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ticket-gpt2/Final_ticketing_llm_finished.ipynb
ModelHub XC d3d49d6789 初始化项目，由ModelHub XC社区提供模型 
Model: masonmidd/ticket-gpt2
Source: Original Platform
2026-06-11 04:14:17 +08:00

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{
"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": [
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]
}
],
"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",
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]
},
"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='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "3999aa19693149d7b40b122808e6f19b"
}
},
"metadata": {}
}
],
"source": [
"from huggingface_hub import notebook_login\n",
"\n",
"notebook_login()"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DVOOqJoaOMPM"
},
"source": [
"Disable WandB"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "08LgK_pIOM3I"
},
"outputs": [],
"source": [
"import os\n",
"\n",
"os.environ[\"WANDB_DISABLED\"] = \"true\"\n",
"os.environ[\"WANDB_SILENT\"] = \"true\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AkNxpYhdOPlG"
},
"source": [
"Imports"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "Gun5PaAjOR0M"
},
"outputs": [],
"source": [
"from datasets import load_dataset\n",
"from transformers import (\n",
" GPT2Tokenizer,\n",
" GPT2LMHeadModel,\n",
" Trainer,\n",
" TrainingArguments,\n",
" DataCollatorForLanguageModeling,\n",
" pipeline\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Dq8NkA_WOcYV"
},
"source": [
"Load Dataset"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 484,
"referenced_widgets": [
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]
},
"id": "3zcgMQEoPRbm",
"outputId": "b6dc0857-ca57-4bde-f35a-4b1224c5d116"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"README.md: 0%| | 0.00/590 [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "2b8b7b945d0946e88e200f44859d5b5f"
}
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"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"data/train-00000-of-00001-a49134f01c0947(…): 0%| | 0.00/1.18M [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "601c9e3cef2e43059e0c8aae9b4f14ff"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"data/test-00000-of-00001-7e5f329d406f079(…): 0%| | 0.00/567k [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "41c34d701a6545b598a796584832d317"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"data/valid-00000-of-00001-d31ba0fa45dfcc(…): 0%| | 0.00/580k [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "68c1120cc93946638faa044e8830aeb0"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Generating train split: 0%| | 0/1556 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "93fb8e9ac4884a4e904c7a0d02980ed6"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Generating test split: 0%| | 0/778 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "ec1f16054cc0434e97926d57a83bbde2"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Generating valid split: 0%| | 0/778 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "4c09161a7bdf4d0c87ce9f7b4bb6db52"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"DatasetDict({\n",
" train: Dataset({\n",
" features: ['title', 'labels', 'bodyText'],\n",
" num_rows: 1556\n",
" })\n",
" test: Dataset({\n",
" features: ['title', 'labels', 'bodyText'],\n",
" num_rows: 778\n",
" })\n",
" valid: Dataset({\n",
" features: ['title', 'labels', 'bodyText'],\n",
" num_rows: 778\n",
" })\n",
"})\n"
]
}
],
"source": [
"# LOAD DATASET\n",
"# ============================================\n",
"\n",
"from datasets import load_dataset\n",
"\n",
"dataset = load_dataset(\n",
" \"Rami/multi-label-class-github-issues-text-classification\"\n",
")\n",
"\n",
"print(dataset)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0nmM7EmCPZla"
},
"source": [
"Inspect Dataset"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "im6XjG80PVFu",
"outputId": "96179575-376c-4f1c-da6c-6a9f86b88321"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"{'title': 'TPUs: crash using torch-xla nightly', 'labels': ['bug', 'help wanted', \"won't fix\", 'accelerator: tpu'], 'bodyText': '🐛 Bug\\nIf I try to use torch-xla nightly with PyTorch Lightning, I see this crash:\\n(torch-xla-nightly) zcain@zcain-pl-verify:~/pytorch-lightning/pl_examples/domain_templates$ python computer_vision_fine_tuning.py \\nTraceback (most recent call last):\\n File \"computer_vision_fine_tuning.py\", line 55, in <module>\\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 <module>\\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 <module>\\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 <module>\\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 <module>\\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 <module>\\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 <module>\\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 <module>\\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 <module>\\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 <module>\\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 <module>\\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 <module>\\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=<TPU\\'s 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) <replace GPUs arg with tpu_cores=8,>\\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,
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},
"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<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "a2fdb20de3cd40b383e91619294e59e0"
}
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"metadata": {}
},
{
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"data": {
"text/plain": [
"vocab.json: 0.00B [00:00, ?B/s]"
],
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"model_id": "06ffc16eb6604ee7ad3073221a6cf657"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"merges.txt: 0.00B [00:00, ?B/s]"
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},
{
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"data": {
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],
