BART for extractive (span-based) question answering, trained on Squad 2.0.
F1 score of 87.4.
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!
Here's a quick way to get question answering running locally:
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).
Unknown, no further evaluation has been performed. In a technical sense one big limitation is that it's 1.6G 😬
Modified to freeze shared parameters and encoder embeddings.
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