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--- |
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language: "en" |
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--- |
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# BART-Squad2 |
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## Model description |
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BART for extractive (span-based) question answering, trained on Squad 2.0. |
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F1 score of 87.4. |
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## Intended uses & limitations |
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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! |
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#### How to use |
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Here's a quick way to get question answering running locally: |
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```python |
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from transformers import AutoTokenizer, AutoModelForQuestionAnswering |
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tokenizer = AutoTokenizer.from_pretrained("Primer/bart-squad2") |
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model = AutoModelForQuestionAnswering.from_pretrained("Primer/bart-squad2") |
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model.to('cuda'); model.eval() |
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def answer(question, text): |
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seq = '<s>' + question + ' </s> </s> ' + text + ' </s>' |
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tokens = tokenizer.encode_plus(seq, return_tensors='pt', padding='max_length', max_length=1024) |
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input_ids = tokens['input_ids'].to('cuda') |
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attention_mask = tokens['attention_mask'].to('cuda') |
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start, end, _ = model(input_ids, attention_mask=attention_mask) |
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start_idx = int(start.argmax().int()) |
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end_idx = int(end.argmax().int()) |
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print(tokenizer.decode(input_ids[0, start_idx:end_idx]).strip()) |
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# ^^ it will be an empty string if the model decided "unanswerable" |
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>>> question = "Where does Tom live?" |
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>>> context = "Tom is an engineer in San Francisco." |
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>>> answer(question, context) |
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San Francisco |
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``` |
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(Just drop the `.to('cuda')` stuff if running on CPU). |
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#### Limitations and bias |
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Unknown, no further evaluation has been performed. In a technical sense one big limitation is that it's 1.6G 馃槵 |
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## Training procedure |
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`run_squad.py` with: |
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|param|value| |
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|batch size|8| |
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|max_seq_length|1024| |
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|learning rate|1e-5| |
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|epochs|2| |
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Modified to freeze shared parameters and encoder embeddings. |
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