2023-02-12 20:03:40 +01:00
},
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
"Collecting datasets\n",
" Downloading datasets-2.9.0-py3-none-any.whl (462 kB)\n",
2023-02-12 23:36:28 +01:00
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m462.8/462.8 KB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
2023-02-12 20:03:40 +01:00
"\u001b[?25hCollecting transformers\n",
" Downloading transformers-4.26.1-py3-none-any.whl (6.3 MB)\n",
2023-02-12 23:36:28 +01:00
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.3/6.3 MB\u001b[0m \u001b[31m55.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
2023-02-12 20:03:40 +01:00
"\u001b[?25hRequirement already satisfied: torch in /usr/local/lib/python3.8/dist-packages (1.13.1+cu116)\n",
"Requirement already satisfied: scikit-learn in /usr/local/lib/python3.8/dist-packages (1.0.2)\n",
"Collecting evaluate\n",
" Downloading evaluate-0.4.0-py3-none-any.whl (81 kB)\n",
2023-02-12 23:36:28 +01:00
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.4/81.4 KB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from datasets) (1.21.6)\n",
2023-02-12 20:03:40 +01:00
"Collecting xxhash\n",
" Downloading xxhash-3.2.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (213 kB)\n",
2023-02-12 23:36:28 +01:00
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m213.0/213.0 KB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.8/dist-packages (from datasets) (2023.1.0)\n",
2023-02-12 22:17:41 +01:00
"Requirement already satisfied: aiohttp in /usr/local/lib/python3.8/dist-packages (from datasets) (3.8.3)\n",
2023-02-12 23:36:28 +01:00
"Collecting responses<0.19\n",
" Downloading responses-0.18.0-py3-none-any.whl (38 kB)\n",
"Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from datasets) (6.0)\n",
"Requirement already satisfied: pyarrow>=6.0.0 in /usr/local/lib/python3.8/dist-packages (from datasets) (9.0.0)\n",
2023-02-12 20:03:40 +01:00
"Collecting huggingface-hub<1.0.0,>=0.2.0\n",
" Downloading huggingface_hub-0.12.0-py3-none-any.whl (190 kB)\n",
2023-02-12 23:36:28 +01:00
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.3/190.3 KB\u001b[0m \u001b[31m16.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: dill<0.3.7 in /usr/local/lib/python3.8/dist-packages (from datasets) (0.3.6)\n",
"Requirement already satisfied: tqdm>=4.62.1 in /usr/local/lib/python3.8/dist-packages (from datasets) (4.64.1)\n",
"Requirement already satisfied: requests>=2.19.0 in /usr/local/lib/python3.8/dist-packages (from datasets) (2.25.1)\n",
"Requirement already satisfied: pandas in /usr/local/lib/python3.8/dist-packages (from datasets) (1.3.5)\n",
"Requirement already satisfied: packaging in /usr/local/lib/python3.8/dist-packages (from datasets) (23.0)\n",
2023-02-12 22:17:41 +01:00
"Collecting multiprocess\n",
" Downloading multiprocess-0.70.14-py38-none-any.whl (132 kB)\n",
2023-02-12 23:36:28 +01:00
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.0/132.0 KB\u001b[0m \u001b[31m8.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (2022.6.2)\n",
2023-02-12 22:17:41 +01:00
"Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from transformers) (3.9.0)\n",
"Collecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
" Downloading tokenizers-0.13.2-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.6 MB)\n",
2023-02-12 23:36:28 +01:00
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m51.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
2023-02-12 22:17:41 +01:00
"\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch) (4.4.0)\n",
2023-02-12 23:36:28 +01:00
"Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn) (3.1.0)\n",
2023-02-12 20:03:40 +01:00
"Requirement already satisfied: scipy>=1.1.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn) (1.7.3)\n",
2023-02-12 22:17:41 +01:00
"Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.8/dist-packages (from scikit-learn) (1.2.0)\n",
"Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.8.2)\n",
2023-02-12 20:03:40 +01:00
"Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (22.2.0)\n",
2023-02-12 23:36:28 +01:00
"Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.3.3)\n",
"Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (4.0.2)\n",
"Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (6.0.4)\n",
2023-02-12 20:03:40 +01:00
"Requirement already satisfied: charset-normalizer<3.0,>=2.0 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (2.1.1)\n",
2023-02-12 22:17:41 +01:00
"Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.8/dist-packages (from aiohttp->datasets) (1.3.1)\n",
2023-02-12 20:03:40 +01:00
"Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets) (4.0.0)\n",
2023-02-12 22:17:41 +01:00
"Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets) (1.24.3)\n",
"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets) (2.10)\n",
2023-02-12 20:03:40 +01:00
"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests>=2.19.0->datasets) (2022.12.7)\n",
"Collecting urllib3<1.27,>=1.21.1\n",
" Downloading urllib3-1.26.14-py2.py3-none-any.whl (140 kB)\n",
2023-02-12 23:36:28 +01:00
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m9.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25hRequirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2022.7.1)\n",
"Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.8/dist-packages (from pandas->datasets) (2.8.2)\n",
2023-02-12 20:03:40 +01:00
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7.3->pandas->datasets) (1.15.0)\n",
"Installing collected packages: tokenizers, xxhash, urllib3, multiprocess, responses, huggingface-hub, transformers, datasets, evaluate\n",
" Attempting uninstall: urllib3\n",
" Found existing installation: urllib3 1.24.3\n",
" Uninstalling urllib3-1.24.3:\n",
" Successfully uninstalled urllib3-1.24.3\n",
"Successfully installed datasets-2.9.0 evaluate-0.4.0 huggingface-hub-0.12.0 multiprocess-0.70.14 responses-0.18.0 tokenizers-0.13.2 transformers-4.26.1 urllib3-1.26.14 xxhash-3.2.0\n"
]
}
]
},
{
"cell_type": "markdown",
