Bart Squad2

🚀 BART-Squad2

BART model for extractive (span-based) question answering, trained on Squad 2.0, achieving an F1 score of 87.4.

🚀 Quick Start

If you want to run question answering locally, here's a simple way:

from transformers import AutoTokenizer, AutoModelForQuestionAnswering

tokenizer = AutoTokenizer.from_pretrained("Primer/bart-squad2")
model = AutoModelForQuestionAnswering.from_pretrained("Primer/bart-squad2")
model.to('cuda'); model.eval()

def answer(question, text):
    seq = '<s>' +  question + ' </s> </s> ' + text + ' </s>'
    tokens = tokenizer.encode_plus(seq, return_tensors='pt', padding='max_length', max_length=1024)
    input_ids = tokens['input_ids'].to('cuda')
    attention_mask = tokens['attention_mask'].to('cuda')
    start, end, _ = model(input_ids, attention_mask=attention_mask)
    start_idx = int(start.argmax().int())
    end_idx =  int(end.argmax().int())
    print(tokenizer.decode(input_ids[0, start_idx:end_idx]).strip())
    # ^^ it will be an empty string if the model decided "unanswerable"

>>> question = "Where does Tom live?"
>>> context = "Tom is an engineer in San Francisco."
>>> answer(question, context)
San Francisco

(Just drop the .to('cuda') stuff if running on CPU).

✨ Features

• BART model trained on Squad 2.0 for extractive question answering.
• Achieved an F1 score of 87.4.

📚 Documentation

Intended uses & limitations

Unfortunately, the Huggingface auto-inference API won't run this model, so if you're attempting to try it through the input box above and it complains, don't be discouraged!

Limitations and bias

Unknown, no further evaluation has been performed. In a technical sense one big limitation is that it's 1.6G 😬

🔧 Technical Details

Training procedure

The model was trained using run_squad.py with the following parameters:

The training process was modified to freeze shared parameters and encoder embeddings.