Upload TypePrediction dataset

README.md

@@ -20,6 +20,9 @@ Unified mention-level type prediction data built from all available local Wojood
 ## Main file
 
 - `type_predictor_data.jsonl`
+- `summary.json`
+- `build_type_prediction_dataset.py`
+- `push_to_hf.py`
 
 Each row contains:
 
@@ -40,6 +43,60 @@ Each row contains:
 }
 ```
 
+## Stored row examples
+
+Simple flat/coarse example:
+
+```json
+{
+  "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:
+
+```json
+{
+  "entity": "قضاء رام الله",
+  "type": "GPE",
+  "sentence": """ ديوان قائمقام قضاء رام الله ، الى رئيس بلدية مدينة البيرة بشان المادة الخامسة من قانون الدفاع رقم "" "" 6 "" "" لسنة 1939 بتاريخ ( 9 / 7 / 1952 ) "" .",
+  "start_token": 3,
+  "end_token": 5,
+  "start_char": 17,
+  "end_char": 30,
+  "raw_types": ["GPE", "STATE-OR-PROVINCE"],
+  "sources": [{"dataset": "WojoodFine", "split": "train"}]
+}
+```
+
+Example merged across multiple source datasets:
+
+```json
+{
+  "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:
@@ -52,6 +109,22 @@ Each row contains:
 4. Reconstruct full sentence text and both token and character spans.
 5. Deduplicate by `(sentence, entity, type, start_token, end_token)` and merge provenance.
 
+## 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.
+
 ## Summary
 
 - total rows: `132763`
@@ -96,3 +169,11 @@ Each row contains:
 - `PERCENT`: 312
 - `PRODUCT`: 191
 - `QUANTITY`: 115
+
+## 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

build_type_prediction_dataset.py

@@ -239,6 +239,9 @@ Unified mention-level type prediction data built from all available local Wojood
 ## Main file
 
 - `type_predictor_data.jsonl`
+- `summary.json`
+- `build_type_prediction_dataset.py`
+- `push_to_hf.py`
 
 Each row contains:
 
@@ -259,6 +262,60 @@ Each row contains:
 }}
 ```
 
+## Stored row examples
+
+Simple flat/coarse example:
+
+```json
+{{
+  "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:
+
+```json
+{{
+  "entity": "قضاء رام الله",
+  "type": "GPE",
+  "sentence": "\"\" ديوان قائمقام قضاء رام الله ، الى رئيس بلدية مدينة البيرة بشان المادة الخامسة من قانون الدفاع رقم \"\" \"\" 6 \"\" \"\" لسنة 1939 بتاريخ ( 9 / 7 / 1952 ) \"\" .",
+  "start_token": 3,
+  "end_token": 5,
+  "start_char": 17,
+  "end_char": 30,
+  "raw_types": ["GPE", "STATE-OR-PROVINCE"],
+  "sources": [{{"dataset": "WojoodFine", "split": "train"}}]
+}}
+```
+
+Example merged across multiple source datasets:
+
+```json
+{{
+  "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:
@@ -271,6 +328,22 @@ Each row contains:
 4. Reconstruct full sentence text and both token and character spans.
 5. Deduplicate by `(sentence, entity, type, start_token, end_token)` and merge provenance.
 
+## 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.
+
 ## Summary
 
 - total rows: `{summary['total_rows']}`
@@ -285,6 +358,14 @@ Each row contains:
 ## Top coarse types
 
 {top_types}
+
+## 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
 """