Text Classification
Transformers
Safetensors
French
English
modernbert
devsecops
cybersecurity
fine-tuned
Eval Results (legacy)
text-embeddings-inference
Instructions to use Medyassino/devSECV2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Medyassino/devSECV2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Medyassino/devSECV2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Medyassino/devSECV2") model = AutoModelForSequenceClassification.from_pretrained("Medyassino/devSECV2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 488 Bytes
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"backend": "tokenizers",
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"cls_token": "[CLS]",
"is_local": false,
"local_files_only": false,
"mask_token": "[MASK]",
"max_length": 512,
"model_input_names": [
"input_ids",
"attention_mask"
],
"model_max_length": 8192,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"stride": 0,
"tokenizer_class": "TokenizersBackend",
"truncation_side": "right",
"truncation_strategy": "longest_first",
"unk_token": "[UNK]"
}
|