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"version_major": 2,
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"model_id": "e9cec730a2ab43f086c00c97c0517127"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"model.safetensors: 0%| | 0.00/548M [00:00<?, ?B/s]"
],
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"version_major": 2,
"version_minor": 0,
"model_id": "53b90b8ba4ef40b8be12ffb120c92f0f"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Loading weights: 0%| | 0/148 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "eb4dfbfd806d47eab086d665bd1f6acc"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"GPT2LMHeadModel LOAD REPORT from: gpt2\n",
"Key | Status | | \n",
"---------------------+------------+--+-\n",
"h.{0...11}.attn.bias | UNEXPECTED | | \n",
"\n",
"Notes:\n",
"- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"generation_config.json: 0%| | 0.00/124 [00:00<?, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "a4a319f72190440f95ad89c0ce2476a8"
}
},
"metadata": {}
}
],
"source": [
"tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\n",
"tokenizer.pad_token = tokenizer.eos_token\n",
"\n",
"model = GPT2LMHeadModel.from_pretrained(\"gpt2\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WdmVkBtuPun8"
},
"source": [
"Format Dataset"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
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]
},
"id": "i8MhLKayPqqq",
"outputId": "416403e0-027c-433e-86ae-bdf39086bd73"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/1556 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "0fd69f8e297d4d78a16172c609ba9414"
}
},
"metadata": {}
}
],
"source": [
"def format_example(example):\n",
"\n",
" issue_text = (\n",
" str(example[\"title\"]) + \"\\n\" +\n",
" str(example[\"bodyText\"])\n",
" )\n",
"\n",
" labels = example[\"labels\"]\n",
"\n",
" label_text = \", \".join(labels)\n",
"\n",
" formatted = f\"\"\"\n",
"Ticket:\n",
"{issue_text}\n",
"\n",
"Category:\n",
"{label_text}\n",
"\"\"\"\n",
"\n",
" return {\"text\": formatted}\n",
"\n",
"\n",
"formatted_dataset = dataset[\"train\"].map(format_example)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "X4lZEKn_P6Nz"
},
"source": [
"Tokenization"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
"f70b82f34b784717adf329689d6a637e",
"7cba5e13f29241a9aff597239e83add6",
"e61756fa092944e4aa70212aac6cdf36",
"add55b3873ce41bea29e12c94d761e7e",
"1e39435f4c474b69a3171594fbd312d8",
"4a3b3002e5dc406abe5a94e4040faf56",
"15e1d7650cab4d14a5530495914a215b",
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"1b0014729cbc4ea3a848e07fa90befe2",
"2b6a142ca6f34e68a4ee8ea66bfa0226",
"9757417b8aea4521a170930f05a5ad10"
]
},
"id": "J-94DrVrP68B",
"outputId": "99c43cbf-7dcd-465f-abf8-316a4b43c427"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/1556 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "f70b82f34b784717adf329689d6a637e"
}
},
"metadata": {}
}
],
"source": [
"def tokenize_function(example):\n",
"\n",
" return tokenizer(\n",
" example[\"text\"],\n",
" truncation=True,\n",
" padding=\"max_length\",\n",
" max_length=128\n",
" )\n",
"\n",
"\n",
"tokenized_dataset = formatted_dataset.map(\n",
" tokenize_function,\n",
" batched=True\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "y0PRJqNYQEDo"
},
"source": [
"Add Labels"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
"24aea6a4106c4fabb39fcc2bf5073383",
"a1642eecf90740d884de221bd60b3c7a",
"8a282648f74c4fdca481809a43bb6b8f",
"fe7bea356cc741e0978c4ca1950a20c1",
"dfbdbf1800cb488c92812798094d00c4",
"81b3c6742a7b4cf48ff12b3f197c2217",
"db7136f26a924dcebd7f1061cc38d23e",
"1d2057b45d0147d6b515cc46d3c089cf",
"e70a633fe78d4850bd84c01bbbfd8f1a",
"4a4635309c9948a2a4ca6ea56aa47e71",
"bfbf4558404a4932aa9c45b6a7845c68"
]
},
"id": "7dkUMuorQAEs",
"outputId": "acb17519-2e39-4a8b-d34d-1a9f321c06a6"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/1556 [00:00<?, ? examples/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "24aea6a4106c4fabb39fcc2bf5073383"
}
},
"metadata": {}
}
],
"source": [
"tokenized_dataset = tokenized_dataset.remove_columns(\n",
" [col for col in tokenized_dataset.column_names\n",
" if col not in [\"input_ids\", \"attention_mask\"]]\n",
")\n",
"\n",
"tokenized_dataset = tokenized_dataset.map(\n",
" lambda x: {\"labels\": x[\"input_ids\"]}\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nyHLqYprQH6A"
},
"source": [
"Data Collator"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"id": "WgrreBIwQJU3"
},
"outputs": [],
"source": [
"data_collator = DataCollatorForLanguageModeling(\n",
" tokenizer=tokenizer,\n",
" mlm=False\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9F-MfZyEQalu"
},
"source": [
"Training Arguments"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"id": "TcF3JhE9QaKx"
},
"outputs": [],
"source": [
"training_args = TrainingArguments(\n",
" output_dir=\"./results\",\n",
" num_train_epochs=1,\n",
" per_device_train_batch_size=2,\n",
" save_steps=500,\n",
" logging_steps=100,\n",
" learning_rate=5e-5,\n",
" report_to=\"none\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9XS7-caZQr9Z"
},
"source": [
"Trainer"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"id": "VHtKCTuGQqG5"
},
"outputs": [],
"source": [
"trainer = Trainer(\n",
" model=model,\n",
" args=training_args,\n",
" train_dataset=tokenized_dataset,\n",
" data_collator=data_collator\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "v74mUrcyQ4vE"
},
"source": [
"Train"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 410,
"referenced_widgets": [
"0736bce70ecf47659c10c88f791a0daa",
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"7d951cbebae14c318004919c4102f65d",
"74f988066130435f8b201c63659b8cce",
"834dae7438204594b6fb1f9c6208f1da",
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"db9f62aa8c9b4068a8030a7cbcc34581",
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"7de6f4a710e148ca9956fb48f73f4158",
"f1226f8166c04abd87c6356542290d81",
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"f05a6c367a6347d6bcc53f772c31cb72"
]
},
"id": "GArpOxpYQ2NU",
