Text Classification
Transformers
Safetensors
Russian
roberta
vulnerability
severity
cybersecurity
fstec
Generated from Trainer
text-embeddings-inference
Instructions to use CIRCL/vulnerability-severity-classification-russian-ruRoberta-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CIRCL/vulnerability-severity-classification-russian-ruRoberta-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="CIRCL/vulnerability-severity-classification-russian-ruRoberta-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("CIRCL/vulnerability-severity-classification-russian-ruRoberta-large") model = AutoModelForSequenceClassification.from_pretrained("CIRCL/vulnerability-severity-classification-russian-ruRoberta-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from CIRCL/vulnerability-severity-classification-russian-ruRoberta-large: direct link, hf CLI and curl.
- Browser
- Download file 993 Bytes
-
https://huggingface.co/CIRCL/vulnerability-severity-classification-russian-ruRoberta-large/resolve/main/config.json
- Command line
-
hf download hf://CIRCL/vulnerability-severity-classification-russian-ruRoberta-large/config.json
-
curl -L -o config.json https://huggingface.co/CIRCL/vulnerability-severity-classification-russian-ruRoberta-large/resolve/main/config.json
993 Bytes
| { | |
| "add_cross_attention": false, | |
| "architectures": [ | |
| "RobertaForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "bos_token_id": 1, | |
| "classifier_dropout": null, | |
| "dtype": "float32", | |
| "eos_token_id": 2, | |
| "gradient_checkpointing": false, | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 1024, | |
| "id2label": { | |
| "0": "Low", | |
| "1": "Medium", | |
| "2": "High", | |
| "3": "Critical" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 4096, | |
| "is_decoder": false, | |
| "label2id": { | |
| "Critical": 3, | |
| "High": 2, | |
| "Low": 0, | |
| "Medium": 1 | |
| }, | |
| "layer_norm_eps": 1e-05, | |
| "max_position_embeddings": 514, | |
| "model_type": "roberta", | |
| "num_attention_heads": 16, | |
| "num_hidden_layers": 24, | |
| "pad_token_id": 1, | |
| "position_embedding_type": "absolute", | |
| "problem_type": "single_label_classification", | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.5.0", | |
| "type_vocab_size": 1, | |
| "use_cache": true, | |
| "vocab_size": 50265 | |
| } | |