Improve dataset card: Add text-classification task and sample usage

This pull request enhances the dataset card by:

1. Adding `text-classification` to the `task_categories` metadata, reflecting the core nature of many sub-tasks within BizFinBench.
2. Including a brief "Sample Usage" section in the README to guide users on how to utilize the dataset and provided scripts.

These additions improve the dataset's discoverability and usability on the Hugging Face Hub.

README.md

@@ -1,60 +1,54 @@
 ---
-license: cc-by-4.0
 language:
 - zh
-tags:
-- finance
-pretty_name: BizFinBench
+license: cc-by-4.0
 size_categories:
 - 10K<n<100K
 task_categories:
 - question-answering
+- text-classification
+pretty_name: BizFinBench
+tags:
+- finance
 configs:
-  - config_name: Anomalous_Event_Attribution
-    data_files:
-      - split: test
-        path: Anomalous_Event_Attribution/*.jsonl
-
-  - config_name: Emotion_Recognition
-    data_files:
-      - split: test
-        path: Emotion_Recognition/*.jsonl
-
-  - config_name: Financial_Data_Description
-    data_files:
-      - split: test
-        path: Financial_Data_Description/*.jsonl
-
-  - config_name: Financial_Knowledge_QA
-    data_files:
-      - split: test
-        path: Financial_Knowledge_QA/*.jsonl
-
-  - config_name: Financial_Named_Entity_Recognition
-    data_files:
-      - split: test
-        path: Financial_Named_Entity_Recognition/*.jsonl
-
-  - config_name: Financial_Numerical_Computation
-    data_files:
-      - split: test
-        path: Financial_Numerical_Computation/*.jsonl
-
-  - config_name: Financial_Time_Reasoning
-    data_files:
-      - split: test
-        path: Financial_Time_Reasoning/*.jsonl
-
-  - config_name: Financial_Tool_Usage
-    data_files:
-      - split: test
-        path: Financial_Tool_Usage/*.jsonl
-
-  - config_name: Stock_Price_Prediction
-    data_files:
-      - split: test
-        path: Stock_Price_Prediction/*.jsonl
+- config_name: Anomalous_Event_Attribution
+  data_files:
+  - split: test
+    path: Anomalous_Event_Attribution/*.jsonl
+- config_name: Emotion_Recognition
+  data_files:
+  - split: test
+    path: Emotion_Recognition/*.jsonl
+- config_name: Financial_Data_Description
+  data_files:
+  - split: test
+    path: Financial_Data_Description/*.jsonl
+- config_name: Financial_Knowledge_QA
+  data_files:
+  - split: test
+    path: Financial_Knowledge_QA/*.jsonl
+- config_name: Financial_Named_Entity_Recognition
+  data_files:
+  - split: test
+    path: Financial_Named_Entity_Recognition/*.jsonl
+- config_name: Financial_Numerical_Computation
+  data_files:
+  - split: test
+    path: Financial_Numerical_Computation/*.jsonl
+- config_name: Financial_Time_Reasoning
+  data_files:
+  - split: test
+    path: Financial_Time_Reasoning/*.jsonl
+- config_name: Financial_Tool_Usage
+  data_files:
+  - split: test
+    path: Financial_Tool_Usage/*.jsonl
+- config_name: Stock_Price_Prediction
+  data_files:
+  - split: test
+    path: Stock_Price_Prediction/*.jsonl
 ---
+
 # BizFinBench: A Business-Driven Real-World Financial Benchmark for Evaluating LLMs
 
