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README.md

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+---
+library_name: lucid
+license: mit
+tags:
+  - token-classification
+  - bert
+  - lucid
+datasets:
+  - conll2003
+pipeline_tag: token-classification
+model-index:
+  - name: bert-base-token-cls
+    results:
+      - task: { type: token-classification }
+        dataset: { name: conll2003, type: conll2003 }
+        metrics:
+          - { type: f1, value: 91.3 }
+---
+
+# BERT-Base (CoNLL-2003 NER)
+
+> https://arxiv.org/abs/1810.04805
+
+[Lucid](https://github.com/ChanLumerico/lucid) port of `transformers/dslim/bert-base-NER`,
+converted to Lucid-native safetensors.
+
+## Available weights
+
+| Tag | f1 | Params | GFLOPs | Size | Source |
+|---|---|---|---|---|---|
+| `CONLL2003` *(default)* | 91.3 | 108.3M | — | 413.22 MB | transformers |
+
+## Usage
+
+```python
+import lucid
+import lucid.models as models
+from lucid.models.weights import BERTBaseNERWeights
+
+# default tag
+model = models.bert_base_token_cls(pretrained=True)
+
+# explicit tag (enum or string)
+model = models.bert_base_token_cls(weights=BERTBaseNERWeights.CONLL2003)
+model = models.bert_base_token_cls(pretrained="CONLL2003")
+
+# feed token ids (tokenize with the matching lucid.utils.tokenizer)
+input_ids = lucid.tensor([[101, 7592, 2088, 102]], dtype=lucid.int64)
+out = model(input_ids)
+logits = out.logits  # classification logits
+```
+
+## Conversion
+
+Converted from `transformers/dslim/bert-base-NER` via
+`python -m tools.convert_weights bert_base_token_cls --tag CONLL2003`.
+Key mapping + numerical parity verified against the source.
+
+## License
+
+`mit` — inherited from the original weights.
+
+## Citation
+
+```
+Devlin et al., "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding", NAACL 2019. Miniatures: Turc et al., "Well-Read Students Learn Better", 2019.
+```