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Verified signature# DistilBERT for Named Entity Recognition This project demonstrates fine-tuning **DistilBERT** for Named Entity Recognition (NER) using the **CoNLL-2003** dataset. The model is designed to detect and classify named entities in English text. ## Model Information - **Pretrained Model:** distilbert-base-uncased - **Dataset:** CoNLL-2003 - **Task:** Token Classification (NER) - **Libraries:** PyTorch, Hugging Face Transformers The model recognizes four main entity categories: - **PER:** Person names - **ORG:** Organizations - **LOC:** Locations - **MISC:** Miscellaneous entities The predictions follow the BIO tagging scheme (`B-`, `I-`, and `O`) to distinguish entity boundaries. ## Using the Model The fine-tuned model is available on Hugging Face Hub and can be used for inference with the `transformers` library. ```python from transformers import pipeline # Initialize the NER pipeline ner = pipeline( task="token-classification", model="mohammad748/distilbert-conll2003-ner…2d337cd -
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