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| #!pip install -q transformers | |
| from transformers import pipeline | |
| ner_pipeline = pipeline("ner", model="Tirendaz/roberta-base-NER") | |
| text = "I am Tim and I work at Google" | |
| ner_pipeline(text) | |
| text_tr = "Benim adım Ali ve Trendyol'da çalışıyorum." | |
| ner_pipeline(text_tr) | |
| ner_pipeline(text_tr, aggregation_strategy = "simple") | |
| def ner(text): | |
| output = ner_pipeline(text, aggregation_strategy = "simple") | |
| return {"text": text, "entities": output} | |
| #!pip install -q gradio | |
| import gradio as gr | |
| examples=[ | |
| "My name is Tim and I live in California", | |
| "Ich arbeite bei Google in Berling", | |
| "Ali, Ankaralı mı?" | |
| ] | |
| demo = gr.Interface( | |
| ner, | |
| gr.Textbox(placeholder="Enter sentence here..."), | |
| gr.HighlightedText(), | |
| examples=examples | |
| ) | |
| demo.launch(share=True) |