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import gradio as gr
import numpy as np
from transformers import pipeline
title = "Token Classification"
description = """
Label the entities of a sentence as:
1. person(PER),
2. organization(ORG),
3. location(LOC)
4. miscellaneous(MISC).
<img src="https://huggingface.co/spaces/course-demos/Rick_and_Morty_QA/resolve/main/rick.png" width=200px>
"""
article = "Check out [my github repository](https://github.com/Neural-Net-Rahul/P2-Token-Classification-using-Fine-tuned-Hugging-face-transformer) and my [fine tuned model](https://huggingface.co/neural-net-rahul/bert-finetuned-ner)"
textbox = gr.Textbox(label="Type your sentence here :", placeholder="My name is Bill Gates.", lines=3)
model = pipeline('token-classification',model='neural-net-rahul/bert-finetuned-ner')
def predict(text):
result = []
for dicti in model(text):
entity,word = dicti['entity'],dicti['word']
if entity == "B-PER" or entity=='I-PER':
entity = "Person"
elif entity == "B-LOC" or entity=='I-LOC':
entity = "Location"
elif entity == "B-ORG" or entity=='I-ORG':
entity = "Organization"
elif entity == "B-MISC" or entity=='I-MISC':
entity = "Miscellaneous"
result.append({entity,word})
return result
gr.Interface(
fn=predict,
inputs=textbox,
outputs=[gr.Text()],
title=title,
description=description,
article=article,
examples=[["Mark founded Facebook, shaping global social media connectivity."], ["Delhi is the most beautiful state after Kerala"]],
).launch() |