docs(readme): update

README.md

@@ -37,6 +37,8 @@ configs:
 
 Github Repo: https://github.com/TreeAI-Lab/NumericBench
 
+Arxiv Paper: https://arxiv.org/abs/2502.11075
+
 # How to use it?
 
 ## Loading Data
@@ -116,3 +118,22 @@ Due to the excessive length of the content, "..." is used to indicate omission.
 
 # Dataset statistics
 
+<p align="center">
+  <img src="./figure/data_statistics.png" width=600>
+</p>
+
+For more details, please refer to our paper.
+
+# Citation
+
+```
+@misc{li2025exposingnumeracygapsbenchmark,
+      title={Exposing Numeracy Gaps: A Benchmark to Evaluate Fundamental Numerical Abilities in Large Language Models}, 
+      author={Haoyang Li and Xuejia Chen and Zhanchao XU and Darian Li and Nicole Hu and Fei Teng and Yiming Li and Luyu Qiu and Chen Jason Zhang and Qing Li and Lei Chen},
+      year={2025},
+      eprint={2502.11075},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2502.11075}, 
+}
+```

example.py

@@ -0,0 +1,17 @@
+from huggingface_hub import hf_hub_download
+import pandas as pd
+
+dataset_name_list = ['arithmetic_operation/context_arithmetic_operation.json', 'arithmetic_operation/arithmetic_operation.json',
+            'mixed_number_sting/mixed_number_string_500_per_sample.json', 
+            'num_list/num_list_500_per_sample_1000_length.json', 'num_list/num_list_500_per_sample_100_length.json', 
+            'sequence/sequence_500_sample_100_length.json', 
+            'stock/single-turn/stock_500_per_sample_150_length.json', 'stock/single-turn/stock_500_per_sample_300_length.json', 'stock/multi-turn/stock_multi_turn_100_per_sample_100_length.json',
+            'weather/single-turn/weather_500_per_sample_200_length.json', 'weather/single-turn/weather_500_per_sample_400_length.json', 'weather/multi-turn/weather_multi_turn_100_per_sample_100_length.json'
+            ]
+
+REPO_ID = "TreeAILab/NumericBench"
+
+for dataset_name in dataset_name_list:
+    dataset = pd.read_json(
+        hf_hub_download(repo_id=REPO_ID, filename=dataset_name, repo_type="dataset")
+    )