Update README.md

README.md

@@ -1,47 +1,51 @@
 ---
+base_model: hfl/chinese-macbert-base
+datasets:
+- CIRCL/Vulnerability-CNVD
 library_name: transformers
 license: apache-2.0
-base_model: hfl/chinese-macbert-base
-tags:
-- generated_from_trainer
 metrics:
 - accuracy
+tags:
+- generated_from_trainer
+- text-classification
+- classification
+- nlp
+- chinese
+- vulnerability
+pipeline_tag: text-classification
+language: zh
 model-index:
 - name: vulnerability-severity-classification-chinese-macbert-base
   results: []
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification (Chinese Text) 🇨🇳
 
-# vulnerability-severity-classification-chinese-macbert-base
+This model is a fine-tuned version of [hfl/chinese-macbert-base](https://huggingface.co/hfl/chinese-macbert-base) on the dataset [CIRCL/Vulnerability-CNVD](https://huggingface.co/datasets/CIRCL/Vulnerability-CNVD).
+
+For more information, visit the [Vulnerability-Lookup project page](https://vulnerability.circl.lu) or the [ML-Gateway GitHub repository](https://github.com/vulnerability-lookup/ML-Gateway), which demonstrates its usage in a FastAPI server.
 
-This model is a fine-tuned version of [hfl/chinese-macbert-base](https://huggingface.co/hfl/chinese-macbert-base) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 1.3186
-- Accuracy: 0.7657
-- F1 Macro: 0.6796
-- Low Precision: 0.5544
-- Low Recall: 0.3987
-- Low F1: 0.4638
-- Medium Precision: 0.7805
-- Medium Recall: 0.8196
-- Medium F1: 0.7996
-- High Precision: 0.7787
-- High Recall: 0.7720
-- High F1: 0.7753
 
-## Model description
+## How to use
 
-More information needed
+You can use this model directly with the Hugging Face `transformers` library for text classification:
 
-## Intended uses & limitations
+```python
+from transformers import pipeline
 
-More information needed
+classifier = pipeline(
+    "text-classification",
+    model="CIRCL/vulnerability-severity-classification-chinese-macbert-base"
+)
 
-## Training and evaluation data
+# Example usage for a Chinese vulnerability description
+description_chinese = "TOTOLINK A3600R是中国吉翁电子（TOTOLINK）公司的一款6天线1200M无线路由器。TOTOLINK A3600R存在缓冲区溢出漏洞，该漏洞源于/cgi-bin/cstecgi.cgi文件的UploadCustomModule函数中的File参数未能正确验证输入数据的长度大小，攻击者可利用该漏洞在系统上执行任意代码或者导致拒绝服务。"
+result_chinese = classifier(description_chinese)
+print(result_chinese)
+# Expected output example: [{'label': '高', 'score': 0.9802}]
+```
 
-More information needed
 
 ## Training procedure
 
@@ -56,6 +60,20 @@ The following hyperparameters were used during training:
 - lr_scheduler_type: linear
 - num_epochs: 5
 
+It achieves the following results on the evaluation set:
+- Loss: 1.3186
+- Accuracy: 0.7657
+- F1 Macro: 0.6796
+- Low Precision: 0.5544
+- Low Recall: 0.3987
+- Low F1: 0.4638
+- Medium Precision: 0.7805
+- Medium Recall: 0.8196
+- Medium F1: 0.7996
+- High Precision: 0.7787
+- High Recall: 0.7720
+- High F1: 0.7753
+
 ### Training results
 
 | Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1 Macro | Low Precision | Low Recall | Low F1 | Medium Precision | Medium Recall | Medium F1 | High Precision | High Recall | High F1 |