{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "D9DX5vJ8ImQi" }, "source": [ "## Model Overview\n", "This project uses a fine-tuned version of GPT-2 to analyze and generate responses for technical support tickets. The model was trained using GitHub issue data that resembles real-world IT support requests, including software bugs, access problems, network issues, and technical troubleshooting scenarios.\n", "\n", "\n", "The model processes incoming ticket text and learns patterns between issue descriptions and their associated labels or categories. During training, the model was fine-tuned to recognize technical language commonly found in support environments and generate structured outputs related to ticket analysis.\n", "\n", "The fine-tuned model supports multiple ticketing-related tasks, including:\n", "\n", "* Ticket categorization\n", "* Technical issue understanding\n", "* Routing recommendations\n", "* Troubleshooting suggestion generation\n", "\n", "\n", "The primary goal of the model is to automate portions of the IT help desk workflow by assisting with ticket triage and employee self-service support. Instead of relying entirely on manual review, the model can analyze natural language ticket submissions and generate contextual responses based on learned technical patterns.\n", "\n", "The system uses:\n", "\n", "\n", "* GPT-2 as the base transformer model\n", "* Hugging Face Transformers for training and inference\n", "* Google Colab for model development and experimentation\n", "\n", "The model was fine-tuned rather than trained from scratch, allowing the project to leverage GPT-2s pretrained language understanding while adapting it to technical support and ticketing workflows." ] }, { "cell_type": "markdown", "metadata": { "id": "FZWf5LkRNv5O" }, "source": [ "Install Libraries" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "rzxyN8doNAsE", "outputId": "ee9f549c-dd13-4630-c1ab-be4491e04ebc" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Requirement already satisfied: transformers in 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markdown-it-py>=2.2.0 in /usr/local/lib/python3.12/dist-packages (from rich>=12.3.0->typer->huggingface_hub) (4.0.0)\n", "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.12/dist-packages (from rich>=12.3.0->typer->huggingface_hub) (2.20.0)\n", "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.12/dist-packages (from sympy>=1.13.3->torch>=2.0.0->accelerate) (1.3.0)\n", "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.12/dist-packages (from jinja2->torch>=2.0.0->accelerate) (3.0.3)\n", "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.12/dist-packages (from markdown-it-py>=2.2.0->rich>=12.3.0->typer->huggingface_hub) (0.1.2)\n" ] } ], "source": [ "!pip install transformers datasets accelerate huggingface_hub" ] }, { "cell_type": "markdown", "metadata": { "id": "Vprj7h1MOCA8" }, "source": [ "Login to Hugging Face" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 141, "referenced_widgets": [ "3999aa19693149d7b40b122808e6f19b", "dad3979814bf4038b4d61043d56b93b1", "61971fd5dd624b1e9abd993182427425", "26d2994bd1f342999ea919db66cc4e6e", "de9e58b3117f4d179a55d7ae089b8ffc", "5b914a174a5f4404a1715e80d98603f3", "02af896559e5412f88a6c7337a4d87d6", "3dfa472f6451494ca85b607381f5f48f", "f30c5c323283417aa1be8796dcfd300a", "92e97e0938b241889371290e7efd7fcd", "205172b565a7448291a7aa857235a9c9", "25a5f0de4d034c8c980544dba0ed46e1", "95dfd952375e4af5b96d7f44b8bfff9f", "1089cba20bc541baaed0c3cd09c5164e", "328f65d466a1432b9494628dec6106de", "8b9a8163db2842d397161d111867bda9", "2d8660d4c0904bd38a9fafb776db5664", "4da15fcf625647519b9026cff1264200", "ac2556d4ca3a441e8582da512b2db46a", "12f9c2daae0244d6a132fe54e4a27b50" ] }, "id": "yPiVoZsUODoz", "outputId": "45afe692-ae0e-48cc-d497-d9541310afd8" }, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:93: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "VBox(children=(HTML(value='
\\n import pytorch_lightning as pl\\n File \"/anaconda3/envs/torch-xla-nightly/lib/python3.6/site-packages/pytorch_lightning/__init__.py\", line 65, in \\n from pytorch_lightning.trainer import Trainer\\n File \"/anaconda3/envs/torch-xla-nightly/lib/python3.6/site-packages/pytorch_lightning/trainer/__init__.py\", line 18, in \\n from pytorch_lightning.trainer.trainer import Trainer\\n File \"/anaconda3/envs/torch-xla-nightly/lib/python3.6/site-packages/pytorch_lightning/trainer/trainer.py\", line 30, in \\n from pytorch_lightning.loggers import LightningLoggerBase\\n File \"/anaconda3/envs/torch-xla-nightly/lib/python3.6/site-packages/pytorch_lightning/loggers/__init__.py\", line 18, in \\n from pytorch_lightning.loggers.tensorboard import TensorBoardLogger\\n File \"/anaconda3/envs/torch-xla-nightly/lib/python3.6/site-packages/pytorch_lightning/loggers/tensorboard.py\", line 24, in \\n from torch.utils.tensorboard import SummaryWriter\\n File \"/anaconda3/envs/torch-xla-nightly/lib/python3.6/site-packages/torch/utils/tensorboard/__init__.py\", line 8, in \\n from .writer import FileWriter, SummaryWriter # noqa F401\\n File \"/anaconda3/envs/torch-xla-nightly/lib/python3.6/site-packages/torch/utils/tensorboard/writer.py\", line 9, in \\n from tensorboard.compat.proto.event_pb2 import SessionLog\\n File \"/anaconda3/envs/torch-xla-nightly/lib/python3.6/site-packages/tensorboard/compat/proto/event_pb2.py\", line 17, in \\n from tensorboard.compat.proto import summary_pb2 as tensorboard_dot_compat_dot_proto_dot_summary__pb2\\n File \"/anaconda3/envs/torch-xla-nightly/lib/python3.6/site-packages/tensorboard/compat/proto/summary_pb2.py\", line 17, in \\n from tensorboard.compat.proto import tensor_pb2 as tensorboard_dot_compat_dot_proto_dot_tensor__pb2\\n File \"/anaconda3/envs/torch-xla-nightly/lib/python3.6/site-packages/tensorboard/compat/proto/tensor_pb2.py\", line 16, in \\n from tensorboard.compat.proto import resource_handle_pb2 as tensorboard_dot_compat_dot_proto_dot_resource__handle__pb2\\n File \"/anaconda3/envs/torch-xla-nightly/lib/python3.6/site-packages/tensorboard/compat/proto/resource_handle_pb2.py\", line 150, in \\n \\'__module__\\' : \\'tensorboard.compat.proto.resource_handle_pb2\\'\\nSystemError: google/protobuf/pyext/descriptor.cc:354: bad argument to internal