Add model card README

README.md

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+---
+license: mit
+tags:
+- gguf
+- bert
+- ner
+- token-classification
+- named-entity-recognition
+base_model: dslim/bert-base-NER
+pipeline_tag: token-classification
+---
+
+# BERT Base NER — GGUF
+
+GGUF conversion of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) for use with [CrispEmbed](https://github.com/CrispStrobe/CrispEmbed).
+
+Fixed-label Named Entity Recognition on English text. BERT-base-cased (110M params) fine-tuned on CoNLL-03 with 9 IOB labels.
+
+## Labels
+
+| ID | Label | Description |
+|----|-------|-------------|
+| 0 | O | Outside any entity |
+| 1 | B-MISC | Beginning of miscellaneous entity |
+| 2 | I-MISC | Inside miscellaneous entity |
+| 3 | B-PER | Beginning of person name |
+| 4 | I-PER | Inside person name |
+| 5 | B-ORG | Beginning of organization |
+| 6 | I-ORG | Inside organization |
+| 7 | B-LOC | Beginning of location |
+| 8 | I-LOC | Inside location |
+
+## Available Formats
+
+| File | Format | Size |
+|------|--------|------|
+| `bert-base-ner-f32.gguf` | Float32 | 412 MB |
+| `bert-base-ner-q8_0.gguf` | Q8_0 | 111 MB |
+| `bert-base-ner-q4_k.gguf` | Q4_K | 70 MB |
+
+## Usage
+
+```bash
+crispembed -m bert-base-ner-q8_0.gguf --ner "Barack Obama was born in Hawaii"
+```
+
+```python
+from crispembed import CrispNER
+ner = CrispNER("bert-base-ner-q8_0.gguf")
+entities = ner.extract("Barack Obama was born in Hawaii")
+# [{"text": "Barack Obama", "label": "PER", "start": 0, "end": 12, "score": 0.999},
+#  {"text": "Hawaii", "label": "LOC", "start": 25, "end": 31, "score": 1.000}]
+```
+
+Auto-detected as BERT NER (vs GLiNER zero-shot) from `ner.classifier.weight` in GGUF.
+
+## Parity
+
+Encoder output: cos_min=0.999971 vs HuggingFace transformers (F32).