| # T5 for question-answering | |
| This is T5-base model fine-tuned on SQuAD1.1 for QA using text-to-text approach | |
| ## Model training | |
| This model was trained on colab TPU with 35GB RAM for 4 epochs | |
| ## Results: | |
| | Metric | #Value | | |
| |-------------|---------| | |
| | Exact Match | 81.5610 | | |
| | F1 | 89.9601 | | |
| ## Model in Action 🚀 | |
| ``` | |
| from transformers import AutoModelWithLMHead, AutoTokenizer | |
| tokenizer = AutoTokenizer.from_pretrained("valhalla/t5-base-squad") | |
| model = AutoModelWithLMHead.from_pretrained("valhalla/t5-base-squad") | |
| def get_answer(question, context): | |
| input_text = "question: %s context: %s </s>" % (question, context) | |
| features = tokenizer([input_text], return_tensors='pt') | |
| out = model.generate(input_ids=features['input_ids'], | |
| attention_mask=features['attention_mask']) | |
| return tokenizer.decode(out[0]) | |
| context = "In Norse mythology, Valhalla is a majestic, enormous hall located in Asgard, ruled over by the god Odin." | |
| question = "What is Valhalla ?" | |
| get_answer(question, context) | |
| # output: 'a majestic, enormous hall located in Asgard, ruled over by the god Odin' | |
| ``` | |
| Play with this model [](https://colab.research.google.com/drive/1a5xpJiUjZybfU9Mi-aDkOp116PZ9-wni?usp=sharing) | |
| > Created by Suraj Patil [](https://github.com/patil-suraj/) | |
| [](https://twitter.com/psuraj28) | |