"source": [],
"metadata": {
"id": "a_f-yno_zity"
}
},
{
"cell_type": "code",
"source": [
"!wget 'https://git.wmi.amu.edu.pl/s444465/projekt-glebokie/raw/branch/master/run_glue.py' -O 'run_glue.py'\n",
"!wget 'https://git.wmi.amu.edu.pl/s444465/projekt-glebokie/raw/branch/master/roberta.py' -O 'roberta.py'\n",
"!wget 'https://git.wmi.amu.edu.pl/s444465/projekt-glebokie/raw/branch/master/gpt2.py' -O 'gpt2.py'"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
2023-02-10 22:26:16 +01:00
},
2023-02-12 20:03:40 +01:00
"id": "V_HmRNcmzhsw",
2023-02-12 23:36:28 +01:00
"outputId": "feafb930-4dbf-436c-8e37-de4e8b8a32cc"
2023-02-12 20:03:40 +01:00
},
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
2023-02-12 23:36:28 +01:00
"--2023-02-12 21:57:57-- https://git.wmi.amu.edu.pl/s444465/projekt-glebokie/raw/branch/master/run_glue.py\n",
2023-02-12 20:03:40 +01:00
"Resolving git.wmi.amu.edu.pl (git.wmi.amu.edu.pl)... 150.254.78.40\n",
"Connecting to git.wmi.amu.edu.pl (git.wmi.amu.edu.pl)|150.254.78.40|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
2023-02-12 22:17:41 +01:00
"Length: 30601 (30K) [text/plain]\n",
2023-02-12 20:03:40 +01:00
"Saving to: ‘ ’
2023-02-10 22:26:16 +01:00
"\n",
2023-02-12 23:36:28 +01:00
"run_glue.py 100%[===================>] 29.88K --.-KB/s in 0.1s \n",
2023-02-10 22:26:16 +01:00
"\n",
2023-02-12 23:36:28 +01:00
"2023-02-12 21:57:58 (248 KB/s) - ‘ ’
2023-02-10 22:26:16 +01:00
"\n",
2023-02-12 23:36:28 +01:00
"--2023-02-12 21:57:58-- https://git.wmi.amu.edu.pl/s444465/projekt-glebokie/raw/branch/master/roberta.py\n",
2023-02-12 20:03:40 +01:00
"Resolving git.wmi.amu.edu.pl (git.wmi.amu.edu.pl)... 150.254.78.40\n",
"Connecting to git.wmi.amu.edu.pl (git.wmi.amu.edu.pl)|150.254.78.40|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
2023-02-12 22:17:41 +01:00
"Length: 12783 (12K) [text/plain]\n",
2023-02-12 20:03:40 +01:00
"Saving to: ‘ ’
2023-02-10 22:26:16 +01:00
"\n",
2023-02-12 23:36:28 +01:00
"roberta.py 100%[===================>] 12.48K --.-KB/s in 0s \n",
2023-02-10 22:26:16 +01:00
"\n",
2023-02-12 23:36:28 +01:00
"2023-02-12 21:57:58 (265 MB/s) - ‘ ’
2023-02-10 22:26:16 +01:00
"\n",
2023-02-12 23:36:28 +01:00
"--2023-02-12 21:57:58-- https://git.wmi.amu.edu.pl/s444465/projekt-glebokie/raw/branch/master/gpt2.py\n",
2023-02-12 20:03:40 +01:00
"Resolving git.wmi.amu.edu.pl (git.wmi.amu.edu.pl)... 150.254.78.40\n",
"Connecting to git.wmi.amu.edu.pl (git.wmi.amu.edu.pl)|150.254.78.40|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 7976 (7.8K) [text/plain]\n",
"Saving to: ‘ ’
2023-02-10 22:26:16 +01:00
"\n",
2023-02-12 20:03:40 +01:00
"gpt2.py 100%[===================>] 7.79K --.-KB/s in 0s \n",
2023-02-10 22:26:16 +01:00
"\n",
2023-02-12 23:36:28 +01:00
"2023-02-12 21:57:59 (1.37 GB/s) - ‘ ’
2023-02-12 20:03:40 +01:00
"\n"
2023-02-10 22:26:16 +01:00
]
}
]
},
{
"cell_type": "code",
2023-02-12 20:03:40 +01:00
"source": [
"import json\n",
"from pathlib import Path\n",
"from typing import Dict, List\n",
"from datasets import load_dataset\n",
"\n",
"loaded_data = load_dataset('emotion')\n",
"!mkdir -v -p data\n",
"\n",
"train_path = Path('data/train.json')\n",
"valid_path = Path('data/valid.json')\n",
"test_path = Path('data/test.json')\n",
"data_train, data_valid, data_test = [], [], []\n",
"\n",
"for source_data, dataset, max_size in [\n",
" (loaded_data['train'], data_train, None),\n",
2023-02-12 23:36:28 +01:00
" (loaded_data['validation'], data_valid, None),\n",
" (loaded_data['test'], data_test, None),\n",
2023-02-12 20:03:40 +01:00
"]:\n",
" for i, data in enumerate(source_data):\n",
" if max_size is not None and i >= max_size:\n",
" break\n",
" data_line = {\n",
" 'label': int(data['label']),\n",
" 'text': data['text'],\n",
" }\n",
" dataset.append(data_line)\n",
"\n",
"print(f'Train: {len(data_train):6d}')\n",
"print(f'Valid: {len(data_valid):6d}')\n",
"\n",
"data_class_1, data_class_2 = [], []\n",
"\n",
2023-02-12 23:36:28 +01:00
"\"\"\"for data in data_valid:\n",
2023-02-12 20:03:40 +01:00
" label = data['label']\n",
" if label == 0:\n",
" data_class_1.append(data)\n",
" elif label == 1:\n",
" data_class_2.append(data)\n",
"\n",
"print(f'Label 1: {len(data_class_1):6d}')\n",
"print(f'Label 2: {len(data_class_2):6d}')\n",
"\n",
"size_half_class_1 = int(len(data_class_1) / 2)\n",
"size_half_class_2 = int(len(data_class_2) / 2)\n",
"\n",
"data_valid = data_class_1[:size_half_class_1] + data_class_2[:size_half_class_2]\n",
"data_test = data_class_1[size_half_class_1:] + data_class_2[size_half_class_2:]\n",
2023-02-12 23:36:28 +01:00
"\"\"\"\n",
2023-02-12 20:03:40 +01:00
"\n",
"print(f'Valid: {len(data_valid):6d}')\n",
"print(f'Test : {len(data_test):6d}')\n",
"\n",
"MAP_LABEL_TRANSLATION = {\n",
" 0: 'sadness',\n",
" 1: 'joy',\n",
" 2: 'love',\n",
" 3: 'anger',\n",
" 4: 'fear',\n",
" 5: 'surprise',\n",
"}\n",
"\n",
"def save_as_translations(original_save_path: Path, data_to_save: List[Dict]) -> None:\n",
" file_name = 's2s-' + original_save_path.name\n",
" file_path = original_save_path.parent / file_name\n",
"\n",
" print(f'Saving into: {file_path}')\n",
" with open(file_path, 'wt') as f_write:\n",
" for data_line in data_to_save:\n",
" label = data_line['label']\n",
" new_label = MAP_LABEL_TRANSLATION[label]\n",
" data_line['label'] = new_label\n",
" data_line_str = json.dumps(data_line)\n",
" f_write.write(f'{data_line_str}\\n')\n",
"\n",
"for file_path, data_to_save in [(train_path, data_train), (valid_path, data_valid), (test_path, data_test)]:\n",
" print(f'Saving into: {file_path}')\n",
" with open(file_path, 'wt') as f_write:\n",
" for data_line in data_to_save:\n",
" data_line_str = json.dumps(data_line)\n",
" f_write.write(f'{data_line_str}\\n')\n",
" \n",
" save_as_translations(file_path, data_to_save)\n",
"\n"
],
2023-02-10 22:26:16 +01:00
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
2023-02-12 23:36:28 +01:00
"height": 295,
2023-02-10 22:26:16 +01:00
"referenced_widgets": [
2023-02-12 23:36:28 +01:00
"16f1b324020d48c3a8fd4487c42bbd6b",
"df036cb95c56454998cb7c788d341584",
