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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) |