Initial Commit

app.py

@@ -1,12 +1,46 @@
-
+import gradio as gr
 from transformers import AutoTokenizer,AutoModelForQuestionAnswering
-from transformers import pipeline
+import torch
+
+def inference(question,context):
+    question_first=bool(tokenizer.padding_side=='right')
+    max_answer_len=5
+    encoded_text=tokenizer.encode_plus(question,context,padding='longest',
+                    truncation="longest_first" ,
+                    max_length=512,
+                    stride=30,
+                    return_tensors="pt",
+                    return_token_type_ids=False,
+                    return_overflowing_tokens=False,
+                    return_offsets_mapping=False,
+                    return_special_tokens_mask=False)
+    input_ids=encoded_text['input_ids'].tolist()[0]
+    tokens=tokenizer.convert_ids_to_tokens(input_ids)
+    with torch.no_grad():
+        outputs=model(**encoded_text)
+#     answer_st=outputs.start_logits
+#     answer_et=outputs.end_logits
+    start_,end_=outputs[:2]
+    answer_start=torch.argmax(start_)
+    answer_end=torch.argmax(end_)+1
+    answer=tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end]))
+    print(answer)
+    
+
+
+
 model=AutoModelForQuestionAnswering.from_pretrained('abhilash1910/albert-squad-v2')
 tokenizer=AutoTokenizer.from_pretrained('abhilash1910/albert-squad-v2')
+'''
 nlp_QA=pipeline('question-answering',model=model,tokenizer=tokenizer)
 QA_inp={
     'question': 'How many parameters does Bert large have?',
     'context': 'Bert large is really big... it has 24 layers, for a total of 340M parameters.Altogether it is 1.34 GB so expect it to take a couple minutes to download to your Colab instance.'
 }
+
 result=nlp_QA(QA_inp)
-result
+'''
+question='How many parameters does Bert large have?'
+context='Bert large is really big... it has 24 layers, for a total of 340M parameters.Altogether it is 1.34 GB so expect it to take a couple minutes to download to your Colab instance.'
+title = 'Question Answering demo with Albert QA transformer and gradio'
+interface = gr.Interface.from_pipeline(inference,title = title,theme = "peach",examples = [[context, question]]).launch()