Release verified evaluation data and paper training annotations

LICENSE

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+Generated annotations: Creative Commons Attribution 4.0 International (CC BY 4.0).
+Copyright (c) 2026 Bora Kargi, Arnas Uselis, Seong Joon Oh.
+
+License terms: https://creativecommons.org/licenses/by/4.0/legalcode
+
+This license applies to the authors’ generated annotations. Third-party source captions and images retain their original rights and terms; see README.md.

README.md

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+---
+license: cc-by-4.0
+pretty_name: CS-CLIP Training Annotations
+language:
+- en
+tags:
+- image-text-retrieval
+- compositionality
+- clip
+- arxiv:2602.23906
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*.parquet
+---
+
+# CS-CLIP Training Annotations
+
+Prepared training annotations for **[Half-Truths Break Similarity-Based Retrieval](https://arxiv.org/abs/2602.23906)**, NeurIPS 2026. Bora Kargi, Arnas Uselis, Seong Joon Oh.
+
+This release contains **410,340 caption records** from the eight annotation files used by the reported CS-CLIP run. All reference COCO `train2014` images. Caption counts are not unique image counts. Images are downloaded separately from COCO.
+
+The [verified Half-Truth evaluation dataset](https://huggingface.co/datasets/kbora/Half-Truths) is released separately.
+
+## Train using the original files
+
+The archive preserves the training JSON files exactly, including their original field names. The [CS-CLIP repository](https://github.com/kargibora/CS-CLIP) provides training code.
+
+```bash
+hf download kbora/CS-CLIP-Training original/training-json.tar.gz \
+  --repo-type dataset --local-dir datasets/CS-CLIP-Training
+mkdir -p datasets/CS-CLIP-Training/json
+tar -xzf datasets/CS-CLIP-Training/original/training-json.tar.gz \
+  -C datasets/CS-CLIP-Training/json
+```
+
+`image_path` is relative to the image root, for example `datasets/COCO/train2014/COCO_train2014_000000057870.jpg`. With this directory layout, set `IMAGE_ROOT=.`.
+
+## Browse the annotations
+
+```python
+from datasets import load_dataset
+samples = load_dataset("kbora/CS-CLIP-Training", split="train")
+```
+
+The Parquet view exposes the main training fields in a tabular schema:
+
+- `sample_id`, `original_caption`, `image_path`: caption and image references.
+- `entities`: extracted positive units, called `positive_components` in the original JSON.
+- `entity_foils`: rows of `positive`, `negative`, and `change_type`, flattened from `negative_components`.
+- `relations_json`: JSON-encoded relation units and their matched foils, preserving nested source fields.
+- `swap_negatives`: full-caption shuffled negatives.
+
+Use the original archive for exact training inputs; the Parquet files are a browsing view. `manifest.json` records each original file's SHA-256 and sample count.
+
+## Construction and limitations
+
+The annotations contain automatically generated entity/relation units, matched foils, and shuffled caption negatives. They are **not human-verified training labels**. An image-grounded VLM audit of 1,000 training foils estimated a 24.6% false-negative rate: some nominally incorrect foils are true for the image. The paper's evaluation uses a separate human-verified suite. The training archive is distributed as used, including that noise.
+
+## Source assets and licensing
+
+The authors’ generated annotations are released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). This grant covers their contributions, not third-party source captions or images.
+
+Source captions and images come from [COCO](https://cocodataset.org/). COCO images are not included in this repository and retain their original rights and terms. An annotation license does not relicense source assets.
+
+## Citation
+
+```bibtex
+@inproceedings{kargi2026halftruths,
+  title={Half-Truths Break Similarity-Based Retrieval},
+  author={Kargi, Bora and Uselis, Arnas and Oh, Seong Joon},
+  booktitle={Advances in Neural Information Processing Systems},
+  year={2026},
+  url={https://arxiv.org/abs/2602.23906}
+}
+```

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manifest.json

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+}

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