End of training

README.md

@@ -1,52 +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).
-
-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 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. -->
 
+# 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: 0.5997
+- Accuracy: 0.7846
 
-## 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
 
+More information needed
 
 ## Training procedure
 
@@ -61,19 +46,15 @@ The following hyperparameters were used during training:
 - lr_scheduler_type: linear
 - num_epochs: 5
 
-It achieves the following results on the evaluation set:
-- Loss: 0.6086
-- Accuracy: 0.7746
-
 ### Training results
 
 | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
 |:-------------:|:-----:|:-----:|:---------------:|:--------:|
-| 0.6059        | 1.0   | 3548  | 0.5956          | 0.7432   |
-| 0.5083        | 2.0   | 7096  | 0.5697          | 0.7664   |
-| 0.491         | 3.0   | 10644 | 0.5535          | 0.7730   |
-| 0.4476        | 4.0   | 14192 | 0.5666          | 0.7790   |
-| 0.3577        | 5.0   | 17740 | 0.6086          | 0.7746   |
+| 0.6264        | 1.0   | 3548  | 0.5766          | 0.7565   |
+| 0.5523        | 2.0   | 7096  | 0.5536          | 0.7724   |
+| 0.4184        | 3.0   | 10644 | 0.5440          | 0.7836   |
+| 0.3236        | 4.0   | 14192 | 0.5629          | 0.7889   |
+| 0.2604        | 5.0   | 17740 | 0.5997          | 0.7846   |
 
 
 ### 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,water_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,cpu_utilization_percent,gpu_utilization_percent,ram_utilization_percent,ram_used_gb,on_cloud,pue,wue
-2026-01-09T10:30:02,codecarbon,10fef713-6914-4471-bbfb-6264fe2c2ca7,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,3486.109665968921,0.08144179050348038,2.3361798195423276e-05,70.0001582310907,663.0685817263877,70.0,0.06543725066806824,0.6428308792642952,0.06543004039795555,0.7736981703303191,0.0,Luxembourg,LUX,,,,Linux-6.8.0-88-generic-x86_64-with-glibc2.39,3.12.3,3.2.1,224,Intel(R) Xeon(R) Platinum 8480+,2,2 x NVIDIA H100 NVL,6.1661,49.7498,2015.3354606628418,machine,0.9989925158318941,73.37708693149108,1.1,21.73262945207015,N,1.0,0.0
+2026-01-13T13:57:37,codecarbon,85a26bf8-77b9-458a-9437-1473bb66bf37,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,4124.957966918,0.10612537972916668,2.572762694318052e-05,218.5363534014986,591.4396810084689,70.0,0.25085537126080304,0.6772140403819091,0.08012320807329996,1.008192619716013,0.0,Luxembourg,LUX,,,,Linux-6.8.0-90-generic-x86_64-with-glibc2.39,3.12.3,3.2.1,224,Intel(R) Xeon(R) Platinum 8480+,2,2 x NVIDIA H100 NVL,6.1661,49.7498,2015.3354682922363,machine,0.4483949416342412,61.773589494163424,0.9129863813229573,18.926386515917944,N,1.0,0.0

model.safetensors

@@ -1,3 +1,3 @@
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