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import gradio as gr
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
import re
description = "Named Entity Recognition Using BERT"
title = "NERBERT"
examples = [["Hey, Alex here from London!"], ["My name is Wolfgang and I live in Berlin"]]
def findNER(example):
tokenizer = AutoTokenizer.from_pretrained("dslim/bert-base-NER")
model = AutoModelForTokenClassification.from_pretrained("dslim/bert-base-NER")
ner_pipeline = pipeline("ner", model=model, tokenizer=tokenizer)
pipeline_output = ner_pipeline(example)
final_output = []
# all_words = re.split(r'[^a-zA-Z0-9\s]', example)
for _ in pipeline_output:
final_output.extend([(_['word'], _['entity'])])
return final_output
interface = gr.Interface(fn=findNER, inputs='text', outputs=['highlight'], examples=examples, description=description, title=title, interpretation='default')
interface.launch() |