Update README.md

README.md

@@ -4,4 +4,67 @@ datasets:
 - VenkatManda/KaggleQuestions
 language:
 - en
----
+---
+
+# Kaggle Q&A Model Fine-tuned from GPT-2
+
+## Overview
+
+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.
+
+## Model Details
+
+- **Base Model**: OpenAI's GPT-2
+- **Fine-tuned Dataset**: Kaggle Q&A data
+- **Model Type**: Transformer-based Language Model
+- **Framework**: Hugging Face's Transformers Library
+
+## Usage
+
+To use this model, follow these steps:
+
+1. Install the `transformers` library by Hugging Face:
+
+   ```bash
+   pip install transformers
+   
+# Load the model using its identifier:
+
+  ```bash
+  from transformers import AutoTokenizer, AutoModelForQuestionAnswering
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("VenkatManda/KaggleQuestionsModelGPT2")
+model = AutoModelForQuestionAnswering.from_pretrained("VenkatManda/KaggleQuestionsModelGPT2")
+
+
+
+# Provide context and question
+context = "Your context here"
+question = "Your question here?"
+
+# Tokenize input
+inputs = tokenizer(question, context, return_tensors="pt")
+
+# Perform inference
+outputs = model(**inputs)
+
+# Get answer
+answer_start_scores = outputs.start_logits
+answer_end_scores = outputs.end_logits
+answer_start = torch.argmax(answer_start_scores)
+answer_end = torch.argmax(answer_end_scores) + 1
+answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(inputs["input_ids"][0][answer_start:answer_end]))
+print("Answer:", answer)
+
+
+@article{venkat2024kagglegpt2qa,
+  title={Kaggle Q&A Model Fine-tuned from GPT-2},
+  author={Venkat},
+  journal={GitHub},
+  year={2024},
+  howpublished={\url{https://github.com/venkat/kaggle-gpt2-qa}}
+}
+
+
+