| from transformers import pipeline | |
| import gradio as gr | |
| model_checkpoint = "MuntasirHossain/bert-finetuned-ner" | |
| model = pipeline("token-classification", model=model_checkpoint, aggregation_strategy="simple") | |
| def ner(text): | |
| output = ner_pipeline(text) | |
| return {"text": text, "entities": output} | |
| description = "This AI model is trained to identify and classify named entities in unstructured text." | |
| title = "Named Entity Recognition" | |
| theme = "grass" | |
| examples=[["Mount Everest is Earth's highest mountain, located in the Mahalangur Himal sub-range of the Himalayas. The China-Nepal border runs across it."]] | |
| gr.Interface(fn=predict, | |
| inputs="textbox", | |
| outputs="text", | |
| title=title, | |
| theme = theme, | |
| description=description, | |
| examples=examples, | |
| ).launch() |