End of training

README.md

@@ -1,50 +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).
+# vulnerability-severity-classification-chinese-macbert-base
 
-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"
-)
+## 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
 
@@ -59,24 +46,20 @@ The following hyperparameters were used during training:
 - lr_scheduler_type: linear
 - num_epochs: 5
 
-It achieves the following results on the evaluation set:
-- Loss: 0.5994
-- Accuracy: 0.7858
-
 ### Training results
 
 | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
 |:-------------:|:-----:|:-----:|:---------------:|:--------:|
-| 0.6543        | 1.0   | 3465  | 0.5870          | 0.7529   |
-| 0.5973        | 2.0   | 6930  | 0.5463          | 0.7736   |
-| 0.4937        | 3.0   | 10395 | 0.5417          | 0.7841   |
-| 0.448         | 4.0   | 13860 | 0.5541          | 0.7884   |
-| 0.3624        | 5.0   | 17325 | 0.5994          | 0.7858   |
+| 0.6196        | 1.0   | 3491  | 0.5932          | 0.7498   |
+| 0.4754        | 2.0   | 6982  | 0.5667          | 0.7699   |
+| 0.4553        | 3.0   | 10473 | 0.5629          | 0.7751   |
+| 0.4466        | 4.0   | 13964 | 0.5712          | 0.7747   |
+| 0.3432        | 5.0   | 17455 | 0.6044          | 0.7745   |
 
 
 ### Framework versions
 
-- Transformers 4.56.1
-- Pytorch 2.8.0+cu128
-- Datasets 4.0.0
-- Tokenizers 0.22.0
+- Transformers 4.57.1
+- Pytorch 2.9.0+cu128
+- Datasets 4.3.0
+- Tokenizers 0.22.1

emissions.csv

@@ -1,2 +1,2 @@
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model.safetensors

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