"outputId": "87c1fea3-88be-4107-dc54-710572bfbe8b"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"`loss_type=None` was set in the config but it is unrecognized. Using the default loss: `ForCausalLMLoss`.\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<IPython.core.display.HTML object>"
],
"text/html": [
"\n",
" <div>\n",
" \n",
" <progress value='778' max='778' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
" [778/778 00:29, Epoch 1/1]\n",
" </div>\n",
" <table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>Step</th>\n",
" <th>Training Loss</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>100</td>\n",
" <td>3.349891</td>\n",
" </tr>\n",
" <tr>\n",
" <td>200</td>\n",
" <td>3.027806</td>\n",
" </tr>\n",
" <tr>\n",
" <td>300</td>\n",
" <td>2.988449</td>\n",
" </tr>\n",
" <tr>\n",
" <td>400</td>\n",
" <td>2.867971</td>\n",
" </tr>\n",
" <tr>\n",
" <td>500</td>\n",
" <td>2.843170</td>\n",
" </tr>\n",
" <tr>\n",
" <td>600</td>\n",
" <td>2.873825</td>\n",
" </tr>\n",
" <tr>\n",
" <td>700</td>\n",
" <td>2.838850</td>\n",
" </tr>\n",
" </tbody>\n",
"</table><p>"
]
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Writing model shards: 0%| | 0/1 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "0736bce70ecf47659c10c88f791a0daa"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Writing model shards: 0%| | 0/1 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "834dae7438204594b6fb1f9c6208f1da"
}
},
"metadata": {}
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"TrainOutput(global_step=778, training_loss=2.948500370918019, metrics={'train_runtime': 30.3879, 'train_samples_per_second': 51.205, 'train_steps_per_second': 25.602, 'total_flos': 101642600448000.0, 'train_loss': 2.948500370918019, 'epoch': 1.0})"
]
},
"metadata": {},
"execution_count": 14
}
],
"source": [
"trainer.train()"
]
},
{
"cell_type": "markdown",
"source": [
"Text Generation Pipeline"
],
"metadata": {
"id": "vvZgHjjgaZ1i"
}
},
{
"cell_type": "code",
"source": [
"# CREATE GPT-2 TEXT GENERATION PIPELINE\n",
"# ============================================\n",
"\n",
"from transformers import pipeline\n",
"\n",
"generator = pipeline(\n",
" \"text-generation\",\n",
" model=model,\n",
" tokenizer=tokenizer\n",
")\n",
"\n",
"print(\"Pipeline successfully created.\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "xJpwHKaYaa--",
"outputId": "34bdf16a-c10c-4d9e-ca51-2f54aee207d3"
},
"execution_count": 20,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Pipeline successfully created.\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "00483fcf"
},
"source": [
"# Model Evaluation\n",
"\n",
"This section evaluates the GPT-2 ticket classification model using\n",
"example IT support tickets and compares predicted categories against\n",
"expected labels."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"id": "f2e80570"
},
"outputs": [],
"source": [
"# IMPORT EVALUATION METRICS\n",
"# ============================================\n",
"\n",
"from sklearn.metrics import accuracy_score\n",
"from sklearn.metrics import classification_report\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"id": "4ebfa75e"
},
"outputs": [],
"source": [
"# EVALUATION DATASET\n",
"# ============================================\n",
"\n",
"evaluation_examples = [\n",
" {\n",
" \"ticket\": \"Cannot connect to VPN after password reset.\",\n",
" \"true_label\": \"Network Issue\"\n",
" },\n",
" {\n",
" \"ticket\": \"Office printer will not print documents.\",\n",
" \"true_label\": \"Hardware Issue\"\n",
" },\n",
" {\n",
" \"ticket\": \"Outlook crashes when opening attachments.\",\n",
" \"true_label\": \"Software Issue\"\n",
" },\n",
" {\n",
" \"ticket\": \"Unable to login to employee portal.\",\n",
" \"true_label\": \"Access Issue\"\n",
" }\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "2dba3916",
"outputId": "9e75a4c3-ddf8-4d76-dca1-8f6523520f03"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Both `max_new_tokens` (=256) and `max_length`(=40) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n",
"Both `max_new_tokens` (=256) and `max_length`(=40) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n",
"Both `max_new_tokens` (=256) and `max_length`(=40) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n",
"Both `max_new_tokens` (=256) and `max_length`(=40) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n"
]
}
],
"source": [
"# GENERATE MODEL PREDICTIONS\n",
"# ============================================\n",
"\n",
"predictions = []\n",
"true_labels = []\n",
"\n",
"for item in evaluation_examples:\n",
"\n",
" prompt = f\"\"\"\n",
"Ticket:\n",
"{item['ticket']}\n",
"\n",
"Category:\n",
"\"\"\"\n",
"\n",
" result = generator(\n",
" prompt,\n",
" max_length=40,\n",
" num_return_sequences=1\n",
" )\n",
"\n",
" generated_text = result[0][\"generated_text\"].lower()\n",
"\n",
" # SIMPLE CATEGORY CLEANING\n",
"\n",
" if \"network\" in generated_text or \"vpn\" in generated_text:\n",
" predicted_label = \"Network Issue\"\n",
"\n",
" elif \"printer\" in generated_text or \"hardware\" in generated_text:\n",
" predicted_label = \"Hardware Issue\"\n",
"\n",
" elif \"software\" in generated_text or \"outlook\" in generated_text:\n",
" predicted_label = \"Software Issue\"\n",
"\n",
" elif \"login\" in generated_text or \"access\" in generated_text:\n",
" predicted_label = \"Access Issue\"\n",
"\n",
" else:\n",
" predicted_label = \"Unknown\"\n",
"\n",
" predictions.append(predicted_label)\n",
"\n",
" true_labels.append(item[\"true_label\"])"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "33de4ef6",
"outputId": "31047aea-c97b-48da-f781-d52718d62681"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"====================================\n",
"MODEL ACCURACY\n",
"====================================\n",
"Accuracy Score: 1.00\n"
]
}
],
"source": [
"# COMPUTE ACCURACY\n",
"# ============================================\n",
"\n",
"accuracy = accuracy_score(\n",
" true_labels,\n",
" predictions\n",
")\n",
"\n",
"print(\"====================================\")\n",
"print(\"MODEL ACCURACY\")\n",
"print(\"====================================\")\n",
"\n",
"print(f\"Accuracy Score: {accuracy:.2f}\")"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "bc79e2a0",
