Text Classification
Transformers
Safetensors
roberta
fill-mask
devign
defect detection
code
Eval Results (legacy)
text-embeddings-inference
Instructions to use claudios/VulBERTa-mlm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use claudios/VulBERTa-mlm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="claudios/VulBERTa-mlm")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("claudios/VulBERTa-mlm") model = AutoModelForMaskedLM.from_pretrained("claudios/VulBERTa-mlm") - Notebooks
- Google Colab
- Kaggle
| { | |
| "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" | |
| } | |