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: 0.5043
-- Accuracy: 0.8266
+- Loss: 0.5091
+- Accuracy: 0.8220
 
 ## 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 |
 |:-------------:|:-----:|:------:|:---------------:|:--------:|
-| 0.6699        | 1.0   | 26473  | 0.6352          | 0.7442   |
-| 0.5909        | 2.0   | 52946  | 0.5688          | 0.7736   |
-| 0.4787        | 3.0   | 79419  | 0.5192          | 0.7981   |
-| 0.4534        | 4.0   | 105892 | 0.5025          | 0.8174   |
-| 0.3843        | 5.0   | 132365 | 0.5043          | 0.8266   |
+| 0.6771        | 1.0   | 26510  | 0.6352          | 0.7360   |
+| 0.6633        | 2.0   | 53020  | 0.5819          | 0.7646   |
+| 0.5362        | 3.0   | 79530  | 0.5248          | 0.7932   |
+| 0.4466        | 4.0   | 106040 | 0.5198          | 0.8121   |
+| 0.3886        | 5.0   | 132550 | 0.5091          | 0.8220   |
 
 
 ### 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
-2025-04-04T11:15:16,codecarbon,444803ec-c9b0-400e-aeaf-53be6a2fa5e6,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,21771.339703876525,0.3901343951688573,1.791963197833854e-05,42.5,182.52661961740924,94.34470081329346,0.25685810986615504,2.879254088679204,0.5701701149822527,3.7062823135276144,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
+2025-04-07T11:05:46,codecarbon,736c09be-51f2-4270-8b9b-dc0b1e387939,5b0fa12a-3dd7-45bb-9766-cc326314d9f1,21793.828189492226,0.39047380406020327,1.7916714799489336e-05,42.5,186.42425294851307,94.34470081329346,0.25708567687020156,2.8817499934536954,0.5706710325080956,3.709506702831985,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