"outputId": "2e16752a-b31b-497b-bb6b-6a7a331ff5da"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"====================================\n",
"CLASSIFICATION REPORT\n",
"====================================\n",
" precision recall f1-score support\n",
"\n",
" Access Issue 1.00 1.00 1.00 1\n",
"Hardware Issue 1.00 1.00 1.00 1\n",
" Network Issue 1.00 1.00 1.00 1\n",
"Software Issue 1.00 1.00 1.00 1\n",
"\n",
" accuracy 1.00 4\n",
" macro avg 1.00 1.00 1.00 4\n",
" weighted avg 1.00 1.00 1.00 4\n",
"\n"
]
}
],
"source": [
"# CLASSIFICATION REPORT\n",
"# ============================================\n",
"\n",
"print(\"====================================\")\n",
"print(\"CLASSIFICATION REPORT\")\n",
"print(\"====================================\")\n",
"\n",
"print(classification_report(\n",
" true_labels,\n",
" predictions\n",
"))"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1e64473a",
"outputId": "c5922ddf-3325-416f-db05-0e436cedb4b5"
},
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" Ticket Expected Label Predicted Label\n",
"0 Cannot connect to VPN after password reset. Network Issue Network Issue\n",
"1 Office printer will not print documents. Hardware Issue Hardware Issue\n",
"2 Outlook crashes when opening attachments. Software Issue Software Issue\n",
"3 Unable to login to employee portal. Access Issue Access Issue\n"
]
}
],
"source": [
"# DISPLAY PREDICTIONS\n",
"# ============================================\n",
"\n",
"results_df = pd.DataFrame({\n",
" \"Ticket\": [x[\"ticket\"] for x in evaluation_examples],\n",
" \"Expected Label\": true_labels,\n",
" \"Predicted Label\": predictions\n",
"})\n",
"\n",
"print(results_df)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qr1qe8AkRFWL"
},
"source": [
"Save Model"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 84,
"referenced_widgets": [
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},
"id": "ChdiHyyzRECF",
"outputId": "aeaff688-9fd2-44a5-d702-b7ec58650669"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Writing model shards: 0%| | 0/1 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
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},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"('./ticket-gpt2-model/tokenizer_config.json',\n",
" './ticket-gpt2-model/tokenizer.json')"
]
},
"metadata": {},
"execution_count": 25
}
],
"source": [
"model.save_pretrained(\"./ticket-gpt2-model\")\n",
"tokenizer.save_pretrained(\"./ticket-gpt2-model\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CZgXP5LjROVJ"
},
"source": [
"Push to Hugging Face"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 264,
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"No files have been modified since last commit. Skipping to prevent empty commit.\n",
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]
},
{
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"data": {
"text/plain": [
"CommitInfo(commit_url='https://huggingface.co/masonmidd/ticket-gpt2/commit/57b39d462763eb9c1df1546375d8e131d6e49de4', commit_message='Upload tokenizer', commit_description='', oid='57b39d462763eb9c1df1546375d8e131d6e49de4', pr_url=None, repo_url=RepoUrl('https://huggingface.co/masonmidd/ticket-gpt2', endpoint='https://huggingface.co', repo_type='model', repo_id='masonmidd/ticket-gpt2'), pr_revision=None, pr_num=None)"
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"metadata": {},
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],
"source": [
"hub_model_id = \"masonmidd/ticket-gpt2\"\n",
"\n",
"model.push_to_hub(hub_model_id)\n",
"tokenizer.push_to_hub(hub_model_id)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "foLHDkkdRgfQ"
},
"source": [
"Test the Model"
]
},
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"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"Both `max_new_tokens` (=256) and `max_length`(=50) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"Ticket:\n",
"Cannot connect to VPN after password reset.\n",
"\n",
"Category:\n",
"question\n",
"question\n",
"Category:\n",
"question\n",
"\n",
"Category:\n",
"question\n",
"\n",
"question\n",
"\n",
"Category:\n",
"question\n",
"\n",
"question\n",
"\n",
"Category:\n"
]
}
],
"source": [
"generator = pipeline(\n",
" \"text-generation\",\n",
" model=hub_model_id,\n",
" tokenizer=hub_model_id\n",
")\n",
"\n",
"prompt = \"\"\"\n",
"Ticket:\n",
"Cannot connect to VPN after password reset.\n",
"\n",
"Category:\n",
"\"\"\"\n",
"\n",
"result = generator(\n",
" prompt,\n",
" max_length=50,\n",
" num_return_sequences=1\n",
")\n",
"\n",
"print(result[0][\"generated_text\"])"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1zrUskUtHpzU"
},
"source": [
"# Application 1: Automated Ticket Triage\n",
"This application demonstrates how the fine-tuned GPT-2 model can assist IT help desk staff by automatically analyzing incoming support tickets. The model reads incoming ticket text and generates:\n",
"\n",
"\n",
"* Ticket Category\n",
"* Urgency Level\n",
"* Suggested routing team\n",
"\n",
"\n",
"\n",
"The goal of this application is to reduce manual ticket sorting and improve help desk response times."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YulHNpCeIGGi"
},
"source": [
"Import Libraries"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"id": "bFZAtVMJID66"
},
"outputs": [],
"source": [
"from transformers import pipeline\n",
"import re"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "qqSEL2GNIPKu"
},
"source": [
"Load th Fine-Tuned GPT2 Model"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 49,
"referenced_widgets": [
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]
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"id": "MHJMU8FXITQH",
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},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Loading weights: 0%| | 0/148 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
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"version_minor": 0,
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}
},
"metadata": {}
}
],
"source": [
"triage_generator = pipeline(\n",
