Update README.md

README.md

@@ -1,37 +1,29 @@
 ---
+language:
+- ru
+license: apache-2.0
 library_name: transformers
-base_model: ai-forever/ruRoberta-large
 tags:
+- text-classification
+- vulnerability
+- severity
+- cybersecurity
+- fstec
 - generated_from_trainer
-metrics:
-- accuracy
-model-index:
-- name: vulnerability-severity-classification-russian-ruRoberta-large
-  results: []
+datasets:
+- CIRCL/Vulnerability-FSTEC
+base_model: ai-forever/ruRoberta-large
+pipeline_tag: text-classification
 ---
 
 <!-- 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-russian-ruRoberta-large
+# VLAI: Automated Vulnerability Severity Classification (Chinese Text)
 
-This model is a fine-tuned version of [ai-forever/ruRoberta-large](https://huggingface.co/ai-forever/ruRoberta-large) on an unknown dataset.
-It achieves the following results on the evaluation set:
-- Loss: 2.6495
-- Accuracy: 0.7417
-- F1 Macro: 0.6650
-- Low Precision: 0.6154
-- Low Recall: 0.3380
-- Low F1: 0.4364
-- Medium Precision: 0.7619
-- Medium Recall: 0.8312
-- Medium F1: 0.7951
-- High Precision: 0.6869
-- High Recall: 0.6080
-- High F1: 0.6450
-- Critical Precision: 0.7678
-- Critical Recall: 0.7996
-- Critical F1: 0.7834
+A fine-tuned [ai-forever/ruRoberta-large](https://huggingface.co/ai-forever/ruRoberta-large) model for classifying Russian vulnerability descriptions from the [FSTEC](https://vulnerability.circl.lu/recent#fstec).
+
+Trained on the [CIRCL/Vulnerability-FSTEC](https://huggingface.co/datasets/CIRCL/Vulnerability-FSTEC) dataset as part of the [VulnTrain](https://github.com/vulnerability-lookup/VulnTrain) project.
 
 ## Model description
 
@@ -58,6 +50,24 @@ The following hyperparameters were used during training:
 - lr_scheduler_type: linear
 - num_epochs: 5
 
+It achieves the following results on the evaluation set:
+- Loss: 2.6495
+- Accuracy: 0.7417
+- F1 Macro: 0.6650
+- Low Precision: 0.6154
+- Low Recall: 0.3380
+- Low F1: 0.4364
+- Medium Precision: 0.7619
+- Medium Recall: 0.8312
+- Medium F1: 0.7951
+- High Precision: 0.6869
+- High Recall: 0.6080
+- High F1: 0.6450
+- Critical Precision: 0.7678
+- Critical Recall: 0.7996
+- Critical F1: 0.7834
+
+
 ### Training results
 
 | Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | Low Precision | Low Recall | Low F1 | Medium Precision | Medium Recall | Medium F1 | High Precision | High Recall | High F1 | Critical Precision | Critical Recall | Critical F1 |