"4a0387dee622459498ddc9d7bf201187",
"604840d710474c71a676c8368c9b3f2f",
"7e3d18de5d554030bd6aa801ac7f3192",
"419e5a2aab8147d79490748f633675cd",
"41452374c2a64afd82d30744b36dd801",
"ea94ef66672d4b69bf0d5eac6f7dada3",
"a7e7dd3a259a4a878cfcd6a66ed35c7c",
"275a9313a7e14c57b66cbe484499c8ec",
"c01651c9c010429bbf5507770ce6b6ce"
2023-02-10 22:26:16 +01:00
]
},
2023-02-12 20:03:40 +01:00
"id": "bcR4tWQl0rqt",
2023-02-12 23:36:28 +01:00
"outputId": "6a2bad78-8eb7-4a90-c839-b7cc470438d7"
2023-02-10 22:26:16 +01:00
},
2023-02-12 23:36:28 +01:00
"execution_count": 4,
2023-02-10 22:26:16 +01:00
"outputs": [
{
2023-02-12 20:03:40 +01:00
"output_type": "stream",
"name": "stderr",
"text": [
2023-02-12 23:36:28 +01:00
"WARNING:datasets.builder:No config specified, defaulting to: emotion/split\n",
"WARNING:datasets.builder:Found cached dataset emotion (/root/.cache/huggingface/datasets/emotion/split/1.0.0/cca5efe2dfeb58c1d098e0f9eeb200e9927d889b5a03c67097275dfb5fe463bd)\n"
2023-02-12 20:03:40 +01:00
]
},
2023-02-10 22:26:16 +01:00
{
"output_type": "display_data",
"data": {
"text/plain": [
2023-02-12 20:03:40 +01:00
" 0%| | 0/3 [00:00<?, ?it/s]"
2023-02-10 22:26:16 +01:00
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
2023-02-12 23:36:28 +01:00
"model_id": "16f1b324020d48c3a8fd4487c42bbd6b"
2023-02-10 22:26:16 +01:00
}
},
"metadata": {}
},
{
2023-02-12 20:03:40 +01:00
"output_type": "stream",
"name": "stdout",
"text": [
"mkdir: created directory 'data'\n",
"Train: 16000\n",
"Valid: 2000\n",
2023-02-12 23:36:28 +01:00
"Valid: 2000\n",
"Test : 2000\n",
2023-02-12 20:03:40 +01:00
"Saving into: data/train.json\n",
"Saving into: data/s2s-train.json\n",
"Saving into: data/valid.json\n",
"Saving into: data/s2s-valid.json\n",
"Saving into: data/test.json\n",
"Saving into: data/s2s-test.json\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"\n",
2023-02-12 22:17:41 +01:00
"!head -n 4500 data/train.json > data/train-5k.json\n",
2023-02-12 20:03:40 +01:00
"!tail -n 2500 data/train.json >> data/train-5k.json\n",
"!wc -l data/train-5k.json"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "pRmHIvyB0fZe",
2023-02-12 23:36:28 +01:00
"outputId": "a6f163f0-a393-431c-92e9-aaaf04601832"
2023-02-12 20:03:40 +01:00
},
2023-02-12 23:36:28 +01:00
"execution_count": 5,
2023-02-12 20:03:40 +01:00
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
2023-02-12 22:17:41 +01:00
"7000 data/train-5k.json\n"
2023-02-12 20:03:40 +01:00
]
}
]
},
{
"cell_type": "code",
"source": [
"from pathlib import Path\n",
"\n",
"for file_name in [\"train\", \"valid\", \"test\", \"s2s-train\", \"s2s-valid\", \"s2s-test\"]:\n",
" print(f\"=== {file_name} ===\")\n",
" all_text = Path(f\"data/{file_name}.json\").read_text().split('\\n')\n",
" text = all_text[:2500] + all_text[-2500:]\n",
" Path(f\"data/{file_name}-5k.json\").write_text(\"\\n\".join(text))"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
2023-02-10 22:26:16 +01:00
},
2023-02-12 20:03:40 +01:00
"id": "rFa6ijdx2L28",
2023-02-12 23:36:28 +01:00
"outputId": "3303cd99-beba-4685-d8a0-80f819b1b50d"
2023-02-12 20:03:40 +01:00
},
2023-02-12 23:36:28 +01:00
"execution_count": 6,
2023-02-12 20:03:40 +01:00
"outputs": [
2023-02-10 22:26:16 +01:00
{
2023-02-12 20:03:40 +01:00
"output_type": "stream",
"name": "stdout",
"text": [
"=== train ===\n",
"=== valid ===\n",
"=== test ===\n",
"=== s2s-train ===\n",
"=== s2s-valid ===\n",
"=== s2s-test ===\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"import os\n",
"\n",
"os.environ['TOKENIZERS_PARALLELISM'] = 'true'"
],
"metadata": {
"id": "8opbDvBv3ZlK"
},
2023-02-12 23:36:28 +01:00
"execution_count": 7,
2023-02-12 20:03:40 +01:00
"outputs": []
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "pxuxjHt8P57X"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!python run_glue.py \\\n",
"--cache_dir .cache_training \\\n",
"--model_name_or_path gpt2 \\\n",
"--custom_model gpt2_hidden \\\n",
"--freeze_weights \\\n",
2023-02-12 22:17:41 +01:00
"--train_file data/s2s-train.json \\\n",
"--validation_file data/s2s-valid.json \\\n",
"--test_file data/s2s-test.json \\\n",
2023-02-12 20:03:40 +01:00
"--per_device_train_batch_size 24 \\\n",
"--per_device_eval_batch_size 24 \\\n",
"--do_train \\\n",
"--do_eval \\\n",
"--do_predict \\\n",
"--max_seq_length 128 \\\n",
"--learning_rate 2e-5 \\\n",
"--num_train_epochs 5 \\\n",
"--output_dir out/imdb-5k/gpt2"
],
"metadata": {
"colab": {
2023-02-12 23:36:28 +01:00
"base_uri": "https://localhost:8080/"
2023-02-10 22:26:16 +01:00
},
2023-02-12 20:03:40 +01:00
"id": "XkkeRPG_z3Jc",
2023-02-12 23:36:28 +01:00
"outputId": "ffdacc37-5c06-401a-a588-a1d272dd72b0"
2023-02-12 20:03:40 +01:00
},
2023-02-12 23:36:28 +01:00
"execution_count": 8,
2023-02-12 20:03:40 +01:00
"outputs": [
2023-02-10 22:26:16 +01:00
{
"output_type": "stream",
"name": "stdout",
"text": [
2023-02-12 23:36:28 +01:00
"2023-02-12 22:00:15.880386: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n",
2023-02-12 20:03:40 +01:00
"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
2023-02-12 23:36:28 +01:00
"2023-02-12 22:00:16.771169: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
"2023-02-12 22:00:16.771276: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/lib64-nvidia\n",
"2023-02-12 22:00:16.771294: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.\n",
2023-02-12 20:03:40 +01:00
"WARNING:__main__:Process rank: -1, device: cuda:0, n_gpu: 1distributed training: False, 16-bits training: False\n",
"INFO:__main__:Training/evaluation parameters TrainingArguments(\n",
"_n_gpu=1,\n",
"adafactor=False,\n",
"adam_beta1=0.9,\n",
"adam_beta2=0.999,\n",
"adam_epsilon=1e-08,\n",
"auto_find_batch_size=False,\n",
"bf16=False,\n",
"bf16_full_eval=False,\n",
"data_seed=None,\n",
"dataloader_drop_last=False,\n",
"dataloader_num_workers=0,\n",
"dataloader_pin_memory=True,\n",
"ddp_bucket_cap_mb=None,\n",
"ddp_find_unused_parameters=None,\n",
"ddp_timeout=1800,\n",
"debug=[],\n",
"deepspeed=None,\n",
"disable_tqdm=False,\n",
"do_eval=True,\n",
"do_predict=True,\n",