 📖<a href="https://arxiv.org/abs/2505.19457">Paper</a> |🐙<a href="https://github.com/HiThink-Research/BizFinBench/">Github</a></h3>|🤗<a href="https://huggingface.co/datasets/HiThink-Research/BizFinBench">Huggingface</a></h3>
@@ -118,4 +112,127 @@ The models are evaluated across multiple tasks, with results color-coded to repr
 | DeepSeek-R1 (671B)                   | 80.36           | 🥇 64.04        | 🥉 75.00        | 81.96           | 🥇 91.44        | 98.41           | 39.67           | 55.13           | 🥇 71.46        | 🥈 73.05        |
 | QwQ-32B                              | 84.02           | 52.91           | 64.90           | 84.81           | 89.60           | 94.20           | 34.50           | 🥈 56.68        | 30.27           | 65.77           |
 | DeepSeek-R1-Distill-Qwen-14B         | 71.33           | 44.35           | 16.95           | 81.96           | 85.52           | 92.81           | 39.50           | 50.20           | 52.76           | 59.49           |
-| DeepSeek-R1-Distill-Qwen-32B         | 73.68           | 51.20           | 50.86           | 83.27           | 87.54           | 97.81           | 41.50           | 53.92           | 56.80           | 66.29           |
+| DeepSeek-R1-Distill-Qwen-32B         | 73.68           | 51.20           | 50.86           | 83.27           | 87.54           | 97.81           | 41.50           | 53.92           | 56.80           | 66.29           |
+
+## 🛠️ Usage
+
+### Quick Start – Evaluate a Local Model
+
+```sh
+export MODEL_PATH=model/Qwen2.5-0.5B   # Path to the model to be evaluated
+export REMOTE_MODEL_PORT=16668
+export REMOTE_MODEL_URL=http://127.0.0.1:${REMOTE_MODEL_PORT}/model
+export MODEL_NAME=Qwen2.5-0.5B
+export PROMPT_TYPE=chat_template   # Hithink llama3 llama2 none qwen chat_template; chat_template is recommended
+
+# First start the model as a service
+python inference/predict_multi_gpu.py \
+    --model ${MODEL_PATH} \
+    --server_port ${REMOTE_MODEL_PORT} \
+    --prompt ${PROMPT_TYPE} \
+    --preprocess preprocess \
+    --run_forever \
+    --max_new_tokens 4096 \
+    --tensor_parallel ${TENSOR_PARALLEL} & 
+
+# Pass in the config file path to start evaluation
+python run.py --config config/offical/eval_fin_eval_diamond.yaml --model_name ${MODEL_NAME}
+```
+
+### Quick Start – Evaluate a Local Model and Score with a Judge Model
+
+```sh
+export MODEL_PATH=model/Qwen2.5-0.5B   # Path to the model to be evaluated
+export REMOTE_MODEL_PORT=16668
+export REMOTE_MODEL_URL=http://127.0.0.1:${REMOTE_MODEL_PORT}/model
+export MODEL_NAME=Qwen2.5-0.5B
+export PROMPT_TYPE=chat_template   # llama3 llama2 none qwen chat_template; chat_template is recommended
+
+# First start the model as a service
+python inference/predict_multi_gpu.py \
+    --model ${MODEL_PATH} \
+    --server_port ${REMOTE_MODEL_PORT} \
+    --prompt ${PROMPT_TYPE} \
+    --preprocess preprocess \
+    --run_forever \
+    --max_new_tokens 4096 \
+    --tensor_parallel ${TENSOR_PARALLEL} \
+    --low_vram & 
+
+# Start the judge model
+export JUDGE_MODEL_PATH=/mnt/data/llm/models/base/Qwen2.5-7B
+export JUDGE_TENSOR_PARALLEL=1
+export JUDGE_MODEL_PORT=16667
+python inference/predict_multi_gpu.py \
+    --model ${JUDGE_MODEL_PATH} \
+    --server_port ${JUDGE_MODEL_PORT} \
+    --prompt chat_template \
+    --preprocess preprocess \
+    --run_forever \
+    --manual_start \
+    --max_new_tokens 4096 \
+    --tensor_parallel ${JUDGE_TENSOR_PARALLEL} \
+    --low_vram &
+
+# Pass in the config file path to start evaluation
+python run.py --config "config/offical/eval_fin_eval.yaml" --model_name ${MODEL_NAME}
+```
+
+> **Note**: Add the `--manual_start` argument when launching the judge model, because the judge must wait until the main model finishes inference before starting (this is handled automatically by the `maybe_start_judge_model` function in `run.py`).
+
+
+## ✒️Results
+The models are evaluated across multiple tasks, with results color-coded to represent the top three performers for each task:
+- 🥇 indicates the top-performing model.
+- 🥈 represents the second-best result.
+- 🥉 denotes the third-best performance.
+
+| Model                        | AEA     | FNC     | FTR     | FTU     | FQA     | FDD     | ER      | SP      | FNER    | Average |
+| ---------------------------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- | ------- |