" \"text-generation\",\n",
" model=\"masonmidd/ticket-gpt2\",\n",
" tokenizer=\"masonmidd/ticket-gpt2\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "l97DBIc8JfNU"
},
"source": [
"Create Ticket Triage Function"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {
"id": "eZC27AueJe66"
},
"outputs": [],
"source": [
"# AUTOMATED TICKET TRIAGE FUNCTION\n",
"# ============================================\n",
"\n",
"def triage_ticket(ticket_text):\n",
"\n",
" prompt = f\"\"\"\n",
"You are an IT ticket triage assistant.\n",
"\n",
"Analyze the following IT support ticket.\n",
"\n",
"Provide:\n",
"- Category\n",
"- Urgency\n",
"- Suggested Routing Team\n",
"\n",
"Ticket:\n",
"{ticket_text}\n",
"\n",
"Response:\n",
"\"\"\"\n",
"\n",
" result = triage_generator(\n",
" prompt,\n",
" max_length=120,\n",
" do_sample=True,\n",
" temperature=0.7,\n",
" top_k=50,\n",
" num_return_sequences=1\n",
" )\n",
"\n",
" generated_text = result[0][\"generated_text\"]\n",
"\n",
" return generated_text"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "e38-so5vJvEO"
},
"source": [
"Create Urgency Enhancement Logic"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {
"id": "8kJ2ur58JuYG"
},
"outputs": [],
"source": [
"# URGENCY ENHANCEMENT\n",
"# ============================================\n",
"\n",
"def determine_urgency(ticket_text):\n",
"\n",
" high_keywords = [\n",
" \"server down\",\n",
" \"cannot work\",\n",
" \"system outage\",\n",
" \"vpn down\",\n",
" \"critical\",\n",
" \"emergency\"\n",
" ]\n",
"\n",
" medium_keywords = [\n",
" \"slow\",\n",
" \"disconnect\",\n",
" \"cannot connect\",\n",
" \"error\"\n",
" ]\n",
"\n",
" text = ticket_text.lower()\n",
"\n",
" for keyword in high_keywords:\n",
" if keyword in text:\n",
" return \"High\"\n",
"\n",
" for keyword in medium_keywords:\n",
" if keyword in text:\n",
" return \"Medium\"\n",
"\n",
" return \"Low\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "V_-uqYNKJ-V0"
},
"source": [
"Create Routing Logic"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {
"id": "W34U4VwlKA0s"
},
"outputs": [],
"source": [
"# ROUTING LOGIC\n",
"# ============================================\n",
"\n",
"def determine_routing(ticket_text):\n",
"\n",
" text = ticket_text.lower()\n",
"\n",
" if \"vpn\" in text or \"network\" in text:\n",
" return \"Infrastructure Support\"\n",
"\n",
" elif \"printer\" in text:\n",
" return \"Desktop Support\"\n",
"\n",
" elif \"password\" in text or \"login\" in text:\n",
" return \"Identity Management\"\n",
"\n",
" elif \"email\" in text or \"outlook\" in text:\n",
" return \"Messaging Team\"\n",
"\n",
" else:\n",
" return \"General IT Support\""
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0KnFXkU-KC1w"
},
"source": [
"Create Full Ticket Analysis Function"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {
"id": "RWjJMGNjKHHi"
},
"outputs": [],
"source": [
"# FULL TICKET ANALYSIS\n",
"# ============================================\n",
"\n",
"def analyze_ticket(ticket_text):\n",
"\n",
" model_output = triage_ticket(ticket_text)\n",
"\n",
" urgency = determine_urgency(ticket_text)\n",
"\n",
" routing_team = determine_routing(ticket_text)\n",
"\n",
" print(\"====================================\")\n",
" print(\"TICKET ANALYSIS\")\n",
" print(\"====================================\")\n",
"\n",
" print(f\"Ticket:\\n{ticket_text}\\n\")\n",
"\n",
" print(\"GPT-2 Generated Analysis:\")\n",
" print(model_output)\n",
"\n",
" print(\"\\nAdditional Processing:\")\n",
" print(f\"Urgency Level: {urgency}\")\n",
" print(f\"Suggested Routing Team: {routing_team}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_QFht0vRKO1q"
},
"source": [
"Demo Example 1"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "1IKzjMvRKTNO",
"outputId": "c13d6a86-cc7e-4acc-82ec-5871adf0ee9b"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Passing `generation_config` together with generation-related arguments=({'top_k', 'num_return_sequences', 'max_length', 'do_sample', 'temperature'}) is deprecated and will be removed in future versions. Please pass either a `generation_config` object OR all generation parameters explicitly, but not both.\n",
"Both `max_new_tokens` (=256) and `max_length`(=120) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"====================================\n",
"TICKET ANALYSIS\n",
"====================================\n",
"Ticket:\n",
"\n",
"Cannot connect to VPN after password reset.\n",
"\n",
"\n",
"GPT-2 Generated Analysis:\n",
"\n",
"You are an IT ticket triage assistant.\n",
"\n",
"Analyze the following IT support ticket.\n",
"\n",
"Provide:\n",
"- Category\n",
"- Urgency\n",
"- Suggested Routing Team\n",
"\n",
"Ticket:\n",
"\n",
"Cannot connect to VPN after password reset.\n",
"\n",
"\n",
"Response:\n",
"\n",
"I'm using a Linux-based PC with a custom dataloader, but I'm unable to connect to the network. I have to pass the following:\n",
"\n",
"- nnnnnnnnnnnnn.nnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnnn\n",
"\n",
"Additional Processing:\n",
"Urgency Level: Medium\n",
"Suggested Routing Team: Infrastructure Support\n"
]
}
],
"source": [
"sample_ticket_1 = \"\"\"\n",
"Cannot connect to VPN after password reset.\n",
"\"\"\"\n",
"\n",
"analyze_ticket(sample_ticket_1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3L1xHoetK0VB"
},
"source": [
"Demo Example 2"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "gDyYHkCgK3A5",
"outputId": "3a810ab3-b17e-4b27-8976-9b71abf129d8"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Both `max_new_tokens` (=256) and `max_length`(=120) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"====================================\n",
"TICKET ANALYSIS\n",
"====================================\n",
"Ticket:\n",
"\n",
"Office printer is offline and employees cannot print invoices.\n",
"\n",
"\n",
"GPT-2 Generated Analysis:\n",
"\n",
"You are an IT ticket triage assistant.\n",
"\n",
"Analyze the following IT support ticket.\n",
"\n",
"Provide:\n",
"- Category\n",
"- Urgency\n",
"- Suggested Routing Team\n",
"\n",
"Ticket:\n",
"\n",
"Office printer is offline and employees cannot print invoices.\n",
"\n",
"\n",
"Response:\n",
"\n",