function\\n\\nRepro\\ncreate VM:\\ngcloud compute instances create zcain-vm\\n--zone=us-central1-a\\n--machine-type=e2-highmem-16\\n--image-family=torch-xla\\n--image-project=ml-images\\n--boot-disk-size=300GB\\n--scopes=https://www.googleapis.com/auth/cloud-platform\\ncreate TPU:\\ngcloud compute tpus create zcain-tpu\\n--zone=us-central1-a\\n--network=default\\n--version=pytorch-nightly\\n--accelerator-type=v3-8\\nSSH into VM:\\n(VM) git clone https://github.com/PyTorchLightning/pytorch-lightning.git\\n(VM) conda activate torch-xla-nightly\\n(VM) export TPU_IP_ADDRESS=\\n(VM) export XRT_TPU_CONFIG=\"tpu_worker;0;$TPU_IP_ADDRESS:8470\"\\n(VM) cd pytorch-lightning\\n(VM) pip install .\\n(VM) pip install -r requirements/test.txt\\n(VM) cd pl_examples/domain_templates/\\n(VM) vim computer_vision_fine_tuning.py\\n(VM) \\n(VM) python computer_vision_fine_tuning.py\\n\\nNote: I get the same crash when using pip install pytorch-lightning instead of installing from source\\nIf I use torch-xla-1.7, training works fine.\\nEnvironment\\n* CUDA:\\n - GPU:\\n - available: False\\n - version: None\\n* Packages:\\n - numpy: 1.19.2\\n - pyTorch_debug: False\\n - pyTorch_version: 1.9.0a0+8c185e6\\n - pytorch-lightning: 1.1.8\\n - tqdm: 4.56.0\\n* System:\\n - OS: Linux\\n - architecture:\\n - 64bit\\n - \\n - processor: \\n - python: 3.6.10\\n - version: #1 SMP Debian 4.9.246-2 (2020-12-17)\\n\\nAdditional context\\nThe crash seems related to Tensorboard. I wonder if the issue is related to Tensorboard versioning, however pip install pytorch-lightning doesn\\'t seem to install any new tensorboard version compared to what is already installed in this public torch-xla conda env:\\nInstalling collected packages: PyYAML, fsspec, pytorch-lightning, yarl\\n Attempting uninstall: PyYAML\\n Found existing installation: PyYAML 5.4.1\\n Uninstalling PyYAML-5.4.1:\\n Successfully uninstalled PyYAML-5.4.1\\n Attempting uninstall: fsspec\\n Found existing installation: fsspec 0.7.4\\n Uninstalling fsspec-0.7.4:\\n Successfully uninstalled fsspec-0.7.4\\n Attempting uninstall: yarl\\n Found existing installation: yarl 1.6.3\\n Uninstalling yarl-1.6.3:\\n Successfully uninstalled yarl-1.6.3\\nSuccessfully installed PyYAML-5.3.1 fsspec-0.8.5 pytorch-lightning-1.1.8 yarl-1.5.1\\n\\nLet me know if you can think of anything else I should try'}\n" ] } ], "source": [ "# VIEW SAMPLE DATA\n", "# ============================================\n", "\n", "print(dataset[\"train\"][0])" ] }, { "cell_type": "markdown", "metadata": { "id": "1WjdhccLPmQX" }, "source": [ "Load GPT-2" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 394, "referenced_widgets": [ "a2fdb20de3cd40b383e91619294e59e0", "a55fd63347254a20ac084c4871da1a50", "ee1fdecefdb541dda144abfc5fe033c6", "bb9e849a68054892b3fc99ae3d1c943d", "58248030a8fb4d3690ada4b4555218ab", "108049f9b71449caa7fdffd5ae044da0", "2d10b362633e4eff8e781de08d1be9bc", "e2c02878d08146c18d233470c526b4d6", "e10b739ae5ab456b8fd364f6781f56c1", "a98ee9d9d5a6419ab6a908a6ae5fb87a", "fd000f57e71040f0ab4f113d193e1e56", "06ffc16eb6604ee7ad3073221a6cf657", "0a7221744c804c0b87eaba90cfb2ac50", "294