Datasets:
File size: 907 Bytes
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configs:
- config_name: default
data_files:
- split: train
path:
- rulings/*.jsonl
task_categories:
- text-classification
tags:
- annotation
- data-mentions
- human-feedback
license: apache-2.0
---
# Data-use annotations
Public store of keep/drop rulings from the annotation review app (`human_labeling/review.html`).
## Files
- `rulings/<annotator>.jsonl` — one file per annotator, one JSON object per ruling: `key` (span UID), `ruling` (`DATA_MENTION` keep / `NON_MENTION` drop), `queue` (gold / sample), `annotator` (required, set in the UI), `ts`. Last write per `(queue, key, annotator)` wins.
```python
from datasets import load_dataset
ds = load_dataset("rafmacalaba/data-use-annotations") # train = rulings.jsonl
```
Note: `rulings/_schema.jsonl` is a schema placeholder so the dataset always loads — filter `annotator != "_schema"` in analysis.
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