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from transformers import pipeline
import gradio as gr
get_completion = pipeline("ner", model="dslim/bert-base-NER")
def ner(input):
output = get_completion(input)
return {"text": input, "entities": output}
def merge_tokens(tokens):
'''
WHAT: Faz um loop entre os tokens para concatenar os tokens
com entidades I-* (intermediate token) aos B-* (begining token).
'''
merged_tokens = []
for token in tokens:
if merged_tokens and token['entity'].startswith('I-') and merged_tokens[-1]['entity'].endswith(token['entity'][2:]):
# Se a lista merged_tokens não estiver vazia.
# o token atual for um token intermediário (começa com 'I-').
# a entidade do último token processado terminar com a mesma entidade do token atual, excluindo o prefixo 'I-'.
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:
merged_tokens.append(token)
return merged_tokens
def ner(input):
"""
WHAT: Aplica a task de NER com o hugginface pipeline e concatena os tokens com a mesma entidade.
RETURN: retorna um dicionário com o token e suas entidades.
"""
output = get_completion(input)
merged_tokens = merge_tokens(output)
return {"text": input, "entities": merged_tokens}
gr.close_all()
demo = gr.Interface(fn=ner,
inputs=[gr.Textbox(label="Text to find entities", lines=2)],
outputs=[gr.HighlightedText(label="Text with entities")],
title="NER with dslim/bert-base-NER",
description="Find entities using the `dslim/bert-base-NER` model under the hood!",
allow_flagging="never",
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"])
demo.launch()
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