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: 0.6118
+- Accuracy: 0.7832
 
-## 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: 0.6258
-- Accuracy: 0.7781
-
 ### Training results
 
 | Training Loss | Epoch | Step  | Validation Loss | Accuracy |
 |:-------------:|:-----:|:-----:|:---------------:|:--------:|
-| 0.5987        | 1.0   | 3511  | 0.5940          | 0.7504   |
-| 0.5362        | 2.0   | 7022  | 0.5571          | 0.7702   |
-| 0.5547        | 3.0   | 10533 | 0.5589          | 0.7784   |
-| 0.4246        | 4.0   | 14044 | 0.5903          | 0.7789   |
-| 0.3994        | 5.0   | 17555 | 0.6258          | 0.7781   |
+| 0.5706        | 1.0   | 3511  | 0.5875          | 0.7503   |
+| 0.5364        | 2.0   | 7022  | 0.5596          | 0.7702   |
+| 0.5483        | 3.0   | 10533 | 0.5518          | 0.7768   |
+| 0.4161        | 4.0   | 14044 | 0.5757          | 0.7838   |
+| 0.351         | 5.0   | 17555 | 0.6118          | 0.7832   |
 
 
 ### 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
-2025-11-21T07:13:49,codecarbon,25ecd2bb-56f8-484e-b355-0788c100e255,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,4057.6960604824126,0.0782695276243454,1.928915484493929e-05,42.5,283.16634391516516,94.34468507766725,0.04786272477908186,0.589454613507769,0.10624428948687227,0.7435616277737229,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
+2025-11-25T08:34:42,codecarbon,46016a62-72d2-48da-9d57-e6844c7b1ce7,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,3480.7599397339945,0.14822105400392685,4.2582957908684204e-05,42.5,473.86350310892067,755.7507977485657,0.04105713564181151,0.6370560410333139,0.7299889485153672,1.4081021251904928,Luxembourg,LUX,,,,Linux-6.8.0-88-generic-x86_64-with-glibc2.39,3.12.3,2.8.4,224,Intel(R) Xeon(R) Platinum 8480+,2,2 x NVIDIA H100 NVL,6.1661,49.7498,2015.3354606628418,machine,N,1.0

model.safetensors

@@ -1,3 +1,3 @@
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+oid sha256:e428b8772596f4fccf18f0c0da6ed2141a81dec3f9545aef270078c55eb4efa2
 size 409103316