"do_train=True,\n",
"eval_accumulation_steps=None,\n",
"eval_delay=0,\n",
"eval_steps=None,\n",
"evaluation_strategy=no,\n",
"fp16=False,\n",
"fp16_backend=auto,\n",
"fp16_full_eval=False,\n",
"fp16_opt_level=O1,\n",
"fsdp=[],\n",
"fsdp_min_num_params=0,\n",
"fsdp_transformer_layer_cls_to_wrap=None,\n",
"full_determinism=False,\n",
"gradient_accumulation_steps=1,\n",
"gradient_checkpointing=False,\n",
"greater_is_better=None,\n",
"group_by_length=False,\n",
"half_precision_backend=auto,\n",
"hub_model_id=None,\n",
"hub_private_repo=False,\n",
"hub_strategy=every_save,\n",
"hub_token=<HUB_TOKEN>,\n",
"ignore_data_skip=False,\n",
"include_inputs_for_metrics=False,\n",
"jit_mode_eval=False,\n",
"label_names=None,\n",
"label_smoothing_factor=0.0,\n",
"learning_rate=2e-05,\n",
"length_column_name=length,\n",
"load_best_model_at_end=False,\n",
"local_rank=-1,\n",
"log_level=passive,\n",
"log_level_replica=passive,\n",
"log_on_each_node=True,\n",
2023-02-12 23:36:28 +01:00
"logging_dir=out/imdb-5k/gpt2/runs/Feb12_22-00-19_506c7abe63fb,\n",
2023-02-12 20:03:40 +01:00
"logging_first_step=False,\n",
"logging_nan_inf_filter=True,\n",
"logging_steps=500,\n",
"logging_strategy=steps,\n",
"lr_scheduler_type=linear,\n",
"max_grad_norm=1.0,\n",
"max_steps=-1,\n",
"metric_for_best_model=None,\n",
"mp_parameters=,\n",
"no_cuda=False,\n",
"num_train_epochs=5.0,\n",
"optim=adamw_hf,\n",
"optim_args=None,\n",
"output_dir=out/imdb-5k/gpt2,\n",
"overwrite_output_dir=False,\n",
"past_index=-1,\n",
"per_device_eval_batch_size=24,\n",
"per_device_train_batch_size=24,\n",
"prediction_loss_only=False,\n",
"push_to_hub=False,\n",
"push_to_hub_model_id=None,\n",
"push_to_hub_organization=None,\n",
"push_to_hub_token=<PUSH_TO_HUB_TOKEN>,\n",
"ray_scope=last,\n",
"remove_unused_columns=True,\n",
"report_to=['tensorboard'],\n",
"resume_from_checkpoint=None,\n",
"run_name=out/imdb-5k/gpt2,\n",
"save_on_each_node=False,\n",
"save_steps=500,\n",
"save_strategy=steps,\n",
"save_total_limit=None,\n",
"seed=42,\n",
"sharded_ddp=[],\n",
"skip_memory_metrics=True,\n",
"tf32=None,\n",
"torch_compile=False,\n",
"torch_compile_backend=None,\n",
"torch_compile_mode=None,\n",
"torchdynamo=None,\n",
"tpu_metrics_debug=False,\n",
"tpu_num_cores=None,\n",
"use_ipex=False,\n",
"use_legacy_prediction_loop=False,\n",
"use_mps_device=False,\n",
"warmup_ratio=0.0,\n",
"warmup_steps=0,\n",
"weight_decay=0.0,\n",
"xpu_backend=None,\n",
")\n",
2023-02-12 22:17:41 +01:00
"INFO:__main__:load a local file for train: data/s2s-train.json\n",
"INFO:__main__:load a local file for validation: data/s2s-valid.json\n",
"INFO:__main__:load a local file for test: data/s2s-test.json\n",
2023-02-12 23:36:28 +01:00
"WARNING:datasets.builder:Using custom data configuration default-623c8a7b15a2e58a\n",
2023-02-12 20:03:40 +01:00
"INFO:datasets.info:Loading Dataset Infos from /usr/local/lib/python3.8/dist-packages/datasets/packaged_modules/json\n",
2023-02-12 23:36:28 +01:00
"INFO:datasets.builder:Generating dataset json (/content/.cache_training/json/default-623c8a7b15a2e58a/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51)\n",
"Downloading and preparing dataset json/default to /content/.cache_training/json/default-623c8a7b15a2e58a/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51...\n",
"Downloading data files: 100% 3/3 [00:00<00:00, 14873.42it/s]\n",
2023-02-12 20:03:40 +01:00
"INFO:datasets.download.download_manager:Downloading took 0.0 min\n",
"INFO:datasets.download.download_manager:Checksum Computation took 0.0 min\n",
2023-02-12 23:36:28 +01:00
"Extracting data files: 100% 3/3 [00:00<00:00, 1763.55it/s]\n",
2023-02-12 20:03:40 +01:00
"INFO:datasets.utils.info_utils:Unable to verify checksums.\n",
"INFO:datasets.builder:Generating train split\n",
"INFO:datasets.builder:Generating validation split\n",
"INFO:datasets.builder:Generating test split\n",
"INFO:datasets.utils.info_utils:Unable to verify splits sizes.\n",
2023-02-12 23:36:28 +01:00
"Dataset json downloaded and prepared to /content/.cache_training/json/default-623c8a7b15a2e58a/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51. Subsequent calls will reuse this data.\n",
"100% 3/3 [00:00<00:00, 1028.86it/s]\n",
"Downloading (…)lve/main/config.json: 100% 665/665 [00:00<00:00, 126kB/s]\n",
"[INFO|configuration_utils.py:660] 2023-02-12 22:00:20,342 >> loading configuration file config.json from cache at .cache_training/models--gpt2/snapshots/e7da7f221d5bf496a48136c0cd264e630fe9fcc8/config.json\n",
"[INFO|configuration_utils.py:712] 2023-02-12 22:00:20,343 >> Model config GPT2Config {\n",
2023-02-12 20:03:40 +01:00
" \"_name_or_path\": \"gpt2\",\n",
" \"activation_function\": \"gelu_new\",\n",
" \"architectures\": [\n",
" \"GPT2LMHeadModel\"\n",
" ],\n",
" \"attn_pdrop\": 0.1,\n",
" \"bos_token_id\": 50256,\n",
" \"embd_pdrop\": 0.1,\n",
" \"eos_token_id\": 50256,\n",
" \"id2label\": {\n",
" \"0\": \"LABEL_0\",\n",
" \"1\": \"LABEL_1\",\n",
" \"2\": \"LABEL_2\",\n",
" \"3\": \"LABEL_3\",\n",
" \"4\": \"LABEL_4\",\n",
" \"5\": \"LABEL_5\"\n",
" },\n",
" \"initializer_range\": 0.02,\n",
" \"label2id\": {\n",
" \"LABEL_0\": 0,\n",
" \"LABEL_1\": 1,\n",
" \"LABEL_2\": 2,\n",
" \"LABEL_3\": 3,\n",
" \"LABEL_4\": 4,\n",
" \"LABEL_5\": 5\n",
" },\n",
" \"layer_norm_epsilon\": 1e-05,\n",
" \"model_type\": \"gpt2\",\n",
" \"n_ctx\": 1024,\n",
" \"n_embd\": 768,\n",
" \"n_head\": 12,\n",
" \"n_inner\": null,\n",
" \"n_layer\": 12,\n",
" \"n_positions\": 1024,\n",
" \"reorder_and_upcast_attn\": false,\n",
" \"resid_pdrop\": 0.1,\n",
" \"scale_attn_by_inverse_layer_idx\": false,\n",
" \"scale_attn_weights\": true,\n",
" \"summary_activation\": null,\n",
" \"summary_first_dropout\": 0.1,\n",
" \"summary_proj_to_labels\": true,\n",
" \"summary_type\": \"cls_index\",\n",
" \"summary_use_proj\": true,\n",
" \"task_specific_params\": {\n",
" \"text-generation\": {\n",
" \"do_sample\": true,\n",
" \"max_length\": 50\n",
" }\n",
" },\n",
" \"transformers_version\": \"4.26.1\",\n",
" \"use_cache\": true,\n",
" \"vocab_size\": 50257\n",
