🧠 Model

bert-base-NER

by dslim

--- language: en datasets: - conll2003 license: mit model-index: - name: dslim/bert-base-NER results: - task: type: token-classification name: Token Classificat

πŸ• Updated 12/19/2025

🧠 Architecture Explorer

Neural network architecture

1 Input Layer
2 Hidden Layers
3 Attention
4 Output Layer

About

--- language: en datasets: - conll2003 license: mit model-index: - name: dslim/bert-base-NER results: - task: type: token-classification name: Token Classification dataset: name: conll2003 type: conll2003 config: conll2003 split: test metrics: - name: Accuracy type: accuracy value: 0.9118041001560013 verified: true - name: Precision type: precision value: 0.9211550382257732 verified: true - name: Recall type: recall value: 0.9306415698281261 verified: true - name: F1 type: f1 value: 0.9258740...

πŸ“ Limitations & Considerations

  • β€’ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • β€’ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • β€’ FNI scores are relative rankings and may change as new models are added.
  • β€’ Data source: [{"source_platform":"huggingface","source_url":"https://huggingface.co/dslim/bert-base-NER","fetched_at":"2025-12-19T07:41:01.179Z","adapter_version":"3.2.0"}]

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