README.md

metadata

language:
  - ar
pretty_name: TypePrediction
task_categories:
  - token-classification
tags:
  - named-entity-recognition
  - arabic
  - wojood
  - entity-typing
size_categories:
  - 100K<n<1M

TypePrediction

Unified mention-level type prediction data built from all available local Wojood NER datasets except WojoodRelations.

Main file

• type_predictor_data.jsonl
• type_predictor_train.jsonl
• type_predictor_val.jsonl
• type_predictor_test.jsonl
• summary.json
• split_summary.json
• build_type_prediction_dataset.py
• push_to_hf.py
• split_data.py
• artifacts/ambiguous_span_type_conflicts.jsonl
• artifacts/split_assignments.jsonl

Each row contains:

{
  "id": "tp_000001",
  "entity": "النص",
  "type": "ORG",
  "sentence": "الجملة الكاملة",
  "start_token": 0,
  "end_token": 1,
  "start_char": 0,
  "end_char": 10,
  "raw_types": ["ORG", "NONGOV"],
  "sources": [
    {"dataset": "WojoodFine", "split": "train"},
    {"dataset": "Wojood1_1_nested", "split": "train"}
  ]
}

Stored row examples

Simple flat/coarse example:

{
  "id": "tp_000001",
  "entity": "عبد الحميد عبد العاطي",
  "type": "PERS",
  "sentence": "! ! ! تم النشر قبل بواسطة عبد الحميد عبد العاطي ازهقنا .",
  "start_token": 7,
  "end_token": 10,
  "start_char": 26,
  "end_char": 47,
  "raw_types": ["PERS"],
  "sources": [{"dataset": "WojoodFine", "split": "train"}]
}

Example where a fine label is mapped to a coarse label:

{
  "id": "tp_001001",
  "entity": "للعراقيين",
  "type": "NORP",
  "sentence": ", أثارت محاكمات 11 مشتبها بهم فرنسيين في العراق في مايو / أيار هذه المخاوف ، ليس فقط للمشتبه بهم الأجانب بل للعراقيين أيضا .",
  "start_token": 22,
  "end_token": 22,
  "start_char": 108,
  "end_char": 117,
  "raw_types": ["NORP"],
  "sources": [{"dataset": "WojoodFine", "split": "train"}]
}

Example merged across multiple source datasets:

{
  "id": "tp_050001",
  "entity": "مصر",
  "type": "GPE",
  "sentence": "بعدما أطاح به آية الله الخميني وبنظامه ، حيث توجه بداية إلى مصر قبل أن يستقر به الأمر في المغرب ، ,",
  "start_token": 12,
  "end_token": 12,
  "start_char": 60,
  "end_char": 63,
  "raw_types": ["GPE", "COUNTRY"],
  "sources": [
    {"dataset": "Wojood1_1_flat", "split": "test"},
    {"dataset": "Wojood1_1_nested", "split": "test"},
    {"dataset": "WojoodFine-Flat", "split": "split20"}
  ]
}

Construction

1. Parse all sentence-separated CoNLL-style files from:
   • Wojood1_1_flat
   • Wojood1_1_nested
   • WojoodFine
   • WojoodFine-Flat
2. Extract every entity span, including nested spans when multiple BIO tags are present on the same token sequence.
3. Map fine labels to coarse labels using wojood_ontology.json.
4. Reconstruct full sentence text and both token and character spans.
5. Deduplicate by (sentence, entity, type, start_token, end_token) and merge provenance.
6. Remove exact span/type conflicts from the main file:
   • conflict key: (sentence, entity, start_token, end_token, start_char, end_char)
   • if the same exact span has more than one coarse type, all candidate rows for that span are written to artifacts/ambiguous_span_type_conflicts.jsonl
   • the main type_predictor_data.jsonl keeps only unambiguous rows
7. Assign deterministic ids in final sorted order: tp_000001, tp_000002, ...