"}\n",
"\n",
2023-02-12 23:36:28 +01:00
"[INFO|tokenization_auto.py:458] 2023-02-12 22:00:20,434 >> Could not locate the tokenizer configuration file, will try to use the model config instead.\n",
"[INFO|configuration_utils.py:660] 2023-02-12 22:00:20,525 >> loading configuration file config.json from cache at .cache_training/models--gpt2/snapshots/e7da7f221d5bf496a48136c0cd264e630fe9fcc8/config.json\n",
"[INFO|configuration_utils.py:712] 2023-02-12 22:00:20,525 >> Model config GPT2Config {\n",
2023-02-12 20:03:40 +01:00
" \"_name_or_path\": \"gpt2\",\n",
" \"activation_function\": \"gelu_new\",\n",
" \"architectures\": [\n",
" \"GPT2LMHeadModel\"\n",
" ],\n",
" \"attn_pdrop\": 0.1,\n",
" \"bos_token_id\": 50256,\n",
" \"embd_pdrop\": 0.1,\n",
" \"eos_token_id\": 50256,\n",
" \"initializer_range\": 0.02,\n",
" \"layer_norm_epsilon\": 1e-05,\n",
" \"model_type\": \"gpt2\",\n",
" \"n_ctx\": 1024,\n",
" \"n_embd\": 768,\n",
" \"n_head\": 12,\n",
" \"n_inner\": null,\n",
" \"n_layer\": 12,\n",
" \"n_positions\": 1024,\n",
" \"reorder_and_upcast_attn\": false,\n",
" \"resid_pdrop\": 0.1,\n",
" \"scale_attn_by_inverse_layer_idx\": false,\n",
" \"scale_attn_weights\": true,\n",
" \"summary_activation\": null,\n",
" \"summary_first_dropout\": 0.1,\n",
" \"summary_proj_to_labels\": true,\n",
" \"summary_type\": \"cls_index\",\n",
" \"summary_use_proj\": true,\n",
" \"task_specific_params\": {\n",
" \"text-generation\": {\n",
" \"do_sample\": true,\n",
" \"max_length\": 50\n",
" }\n",
" },\n",
" \"transformers_version\": \"4.26.1\",\n",
" \"use_cache\": true,\n",
" \"vocab_size\": 50257\n",
"}\n",
"\n",
2023-02-12 23:36:28 +01:00
"Downloading (…)olve/main/vocab.json: 100% 1.04M/1.04M [00:00<00:00, 9.19MB/s]\n",
"Downloading (…)olve/main/merges.txt: 100% 456k/456k [00:00<00:00, 4.93MB/s]\n",
"Downloading (…)/main/tokenizer.json: 100% 1.36M/1.36M [00:00<00:00, 11.9MB/s]\n",
"[INFO|tokenization_utils_base.py:1802] 2023-02-12 22:00:21,743 >> loading file vocab.json from cache at .cache_training/models--gpt2/snapshots/e7da7f221d5bf496a48136c0cd264e630fe9fcc8/vocab.json\n",
"[INFO|tokenization_utils_base.py:1802] 2023-02-12 22:00:21,743 >> loading file merges.txt from cache at .cache_training/models--gpt2/snapshots/e7da7f221d5bf496a48136c0cd264e630fe9fcc8/merges.txt\n",
"[INFO|tokenization_utils_base.py:1802] 2023-02-12 22:00:21,743 >> loading file tokenizer.json from cache at .cache_training/models--gpt2/snapshots/e7da7f221d5bf496a48136c0cd264e630fe9fcc8/tokenizer.json\n",
"[INFO|tokenization_utils_base.py:1802] 2023-02-12 22:00:21,744 >> loading file added_tokens.json from cache at None\n",
"[INFO|tokenization_utils_base.py:1802] 2023-02-12 22:00:21,744 >> loading file special_tokens_map.json from cache at None\n",
"[INFO|tokenization_utils_base.py:1802] 2023-02-12 22:00:21,744 >> loading file tokenizer_config.json from cache at None\n",
"[INFO|configuration_utils.py:660] 2023-02-12 22:00:21,744 >> loading configuration file config.json from cache at .cache_training/models--gpt2/snapshots/e7da7f221d5bf496a48136c0cd264e630fe9fcc8/config.json\n",
"[INFO|configuration_utils.py:712] 2023-02-12 22:00:21,745 >> Model config GPT2Config {\n",
2023-02-12 20:03:40 +01:00
" \"_name_or_path\": \"gpt2\",\n",
" \"activation_function\": \"gelu_new\",\n",
" \"architectures\": [\n",
" \"GPT2LMHeadModel\"\n",
" ],\n",
" \"attn_pdrop\": 0.1,\n",
" \"bos_token_id\": 50256,\n",
" \"embd_pdrop\": 0.1,\n",
" \"eos_token_id\": 50256,\n",
" \"initializer_range\": 0.02,\n",
" \"layer_norm_epsilon\": 1e-05,\n",
" \"model_type\": \"gpt2\",\n",
" \"n_ctx\": 1024,\n",
" \"n_embd\": 768,\n",
" \"n_head\": 12,\n",
" \"n_inner\": null,\n",
" \"n_layer\": 12,\n",
" \"n_positions\": 1024,\n",
" \"reorder_and_upcast_attn\": false,\n",
" \"resid_pdrop\": 0.1,\n",
" \"scale_attn_by_inverse_layer_idx\": false,\n",
" \"scale_attn_weights\": true,\n",
" \"summary_activation\": null,\n",
" \"summary_first_dropout\": 0.1,\n",
" \"summary_proj_to_labels\": true,\n",
" \"summary_type\": \"cls_index\",\n",
" \"summary_use_proj\": true,\n",
" \"task_specific_params\": {\n",
" \"text-generation\": {\n",
" \"do_sample\": true,\n",
" \"max_length\": 50\n",
" }\n",
" },\n",
" \"transformers_version\": \"4.26.1\",\n",
" \"use_cache\": true,\n",
" \"vocab_size\": 50257\n",
"}\n",
"\n",
"INFO:__main__:Using hidden states in model: True\n",
"INFO:__main__:Using implementation from class: GPT2ForSequenceClassificationCustom\n",
2023-02-12 23:36:28 +01:00
"Downloading (…)\"pytorch_model.bin\";: 100% 548M/548M [00:05<00:00, 103MB/s]\n",
"[INFO|modeling_utils.py:2275] 2023-02-12 22:00:27,304 >> loading weights file pytorch_model.bin from cache at .cache_training/models--gpt2/snapshots/e7da7f221d5bf496a48136c0cd264e630fe9fcc8/pytorch_model.bin\n",
"[INFO|modeling_utils.py:2857] 2023-02-12 22:00:30,150 >> All model checkpoint weights were used when initializing GPT2ForSequenceClassificationCustom.\n",
2023-02-12 20:03:40 +01:00
"\n",
2023-02-12 23:36:28 +01:00
"[WARNING|modeling_utils.py:2859] 2023-02-12 22:00:30,150 >> Some weights of GPT2ForSequenceClassificationCustom were not initialized from the model checkpoint at gpt2 and are newly initialized: ['score.dense_1_hidden.weight', 'score.dense_2.weight', 'score.out_proj.weight', 'score.dense_2.bias', 'score.dense_1_hidden.bias', 'score.dense_1_input.bias', 'score.dense_1_input.weight']\n",
2023-02-12 20:03:40 +01:00
"You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
"INFO:__main__:Freezing encoder weights\n",
"INFO:__main__:Freezing layer 1\n",
"INFO:__main__:Freezing layer 2\n",
"INFO:__main__:Freezing layer 3\n",
"INFO:__main__:Freezing layer 4\n",
"INFO:__main__:Freezing layer 5\n",
"INFO:__main__:Freezing layer 6\n",
"INFO:__main__:Freezing layer 7\n",
"INFO:__main__:Freezing layer 8\n",
"INFO:__main__:Freezing layer 9\n",
"INFO:__main__:Freezing layer 10\n",
"INFO:__main__:Freezing layer 11\n",
"INFO:__main__:Freezing layer 12\n",
"INFO:__main__:Freezing layer 13\n",
"INFO:__main__:Freezing layer 14\n",
"INFO:__main__:Freezing layer 15\n",
