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--- |
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language: en |
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license: apache-2.0 |
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datasets: daily_dialog |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- gpt2 |
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- conversational |
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- chatbot |
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- nlp |
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base_model: gpt2 |
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--- |
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# GPT-2 Personal Assistant |
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**Model repo:** `hmnshudhmn24/gpt2-personal-assistant` |
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A lightweight conversational assistant based on **GPT-2**, fine-tuned on the **DailyDialog** dataset for chat and casual Q&A. |
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## Model details |
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- **Base model:** gpt2 |
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- **Task:** Conversational text generation / Chatbot |
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- **Dataset used for demo:** daily_dialog (small subset used in training script for quick demo) |
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- **Language:** English |
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- **License:** Apache-2.0 |
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## How to use (inference) |
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```python |
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from transformers import pipeline |
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generator = pipeline("text-generation", model="hmnshudhmn24/gpt2-personal-assistant") |
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prompt = "User: Hello\nAssistant: Hi! How can I help you?\nUser: What's the weather like today?\nAssistant:" |
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print(generator(prompt, max_length=100, num_return_sequences=1)[0]["generated_text"]) |
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``` |
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## Train locally (quick demo) |
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Run: |
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```bash |
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python train_chatbot.py |
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``` |
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This script fine-tunes `gpt2` on a subset of the DailyDialog dataset and saves the model to `./gpt2-personal-assistant` folder. |
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## Files in this repo |
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- `config.json`, `tokenizer_config.json`, `special_tokens_map.json` — model/tokenizer configs |
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- `train_chatbot.py` — training script (demo) |
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- `inference.py` — simple inference example |
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- `utils.py` — helper to build conversation prompts |
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- `example_conversations.txt` — small sample dialogues |
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- `requirements.txt` — Python dependencies |
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## Notes & limitations |
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- GPT-2 is a general-purpose LM; it can generate incorrect or unsafe outputs. Do not rely on it for critical advice. |
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- For production, use larger datasets, more epochs, and safety filtering. |
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- If uploading to Hugging Face, include `pytorch_model.bin` (weights) after training. |
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## License |
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Apache-2.0 |
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