Parsing details and edge cases

• Wojood1_1_flat uses simple space-separated token tag rows.
• Wojood1_1_nested uses space-separated rows with multiple BIO tags on the same token when nested annotations exist.
• WojoodFine and WojoodFine-Flat use tab-separated rows.
• Some WojoodFine rows place multiple tags inside a single tab field, for example:
  • نبيه\tI-OCC B-PERS\t\t\t The builder normalizes this by splitting tag cells on internal whitespace.
• Some fine labels appear with alias spellings that differ from the ontology ids, for example:
  • GPE-ORG -> GPE_ORG
  • Cluster -> CLUSTER
  • Path -> PATH The builder canonicalizes these before mapping to coarse parents.
• Sentence boundaries are read from blank lines in the CoNLL files.
• Character spans are reconstructed after joining sentence tokens with single spaces.
• The main file is single-label by construction. Ambiguous multi-type spans are excluded rather than guessed.

Summary

• total rows: 125996
• unique sentences: 33933
• raw extracted mentions before dedupe: 408035
• rows merged by dedupe: 275272
• ambiguous span groups excluded: 3383
• candidate rows excluded by ambiguity filtering: 6767

Source breakdown

• Wojood1_1_flat/test: 14573 mentions from 6606 sentences
• Wojood1_1_flat/train: 49959 mentions from 23125 sentences
• Wojood1_1_flat/val: 7143 mentions from 3304 sentences
• Wojood1_1_nested/test: 18045 mentions from 6606 sentences
• Wojood1_1_nested/train: 62377 mentions from 23125 sentences
• Wojood1_1_nested/val: 8944 mentions from 3304 sentences
• WojoodFine/test: 27848 mentions from 5748 sentences
• WojoodFine/train: 96188 mentions from 19484 sentences
• WojoodFine/val: 13800 mentions from 2828 sentences
• WojoodFine-Flat/split10: 10859 mentions from 3304 sentences
• WojoodFine-Flat/split20: 22208 mentions from 6606 sentences
• WojoodFine-Flat/split70: 76091 mentions from 23125 sentences

Top coarse types

• GPE: 26902
• ORG: 26593
• DATE: 19762
• PERS: 10712
• NORP: 10217
• ORDINAL: 7807
• OCC: 7696
• EVENT: 3769
• CARDINAL: 3724
• LOC: 2388
• WEBSITE: 1478
• FAC: 1296
• LAW: 904
• TIME: 871
• MONEY: 420
• CURR: 411
• LANGUAGE: 332
• PERCENT: 312
• PRODUCT: 190
• QUANTITY: 106

Validation performed

• verified that all four non-relation Wojood sources were included
• verified nested extraction is enabled when multiple tags occur on one token
• verified fine-to-coarse mapping uses the repo's wojood_ontology.json
• verified sentence[start_char:end_char] == entity across the built file
• verified deduped rows merge provenance rather than silently dropping it
• verified no exact span in the main file has more than one coarse type
• verified ids are unique and deterministic

Split files

The repo also includes a mention-level stratified split built from type_predictor_data.jsonl:

• type_predictor_train.jsonl
• type_predictor_val.jsonl
• type_predictor_test.jsonl
• split_summary.json
• artifacts/split_assignments.jsonl
• split_data.py

Split policy:

1. Start from the clean type_predictor_data.jsonl.
2. Perform a stratified 80/20 split by the type column with random_state = 42.
3. Split the holdout pool again, stratified by type, into equal halves.
4. Keep sentence overlap allowed across splits.
5. Preserve all original fields.
6. Add evaluation_category only to validation and test rows:
   • unseen_sentence
   • seen_sentence_new_entity

Current split sizes:

• train: 100,796
• validation: 12,600
• test: 12,600

Current evaluation-category counts:

• validation seen_sentence_new_entity: 11,661
• validation unseen_sentence: 939
• test seen_sentence_new_entity: 11,606
• test unseen_sentence: 994