"INFO:__main__:Freezing layer 16\n",
"INFO:__main__:Freezing layer 17\n",
"INFO:__main__:Freezing layer 18\n",
"INFO:__main__:Freezing layer 19\n",
"INFO:__main__:Freezing layer 20\n",
"INFO:__main__:Freezing layer 21\n",
"INFO:__main__:Freezing layer 22\n",
"INFO:__main__:Freezing layer 23\n",
"INFO:__main__:Freezing layer 24\n",
"INFO:__main__:Freezing layer 25\n",
"INFO:__main__:Freezing layer 26\n",
"INFO:__main__:Freezing layer 27\n",
"INFO:__main__:Freezing layer 28\n",
"INFO:__main__:Freezing layer 29\n",
"INFO:__main__:Freezing layer 30\n",
"INFO:__main__:Freezing layer 31\n",
"INFO:__main__:Freezing layer 32\n",
"INFO:__main__:Freezing layer 33\n",
"INFO:__main__:Freezing layer 34\n",
"INFO:__main__:Freezing layer 35\n",
"INFO:__main__:Freezing layer 36\n",
"INFO:__main__:Freezing layer 37\n",
"INFO:__main__:Freezing layer 38\n",
"INFO:__main__:Freezing layer 39\n",
"INFO:__main__:Freezing layer 40\n",
"INFO:__main__:Ignoring layer 41\n",
"INFO:__main__:Ignoring layer 42\n",
"INFO:__main__:Ignoring layer 43\n",
"INFO:__main__:Ignoring layer 44\n",
"INFO:__main__:Ignoring layer 45\n",
"INFO:__main__:Ignoring layer 46\n",
"INFO:__main__:Ignoring layer 47\n",
"INFO:__main__:Ignoring layer 48\n",
"INFO:__main__:Ignoring layer 49\n",
"INFO:__main__:Ignoring layer 50\n",
"INFO:__main__:Ignoring layer 51\n",
"INFO:__main__:Ignoring layer 52\n",
"INFO:__main__:Ignoring layer 53\n",
"INFO:__main__:Ignoring layer 54\n",
"INFO:__main__:Ignoring layer 55\n",
"INFO:__main__:Ignoring layer 56\n",
"INFO:__main__:Ignoring layer 57\n",
"INFO:__main__:Ignoring layer 58\n",
"INFO:__main__:Ignoring layer 59\n",
"INFO:__main__:Ignoring layer 60\n",
"INFO:__main__:Ignoring layer 61\n",
"INFO:__main__:Ignoring layer 62\n",
"INFO:__main__:Ignoring layer 63\n",
"INFO:__main__:Ignoring layer 64\n",
"INFO:__main__:Ignoring layer 65\n",
"INFO:__main__:Ignoring layer 66\n",
"INFO:__main__:Ignoring layer 67\n",
"INFO:__main__:Ignoring layer 68\n",
"INFO:__main__:Ignoring layer 69\n",
"INFO:__main__:Ignoring layer 70\n",
"INFO:__main__:Ignoring layer 71\n",
"INFO:__main__:Ignoring layer 72\n",
"INFO:__main__:Ignoring layer 73\n",
"INFO:__main__:Ignoring layer 74\n",
"INFO:__main__:Ignoring layer 75\n",
"INFO:__main__:Ignoring layer 76\n",
"INFO:__main__:Ignoring layer 77\n",
"INFO:__main__:Ignoring layer 78\n",
"INFO:__main__:Ignoring layer 79\n",
"INFO:__main__:Ignoring layer 80\n",
"INFO:__main__:Ignoring layer 81\n",
"INFO:__main__:Ignoring layer 82\n",
"INFO:__main__:Ignoring layer 83\n",
"INFO:__main__:Ignoring layer 84\n",
"INFO:__main__:Ignoring layer 85\n",
"INFO:__main__:Ignoring layer 86\n",
"INFO:__main__:Ignoring layer 87\n",
"INFO:__main__:Ignoring layer 88\n",
"INFO:__main__:Ignoring layer 89\n",
"INFO:__main__:Ignoring layer 90\n",
"INFO:__main__:Ignoring layer 91\n",
"INFO:__main__:Ignoring layer 92\n",
"INFO:__main__:Ignoring layer 93\n",
"INFO:__main__:Ignoring layer 94\n",
"INFO:__main__:Ignoring layer 95\n",
"INFO:__main__:Ignoring layer 96\n",
"INFO:__main__:Ignoring layer 97\n",
"INFO:__main__:Ignoring layer 98\n",
"INFO:__main__:Ignoring layer 99\n",
"INFO:__main__:Ignoring layer 100\n",
"INFO:__main__:Ignoring layer 101\n",
"INFO:__main__:Ignoring layer 102\n",
"INFO:__main__:Ignoring layer 103\n",
"INFO:__main__:Ignoring layer 104\n",
"INFO:__main__:Ignoring layer 105\n",
"INFO:__main__:Ignoring layer 106\n",
"INFO:__main__:Ignoring layer 107\n",
"INFO:__main__:Ignoring layer 108\n",
"INFO:__main__:Ignoring layer 109\n",
"INFO:__main__:Ignoring layer 110\n",
"INFO:__main__:Ignoring layer 111\n",
"INFO:__main__:Ignoring layer 112\n",
"INFO:__main__:Ignoring layer 113\n",
"INFO:__main__:Ignoring layer 114\n",
"INFO:__main__:Ignoring layer 115\n",
"INFO:__main__:Ignoring layer 116\n",
"INFO:__main__:Ignoring layer 117\n",
"INFO:__main__:Ignoring layer 118\n",
"INFO:__main__:Ignoring layer 119\n",
"INFO:__main__:Ignoring layer 120\n",
"INFO:__main__:Ignoring layer 121\n",
"INFO:__main__:Ignoring layer 122\n",
"INFO:__main__:Ignoring layer 123\n",
"INFO:__main__:Ignoring layer 124\n",
"INFO:__main__:Ignoring layer 125\n",
"INFO:__main__:Ignoring layer 126\n",
"INFO:__main__:Ignoring layer 127\n",
"INFO:__main__:Ignoring layer 128\n",
"INFO:__main__:Ignoring layer 129\n",
"INFO:__main__:Ignoring layer 130\n",
"INFO:__main__:Ignoring layer 131\n",
"INFO:__main__:Ignoring layer 132\n",
"INFO:__main__:Ignoring layer 133\n",
"INFO:__main__:Ignoring layer 134\n",
"INFO:__main__:Ignoring layer 135\n",
"INFO:__main__:Ignoring layer 136\n",
"INFO:__main__:Ignoring layer 137\n",
"INFO:__main__:Ignoring layer 138\n",
"INFO:__main__:Ignoring layer 139\n",
"INFO:__main__:Ignoring layer 140\n",
"INFO:__main__:Ignoring layer 141\n",
"INFO:__main__:Ignoring layer 142\n",
"INFO:__main__:Ignoring layer 143\n",
"INFO:__main__:Ignoring layer 144\n",
"INFO:__main__:Ignoring layer 145\n",
"INFO:__main__:Ignoring layer 146\n",
"INFO:__main__:Ignoring layer 147\n",
"INFO:__main__:Ignoring layer 148\n",
"INFO:__main__:Ignoring layer 149\n",
"INFO:__main__:Ignoring layer 150\n",
"INFO:__main__:Ignoring layer 151\n",
"INFO:__main__:Ignoring layer 152\n",
"INFO:__main__:Ignoring layer 153\n",
"INFO:__main__:Ignoring layer 154\n",
"INFO:__main__:Ignoring layer 155\n",
2023-02-12 23:36:28 +01:00
"[ERROR|tokenization_utils_base.py:1042] 2023-02-12 22:00:30,162 >> Using pad_token, but it is not set yet.\n",
2023-02-12 20:03:40 +01:00
"INFO:__main__:Set PAD token to EOS: <|endoftext|>\n",
2023-02-12 23:36:28 +01:00
"Running tokenizer on dataset: 0% 0/16 [00:00<?, ?ba/s]INFO:datasets.arrow_dataset:Caching processed dataset at /content/.cache_training/json/default-623c8a7b15a2e58a/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51/cache-c8be49ce7f75c3ae.arrow\n",
"Running tokenizer on dataset: 100% 16/16 [00:01<00:00, 11.14ba/s]\n",
"Running tokenizer on dataset: 0% 0/2 [00:00<?, ?ba/s]INFO:datasets.arrow_dataset:Caching processed dataset at /content/.cache_training/json/default-623c8a7b15a2e58a/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51/cache-01058c80c2124fd4.arrow\n",
