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--- |
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license: apache-2.0 |
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datasets: |
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- VenkatManda/KaggleQuestions |
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language: |
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- en |
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--- |
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# Kaggle Q&A Model Fine-tuned from GPT-2 |
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## Overview |
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This repository contains a question-answering (Q&A) model fine-tuned from OpenAI's GPT-2 on Kaggle data. The model is hosted on Hugging Face's model hub and can be easily used for various question-answering tasks. |
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## Model Details |
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- **Base Model**: OpenAI's GPT-2 |
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- **Fine-tuned Dataset**: Kaggle Q&A data |
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- **Model Type**: Transformer-based Language Model |
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- **Framework**: Hugging Face's Transformers Library |
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## Usage |
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To use this model, follow these steps: |
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1. Install the `transformers` library by Hugging Face: |
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```bash |
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pip install transformers |
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# Load the model using its identifier: |
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```bash |
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from transformers import AutoTokenizer, AutoModelForQuestionAnswering |
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# Load tokenizer and model |
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tokenizer = AutoTokenizer.from_pretrained("VenkatManda/KaggleQuestionsModelGPT2") |
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model = AutoModelForQuestionAnswering.from_pretrained("VenkatManda/KaggleQuestionsModelGPT2") |
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# Provide context and question |
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context = "Your context here" |
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question = "Your question here?" |
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# Tokenize input |
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inputs = tokenizer(question, context, return_tensors="pt") |
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# Perform inference |
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outputs = model(**inputs) |
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# Get answer |
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answer_start_scores = outputs.start_logits |
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answer_end_scores = outputs.end_logits |
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answer_start = torch.argmax(answer_start_scores) |
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answer_end = torch.argmax(answer_end_scores) + 1 |
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answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(inputs["input_ids"][0][answer_start:answer_end])) |
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print("Answer:", answer) |
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@article{venkat2024kagglegpt2qa, |
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title={Kaggle Q&A Model Fine-tuned from GPT-2}, |
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author={Venkat}, |
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journal={GitHub}, |
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year={2024}, |
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howpublished={\url{https://github.com/venkat/kaggle-gpt2-qa}} |
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} |
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