Upload README.md with huggingface_hub

README.md

@@ -8,13 +8,21 @@ tags:
 - data-use
 size_categories:
 - 1K-10K
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: "data/train.jsonl"
+  - split: validation
+    path: "data/validation.jsonl"
+  - split: holdout
+    path: "data/holdout.jsonl"
 ---
 
 # Dataset Card for Datause Dataset
 
-Combined data-mention extraction dataset for the GLiNER2 data-use swarm. A single
-JSONL file (`datause-dataset.jsonl`) carries all three splits, tagged with a
-top-level `split` field: `train`, `validation`, or `holdout`.
+Combined data-mention extraction dataset for the GLiNER2 data-use swarm, with
+three splits: `train`, `validation`, `holdout`.
 
 ## Dataset Summary
 The dataset is designed to teach Named Entity Recognition (NER) models to extract
@@ -28,10 +36,9 @@ references to datasets, databases, and surveys from PDF-extracted text.
 | `holdout` | 1,149 | canonical `holdout_v10` prose-only evaluation benchmark |
 
 ## Schema (GLiNER2 Flat-NER Format)
-Each record has three fields:
-* `split`: one of `train`, `validation`, `holdout`.
+Each record has two fields:
 * `input`: The raw text paragraph containing potential data mentions.
-* `output`: 
+* `output`:
     * `entities`: Dictionaries containing lists of span strings extracted for three categories:
         * `named_data`: Proper name of a dataset (e.g. `National Education Outcomes Registry (NEOR)`).
         * `descriptive_data`: Described data source (e.g. `school enrolment and retention indicators`).
@@ -40,7 +47,6 @@ Each record has three fields:
 Example:
 ```json
 {
-  "split": "train",
   "input": "We use data from the Demographic and Health Surveys (DHS) 2020 and MICS 2019 to analyze child nutrition.",
   "output": {
     "entities": {
@@ -61,6 +67,5 @@ Example:
 ## Loading
 ```python
 from datasets import load_dataset
-ds = load_dataset("json", data_files="https://huggingface.co/datasets/ai4data/datause-dataset/resolve/main/datause-dataset.jsonl", split="train")
-splits = {s: ds.filter(lambda x: x["split"] == s) for s in ("train", "validation", "holdout")}
+ds = load_dataset("ai4data/datause-dataset")  # DatasetDict: train / validation / holdout
 ```