| --- |
| 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}} |
| } |
| ``` |
| |