c3a630dd64922bfcacb09a5a4b563", "5a212441dd2244ec838531cf751894b9", "15cccfb089eb42c7ac36c8b07252591e", "66f785b2372d4bcf8989ed088b720ef2", "8f953a7047914d398367e9447cf3acda", "746d104bc65745b89430a2ac5b240fa5", "a02099b8cd1c4a49b4be251c99524323", "d3b956d9376c4c7c8828eff2014f5d7e", "6da4587379e546fcbe3b8ceff49f8349", "558ad8688ccb47fcaae90c4d7271ad7a", "69c50b043b734bf5bdea59a2e19da424", "3a5de76f129048cab93d15833f97c717", "746481134de3499aae6c469b97443d18", "21a84ea20f104bca898669ae0aec203a", "bd8f836e53ae4842ac9cd64d92649494", "c97b21a44f6247c8a06c32de410ea757", "e5e1b221f0034c99879499ff5f55b6ba", "35946632ec514c10a36ae1284c4bea0b", "2f1b4d6d7c6b4f14887fe8c2d3311836", "ab5baf79111a4e6499f4676256fe9b5d", "fe9d311da47a48f49f6de6d7cd892235", "b01d3bf4ab914810b8387cfb35cfa3df", "7471c11250c2413d98225f75610fa114", "44cf4d52488c4d06ae663a8d2823704e", "0df7f459a563430ea12c2f88f0a6333b", "fc78d1f8ede94854ab03119166ffb70f", "f751c3ca6ac7468ca561391cb1bdd9bb", "9f9890af770e446ebc8bf733f89b23b0", "76c40db95fc94044b7b1dd4415ae42da", "5ed194376b604066ab0e7612cff80f03", "38ff991854a747aaaeadb161b8c3b42f", "e9cec730a2ab43f086c00c97c0517127", "2ecd6ddda62843309c68e34c1e6ced5c", "87186da367a34ab2a858236d56f97f5e", "8fcc8e9a903f4306bd9b5004e498ea50", "bc53647e62a842c9b1c2dce6262510b0", "7018964de5fb48fabceeda70de894a32", "bb8842517a1a45d183ff42c20f0cccea", "6a80a0ec8c2d4404a1721a158cd652fe", "a0eecbd0246d4c34811f1204a049a54e", "3d83c0fa68494dcab7e038d5d71965bd", "70959ad5b4f14bd785d9a7ac4959a490", "53b90b8ba4ef40b8be12ffb120c92f0f", "c2d15d0fbeac42d784f336358d89243e", "c62d3d8154864e319d43cc866ff8e9e4", "a89cd43bc0f6434ca684e4cf19d1b550", "edc5f84f59f74a9492b12e76b920f761", "01d3aa523c014471b806a816a4a443ab", "ba8681db9d22416e8d57549ec0cf5bdf", "9e16e446af074a3b884782562a45b274", "3e68385159ad422c838d7d9fc1ad4bc0", "6601f86ae25a42ad845b93f50ce4865c", "de9ba6471f094e0c964f8775f8e44e0a", "eb4dfbfd806d47eab086d665bd1f6acc", "e3a251f8d7364569b5d9677162f5048e", "d7c0007fbb284b6bb8fa98500a4b2adf", "069e683fda994c77aa3805434e061a63", "9caf4eae5e80442199a4f2e3a44770b4", "f601bbc536fd4349a818f4bdbf547889", "936b749694fa455085e3d42f6bbc41ed", "746224ba100b4ae494e320da3a5ff50d", "7d5818b2862c4be589ac9b2999e159eb", "298db9289a6848669021aba495943723", "bfb3e315dca44b6bb48a4c8f24cb5fad", "a4a319f72190440f95ad89c0ce2476a8", "32fdc2d892de4029895579602f82ed36", "8bd9f9afea6e4090bf401b5dac5d84d3", "c4f62bd2ca614da2999c205d30345195", "aa17a604da504b4fba13a3c558674892", "25bbd0ab644548868cfc9bb330100719", "6d8b309a0f3543239ccabc96681212c0", "d6a64c7d6a1c411e87a91c986ef65f0c", "92bc1eb7e5ae4eeda180ab4895da6eb5", "d572be861e72404a84181fab637985c6", "356c226511a64a0883fde7efee98fd6a" ] }, "id": "RKzrqHKWPnCV", "outputId": "00c0c528-af8c-40be-8d4f-4a52e2cd8158" }, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "tokenizer_config.json: 0%| | 0.00/26.0 [00:00" ], "text/html": [ "\n", "
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Copy a token from your Hugging Face\ntokens page and paste it below.
Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file.

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