End of training

README.md

@@ -1,56 +1,39 @@
- 
 ---
-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: 0.5994
+- Accuracy: 0.7858
 
-## How to use
+## Model description
 
-You can use this model directly with the Hugging Face `transformers` library for text classification:
+More information needed
 
-```python
-from transformers import pipeline
+## Intended uses & limitations
 
-classifier = pipeline(
-    "text-classification",
-    model="CIRCL/vulnerability-severity-classification-chinese-macbert-base"
-)
+More information needed
 
-# 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
 
-## Training procedure
+More information needed
 
-It achieves the following results on the evaluation set:
-- Loss: 0.6282
-- Accuracy: 0.7798
+## Training procedure
 
 ### Training hyperparameters
 
@@ -67,11 +50,11 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
 |:-------------:|:-----:|:-----:|:---------------:|:--------:|
-| 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   |
+| 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   |
 
 
 ### Framework versions

emissions.csv

@@ -1,2 +1,2 @@
 timestamp,project_name,run_id,experiment_id,duration,emissions,emissions_rate,cpu_power,gpu_power,ram_power,cpu_energy,gpu_energy,ram_energy,energy_consumed,country_name,country_iso_code,region,cloud_provider,cloud_region,os,python_version,codecarbon_version,cpu_count,cpu_model,gpu_count,gpu_model,longitude,latitude,ram_total_size,tracking_mode,on_cloud,pue
-2025-09-23T07:11:38,codecarbon,1f17e608-4fa9-4987-90dd-91fd7affc58e,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,3959.345899205655,0.07726378551046184,1.951428025673708e-05,42.5,400.0415744241799,94.34468507766725,0.04671087107853902,0.5836095666095105,0.10368662567193301,0.7340070633599824,Luxembourg,LUX,luxembourg,,,Linux-6.8.0-71-generic-x86_64-with-glibc2.39,3.12.3,2.8.4,64,AMD EPYC 9124 16-Core Processor,2,2 x NVIDIA L40S,6.1294,49.6113,251.5858268737793,machine,N,1.0
+2025-10-06T06:47:09,codecarbon,15aeb803-7d38-4051-a214-da3130a4bed2,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,3953.308398708701,0.07718071857326264,1.9523070499248874e-05,42.5,315.5237593318902,94.34468507766725,0.04663262536316761,0.5830715406235214,0.10351376028619633,0.7332179262728844,Luxembourg,LUX,luxembourg,,,Linux-6.8.0-71-generic-x86_64-with-glibc2.39,3.12.3,2.8.4,64,AMD EPYC 9124 16-Core Processor,2,2 x NVIDIA L40S,6.1294,49.6113,251.5858268737793,machine,N,1.0

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d5e4ec007e7175b32c214877c243184f0906c6ccee16494456164e04d6478820
+oid sha256:607cde963c9cae8369b2c23dbf2c38da3a480afaf902cbe8303220ac33cbfc8e
 size 409103316