Text Classification
Transformers
Safetensors
roberta
vuldeepecker
defect detection
code
text-embeddings-inference
Instructions to use claudios/VulBERTa-MLP-VulDeePecker with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use claudios/VulBERTa-MLP-VulDeePecker with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="claudios/VulBERTa-MLP-VulDeePecker")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("claudios/VulBERTa-MLP-VulDeePecker") model = AutoModelForSequenceClassification.from_pretrained("claudios/VulBERTa-MLP-VulDeePecker", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 620 Bytes
30d008e 4e8a8fe 30d008e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | {
"added_tokens_decoder": {
"1": {
"content": "<pad>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
}
},
"clean_up_tokenization_spaces": true,
"max_length": 1026,
"model_max_length": 1026,
"pad_to_multiple_of": null,
"pad_token": "<pad>",
"pad_token_type_id": 0,
"padding_side": "right",
"stride": 0,
"tokenizer_class": "VulBERTaTokenizer",
"auto_map": {
"AutoTokenizer": ["tokenization_vulberta.VulBERTaTokenizer", null]
},
"truncation_side": "right",
"truncation_strategy": "longest_first"
}
|