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--- |
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base_model: |
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- google-t5/t5-base |
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pipeline_tag: question-answering |
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license: mit |
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datasets: |
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- rajpurkar/squad_v2 |
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metrics: |
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- accuracy |
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library_name: transformers |
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--- |
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# I-Comprehend Answer Generation Model |
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## Overview |
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The **I-Comprehend Answer Generation Model** is a T5-based model designed to generate answers from a given question and context. This model is particularly useful for applications in automated question answering systems, educational tools, and enhancing information retrieval processes. |
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## Model Details |
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- **Model Architecture:** T5 (Text-to-Text Transfer Transformer) |
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- **Model Type:** Conditional Generation |
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- **Training Data:** [Specify the dataset or type of data used for training] |
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- **Use Cases:** Answer generation, question answering systems, educational tools |
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## Installation |
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To use this model, you need to have the `transformers` library installed. You can install it via pip: |
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```bash |
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pip install transformers |
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pip install torch |
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``` |
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## Usage |
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To use the model, load it with the appropriate tokenizer and model classes from the `transformers` library. Ensure you have the correct repository ID or local path. |
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```bash |
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from transformers import T5ForConditionalGeneration, T5Tokenizer |
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import torch |
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# Load the model and tokenizer |
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t5ag_model = T5ForConditionalGeneration.from_pretrained("miiiciiii/I-Comprehend_ag") |
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t5ag_tokenizer = T5Tokenizer.from_pretrained("miiiciiii/I-Comprehend_ag") |
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def answer_question(question, context): |
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"""Generate an answer for a given question and context.""" |
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input_text = f"question: {question} context: {context}" |
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input_ids = t5ag_tokenizer.encode(input_text, return_tensors="pt", max_length=512, truncation=True) |
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with torch.no_grad(): |
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output = t5ag_model.generate(input_ids, max_length=512, num_return_sequences=1, max_new_tokens=200) |
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return t5ag_tokenizer.decode(output[0], skip_special_tokens=True) |
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# Example usage |
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question = "What is the location of the Eiffel Tower?" |
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context = "The Eiffel Tower is located in Paris and is one of the most famous landmarks in the world." |
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answer = answer_question(question, context) |
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print("Generated Answer:", answer) |
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``` |
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## Model Performance |
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- **Evaluation Metrics:** [BLEU, ROUGE] |
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- **Performance Results:** [Accuracy] |
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## Limitations |
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- The model may not perform well on contexts that are significantly different from the training data. |
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- It may generate answers that are too generic or not contextually relevant in some cases. |
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## Contributing |
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We welcome contributions to improve the model or expand its capabilities. Please feel free to open issues or submit pull requests. |
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## License |
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[MIT License] |
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## Acknowledgments |
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- [Acknowledge any datasets, libraries, or collaborators that contributed to the model] |
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## Contact |
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For any questions or issues, please contact [icomprehend.system@gmail.com]. |
