Text Classification
Transformers
Safetensors
deberta-v2
agents
agent-safety
tool-use
prompt-injection
deberta-v3
text-embeddings-inference
Instructions to use kontext-security/Merlin with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kontext-security/Merlin with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="kontext-security/Merlin")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("kontext-security/Merlin") model = AutoModelForSequenceClassification.from_pretrained("kontext-security/Merlin", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 601 Bytes
21b6f2c | 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 | {
"add_prefix_space": true,
"backend": "tokenizers",
"bos_token": "[CLS]",
"cls_token": "[CLS]",
"do_lower_case": false,
"eos_token": "[SEP]",
"extra_special_tokens": [
"[USER_REQUEST]",
"[INTERACTION_HISTORY]",
"[CURRENT_ACTION]",
"[TOOL_DESCRIPTIONS]"
],
"is_local": false,
"local_files_only": false,
"mask_token": "[MASK]",
"model_max_length": 1000000000000000019884624838656,
"pad_token": "[PAD]",
"sep_token": "[SEP]",
"split_by_punct": false,
"tokenizer_class": "DebertaV2Tokenizer",
"unk_id": 3,
"unk_token": "[UNK]",
"vocab_type": "spm"
}
|