End of training

README.md

@@ -1,53 +1,37 @@
 ---
-base_model: hfl/chinese-macbert-base
-datasets:
-- CIRCL/Vulnerability-CNVD
 library_name: transformers
 license: apache-2.0
-metrics:
-- accuracy
+base_model: hfl/chinese-macbert-base
 tags:
 - generated_from_trainer
-- text-classification
-- classification
-- nlp
-- chinese
-- vulnerability
-pipeline_tag: text-classification
-language: zh
+metrics:
+- accuracy
 model-index:
 - name: vulnerability-severity-classification-chinese-macbert-base
   results: []
 ---
 
-# VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification (Chinese Text)
+<!-- 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. -->
 
-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.
+# 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 an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.5994
-- Accuracy: 0.7900
+- Loss: 0.6172
+- Accuracy: 0.7817
+
+## 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
 
@@ -66,11 +50,11 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
 |:-------------:|:-----:|:-----:|:---------------:|:--------:|
-| 0.65          | 1.0   | 3388  | 0.5772          | 0.7561   |
-| 0.582         | 2.0   | 6776  | 0.5656          | 0.7620   |
-| 0.5284        | 3.0   | 10164 | 0.5274          | 0.7881   |
-| 0.3406        | 4.0   | 13552 | 0.5555          | 0.7869   |
-| 0.3224        | 5.0   | 16940 | 0.5994          | 0.7900   |
+| 0.6329        | 1.0   | 3412  | 0.5832          | 0.7546   |
+| 0.5215        | 2.0   | 6824  | 0.5531          | 0.7750   |
+| 0.4827        | 3.0   | 10236 | 0.5521          | 0.7768   |
+| 0.3448        | 4.0   | 13648 | 0.5822          | 0.7814   |
+| 0.3865        | 5.0   | 17060 | 0.6172          | 0.7817   |
 
 
 ### Framework versions
@@ -78,4 +62,4 @@ The following hyperparameters were used during training:
 - Transformers 4.51.3
 - Pytorch 2.7.1+cu126
 - Datasets 3.6.0
-- Tokenizers 0.21.1
+- Tokenizers 0.21.1

emissions.csv

@@ -1,2 +1,2 @@
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-2025-07-03T08:48:19,codecarbon,6b6ee9a5-bd5e-4b19-a09c-85d1371fcaf6,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,5843.158349264413,0.11051319342331094,1.8913263481422386e-05,42.5,416.29527770671575,94.34468364715576,0.06893670656428569,0.8279147587201692,0.15302544705236684,1.0498769123368223,Luxembourg,LUX,luxembourg,,,Linux-6.8.0-60-generic-x86_64-with-glibc2.39,3.12.3,2.8.4,64,AMD EPYC 9124 16-Core Processor,2,2 x NVIDIA L40S,6.1294,49.6113,251.58582305908203,machine,N,1.0
+2025-07-16T08:11:24,codecarbon,fdc7d841-f907-4035-9903-95cdd678e97f,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,5853.097029601224,0.1108801330521463,1.894383990072016e-05,42.5,397.9241498104493,94.34468364715576,0.06905461438023941,0.8310216200945035,0.15328660956491294,1.0533628440396559,Luxembourg,LUX,luxembourg,,,Linux-6.8.0-60-generic-x86_64-with-glibc2.39,3.12.3,2.8.4,64,AMD EPYC 9124 16-Core Processor,2,2 x NVIDIA L40S,6.1294,49.6113,251.58582305908203,machine,N,1.0

model.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:59714d02dcbe4adcb513d9ff60047c50056f899073586494b5c1f85cb8230a7c
 size 409103316