Instructions to use WhiteRoomProdigy/amicus-ner-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WhiteRoomProdigy/amicus-ner-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="WhiteRoomProdigy/amicus-ner-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("WhiteRoomProdigy/amicus-ner-v1") model = AutoModelForTokenClassification.from_pretrained("WhiteRoomProdigy/amicus-ner-v1") - Notebooks
- Google Colab
- Kaggle
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README.md
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# Amicus NER (amicus-ner-v1)
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This is a legal-domain Named Entity Recognition (NER) model built by fine-tuning **legal-bert-base-uncased** on
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## Model Description
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The model extracts key legal entities from unstructured legal text. It is designed to assist in legal document parsing, case law summary, and legal search applications.
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# Amicus NER (amicus-ner-v1)
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This is a legal-domain Named Entity Recognition (NER) model built by fine-tuning **legal-bert-base-uncased** on court judgments and legal texts.
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## Model Description
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The model extracts key legal entities from unstructured legal text. It is designed to assist in legal document parsing, case law summary, and legal search applications.
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