Add dataset card, paper link, and evaluation instructions

Hi! I'm Niels, part of the community science team at Hugging Face.

This PR improves the dataset card for TOC-Bench by adding:
- Metadata for the `video-text-to-text` task category.
- Links to the research paper and the official GitHub repository.
- A summary of the benchmark's scope and dimensions.
- Standardized evaluation instructions and code snippets from the repository.

Please let me know if you have any questions!

README.md

@@ -1,3 +1,63 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+task_categories:
+- video-text-to-text
+tags:
+- video-understanding
+- temporal-consistency
+- benchmark
+---
+
+# TOC-Bench: A Temporal Object Consistency Benchmark for Video Large Language Models
+
+[Paper](https://huggingface.co/papers/2605.09904) | [GitHub Repository](https://github.com/cjzcjz666/toc_bench)
+
+TOC-Bench is a diagnostic benchmark designed to evaluate **temporal object consistency** in Video Large Language Models (Video-LLMs). While models often excel at general video understanding, TOC-Bench specifically tests their ability to maintain the identity, state, and continuity of objects across challenges such as occlusion, disappearance, reappearance, and state transitions.
+
+## Benchmark Details
+
+- **Size**: 2,323 high-quality, human-verified QA pairs over 1,951 videos.
+- **Grounded reasoning**: Each item is object-track grounded, linking subjects to per-frame trajectories and structured temporal event timelines.
+- **Dimensions**: Covers 10 diagnostic dimensions, including event counting, event ordering, identity-sensitive reasoning, and hallucination-aware verification.
+- **Filtering**: Employs a three-layer temporal-necessity filtering protocol to ensure that questions require temporal visual evidence rather than language priors or single-frame shortcuts.
+
+## Evaluation
+
+The official repository provides a standardized evaluation harness to score model predictions.
+
+### Running Inference
+To run a model on the benchmark using `eval_runner.py`:
+
+```bash
+python eval_runner.py \
+  --bench <REPAIRED_BENCHMARK_JSON> \
+  --videos-root <VIDEO_ROOT> \
+  --video-registry <VIDEO_REGISTRY_JSON> \
+  --model <MODEL_KEY> \
+  --frames 16 \
+  --concurrency 4 \
+  --out predictions.jsonl
+```
+
+### Computing Metrics
+After generating predictions, you can compute accuracy and diagnostic metrics using:
+
+```bash
+python compute_metrics.py \
+  --bench <REPAIRED_BENCHMARK_JSON> \
+  --preds predictions.jsonl \
+  --save-summary summary.json
+```
+
+## Citation
+
+If you find this benchmark useful in your research, please cite the following paper:
+
+```bibtex
+@article{tocbench2024,
+  title={TOC-Bench: A Temporal Object Consistency Benchmark for Video Large Language Models},
+  author={...},
+  journal={arXiv preprint arXiv:2605.09904},
+  year={2024}
+}
+```