Improve dataset card: add task category, paper/code links, and citation

Hi, I'm Niels from the Hugging Face community science team!

This PR improves the dataset card for JF-ICR by:
- Adding the `text-classification` task category and `finance` tag to the YAML metadata.
- Linking the paper to the Hugging Face paper page (https://huggingface.co/papers/2602.01479).
- Adding the official GitHub repository link (https://github.com/The-FinAI/Ebisu).
- Including the evaluation command provided in the GitHub repository to help users run the benchmark.
- Updating the BibTeX citation with the actual authors and title from the paper.

Let me know if you have any questions!

README.md

@@ -1,11 +1,17 @@
 ---
-license: apache-2.0
 language:
 - ja
+license: apache-2.0
+task_categories:
+- text-classification
+tags:
+- finance
 ---
 
 # Dataset Card for JF-ICR
 
+ [**Paper**](https://huggingface.co/papers/2602.01479) | [**Code**](https://github.com/The-FinAI/Ebisu)
+
 ### Dataset Summary
 
 **JF-ICR (Japanese Financial Implicit Commitment Recognition)** is a benchmark dataset for evaluating implicit commitment and refusal recognition in Japanese investor-facing Q&A. The dataset consists of 94 single-turn question-answer pairs from 4 companies spanning 3 years, annotated into 5 implicit stance categories that distinguish agreement, hedging, and refusal under high-context discourse. This dataset addresses the challenge of understanding indirect expression and implicit commitment in Japanese financial communication, where refusals are frequently conveyed through pragmatic cues rather than explicit statements.
@@ -17,7 +23,22 @@ language:
 
 ### Languages
 
-* Japanese (jp)
+* Japanese (ja)
+
+## Usage
+
+This dataset is part of the **Ebisu** benchmark. To evaluate a model using the official evaluation harness, you can use a command like the following:
+
+```bash
+lm_eval --model vllm \
+    --model_args "pretrained=$MODEL,tensor_parallel_size=4,gpu_memory_utilization=0.95,max_model_len=1024" \
+    --tasks jp \
+    --batch_size auto \
+    --output_path ../results/jp \
+    --log_samples \
+    --apply_chat_template \
+    --include_path ../tasks/jp
+```
 
 ## Dataset Structure
 
@@ -159,15 +180,13 @@ JF-ICR contributes to research in Japanese financial NLP by enabling evaluation
 If you use this dataset, please cite:
 
 ```bibtex
-@misc{ebisu2025,
-      title={EBISU: Benchmarking Large Language Models in Japanese Finance}, 
-      author={[Authors]},
-      year={2025},
-      eprint={[arXiv number]},
+@misc{peng2026ebisu,
+      title={Ebisu: Benchmarking Large Language Models in Japanese Finance}, 
+      author={Xueqing Peng and Ruoyu Xiang and Fan Zhang and Mingzi Song and Mingyang Jiang and Yan Wang and Lingfei Qian and Taiki Hara and Yuqing Guo and Jimin Huang and Junichi Tsujii and Sophia Ananiadou},
+      year={2026},
+      eprint={2602.01479},
       archivePrefix={arXiv},
       primaryClass={cs.CL},
-      url={https://arxiv.org/abs/[number]}, 
+      url={https://huggingface.co/papers/2602.01479}, 
 }
-```
-
-**Note:** Please check the official paper for the complete citation once published.
+```