Model card for primer/BART-Squad2 (#6801)

model_cards/Primer/bart-squad2/README.md

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+---
+language: "en"
+---
+
+# BART-Squad2
+
+## Model description
+
+BART for extractive (span-based) question answering, trained on Squad 2.0.
+
+F1 score of 87.4.
+
+## 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!
+
+#### How to use
+
+Here's a quick way to get question answering running locally:
+
+```python
+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).
+
+#### Limitations and bias
+
+Unknown, no further evaluation has been performed. In a technical sense one big limitation is that it's 1.6G 😬
+
+## Training procedure
+
+`run_squad.py` with:
+
+|param|value|
+|---|---|
+|batch size|8|
+|max_seq_length|1024|
+|learning rate|1e-5|
+|epochs|2|
+
+Modified to freeze shared parameters and encoder embeddings.
+