bconsolvo commited on
Commit
737b989
1 Parent(s): 2740af3

Update app.py

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Files changed (1) hide show
  1. app.py +2 -2
app.py CHANGED
@@ -30,7 +30,7 @@ def predict(context,question):
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  # dense_duration = (dense_end_time - dense_start_time) * 1000
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  # dense_answer = dense_predictions['answer']
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- return sparse_answer,sparse_score,sparse_start,sparse_duration #,dense_answer,dense_duration
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  md = """This prediction model is designed to answer a question about a given input text--reading comprehension. The model does not just answer questions in general -- it only works from the text that you provide. However, automated reading comprehension can be a valuable task.
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@@ -61,7 +61,7 @@ iface = gr.Interface(
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  fn=predict,
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  inputs=[context,question],
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  # outputs=[sparse_answer,sparse_duration,dense_answer,dense_duration],
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- outputs=[sparse_answer,sparse_score,sparse_start,sparse_duration],
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  examples=[[apple_context,apple_question]],
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  title = "Question & Answer with Sparse BERT using the SQuAD dataset",
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  description = md,
 
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  # dense_duration = (dense_end_time - dense_start_time) * 1000
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  # dense_answer = dense_predictions['answer']
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+ return sparse_answer,sparse_score,sparse_start #,sparse_duration #,dense_answer,dense_duration
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  md = """This prediction model is designed to answer a question about a given input text--reading comprehension. The model does not just answer questions in general -- it only works from the text that you provide. However, automated reading comprehension can be a valuable task.
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  fn=predict,
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  inputs=[context,question],
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  # outputs=[sparse_answer,sparse_duration,dense_answer,dense_duration],
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+ outputs=[sparse_answer,sparse_score,sparse_start],
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  examples=[[apple_context,apple_question]],
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  title = "Question & Answer with Sparse BERT using the SQuAD dataset",
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  description = md,