Update README.md

README.md

@@ -1,37 +1,52 @@
 ---
+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: 0.6044
-- Accuracy: 0.7745
 
-## 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"
+)
+
+# 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}]
+```
 
-## Training and evaluation data
 
-More information needed
 
 ## Training procedure
 
@@ -46,6 +61,10 @@ The following hyperparameters were used during training:
 - lr_scheduler_type: linear
 - num_epochs: 5
 
+It achieves the following results on the evaluation set:
+- Loss: 0.6044
+- Accuracy: 0.7745
+
 ### Training results
 
 | Training Loss | Epoch | Step  | Validation Loss | Accuracy |