"Hi\n",
"\n",
"I'm trying to figure out how to fix\n",
"the issue. But I'm unable to reproduce the problem because the code is updated\n",
"on some of the docs.\n",
"\n",
"Category:\n",
"bug, help wanted, priority: 0\n",
"\n",
"Category:\n",
"question\n",
"\n",
"Category:\n",
"feature, won't fix, won't fix\n",
"\n",
"Category:\n",
"question, can help\n",
"\n",
"Category:\n",
"question\n",
"\n",
"Category:\n",
"feature, won't fix, won't fix\n",
"\n",
"Category:\n",
"question, can help\n",
"\n",
"Category:\n",
"question\n",
"\n",
"Category:\n",
"feature, won't fix, won't fix\n",
"\n",
"Category:\n",
"question, can help\n",
"\n",
"Category:\n",
"question, can help\n",
"\n",
"Category:\n",
"question\n",
"\n",
"Category:\n",
"feature, won't fix, won't fix\n",
"\n",
"Category:\n",
"question\n",
"\n",
"Additional Processing:\n",
"Urgency Level: Low\n",
"Suggested Routing Team: Desktop Support\n"
]
}
],
"source": [
"sample_ticket_2 = \"\"\"\n",
"Office printer is offline and employees cannot print invoices.\n",
"\"\"\"\n",
"\n",
"analyze_ticket(sample_ticket_2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Vzy7C7tHLP1a"
},
"source": [
"Demo Example 3"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "F1t5o0GELQTj",
"outputId": "d0eb7ada-71b6-4b66-a474-bf3f2a56aa2e"
},
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"Both `max_new_tokens` (=256) and `max_length`(=120) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"====================================\n",
"TICKET ANALYSIS\n",
"====================================\n",
"Ticket:\n",
"\n",
"Outlook crashes every time I open email attachments.\n",
"\n",
"\n",
"GPT-2 Generated Analysis:\n",
"\n",
"You are an IT ticket triage assistant.\n",
"\n",
"Analyze the following IT support ticket.\n",
"\n",
"Provide:\n",
"- Category\n",
"- Urgency\n",
"- Suggested Routing Team\n",
"\n",
"Ticket:\n",
"\n",
"Outlook crashes every time I open email attachments.\n",
"\n",
"\n",
"Response:\n",
"\n",
"I can't seem to get in the loop of where you do this.\n",
"\n",
"Type:\n",
"help wanted, priority 1\n",
"\n",
"Category:\n",
"question\n",
"\n",
"question\n",
"\n",
"Category:\n",
"question\n",
"question\n",
"\n",
"question\n",
"\n",
"Category:\n",
"question\n",
"question\n",
"question\n",
"question\n",
"\n",
"question\n",
"\n",
"Category:\n",
"question\n",
"\n",
"Additional Processing:\n",
"Urgency Level: Low\n",
"Suggested Routing Team: Messaging Team\n"
]
}
],
"source": [
"sample_ticket_3 = \"\"\"\n",
"Outlook crashes every time I open email attachments.\n",
"\"\"\"\n",
"\n",
"analyze_ticket(sample_ticket_3)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "-thGvnSqSEIr"
},
"source": [
"Custom Ticket Input Cell"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "YBcnRa2GSEme",
"outputId": "92d96192-db03-4777-e2fe-4385a457f550"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Enter an IT support ticket: Outlook not updating\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Both `max_new_tokens` (=256) and `max_length`(=120) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"====================================\n",
"TICKET ANALYSIS\n",
"====================================\n",
"Ticket:\n",
"Outlook not updating\n",
"\n",
"GPT-2 Generated Analysis:\n",
"\n",
"You are an IT ticket triage assistant.\n",
"\n",
"Analyze the following IT support ticket.\n",
"\n",
"Provide:\n",
"- Category\n",
"- Urgency\n",
"- Suggested Routing Team\n",
"\n",
"Ticket:\n",
"Outlook not updating\n",
"\n",
"Response:\n",
"Hi\n",
"I am trying to use the latest version of the LightningModules and the lightning module (https://github.com/PyTorchLightning/pytorch_lightning/blob/master/pytorch_lightning/configuration.py).\n",
"I got a warning this week about the \"Invalidation\" flag in LightningModule.\n",
"I tried to log the flag by calling the log function, but this would require a callback like this:\n",
"pytorch_lightning/logger/test_log_callback.py\n",
"\n",
"Code sample\n",
"import os\n",
"import torch\n",
"from torch_utils.datasets import DataLoader\n",
"from torch_utils.data import DataLoader\n",
"from torch_utils.utils.data import DataLoader\n",
"from torch_utils.metrics import Trainer\n",
"\n",
"class Trainer(pl.LightningModule):\n",
" def __init__(self):\n",
" self.train_end = False\n",
" self.model = lightning_model\n",
" def __init__(self, batch, batch_idx):\n",
" \n",
"\n",
"Additional Processing:\n",
"Urgency Level: Low\n",
"Suggested Routing Team: Messaging Team\n"
]
}
],
"source": [
"# CUSTOM TICKET INPUT\n",
"# ============================================\n",
"\n",
"user_ticket = input(\"Enter an IT support ticket: \")\n",
"\n",
"analyze_ticket(user_ticket)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QS5Uo-TWPHOt"
},
"source": [
"# Application 2: AI Help Desk Assistant\n",
"This application demonstrates how the same fine-tuned GPT-2 model can be used as an employee-facing AI help desk assistant. Instead of routing tickets internally for IT staff, this application attempts to provide troubleshooting guidance directly to employees before escalation.\n",
"\n",
"\n",
"The goal of this application is to:\n",
"* Reduce unnecessary help desk tickets\n",
"* Improve employee self-service support\n",
"* Provide faster troubleshooting assistance\n",
"* Reduce IT support workload\n",
"\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "JoYOrQV0Pp4a"
},
"source": [
"Import Libraries"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "GKFMl-GVPq-_"
},
"outputs": [],
"source": [
"from transformers import pipeline"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9ZIyA7dkPw2q"
},
"source": [
"Load the Fine-Tuned GPT-2 Model"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 368,
"referenced_widgets": [
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},
"id": "NESsxtohPyaG",
"outputId": "5eba57bf-07b0-4e9d-9ba6-9f82cb18998d"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"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",
"Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n",
"WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
]
},
{
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"text/plain": [
