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| from transformers import pipeline | |
| import gradio as gr | |
| get_completion = pipeline("ner", model="dslim/bert-base-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 ner(input): | |
| 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 `BERT-base` model under the hood!", | |
| allow_flagging="never", | |
| #Here we introduce a new tag, examples, easy to use examples for your application | |
| examples=["My name is Raul and I live in Niterói, Rio de Janeiro, Brazil", | |
| "Lionel Messi is the greatest footballer of the new century", | |
| "Toronto is hockey capital of the world", | |
| "S&P 500 has gained 400 points in last trailing 7 days", | |
| "Paris is one of most visited cities in the world every year."]) | |
| demo.launch() |