API_URL = "https://api-inference.huggingface.co/models/dslim/bert-base-NER" # Helper function import requests, json #Summarization endpoint def get_completion(inputs,ENDPOINT_URL, parameters=None): hf_api_key = "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" headers = { "Content-Type": "application/json" } data = { "inputs": inputs } if parameters is not None: data.update({"parameters": parameters}) response = requests.request("POST", ENDPOINT_URL, headers=headers, data=json.dumps(data) ) return json.loads(response.content.decode("utf-8")) import gradio as gr def merge_tokens(tokens): merged_tokens = [] for token in tokens: if merged_tokens and token['entity_group'].startswith('I-') and merged_tokens[-1]['entity_group'].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, parameters=None, ENDPOINT_URL=API_URL) 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(inline= False)