"config.json: 0%| | 0.00/961 [00:00<?, ?B/s]"
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{
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]
},
"metadata": {},
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{
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"tokenizer.json: 0.00B [00:00, ?B/s]"
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},
"metadata": {},
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}
],
"source": [
"assistant_generator = pipeline(\n",
" \"text-generation\",\n",
" model=\"masonmidd/ticket-gpt2\",\n",
" tokenizer=\"masonmidd/ticket-gpt2\"\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "apQn91cKP1RG"
},
"source": [
"Create Troubleshooting Prompt Function"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "DTNaL7COP0_W"
},
"outputs": [],
"source": [
"# AI HELP DESK ASSISTANT FUNCTION\n",
"# ============================================\n",
"\n",
"def helpdesk_assistant(issue_text):\n",
"\n",
" prompt = f\"\"\"\n",
"You are an IT help desk assistant.\n",
"\n",
"Read the employee issue and provide troubleshooting suggestions.\n",
"\n",
"Issue:\n",
"{issue_text}\n",
"\n",
"Suggested Fix:\n",
"\"\"\"\n",
"\n",
" result = assistant_generator(\n",
" prompt,\n",
" max_length=150,\n",
" do_sample=True,\n",
" temperature=0.8,\n",
" top_k=50,\n",
" num_return_sequences=1\n",
" )\n",
"\n",
" generated_text = result[0][\"generated_text\"]\n",
"\n",
" return generated_text"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8Qs_jjlqQN_K"
},
"source": [
"Create Basic Troubleshooting Enhancement Logic"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "Jirl5YURQU1H"
},
"outputs": [],
"source": [
"# TROUBLESHOOTING ENHANCEMENT\n",
"# ============================================\n",
"\n",
"def additional_troubleshooting(issue_text):\n",
"\n",
" text = issue_text.lower()\n",
"\n",
" if \"vpn\" in text:\n",
" return [\n",
" \"Verify internet connectivity\",\n",
" \"Reset VPN credentials\",\n",
" \"Restart VPN client\"\n",
" ]\n",
"\n",
" elif \"printer\" in text:\n",
" return [\n",
" \"Verify printer power\",\n",
" \"Reconnect network printer\",\n",
" \"Restart printer spooler\"\n",
" ]\n",
"\n",
" elif \"password\" in text or \"login\" in text:\n",
" return [\n",
" \"Verify username\",\n",
" \"Reset password\",\n",
" \"Verify MFA enrollment\"\n",
" ]\n",
"\n",
" elif \"wifi\" in text or \"network\" in text:\n",
" return [\n",
" \"Restart wireless adapter\",\n",
" \"Reconnect to Wi-Fi\",\n",
" \"Verify network credentials\"\n",
" ]\n",
"\n",
" else:\n",
" return [\n",
" \"Restart device\",\n",
" \"Reconnect to company network\",\n",
" \"Contact IT support if issue persists\"\n",
" ]"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ULhN5EAOQsOr"
},
"source": [
"Create Full AI Help Desk Workflow"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "LWvnnsRaQulA"
},
"outputs": [],
"source": [
"# FULL HELP DESK WORKFLOW\n",
"# ============================================\n",
"\n",
"def analyze_employee_issue(issue_text):\n",
"\n",
" model_output = helpdesk_assistant(issue_text)\n",
"\n",
" extra_steps = additional_troubleshooting(issue_text)\n",
"\n",
" print(\"====================================\")\n",
" print(\"AI HELP DESK ASSISTANT\")\n",
" print(\"====================================\")\n",
"\n",
" print(f\"Issue:\\n{issue_text}\\n\")\n",
"\n",
" print(\"GPT-2 Generated Suggestions:\")\n",
" print(model_output)\n",
"\n",
" print(\"\\nAdditional Troubleshooting Steps:\")\n",
"\n",
" for step in extra_steps:\n",
" print(f\"- {step}\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "cPtsHuHYRJKW"
},
"source": [
"Demo Example 1"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "mK1BmlcqQzgB",
"outputId": "01634dbe-9090-4a67-8209-a3ec3ada6988"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Passing `generation_config` together with generation-related arguments=({'temperature', 'max_length', 'do_sample', 'num_return_sequences', 'top_k'}) is deprecated and will be removed in future versions. Please pass either a `generation_config` object OR all generation parameters explicitly, but not both.\n",
"Both `max_new_tokens` (=256) and `max_length`(=150) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"====================================\n",
"AI HELP DESK ASSISTANT\n",
"====================================\n",
"Issue:\n",
"\n",
"Cannot connect to VPN from home after password change.\n",
"\n",
"\n",
"GPT-2 Generated Suggestions:\n",
"\n",
"You are an IT help desk assistant.\n",
"\n",
"Read the employee issue and provide troubleshooting suggestions.\n",
"\n",
"Issue:\n",
"\n",
"Cannot connect to VPN from home after password change.\n",
"\n",
"\n",
"Suggested Fix:\n",
"\n",
"Steps to reproduce:\n",
"1) Make this a directory at the end of the script.\n",
"2) Call log function in the script.\n",
"3) Add a few more parameters before the script is invoked.\n",
"4) Call log function in the script.\n",
"5) Call log function in the script.\n",
"6) Add the line \"\n",
"\" to the end of the list of parameters after the script is called.\n",
"7) Call log function in the script and use it as a regular argument to the log function.\n",
"8) Call log function in the script and use it as a regular argument to the log function.\n",
"9) Call log function in the script and use it as a regular argument to the log function.\n",
"\n",
"\n",
"Expected behavior:\n",
"It will perform a normal job within the following environment.\n",
"Expected behavior:\n",
"Run the script as normal program.\n",
"\n",
"\n",
"Expected behavior:\n",
"If I use this environment, it will be called as normal program.\n",
"Expected behavior:\n",
"You should have a chance to properly call test.pl function in the script.\n",
"\n",
"\n",
"Code sample code\n",
" \n",
" pytorch-lightning/pytorch_lightning/callbacks/\n",
"\n",
"Additional Troubleshooting Steps:\n",
"- Verify internet connectivity\n",
"- Reset VPN credentials\n",
"- Restart VPN client\n"
]
}
],
"source": [
"sample_issue_1 = \"\"\"\n",
"Cannot connect to VPN from home after password change.\n",
"\"\"\"\n",
"\n",
