End of training

README.md

@@ -9,51 +9,31 @@ metrics:
 model-index:
 - name: vulnerability-severity-classification-roberta-base
   results: []
-datasets:
-- CIRCL/vulnerability-scores
 ---
 
-# vulnerability-severity-classification-roberta-base
+<!-- 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 [roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the dataset [CIRCL/vulnerability-scores](https://huggingface.co/datasets/CIRCL/vulnerability-scores).
+# vulnerability-severity-classification-roberta-base
 
+This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.5372
-- Accuracy: 0.8138
+- Loss: 0.5110
+- Accuracy: 0.8265
 
 ## Model description
 
-It is a classification model and is aimed to assist in classifying vulnerabilities by severity based on their descriptions.
-
-
-## How to get started with the model
-
-```python
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-import torch
-
-labels = ["low", "medium", "high", "critical"]
-
-model_name = "CIRCL/vulnerability-severity-classification-roberta-base"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
-model.eval()
+More information needed
 
-test_description = "langchain_experimental 0.0.14 allows an attacker to bypass the CVE-2023-36258 fix and execute arbitrary code via the PALChain in the python exec method."
-inputs = tokenizer(test_description, return_tensors="pt", truncation=True, padding=True)
+## Intended uses & limitations
 
-# Run inference
-with torch.no_grad():
-    outputs = model(**inputs)
-    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+More information needed
 
+## Training and evaluation data
 
-# Print results
-print("Predictions:", predictions)
-predicted_class = torch.argmax(predictions, dim=-1).item()
-print("Predicted severity:", labels[predicted_class])
-```
+More information needed
 
+## Training procedure
 
 ### Training hyperparameters
 
@@ -70,11 +50,11 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step   | Validation Loss | Accuracy |
 |:-------------:|:-----:|:------:|:---------------:|:--------:|
-| 0.7239        | 1.0   | 24058  | 0.6421          | 0.7359   |
-| 0.6718        | 2.0   | 48116  | 0.5911          | 0.7598   |
-| 0.5085        | 3.0   | 72174  | 0.5567          | 0.7878   |
-| 0.4282        | 4.0   | 96232  | 0.5377          | 0.8059   |
-| 0.3508        | 5.0   | 120290 | 0.5372          | 0.8138   |
+| 0.5348        | 1.0   | 25731  | 0.6370          | 0.7410   |
+| 0.4628        | 2.0   | 51462  | 0.5785          | 0.7692   |
+| 0.5154        | 3.0   | 77193  | 0.5256          | 0.7953   |
+| 0.4049        | 4.0   | 102924 | 0.5046          | 0.8145   |
+| 0.2862        | 5.0   | 128655 | 0.5110          | 0.8265   |
 
 
 ### Framework versions
@@ -82,4 +62,4 @@ The following hyperparameters were used during training:
 - Transformers 4.49.0
 - Pytorch 2.6.0+cu124
 - Datasets 3.3.2
-- Tokenizers 0.21.0
+- Tokenizers 0.21.0

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
-2025-03-07T18:34:32,codecarbon,0d870122-7bf3-4a74-9532-05a1c879af32,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,21096.269472956657,0.369204716185761,1.7500948054301502e-05,42.5,181.08759584722017,94.34470081329346,0.24886044327972778,2.706183070500259,0.5524065511598465,3.507450064939827,Luxembourg,LUX,luxembourg,,,Linux-6.8.0-48-generic-x86_64-with-glibc2.39,3.12.3,2.8.3,64,AMD EPYC 9124 16-Core Processor,2,2 x NVIDIA L40S,6.1294,49.6113,251.58586883544922,machine,N,1.0
+2025-03-11T13:42:52,codecarbon,10e7964a-d760-482a-a624-97b76663bf5d,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,22333.945179948583,0.3920252870180382,1.755289017947436e-05,42.5,182.39382883775872,94.34470081329346,0.26345167142701137,2.8759955588501214,0.5847985827629493,3.7242458130400817,Luxembourg,LUX,luxembourg,,,Linux-6.8.0-48-generic-x86_64-with-glibc2.39,3.12.3,2.8.3,64,AMD EPYC 9124 16-Core Processor,2,2 x NVIDIA L40S,6.1294,49.6113,251.58586883544922,machine,N,1.0

model.safetensors

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:50e5aab1f42c2c684462268d79570c4bdb1c43763b5e556f3e2df7af4dc9189e
+oid sha256:0498bd5b2eaa2d43fc76c8a938941c36b96c014d79b9e0790d3604c49de6d19e
 size 498618976