Upload app.py

app.py

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+import jax
+import jax.numpy as jnp
+from transformers import FlaxBigBirdForQuestionAnswering, BigBirdTokenizerFast
+import gradio as gr
+
+FLAX_MODEL_ID = "vasudevgupta/flax-bigbird-natural-questions"
+
+if __name__ == "__main__":  
+  model = FlaxBigBirdForQuestionAnswering.from_pretrained(FLAX_MODEL_ID, block_size=64, num_random_blocks=3)
+  tokenizer = BigBirdTokenizerFast.from_pretrained(FLAX_MODEL_ID)
+  
+  @jax.jit
+  def forward(*args, **kwargs):
+    return model(*args, **kwargs)
+  
+  def get_answer(question, context):
+  
+      encoding = tokenizer(question, context, return_tensors="jax", max_length=512, padding="max_length", truncation=True)
+      start_scores, end_scores = forward(**encoding).to_tuple()
+  
+      # Let's take the most likely token using `argmax` and retrieve the answer
+      all_tokens = tokenizer.convert_ids_to_tokens(encoding["input_ids"][0].tolist())
+  
+      answer_tokens = all_tokens[jnp.argmax(start_scores): jnp.argmax(end_scores)+1]
+      answer = tokenizer.decode(tokenizer.convert_tokens_to_ids(answer_tokens))
+  
+      return answer
+  
+  default_context = "Models like BERT, RoBERTa have a token limit of 512. But BigBird supports up to 4096 tokens! How does it do that? How can transformers be applied to longer sequences? In Abhishek Thakur's next Talks, I will discuss BigBird!! Attend this Friday, 9:30 PM IST Live link: https://www.youtube.com/watch?v=G22vNvHmHQ0.\nBigBird is a transformer based model which can process long sequences (upto 4096) very efficiently. RoBERTa variant of BigBird has shown outstanding results on long document question answering."
+  question = gr.inputs.Textbox(lines=2, default="When is talk happening?", label="Question")
+  context = gr.inputs.Textbox(lines=10, default=default_context, label="Context")
+
+  title = "BigBird-RoBERTa"
+  desc = "BigBird is a transformer based model which can process long sequences (upto 4096) very efficiently. RoBERTa variant of BigBird has shown outstanding results on long document question answering."
+
+  gr.Interface(fn=get_answer, inputs=[question, context], outputs="text", title=title, description=desc).launch()