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# 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)
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