End of training

README.md

@@ -9,8 +9,6 @@ metrics:
 model-index:
 - name: vulnerability-severity-classification-roberta-base
   results: []
-datasets:
-- CIRCL/vulnerability-scores
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -18,45 +16,22 @@ should probably proofread and complete it, then remove this comment. -->
 
 # vulnerability-severity-classification-roberta-base
 
-This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the dataset [CIRCL/vulnerability-scores](https://huggingface.co/datasets/CIRCL/vulnerability-scores).
-
+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: 0.5004
-- Accuracy: 0.8269
+- Loss: 0.5006
+- Accuracy: 0.8308
 
 ## 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-scores"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
-model.eval()
-
-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)
+More information needed
 
-# Run inference
-with torch.no_grad():
-    outputs = model(**inputs)
-    predictions = torch.nn.functional.softmax(outputs.logits, dim=-1)
+## Intended uses & limitations
 
+More information needed
 
-# Print results
-print("Predictions:", predictions)
-predicted_class = torch.argmax(predictions, dim=-1).item()
-print("Predicted severity:", labels[predicted_class])
-```
+## Training and evaluation data
 
+More information needed
 
 ## Training procedure
 
@@ -75,11 +50,11 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step   | Validation Loss | Accuracy |
 |:-------------:|:-----:|:------:|:---------------:|:--------:|
-| 0.6222        | 1.0   | 26721  | 0.6425          | 0.7424   |
-| 0.5253        | 2.0   | 53442  | 0.5840          | 0.7720   |
-| 0.4666        | 3.0   | 80163  | 0.5480          | 0.7959   |
-| 0.2633        | 4.0   | 106884 | 0.5039          | 0.8181   |
-| 0.2508        | 5.0   | 133605 | 0.5004          | 0.8269   |
+| 0.6907        | 1.0   | 26806  | 0.6340          | 0.7465   |
+| 0.5341        | 2.0   | 53612  | 0.5606          | 0.7783   |
+| 0.4568        | 3.0   | 80418  | 0.5162          | 0.8029   |
+| 0.437         | 4.0   | 107224 | 0.5003          | 0.8204   |
+| 0.338         | 5.0   | 134030 | 0.5006          | 0.8308   |
 
 
 ### Framework versions
@@ -87,4 +62,4 @@ The following hyperparameters were used during training:
 - Transformers 4.49.0
 - Pytorch 2.6.0+cu124
 - Datasets 3.4.0
-- Tokenizers 0.21.1
+- Tokenizers 0.21.1

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
-2025-04-17T20:43:34,codecarbon,b3b235ad-c7c2-4aae-9bf8-6f06a3844b82,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,21964.696577124298,0.3937361711972296,1.7925864343935274e-05,42.5,181.96671727190795,94.34470081329346,0.25912773687640994,2.9061641099294206,0.5752073903167013,3.7404992371225365,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
+2025-04-24T17:50:45,codecarbon,d248c2a9-dc29-49db-8bbf-9174c4818b38,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,22025.923609932885,0.3948230483918265,1.7925379901607204e-05,42.5,183.7675567277129,94.34470081329346,0.25984539876734164,2.9141783866185733,0.5768008005733226,3.7508245859592306,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 @@
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+oid sha256:221bc25146c5d047b93d332386be1022b03d4872572db55170296dc13a493c3a
 size 498618976