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import gradio as gr
from transformers import pipeline
ner = pipeline('ner')
def merge_tokens(tokens):
merged_tokens = []
for token in tokens:
if merged_tokens and token['entity'].startswith('I-') and merged_tokens[-1]['entity'].endswith(token['entity'][2:]):
# If current token continues the entity of the last one, merge them
last_token = merged_tokens[-1]
last_token['word'] += token['word'].replace('##', '')
last_token['end'] = token['end']
last_token['score'] = (last_token['score'] + token['score']) / 2
else:
# Otherwise, add the token to the list
merged_tokens.append(token)
return merged_tokens
def named(input):
output = ner(input)
merged_word = merge_tokens(output)
return {'text': input, 'entities': merged_word}
a = gr.Interface(fn=named,
inputs=[gr.Textbox(label="Text input", lines= 2)],
outputs=[gr.HighlightedText(label='Text with entities')],
title='Named Entity Recognition', examples=["My name is Andrew, I'm building DeeplearningAI and I live in California", "My name is Poli, I live in Vienna and work at HuggingFace"])
a.launch()
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