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README.md

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+---
+library_name: lucid
+license: mit
+tags:
+  - question-answering
+  - bert
+  - lucid
+datasets:
+  - squad
+pipeline_tag: question-answering
+model-index:
+  - name: bert-base-qa
+    results:
+      - task: { type: question-answering }
+        dataset: { name: squad, type: squad }
+        metrics:
+          - { type: exact_match, value: 80.9 }
+          - { type: f1, value: 88.1 }
+---
+
+# BERT-Base (SQuAD v1.1)
+
+> https://arxiv.org/abs/1810.04805
+
+[Lucid](https://github.com/ChanLumerico/lucid) port of `transformers/csarron/bert-base-uncased-squad-v1`,
+converted to Lucid-native safetensors.
+
+## Available weights
+
+| Tag | exact_match | f1 | Params | GFLOPs | Size | Source |
+|---|---|---|---|---|---|---|
+| `SQUAD_V1` *(default)* | 80.9 | 88.1 | 109.5M | — | 417.67 MB | transformers |
+
+## Usage
+
+```python
+import lucid
+import lucid.models as models
+from lucid.models.weights import BERTBaseQAWeights
+
+# default tag
+model = models.bert_base_qa(pretrained=True)
+
+# explicit tag (enum or string)
+model = models.bert_base_qa(weights=BERTBaseQAWeights.SQUAD_V1)
+model = models.bert_base_qa(pretrained="SQUAD_V1")
+
+# 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)
+start, end = out.start_logits, out.end_logits  # (B, T) each
+```
+
+## Conversion
+
+Converted from `transformers/csarron/bert-base-uncased-squad-v1` via
+`python -m tools.convert_weights bert_base_qa --tag SQUAD_V1`.
+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.
+```