Add dataset card with split configs for train/val/test and heldout_reviews

README.md

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+---
+language:
+- en
+license: other
+task_categories:
+- text-classification
+- text-generation
+pretty_name: Cochrane Screening SFT
+tags:
+- systematic-review
+- cochrane
+- title-abstract-screening
+- medical
+- peft
+- sft
+size_categories:
+- 100K<n<1M
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: train.jsonl
+  - split: validation
+    path: val.jsonl
+  - split: test
+    path: test.jsonl
+- config_name: heldout_reviews1
+  data_files:
+  - split: test
+    path: heldout_reviews1.jsonl
+- config_name: heldout_reviews2
+  data_files:
+  - split: test
+    path: heldout_reviews2.jsonl
+- config_name: heldout_reviews3
+  data_files:
+  - split: test
+    path: heldout_reviews3.jsonl
+dataset_info:
+- config_name: default
+  features:
+  - name: messages
+    list:
+    - name: role
+      dtype: string
+    - name: content
+      dtype: string
+  - name: row_id
+    dtype: int64
+  - name: label
+    dtype: string
+  splits:
+  - name: train
+    num_examples: 416799
+  - name: validation
+    num_examples: 46311
+  - name: test
+    num_examples: 26673
+- config_name: heldout_reviews1
+  features:
+  - name: messages
+    list:
+    - name: role
+      dtype: string
+    - name: content
+      dtype: string
+  - name: row_id
+    dtype: int64
+  - name: label
+    dtype: string
+  splits:
+  - name: test
+    num_examples: 26858
+- config_name: heldout_reviews2
+  features:
+  - name: messages
+    list:
+    - name: role
+      dtype: string
+    - name: content
+      dtype: string
+  - name: row_id
+    dtype: int64
+  - name: label
+    dtype: string
+  splits:
+  - name: test
+    num_examples: 4391
+- config_name: heldout_reviews3
+  features:
+  - name: messages
+    list:
+    - name: role
+      dtype: string
+    - name: content
+      dtype: string
+  - name: row_id
+    dtype: int64
+  - name: label
+    dtype: string
+  splits:
+  - name: test
+    num_examples: 19007
+---
+
+# Cochrane Screening SFT
+
+Supervised fine-tuning (SFT) chat dataset for **Cochrane-style title and abstract screening**.
+
+Each example is a chat conversation that asks a model to predict a screening decision
+(`include` / `exclude` / `uncertain`) and a short justification (`reason`).
+
+Code: [ljwa2323/cochrane-screening-slm](https://github.com/ljwa2323/cochrane-screening-slm)
+
+## Dataset summary
+
+| Split / config | Records | Role |
+| --- | ---: | --- |
+| `train` | 416,799 | LoRA SFT training |
+| `validation` | 46,311 | Training-time validation (10% stratified holdout from development data) |
+| `test` | 26,673 | Internal held-out test split |
+| `heldout_reviews1` | 26,858 | External reviews (random Cochrane set) |
+| `heldout_reviews2` | 4,391 | External reviews (HIV-focused set) |
+| `heldout_reviews3` | 19,007 | External reviews (heart/CVD-focused set) |
+
+Label mapping used when building the dataset:
+
+- `0.0` -> `exclude`
+- `0.5` -> `uncertain`
+- `1.0` -> `include`
+
+Approximate label counts on the development-derived set (train+val source):
+
+- exclude: 221,129
+- uncertain: 160,861
+- include: 81,120
+
+## Data fields
+
+Each `*.jsonl` line contains:
+
+| Field | Type | Description |
+| --- | --- | --- |
+| `messages` | list | Chat turns: `system`, `user`, `assistant` |
+| `row_id` | int | Source row id |
+| `label` | string | Gold label: `include` / `exclude` / `uncertain` |
+
+The assistant target is a JSON object:
+
+```json
+{"label": "include|exclude|uncertain", "reason": "<brief explanation>"}
+```
+
+## How to load
+
+```python
+from datasets import load_dataset
+
+# Internal splits
+ds = load_dataset("deepcoder2024/cochrane-screening-sft")
+print(ds)
+
+# External held-out reviews
+hr1 = load_dataset("deepcoder2024/cochrane-screening-sft", "heldout_reviews1")
+hr2 = load_dataset("deepcoder2024/cochrane-screening-sft", "heldout_reviews2")
+hr3 = load_dataset("deepcoder2024/cochrane-screening-sft", "heldout_reviews3")
+```
+
+> Tip: ignore `*_manifest.json` files when loading. They are metadata only and should not be parsed as chat examples.
+
+## Intended use
+
+- Fine-tune small language models (e.g., Qwen3 LoRA) for title/abstract screening
+- Evaluate screening label + reason generation on internal and external review sets
+
+## Out-of-scope use
+
+- Not a substitute for expert systematic-review judgment
+- Not intended for clinical decision-making about individual patients
+- Labels and reasons are for research / screening-assistance experiments only
+
+## Related models
+
+- [`deepcoder2024/Qwen3-1.7B-LoRA-Cochrane-Screening`](https://huggingface.co/deepcoder2024/Qwen3-1.7B-LoRA-Cochrane-Screening)
+- [`deepcoder2024/Qwen3-4B-LoRA-Cochrane-Screening`](https://huggingface.co/deepcoder2024/Qwen3-4B-LoRA-Cochrane-Screening)
+- [`deepcoder2024/Qwen3-8B-LoRA-Cochrane-Screening`](https://huggingface.co/deepcoder2024/Qwen3-8B-LoRA-Cochrane-Screening)
+
+## Citation
+
+If you use this dataset, please cite the associated project repository:
+
+```bibtex
+@misc{cochrane_screening_sft,
+  title        = {Cochrane Screening SFT Dataset},
+  author       = {deepcoder2024},
+  year         = {2026},
+  howpublished = {\\url{https://huggingface.co/datasets/deepcoder2024/cochrane-screening-sft}}
+}
+```