Add library name and link to code

README.md

@@ -1,11 +1,13 @@
 ---
-license: cc-by-nc-4.0
+datasets:
+- zjunlp/ChineseHarm-bench
 language:
 - zh
+license: cc-by-nc-4.0
 pipeline_tag: text-classification
-datasets:
-- zjunlp/ChineseHarm-bench
+library_name: transformers
 ---
+
 ## 🚀Installation
 
 1. Clone the repositories:
@@ -27,13 +29,17 @@ datasets:
 
 We release the following variants of our harmful content detection model:
 
+- [**ChineseGuard-1.5B**](https://huggingface.co/zjunlp/ChineseGuard-1.5B)
+- [**ChineseGuard-3B**](https://huggingface.co/zjunlp/ChineseGuard-3B)
+- [**ChineseGuard-7B**](https://huggingface.co/zjunlp/ChineseGuard-7B)
+
 🔹 Single Inference (Example)
 
-Run single-input inference using the ChineseGuard-3B model:
+Run single-input inference using the ChineseGuard-1.5B model:
 
 ```
 SCRIPT_PATH="../infer/single_infer.py"
-model_name="zjunlp/ChineseGuard-3B"
+model_name="zjunlp/ChineseGuard-1.5B"
 text="代发短信，有想做的联系我，无押金"
 
 python $SCRIPT_PATH \
@@ -43,13 +49,13 @@ python $SCRIPT_PATH \
 
 🔸 Batch Inference (Multi-NPU or Multi-GPU)
 
-To run inference on the entire ChineseHarm-Bench using ChineseGuard-3B and 8 NPUs:
+To run inference on the entire ChineseHarm-Bench using ChineseGuard-1.5B and 8 NPUs:
 
 ```
 SCRIPT_PATH="../infer/batch_infer.py"
-model_name="zjunlp/ChineseHarm-3B"
+model_name="zjunlp/ChineseHarm-1.5B"
 file_name="../benchmark/bench.json"
-output_file="../benchmark/bench_ChineseHarm-3B.json"
+output_file="../benchmark/bench_ChineseHarm-1.5B.json"
 
 python $SCRIPT_PATH \
     --model_name $model_name \
@@ -63,9 +69,20 @@ python $SCRIPT_PATH \
 >
 > **Note:** The inference scripts support both NPU and GPU devices.
 
+**Evaluation: Calculating F1 Score**
+
+After inference, evaluate the predictions by computing the F1 score with the following command:
+
+```
+python ../calculate_metrics.py \
+    --file_path "../benchmark/bench_ChineseHarm-1.5B.json" \
+    --true_label_field "标签" \
+    --predicted_label_field "predict_label"
+```
+
 ## 🚩Citation
 
-Please cite our repository if you use ChineseGuard in your work. Thanks!
+Please cite our repository if you use ChineseHarm-bench in your work. Thanks!
 
 ```bibtex
 @misc{liu2025chineseharmbenchchineseharmfulcontent,
@@ -77,4 +94,6 @@ Please cite our repository if you use ChineseGuard in your work. Thanks!
       primaryClass={cs.CL},
       url={https://arxiv.org/abs/2506.10960}, 
 }
-```
+```
+
+Codebase: https://github.com/zjunlp/ChineseHarm-bench