Token Classification
Transformers
Safetensors
English
modernbert
Generated from Trainer
named-entity-recognition
Eval Results (legacy)
Instructions to use MatteoFasulo/ModernBERT-base-NER with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MatteoFasulo/ModernBERT-base-NER with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="MatteoFasulo/ModernBERT-base-NER")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("MatteoFasulo/ModernBERT-base-NER") model = AutoModelForTokenClassification.from_pretrained("MatteoFasulo/ModernBERT-base-NER") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
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@@ -141,6 +141,13 @@ for entity in entities:
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print(
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f"{entity['word']}: {entity['entity_group']} (confidence: {entity['score']:.4f})"
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)
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```
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## Ethical Considerations
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print(
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f"{entity['word']}: {entity['entity_group']} (confidence: {entity['score']:.4f})"
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)
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+
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# Apple Inc.: ORG (confidence: 0.9673)
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# founded: MISC (confidence: 0.4503)
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# by: PER (confidence: 0.6405)
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# Steve Jobs: PER (confidence: 0.9905)
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# Cupertino: LOC (confidence: 0.9894)
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# California: LOC (confidence: 0.9859)
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```
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## Ethical Considerations
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