"Running tokenizer on dataset: 100% 2/2 [00:00<00:00, 6.04ba/s]\n",
"Running tokenizer on dataset: 0% 0/2 [00:00<?, ?ba/s]INFO:datasets.arrow_dataset:Caching processed dataset at /content/.cache_training/json/default-623c8a7b15a2e58a/0.0.0/0f7e3662623656454fcd2b650f34e886a7db4b9104504885bd462096cc7a9f51/cache-53937ce94683c2e2.arrow\n",
"Running tokenizer on dataset: 100% 2/2 [00:00<00:00, 11.40ba/s]\n",
2023-02-12 22:17:41 +01:00
"INFO:__main__:Sample 10476 of the training set: {'label': 4, 'text': 'i do find new friends i m going to try extra hard to make them stay and if i decide that i don t want to feel hurt again and just ride out the last year of school on my own i m going to have to try extra hard not to care what people think of me being a loner', 'input_ids': [72, 466, 1064, 649, 2460, 1312, 285, 1016, 284, 1949, 3131, 1327, 284, 787, 606, 2652, 290, 611, 1312, 5409, 326, 1312, 836, 256, 765, 284, 1254, 5938, 757, 290, 655, 6594, 503, 262, 938, 614, 286, 1524, 319, 616, 898, 1312, 285, 1016, 284, 423, 284, 1949, 3131, 1327, 407, 284, 1337, 644, 661, 892, 286, 502, 852, 257, 300, 14491, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}.\n",
"INFO:__main__:Sample 1824 of the training set: {'label': 2, 'text': 'i asked them to join me in creating a world where all year old girls could grow up feeling hopeful and powerful', 'input_ids': [72, 1965, 606, 284, 4654, 502, 287, 4441, 257, 995, 810, 477, 614, 1468, 4813, 714, 1663, 510, 4203, 17836, 290, 3665, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}.\n",
"INFO:__main__:Sample 409 of the training set: {'label': 3, 'text': 'i feel when you are a caring person you attract other caring people into your life', 'input_ids': [72, 1254, 618, 345, 389, 257, 18088, 1048, 345, 4729, 584, 18088, 661, 656, 534, 1204, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256, 50256], 'attention_mask': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}.\n",
2023-02-12 23:36:28 +01:00
"Downloading builder script: 100% 4.20k/4.20k [00:00<00:00, 4.82MB/s]\n",
"[INFO|trainer.py:710] 2023-02-12 22:00:38,984 >> The following columns in the training set don't have a corresponding argument in `GPT2ForSequenceClassificationCustom.forward` and have been ignored: text. If text are not expected by `GPT2ForSequenceClassificationCustom.forward`, you can safely ignore this message.\n",
2023-02-12 20:03:40 +01:00
"/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:306: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n",
" warnings.warn(\n",
2023-02-12 23:36:28 +01:00
"[INFO|trainer.py:1650] 2023-02-12 22:00:38,993 >> ***** Running training *****\n",
"[INFO|trainer.py:1651] 2023-02-12 22:00:38,993 >> Num examples = 16000\n",
"[INFO|trainer.py:1652] 2023-02-12 22:00:38,993 >> Num Epochs = 5\n",
"[INFO|trainer.py:1653] 2023-02-12 22:00:38,993 >> Instantaneous batch size per device = 24\n",
"[INFO|trainer.py:1654] 2023-02-12 22:00:38,993 >> Total train batch size (w. parallel, distributed & accumulation) = 24\n",
"[INFO|trainer.py:1655] 2023-02-12 22:00:38,993 >> Gradient Accumulation steps = 1\n",
"[INFO|trainer.py:1656] 2023-02-12 22:00:38,993 >> Total optimization steps = 3335\n",
"[INFO|trainer.py:1657] 2023-02-12 22:00:38,994 >> Number of trainable parameters = 68517888\n",
2023-02-12 22:17:41 +01:00
"{'loss': 1.0593, 'learning_rate': 1.7001499250374815e-05, 'epoch': 0.75}\n",
2023-02-12 23:36:28 +01:00
" 15% 500/3335 [04:16<24:04, 1.96it/s][INFO|trainer.py:2709] 2023-02-12 22:04:55,709 >> Saving model checkpoint to out/imdb-5k/gpt2/checkpoint-500\n",
"[INFO|configuration_utils.py:453] 2023-02-12 22:04:55,710 >> Configuration saved in out/imdb-5k/gpt2/checkpoint-500/config.json\n",
"[INFO|modeling_utils.py:1704] 2023-02-12 22:04:57,444 >> Model weights saved in out/imdb-5k/gpt2/checkpoint-500/pytorch_model.bin\n",
"[INFO|tokenization_utils_base.py:2160] 2023-02-12 22:04:57,444 >> tokenizer config file saved in out/imdb-5k/gpt2/checkpoint-500/tokenizer_config.json\n",
"[INFO|tokenization_utils_base.py:2167] 2023-02-12 22:04:57,444 >> Special tokens file saved in out/imdb-5k/gpt2/checkpoint-500/special_tokens_map.json\n",
2023-02-12 22:17:41 +01:00
"{'loss': 0.3829, 'learning_rate': 1.4002998500749626e-05, 'epoch': 1.5}\n",
2023-02-12 23:36:28 +01:00
" 30% 1000/3335 [08:36<19:51, 1.96it/s][INFO|trainer.py:2709] 2023-02-12 22:09:15,813 >> Saving model checkpoint to out/imdb-5k/gpt2/checkpoint-1000\n",
"[INFO|configuration_utils.py:453] 2023-02-12 22:09:15,814 >> Configuration saved in out/imdb-5k/gpt2/checkpoint-1000/config.json\n",
"[INFO|modeling_utils.py:1704] 2023-02-12 22:09:17,628 >> Model weights saved in out/imdb-5k/gpt2/checkpoint-1000/pytorch_model.bin\n",
"[INFO|tokenization_utils_base.py:2160] 2023-02-12 22:09:17,629 >> tokenizer config file saved in out/imdb-5k/gpt2/checkpoint-1000/tokenizer_config.json\n",
"[INFO|tokenization_utils_base.py:2167] 2023-02-12 22:09:17,630 >> Special tokens file saved in out/imdb-5k/gpt2/checkpoint-1000/special_tokens_map.json\n",
2023-02-12 22:17:41 +01:00
"{'loss': 0.256, 'learning_rate': 1.100449775112444e-05, 'epoch': 2.25}\n",
2023-02-12 23:36:28 +01:00
" 45% 1500/3335 [12:56<15:43, 1.95it/s][INFO|trainer.py:2709] 2023-02-12 22:13:36,008 >> Saving model checkpoint to out/imdb-5k/gpt2/checkpoint-1500\n",
"[INFO|configuration_utils.py:453] 2023-02-12 22:13:36,009 >> Configuration saved in out/imdb-5k/gpt2/checkpoint-1500/config.json\n",
"[INFO|modeling_utils.py:1704] 2023-02-12 22:13:37,703 >> Model weights saved in out/imdb-5k/gpt2/checkpoint-1500/pytorch_model.bin\n",
"[INFO|tokenization_utils_base.py:2160] 2023-02-12 22:13:37,704 >> tokenizer config file saved in out/imdb-5k/gpt2/checkpoint-1500/tokenizer_config.json\n",
"[INFO|tokenization_utils_base.py:2167] 2023-02-12 22:13:37,704 >> Special tokens file saved in out/imdb-5k/gpt2/checkpoint-1500/special_tokens_map.json\n",
2023-02-12 22:17:41 +01:00
"{'loss': 0.2101, 'learning_rate': 8.005997001499251e-06, 'epoch': 3.0}\n",
2023-02-12 23:36:28 +01:00
