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

Created by Suraj Patil