Improve dataset card: Add text-classification task and sample usage (#2)

- Improve dataset card: Add text-classification task and sample usage (631763c045b77790586ca329db2739e1f11d7cf9)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

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