" 60% 2000/3335 [17:17<11:23, 1.95it/s][INFO|trainer.py:2709] 2023-02-12 22:17:56,308 >> Saving model checkpoint to out/imdb-5k/gpt2/checkpoint-2000\n",
"[INFO|configuration_utils.py:453] 2023-02-12 22:17:56,309 >> Configuration saved in out/imdb-5k/gpt2/checkpoint-2000/config.json\n",
"[INFO|modeling_utils.py:1704] 2023-02-12 22:17:58,005 >> Model weights saved in out/imdb-5k/gpt2/checkpoint-2000/pytorch_model.bin\n",
"[INFO|tokenization_utils_base.py:2160] 2023-02-12 22:17:58,006 >> tokenizer config file saved in out/imdb-5k/gpt2/checkpoint-2000/tokenizer_config.json\n",
"[INFO|tokenization_utils_base.py:2167] 2023-02-12 22:17:58,006 >> Special tokens file saved in out/imdb-5k/gpt2/checkpoint-2000/special_tokens_map.json\n",
2023-02-12 22:17:41 +01:00
"{'loss': 0.17, 'learning_rate': 5.0074962518740634e-06, 'epoch': 3.75}\n",
2023-02-12 23:36:28 +01:00
" 75% 2500/3335 [21:37<07:05, 1.96it/s][INFO|trainer.py:2709] 2023-02-12 22:22:16,686 >> Saving model checkpoint to out/imdb-5k/gpt2/checkpoint-2500\n",
"[INFO|configuration_utils.py:453] 2023-02-12 22:22:16,687 >> Configuration saved in out/imdb-5k/gpt2/checkpoint-2500/config.json\n",
"[INFO|modeling_utils.py:1704] 2023-02-12 22:22:18,356 >> Model weights saved in out/imdb-5k/gpt2/checkpoint-2500/pytorch_model.bin\n",
"[INFO|tokenization_utils_base.py:2160] 2023-02-12 22:22:18,357 >> tokenizer config file saved in out/imdb-5k/gpt2/checkpoint-2500/tokenizer_config.json\n",
"[INFO|tokenization_utils_base.py:2167] 2023-02-12 22:22:18,357 >> Special tokens file saved in out/imdb-5k/gpt2/checkpoint-2500/special_tokens_map.json\n",
2023-02-12 22:17:41 +01:00
"{'loss': 0.1569, 'learning_rate': 2.008995502248876e-06, 'epoch': 4.5}\n",
2023-02-12 23:36:28 +01:00
" 90% 3000/3335 [25:57<02:51, 1.95it/s][INFO|trainer.py:2709] 2023-02-12 22:26:36,938 >> Saving model checkpoint to out/imdb-5k/gpt2/checkpoint-3000\n",
"[INFO|configuration_utils.py:453] 2023-02-12 22:26:36,939 >> Configuration saved in out/imdb-5k/gpt2/checkpoint-3000/config.json\n",
"[INFO|modeling_utils.py:1704] 2023-02-12 22:26:38,608 >> Model weights saved in out/imdb-5k/gpt2/checkpoint-3000/pytorch_model.bin\n",
"[INFO|tokenization_utils_base.py:2160] 2023-02-12 22:26:38,608 >> tokenizer config file saved in out/imdb-5k/gpt2/checkpoint-3000/tokenizer_config.json\n",
"[INFO|tokenization_utils_base.py:2167] 2023-02-12 22:26:38,608 >> Special tokens file saved in out/imdb-5k/gpt2/checkpoint-3000/special_tokens_map.json\n",
"100% 3335/3335 [28:53<00:00, 2.15it/s][INFO|trainer.py:1901] 2023-02-12 22:29:32,259 >> \n",
2023-02-12 20:03:40 +01:00
"\n",
"Training completed. Do not forget to share your model on huggingface.co/models =)\n",
"\n",
"\n",
2023-02-12 23:36:28 +01:00
"{'train_runtime': 1733.281, 'train_samples_per_second': 46.155, 'train_steps_per_second': 1.924, 'train_loss': 0.35007504373118614, 'epoch': 5.0}\n",
"100% 3335/3335 [28:53<00:00, 1.92it/s]\n",
"[INFO|trainer.py:2709] 2023-02-12 22:29:32,277 >> Saving model checkpoint to out/imdb-5k/gpt2\n",
"[INFO|configuration_utils.py:453] 2023-02-12 22:29:32,278 >> Configuration saved in out/imdb-5k/gpt2/config.json\n",
"[INFO|modeling_utils.py:1704] 2023-02-12 22:29:33,934 >> Model weights saved in out/imdb-5k/gpt2/pytorch_model.bin\n",
"[INFO|tokenization_utils_base.py:2160] 2023-02-12 22:29:33,934 >> tokenizer config file saved in out/imdb-5k/gpt2/tokenizer_config.json\n",
"[INFO|tokenization_utils_base.py:2167] 2023-02-12 22:29:33,934 >> Special tokens file saved in out/imdb-5k/gpt2/special_tokens_map.json\n",
2023-02-12 20:03:40 +01:00
"***** train metrics *****\n",
" epoch = 5.0\n",
2023-02-12 22:17:41 +01:00
" train_loss = 0.3501\n",
2023-02-12 23:36:28 +01:00
" train_runtime = 0:28:53.28\n",
2023-02-12 22:17:41 +01:00
" train_samples = 16000\n",
2023-02-12 23:36:28 +01:00
" train_samples_per_second = 46.155\n",
" train_steps_per_second = 1.924\n",
2023-02-12 20:03:40 +01:00
"INFO:__main__:*** Evaluate ***\n",
2023-02-12 23:36:28 +01:00
"[INFO|trainer.py:710] 2023-02-12 22:29:34,047 >> The following columns in the evaluation set don't have a corresponding argument in `GPT2ForSequenceClassificationCustom.forward` and have been ignored: text. If text are not expected by `GPT2ForSequenceClassificationCustom.forward`, you can safely ignore this message.\n",
"[INFO|trainer.py:2964] 2023-02-12 22:29:34,108 >> ***** Running Evaluation *****\n",
"[INFO|trainer.py:2966] 2023-02-12 22:29:34,108 >> Num examples = 2000\n",
"[INFO|trainer.py:2969] 2023-02-12 22:29:34,108 >> Batch size = 24\n",
"100% 84/84 [00:17<00:00, 4.83it/s]\n",
2023-02-12 20:03:40 +01:00
"***** eval metrics *****\n",
" epoch = 5.0\n",
2023-02-12 23:36:28 +01:00
" eval_accuracy = 0.93\n",
" eval_loss = 0.1531\n",
" eval_runtime = 0:00:17.72\n",
" eval_samples = 2000\n",
" eval_samples_per_second = 112.855\n",
" eval_steps_per_second = 4.74\n",
2023-02-12 20:03:40 +01:00
"INFO:__main__:*** Predict ***\n",
2023-02-12 23:36:28 +01:00
"[INFO|trainer.py:710] 2023-02-12 22:29:51,834 >> The following columns in the test set don't have a corresponding argument in `GPT2ForSequenceClassificationCustom.forward` and have been ignored: text. If text are not expected by `GPT2ForSequenceClassificationCustom.forward`, you can safely ignore this message.\n",
"[INFO|trainer.py:2964] 2023-02-12 22:29:51,836 >> ***** Running Prediction *****\n",
"[INFO|trainer.py:2966] 2023-02-12 22:29:51,836 >> Num examples = 2000\n",
"[INFO|trainer.py:2969] 2023-02-12 22:29:51,836 >> Batch size = 24\n",
"100% 84/84 [00:17<00:00, 4.81it/s]\n",
2023-02-12 20:03:40 +01:00
"INFO:__main__:***** Predict results None *****\n",
2023-02-12 23:36:28 +01:00
"[INFO|modelcard.py:449] 2023-02-12 22:30:09,657 >> Dropping the following result as it does not have all the necessary fields:\n",
"{'task': {'name': 'Text Classification', 'type': 'text-classification'}, 'metrics': [{'name': 'Accuracy', 'type': 'accuracy', 'value': 0.9300000071525574}]}\n"
2023-02-10 22:26:16 +01:00
]
2023-02-12 20:03:40 +01:00
}
]
2023-02-10 22:26:16 +01:00
}
]
}