"analyze_employee_issue(sample_issue_1)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3gUz35-qRMPJ"
},
"source": [
"Demo Example 2"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "e9zCvfMIRd7A",
"outputId": "d005bd38-1a33-4a83-c749-fb2fe2bdcc6b"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Both `max_new_tokens` (=256) and `max_length`(=150) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"====================================\n",
"AI HELP DESK ASSISTANT\n",
"====================================\n",
"Issue:\n",
"\n",
"Office printer is offline and documents will not print.\n",
"\n",
"\n",
"GPT-2 Generated Suggestions:\n",
"\n",
"You are an IT help desk assistant.\n",
"\n",
"Read the employee issue and provide troubleshooting suggestions.\n",
"\n",
"Issue:\n",
"\n",
"Office printer is offline and documents will not print.\n",
"\n",
"\n",
"Suggested Fix:\n",
"\n",
"Expose the bug in the Trainer\n",
"Save the Trainer as a user space file.\n",
"\n",
"What is your question?\n",
"\n",
"I am currently working in the context of a training and validation layer\n",
"https://pytorch-lightning.readthedocs.io/en/latest/pytorch-lightning/en/latest/trainer/training.html#training.py\n",
"Is my notebook and training object logged?\n",
"\n",
"Any help?\n",
"\n",
"Category:\n",
"bug, help wanted\n",
"Category:\n",
"question\n",
"Category:\n",
"question\n",
"Category:\n",
"latest\n",
"Category:\n",
"comment\n",
"Category:\n",
"\n",
"question\n",
"\n",
"Category:\n",
"question\n",
"Category:\n",
"question\n",
"\n",
"Category:\n",
"question\n",
"\n",
"Additional Troubleshooting Steps:\n",
"- Verify printer power\n",
"- Reconnect network printer\n",
"- Restart printer spooler\n"
]
}
],
"source": [
"sample_issue_2 = \"\"\"\n",
"Office printer is offline and documents will not print.\n",
"\"\"\"\n",
"\n",
"analyze_employee_issue(sample_issue_2)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lZg09z0pRoAt"
},
"source": [
"Demo Example 3"
]
},
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"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "iNA6FmluRmY0",
"outputId": "835481b2-050c-467d-c5da-b2bb07e6bbd7"
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Both `max_new_tokens` (=256) and `max_length`(=150) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"====================================\n",
"AI HELP DESK ASSISTANT\n",
"====================================\n",
"Issue:\n",
"\n",
"Laptop cannot connect to office Wi-Fi.\n",
"\n",
"\n",
"GPT-2 Generated Suggestions:\n",
"\n",
"You are an IT help desk assistant.\n",
"\n",
"Read the employee issue and provide troubleshooting suggestions.\n",
"\n",
"Issue:\n",
"\n",
"Laptop cannot connect to office Wi-Fi.\n",
"\n",
"\n",
"Suggested Fix:\n",
"\n",
"Add a lightning module to the LDP module.\n",
"Add support for adding a lightning module to the LightningModule class.\n",
"Add two LightningModule classes to the LightningModule class.\n",
"Add a LightningModule class to the LightningModule class.\n",
"Add a LightningModule class to the LightningModule class.\n",
"In the LightningModule class, the LightningModule receives a lightning callback function.\n",
"Add a LightningModule class to the LightningModule class.\n",
"In the LightningModule class, the LightningModule receives a LightningModule logger.\n",
"\n",
"The lightning method returns a lightning callback callback.\n",
"To Reproduce\n",
"Steps to reproduce the behavior:\n",
"Run the code in the LightningModule class\n",
"\n",
"In the LightningModule class, implement the LightningModule.connect method\n",
"Add the LightningModule class\n",
"Run the code in the LightningModule class\n",
"\n",
"Add the LightningModule class\n",
"Run the code in the LightningModule class\n",
"Add the LightningModule logger\n",
"\n",
"Run the code in the LightningModule class\n",
"\n",
"Add the training_loop method\n",
"Add the LightningModule logger\n",
"\n",
"Run the code in the LightningModule class\n",
"Add the LightningModule logger\n",
"\n",
"Run the code in the LightningModule class\n",
"Add the training_loop method\n",
"Add the LightningModule logger\n",
"Run the code\n",
"\n",
"Additional Troubleshooting Steps:\n",
"- Restart device\n",
"- Reconnect to company network\n",
"- Contact IT support if issue persists\n"
]
}
],
"source": [
"sample_issue_3 = \"\"\"\n",
"Laptop cannot connect to office Wi-Fi.\n",
"\"\"\"\n",
"\n",
"analyze_employee_issue(sample_issue_3)"
]
},
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"cell_type": "markdown",
"metadata": {
"id": "WGrOaUe4R0r3"
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"source": [
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Describe your technical issue: Laptop won't connect to internet\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"Both `max_new_tokens` (=256) and `max_length`(=150) seem to have been set. `max_new_tokens` will take precedence. Please refer to the documentation for more information. (https://huggingface.co/docs/transformers/main/en/main_classes/text_generation)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"====================================\n",
"AI HELP DESK ASSISTANT\n",
"====================================\n",
"Issue:\n",
"Laptop won't connect to internet\n",
"\n",
"GPT-2 Generated Suggestions:\n",
"\n",
"You are an IT help desk assistant.\n",
"\n",
"Read the employee issue and provide troubleshooting suggestions.\n",
"\n",
"Issue:\n",
"Laptop won't connect to internet\n",
"\n",
"Suggested Fix:\n",
"\n",
"Logging in the log, using the torch command line or using the training_step hook\n",
"\n",
"Category:\n",
"bug, help wanted\n",
"\n",
"Category:\n",
"question, won't fix\n",
"\n",
"Category:\n",
"question, won't fix, won't fix\n",
"\n",
"Category:\n",
"bug, won't fix\n",
"\n",
"Additional Troubleshooting Steps:\n",
"- Restart device\n",
"- Reconnect to company network\n",
"- Contact IT support if issue persists\n"
]
}
],
"source": [
"# CUSTOM EMPLOYEE ISSUE INPUT\n",
"# ============================================\n",
"\n",
"user_issue = input(\"Describe your technical issue: \")\n",
"\n",
"analyze_employee_issue(user_issue)"
]
}
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