Kabeer Akande
adds further arguments
0294383
from transformers import pipeline
import gradio as gr
#reference appropriate Hugging Face model
model_name = 'koakande/bert-finetuned-ner'
# Load token classification pipeline modelfrom Hugging Face
model = pipeline("token-classification", model=model_name, aggregation_strategy="simple")
# write a prediction method for the model
def predict_entities(text):
# Use the loaded model to identify entities in the text
entities = model(text)
# Highlight identified entities in the input text
highlighted_text = text
for entity in entities:
entity_text = text[entity['start']:entity['end']]
replacement = f"<span style='border: 2px solid green;'>{entity_text}</span>"
highlighted_text = highlighted_text.replace(entity_text, replacement)
return highlighted_text
# gradio interface
title = "Named Entity Recognizer"
description = """
This model has been trained to identify entities in a given text. It returns the input text with the entities highlighted in green. Give it a try!
"""
article = "The model is trained using bert-finetuned-ner."
iface = gr.Interface(
fn=predict_entities,
inputs=gr.Textbox(lines=5, placeholder="Enter text..."),
outputs=gr.HTML(),
title=title,
description=description,
article=article,
examples=[["Hello, I am Kabeer. I work as a machine learning engineer at OVO in the UK"], ["This is Maryam who is a Leicester based NHS Doctor"]],
)
iface.launch()