+| **Proprietary LLMs**         |         |         |         |         |         |         |         |         |         |         |
+| ChatGPT-o3                   | 🥈 86.23 | 61.30   | 🥈 75.36 | 🥇 89.15 | 🥈 91.25 | 🥉 98.55 | 🥉 44.48 | 53.27   | 65.13   | 🥇 73.86 |
+| ChatGPT-o4-mini              | 🥉 85.62 | 60.10   | 71.23   | 74.40   | 90.27   | 95.73   | 🥇 47.67 | 52.32   | 64.24   | 71.29   |
+| GPT-4o                       | 79.42   | 56.51   | 🥇 76.20 | 82.37   | 87.79   | 🥇 98.84 | 🥈 45.33 | 54.33   | 65.37   | 🥉 71.80 |
+| Gemini-2.0-Flash             | 🥇 86.94 | 🥉 62.67 | 73.97   | 82.55   | 90.29   | 🥈 98.62 | 22.17   | 🥉 56.14 | 54.43   | 69.75   |
+| Claude-3.5-Sonnet            | 84.68   | 🥈 63.18 | 42.81   | 🥈 88.05 | 87.35   | 96.85   | 16.67   | 47.60   | 63.09   | 65.59   |
+| **Open Source LLMs**         |         |         |         |         |         |         |         |         |         |         |
+| Qwen2.5-7B-Instruct          | 73.87   | 32.88   | 39.38   | 79.03   | 83.34   | 78.93   | 37.50   | 51.91   | 30.31   | 56.35   |
+| Qwen2.5-72B-Instruct         | 69.27   | 54.28   | 70.72   | 85.29   | 87.79   | 97.43   | 35.33   | 55.13   | 54.02   | 67.70   |
+| Qwen2.5-VL-3B                | 53.85   | 15.92   | 17.29   | 8.95    | 81.60   | 59.44   | 39.50   | 52.49   | 21.57   | 38.96   |
+| Qwen2.5-VL-7B                | 73.87   | 32.71   | 40.24   | 77.85   | 83.94   | 77.41   | 38.83   | 51.91   | 33.40   | 56.68   |
+| Qwen2.5-VL-14B               | 37.12   | 41.44   | 53.08   | 82.07   | 84.23   | 7.97    | 37.33   | 54.93   | 47.47   | 49.52   |
+| Qwen2.5-VL-32B               | 76.79   | 50.00   | 62.16   | 83.57   | 85.30   | 95.95   | 40.50   | 54.93   | 🥉 68.36 | 68.62   |
+| Qwen2.5-VL-72B               | 69.55   | 54.11   | 69.86   | 85.18   | 87.37   | 97.34   | 35.00   | 54.94   | 54.41   | 67.53   |
+| Qwen3-1.7B                   | 77.40   | 35.80   | 33.40   | 75.82   | 73.81   | 78.62   | 22.40   | 48.53   | 11.23   | 50.78   |
+| Qwen3-4B                     | 83.60   | 47.40   | 50.00   | 78.19   | 82.24   | 80.16   | 42.20   | 50.51   | 25.19   | 59.94   |
+| Qwen3-14B                    | 84.20   | 58.20   | 65.80   | 82.19   | 84.12   | 92.91   | 33.00   | 52.31   | 50.70   | 67.05   |
+| Qwen3-32B                    | 83.80   | 59.60   | 64.60   | 85.12   | 85.43   | 95.37   | 39.00   | 52.26   | 49.19   | 68.26   |
+| Xuanyuan3-70B                | 12.14   | 19.69   | 15.41   | 80.89   | 86.51   | 83.90   | 29.83   | 52.62   | 37.33   | 46.48   |
+| Llama-3.1-8B-Instruct        | 73.12   | 22.09   | 2.91    | 77.42   | 76.18   | 69.09   | 29.00   | 54.21   | 36.56   | 48.95   |
+| Llama-3.1-70B-Instruct       | 16.26   | 34.25   | 56.34   | 80.64   | 79.97   | 86.90   | 33.33   | 🥇 62.16 | 45.95   | 55.09   |
+| Llama 4 Scout                | 73.60   | 45.80   | 44.20   | 85.02   | 85.21   | 92.32   | 25.60   | 55.76   | 43.00   | 61.17   |
+| DeepSeek-V3 (671B)           | 74.34   | 61.82   | 72.60   | 🥈 86.54 | 🥉 91.07 | 98.11   | 32.67   | 55.73   | 🥈 71.24 | 71.57   |
+| DeepSeek-R1 (671B)           | 80.36   | 🥇 64.04 | 🥉 75.00 | 81.96   | 🥇 91.44 | 98.41   | 39.67   | 55.13   | 🥇 71.46 | 🥈 73.05 |
+| QwQ-32B                      | 84.02   | 52.91   | 64.90   | 84.81   | 89.60   | 94.20   | 34.50   | 🥈 56.68 | 30.27   | 65.77   |
+| DeepSeek-R1-Distill-Qwen-14B | 71.33   | 44.35   | 16.95   | 81.96   | 85.52   | 92.81   | 39.50   | 50.20   | 52.76   | 59.49   |
+| DeepSeek-R1-Distill-Qwen-32B | 73.68   | 51.20   | 50.86   | 83.27   | 87.54   | 97.81   | 41.50   | 53.92   | 56.80   | 66.29   |
+
+
+## 📚 Example
+<img src="static/Anomalous Event Attribution.drawio.png" alt="Data Distribution">
+
+
+## ✒️Citation
+
+```
+@article{lu2025bizfinbench,
+  title={BizFinBench: A Business-Driven Real-World Financial Benchmark for Evaluating LLMs},
+  author={Lu, Guilong and Guo, Xuntao and Zhang, Rongjunchen and Zhu, Wenqiao and Liu, Ji},
+  journal={arXiv preprint arXiv:2505.19457},
+  year={2025}
+}
+```
+
+## 📄 License
+![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-green.svg) ![Data License](https://img.shields.io/badge/Data%20License-CC%20By%20NC%204.0-red.svg) **Usage and License Notices**: The data and code are intended and licensed for research use only.
+License: Attribution-NonCommercial 4.0 International It should abide by the policy of OpenAI: https://openai.com/policies/terms-of-use