vasudevgupta commited on
Commit
06ccb0a
·
1 Parent(s): 2b0b74a

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +5 -2
app.py CHANGED
@@ -15,7 +15,7 @@ if __name__ == "__main__":
15
 
16
  def get_answer(question, context):
17
 
18
- encoding = tokenizer(question, context, return_tensors="jax", max_length=512, padding="max_length", truncation=True)
19
  start_scores, end_scores = forward(**encoding).to_tuple()
20
 
21
  # Let's take the most likely token using `argmax` and retrieve the answer
@@ -30,4 +30,7 @@ if __name__ == "__main__":
30
  question = gr.inputs.Textbox(lines=2, default="Who added BigBird to HuggingFace Transformers?", label="Question")
31
  context = gr.inputs.Textbox(lines=10, default=default_context, label="Context")
32
 
33
- gr.Interface(fn=get_answer, inputs=[question, context], outputs="text").launch()
 
 
 
 
15
 
16
  def get_answer(question, context):
17
 
18
+ encoding = tokenizer(question, context, return_tensors="jax", max_length=4096, padding="max_length", truncation=True)
19
  start_scores, end_scores = forward(**encoding).to_tuple()
20
 
21
  # Let's take the most likely token using `argmax` and retrieve the answer
 
30
  question = gr.inputs.Textbox(lines=2, default="Who added BigBird to HuggingFace Transformers?", label="Question")
31
  context = gr.inputs.Textbox(lines=10, default=default_context, label="Context")
32
 
33
+ title = "BigBird-RoBERTa"
34
+ desc = "BigBird is a transformer based model which can process long sequences (upto 4096) very efficiently. It has shown outstanding results on long document question answering."
35
+
36
+ gr.Interface(fn=get_answer, inputs=[question, context], outputs="text", title=title, description=desc).launch()