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import gradio as gr
from transformers import pipeline
get_completion = pipeline("ner", model="dslim/bert-base-NER")
def merge_tokens(tokens):
merged_tokens = []
for token in tokens:
if (merged_tokens and token['word'].startswith('##')) or (merged_tokens and token['entity'].startswith('I-') and merged_tokens[-1]['entity'].endswith(token['entity'][2:])):
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
merged_tokens[-1] = last_token
else:
# Otherwise, add the token to the list
merged_tokens.append(token)
return merged_tokens
def ner_merged(input):
output = get_completion(input)
merged_tokens = merge_tokens(output)
return {"text": input, "entities": merged_tokens}
demo = gr.Interface(fn=ner_merged,
# 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!",
inputs=[gr.Textbox(label="Type or paste text to find Named Entities or even select and submit below examples", lines=2)],
outputs=[gr.HighlightedText(label="Text with Named Entities identified")],
title="Named Entity Recognition test and demo app by Srinivas.V ",
description="Find entities",
allow_flagging="never",
examples=["My name is Srinivas and I live in Dubai, United Arab Emirates. I love DeepLearningAI",
"I am a Data Scientist and I am a citizen of Bharat"])
demo.launch() |