End of training

README.md

@@ -1,55 +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 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).
+<!-- 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. -->
 
-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: 0.6282
+- Accuracy: 0.7798
 
-- 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': 'High', 'score': 0.9644894003868103}]
-```
+More information needed
 
 ## Training procedure
 
@@ -60,7 +42,7 @@ The following hyperparameters were used during training:
 - train_batch_size: 32
 - eval_batch_size: 32
 - seed: 42
-- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: linear
 - num_epochs: 5
 
@@ -68,16 +50,16 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
 |:-------------:|:-----:|:-----:|:---------------:|:--------:|
-| 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   |
+| 0.5427        | 1.0   | 3447  | 0.6015          | 0.7505   |
+| 0.5167        | 2.0   | 6894  | 0.5665          | 0.7747   |
+| 0.365         | 3.0   | 10341 | 0.5643          | 0.7846   |
+| 0.3289        | 4.0   | 13788 | 0.5923          | 0.7777   |
+| 0.3408        | 5.0   | 17235 | 0.6282          | 0.7798   |
 
 
 ### Framework versions
 
-- Transformers 4.51.3
-- Pytorch 2.7.1+cu126
-- Datasets 3.6.0
-- Tokenizers 0.21.1
+- Transformers 4.56.1
+- Pytorch 2.8.0+cu128
+- Datasets 4.0.0
+- Tokenizers 0.22.0

emissions.csv

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

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