Update dataset card with paper link, code link, and metadata

Hi! I'm Niels, part of the community science team at Hugging Face. I'm opening this PR to improve the documentation of the SupraBench dataset.

This PR updates the dataset card with:
- A link to the research paper on Hugging Face.
- A link to the official GitHub repository.
- Corrected license information (`cc-by-4.0`).
- Added `text-generation` to the `task_categories`.
- Added a sample usage section demonstrating how to run the benchmark code.

README.md

@@ -1,21 +1,23 @@
 ---
-license: other
-tags:
-  - chemistry
-  - supramolecular
-  - host-guest
-  - molecular-recognition
-  - macrocyclic
-  - continued-pretraining
+license: cc-by-4.0
 size_categories:
-  - 100K<n<1M
+- 100K<n<1M
+task_categories:
+- text-generation
+tags:
+- chemistry
+- supramolecular
+- host-guest
+- molecular-recognition
+- macrocyclic
+- continued-pretraining
 configs:
-  - config_name: default
-    data_files:
-      - split: raw
-        path: data/raw-*.parquet
-      - split: filtered
-        path: data/filtered-*.parquet
+- config_name: default
+  data_files:
+  - split: raw
+    path: data/raw-*.parquet
+  - split: filtered
+    path: data/filtered-*.parquet
 ---
 
 # SupraBench
@@ -25,6 +27,9 @@ configs:
 tasks plus an auxiliary vision task, and ships a domain text corpus for
 domain-adaptive pretraining (DAPT).
 
+- 📄 **Paper:** [SupraBench: A Benchmark for Supramolecular Chemistry](https://huggingface.co/papers/2606.13477)
+- 💻 **Code:** [Tianyi-Billy-Ma/SupraBench](https://github.com/Tianyi-Billy-Ma/SupraBench)
+
 > Supramolecular chemistry studies non-covalent host-guest assemblies that
 > underpin drug delivery, chemical sensing, and in-vivo toxin sequestration.
 > Designing host-guest systems is slow (days of dry-lab verification per pair);
@@ -45,6 +50,17 @@ Each task dataset has a `test` split (merged records) and a `cb7` split (the
 CB[7] supplement, for add-on evaluation). Each `base`/`fewshot`/`cot` rendering is
 tagged by the `prompt_strategy` field.
 
+## Sample usage
+
+To run a task against a model using the provided source code:
+
+```bash
+uv run python src/main.py \
+    --task-config  configs/tasks/bap_base.yaml \
+    --model-config configs/models/openrouter_qwen35_27b.yaml \
+    --output-dir   outputs/
+```
+
 ## Dataset statistics
 
 | Task | # Samples |
@@ -79,32 +95,6 @@ column. Arrows give the optimization direction.
 | Gemini-3-Flash | **1.248** | **1.679** | **0.498** | **0.647** | **0.350** | **0.470** | **0.506** | 0.067 | 0.118 |
 | DeepSeek-v4 | *1.433* | *1.994* | *0.461* | *0.730* | *0.309* | *0.381* | *0.500* | *0.090* | *0.141* |
 
-### Few-Shot
-
-| Model | BAP MAE down | BAP RMSE down | TBS ACC up | TBS Regret down | SID F1 up | SID B.Acc up | HGD Recall up | HGD Prec up | HGD F1 up |
-|---|---|---|---|---|---|---|---|---|---|
-| Qwen3.5-9B | 3.650 | 4.820 | 0.370 | 0.951 | 0.154 | 0.150 | 0.000 | 0.022 | 0.042 |
-| Qwen3.5-27B | 2.258 | 3.256 | 0.392 | 0.889 | 0.178 | 0.257 | 0.636 | **0.585** | **0.580** |
-| Llama3.1-8B | 5.504 | 6.940 | 0.283 | 1.227 | 0.142 | 0.182 | 0.655 | 0.369 | 0.456 |
-| Llama3.1-70B | 1.774 | 2.359 | 0.354 | 1.026 | 0.144 | 0.185 | 0.631 | *0.474* | *0.531* |
-| GPT-5.4-Mini | 1.958 | 2.808 | 0.430 | 0.824 | 0.141 | *0.291* | 0.542 | 0.228 | 0.307 |
-| GPT-5.4-Nano | 2.176 | 2.894 | 0.419 | 0.819 | 0.190 | 0.270 | 0.532 | 0.095 | 0.152 |
-| Gemini-3-Flash | **1.257** | **1.702** | **0.513** | **0.619** | **0.389** | **0.421** | *0.660* | 0.364 | 0.448 |
-| DeepSeek-v4 | *1.618* | *2.276* | *0.470* | *0.713* | *0.203* | 0.225 | **0.720** | 0.303 | 0.352 |
-
-### CoT
-
-| Model | BAP MAE down | BAP RMSE down | TBS ACC up | TBS Regret down | SID F1 up | SID B.Acc up | HGD Recall up | HGD Prec up | HGD F1 up |
-|---|---|---|---|---|---|---|---|---|---|
-| Qwen3.5-9B | 3.664 | 4.885 | 0.382 | 0.944 | 0.167 | 0.197 | 0.300 | 0.039 | 0.068 |
-| Qwen3.5-27B | 2.438 | 3.468 | 0.398 | 0.898 | 0.254 | *0.415* | **0.526** | 0.051 | 0.092 |
-| Llama3.1-8B | 4.911 | 6.279 | 0.293 | 1.220 | 0.154 | 0.153 | 0.380 | **0.102** | **0.144** |
-| Llama3.1-70B | 1.833 | 2.512 | 0.373 | 0.985 | 0.106 | 0.380 | 0.421 | 0.055 | 0.097 |
-| GPT-5.4-Mini | 2.036 | 2.887 | 0.429 | 0.828 | 0.220 | 0.282 | 0.444 | 0.080 | 0.129 |
-| GPT-5.4-Nano | 2.160 | 2.881 | 0.410 | 0.822 | 0.174 | 0.257 | 0.492 | 0.056 | 0.098 |
-| Gemini-3-Flash | **1.261** | **1.723** | **0.510** | **0.609** | **0.331** | **0.432** | 0.512 | 0.062 | 0.110 |
-| DeepSeek-v4 | *1.541* | *2.183* | *0.445* | *0.743* | *0.307* | 0.414 | *0.522* | *0.080* | *0.134* |
-
 Takeaways: frontier proprietary LLMs (Gemini-3-Flash, DeepSeek-v4) lead the
 quantitative tasks, yet every task leaves substantial headroom; no single
 prompting strategy is universally best; and CoT amplifies rather than fixes the
@@ -119,3 +109,14 @@ articles subject to each article's individual license; molecular structures use
 [OPSIN](https://github.com/dan2097/opsin).
 
 If you use SupraBench, please cite the paper and the upstream data sources.
+
+## Citation
+
+```bibtex
+@article{ma2026suprabench,
+  title   = {SupraBench: A Benchmark for Supramolecular Host--Guest Chemistry Reasoning in Large Language Models},
+  author  = {Ma, Tianyi and Ma, Yijun and Wang, Zehong and Sun, Weixiang and Li, Ziming and Schmidt, Connor R. and Zhang, Chuxu and Webber, Matthew J. and Ye, Yanfang},
+  year    = {2026},
+  note    = {arXiv preprint, link coming soon}
+}
+```