README.md

metadata

task_categories:
  - token-classification
tags:
  - ner
  - span-extraction
  - data-mentions
  - economics
license: cc-by-4.0
configs:
  - config_name: bio
    data_files:
      - split: train
        path: bio_train.jsonl
      - split: val
        path: bio_val.jsonl
      - split: holdout
        path: bio_holdout.jsonl
  - config_name: gliner
    data_files:
      - split: train
        path: gliner_train.jsonl
      - split: val
        path: gliner_val.jsonl
      - split: holdout
        path: gliner_holdout.jsonl
  - config_name: gliner2
    data_files:
      - split: train
        path: gliner2_train.jsonl
      - split: val
        path: gliner2_val.jsonl
      - split: holdout
        path: gliner2_holdout.jsonl
dataset_info:
  config_name: gliner2
  features:
    - name: input
      dtype: string
    - name: output
      struct:
        - name: entities
          struct:
            - name: named_data
              list: string
            - name: descriptive_data
              list: string
            - name: vague_data
              list: string
        - name: entity_descriptions
          struct:
            - name: named_data
              dtype: string
            - name: descriptive_data
              dtype: string
            - name: vague_data
              dtype: string
    - name: corpus
      dtype: string
    - name: origin
      dtype: string
    - name: corpus_id
      dtype: string
    - name: page
      dtype: int64
    - name: chunk
      dtype: int64
    - name: title
      dtype: string
    - name: pdf_url
      dtype: string
    - name: extractor
      dtype: string
    - name: footnote_link
      dtype: bool
    - name: dedupe_overlap
      dtype: bool
    - name: has_data_score
      dtype: float64
    - name: split
      dtype: string
    - name: spans
      list:
        - name: text
          dtype: string
        - name: label
          dtype: string
        - name: score
          dtype: float64
        - name: start
          dtype: int64
        - name: end
          dtype: int64
    - name: singlepass_model
      dtype: string
    - name: singlepass_keep_thr
      dtype: float64
    - name: singlepass_entities
      list:
        - name: text
          dtype: string
        - name: start
          dtype: int64
        - name: end
          dtype: int64
        - name: extractor_score
          dtype: float64
        - name: probe_score
          dtype: float64
        - name: keep
          dtype: bool
  splits:
    - name: train
      num_bytes: 434822580
      num_examples: 201025
    - name: val
      num_bytes: 188660358
      num_examples: 42414
    - name: holdout
      num_bytes: 196872595
      num_examples: 44174
  download_size: 497942440
  dataset_size: 820355533

Data-use mentions (NER / span extraction)

Data mentions extracted from World Bank Policy Research Working Papers and FCV documents, predicted by a span-extraction model with no human or LLM-judge validation, and formatted for span-extraction (GLiNER / GLiNER2) and token-classification (LFM2.5-encoder) fine-tuning.

Labels

Three entity types:

• NAMED_DATA — a proper name, title, or acronym of a specific data source
• DESCRIPTIVE_DATA — a source described in words but not named
• VAGUE_DATA — generic data wording with no identifiable source

O (BIO) = background, including hard negatives.

Negative strategy

• No hard negatives — every predicted span is kept as a positive.
• Negative-only chunks (no predicted spans) are sampled at a controlled ratio per split.

Re-chunking (why windows are <= 384 tokens)

The upstream input_text is already an extractor chunk of at most max_tokens (default 384) whitespace tokens, so it fits GLiNER/GLiNER2's context window as-is; a sliding-window re-chunk pass runs only as a safety net.

Provenance

Every row contains corpus (prwp or fcv) and origin (the extraction config, e.g. general_prwp, fcv_pads_east_africa, jdc_operational, refugee_pads, reliefweb). See split_stats.json for per-split provenance counts.

Configs

• gliner — {"tokenized_text": [...], "ner": [[start, end, label], ...]} (word-level spans)
• bio — {"tokens": [...], "ner_tags": ["O", "B-NAMED_DATA", ...]}
• gliner2 — {"input": "...", "output": {"entities": {...}, "entity_descriptions": {...}}} (GLiNER2 flat-NER, span strings + descriptions)

Source (raw extraction, no judge)

Unlike the judged build, every span here is a raw prediction of rafmacalaba/gliner_datause (labels NAMED_DATA, DESCRIPTIVE_DATA, VAGUE_DATA, footnote_link=True, dedupe_overlap=True). There are no hard negatives; negatives are entity-less chunks sampled at ~0.25 per positive per split. Treat spans as noisy positives, not gold labels.

Traceability columns

Every config carries the same flat provenance columns alongside its training keys, so any row joins back to the parsed .md (<origin>/<corpus_id with ':' -> '_'>.md), the source PDF, and the extraction run:

• corpus_id — document id (<origin>:<NNNNNN>); page, chunk — position within the document
• title, pdf_url — document title and source PDF URL
• extractor — model id that produced the spans; footnote_link, dedupe_overlap — pipeline flags baked into input/text
• has_data_score — chunk-level data score (1.0 iff spans present)
• split — which file the row came from (train / val / holdout)
• spans — full span detail the training keys collapse: [{text, label, score, start, end}] with char offsets into input/text

Splits

Document-disjoint train / val / holdout (70% / 15% / 15%). See split_stats.json.

Usage

from datasets import load_dataset
gliner  = load_dataset("rafmacalaba/datause-extracted", "gliner")
bio     = load_dataset("rafmacalaba/datause-extracted", "bio")
gliner2 = load_dataset("rafmacalaba/datause-extracted", "gliner2")

Files

• gliner_{train,val,holdout}.jsonl, bio_{train,val,holdout}.jsonl, gliner2_{train,val,holdout}.jsonl
• labels.json (label lists), split_stats.json (per-split counts)