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

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}
+}
+```