---
license: apache-2.0
tags:
- ner
- gliner
- zero-shot
- bootstrap
- uv-script
size_categories:
- n<10K
---

# davanstrien/training-methods-bootstrap

Bootstrap NER dataset produced by [`urchade/gliner_multi-v2.1`](https://huggingface.co/urchade/gliner_multi-v2.1) over [`/input/cleaned-cards.parquet`](https://huggingface.co/datasets//input/cleaned-cards.parquet).

Generated using [`uv-scripts/gliner/extract-entities.py`](https://huggingface.co/datasets/uv-scripts/gliner).

## Provenance

| | |
|---|---|
| Source dataset | `/input/cleaned-cards.parquet` (split `train`) |
| Text column | `card` |
| Bootstrap model | `urchade/gliner_multi-v2.1` |
| Entity types | `training method` |
| Confidence threshold | 0.7 |
| Samples processed | 10000 |
| Total entities extracted | 4278 |
| Inference device | `cuda` |
| Wall clock | 949.8s (10.53 samples/s) |

## Schema

Original `/input/cleaned-cards.parquet` columns plus an `entities` column:

```python
entities: list of {
    "start": int,    # character offset, inclusive
    "end": int,      # character offset, exclusive
    "text": str,     # the matched span
    "label": str,    # one of ['training method']
    "score": float,  # GLiNER confidence in [0, 1]
}
```

## Caveats

- These are **bootstrap labels**, not human-reviewed. Treat low-confidence (< 0.7) entities as candidates for review.
- GLiNER is zero-shot: changing `--entity-types` changes what it extracts, but quality varies by entity type.
- Long texts were truncated at 8000 characters before inference.