---
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).