End of training

README.md

@@ -1,51 +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: 0.6086
+- Accuracy: 0.7746
 
+## 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
 
@@ -53,27 +39,22 @@ print(result_chinese)
 
 The following hyperparameters were used during training:
 - learning_rate: 3e-05
-- train_batch_size: 64
-- eval_batch_size: 64
+- train_batch_size: 32
+- eval_batch_size: 32
 - seed: 42
 - 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
 
-It achieves the following results on the evaluation set:
-- Loss: 0.6059
-- Accuracy: 0.7771
-
-
 ### Training results
 
-| Training Loss | Epoch | Step | Validation Loss | Accuracy |
-|:-------------:|:-----:|:----:|:---------------:|:--------:|
-| 0.5764        | 1.0   | 1772 | 0.6157          | 0.7462   |
-| 0.5644        | 2.0   | 3544 | 0.5618          | 0.7663   |
-| 0.4589        | 3.0   | 5316 | 0.5615          | 0.7781   |
-| 0.3881        | 4.0   | 7088 | 0.5791          | 0.7823   |
-| 0.3433        | 5.0   | 8860 | 0.6059          | 0.7771   |
+| 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   |
 
 
 ### Framework versions
@@ -81,4 +62,4 @@ It achieves the following results on the evaluation set:
 - Transformers 4.57.3
 - Pytorch 2.9.1+cu128
 - Datasets 4.4.2
-- Tokenizers 0.22.1
+- Tokenizers 0.22.2

emissions.csv

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

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