Text Classification
Transformers
Safetensors
deberta-v2
prompt-injection
injection
security
llm-security
Generated from Trainer
text-embeddings-inference
Instructions to use proventra/mdeberta-v3-base-prompt-injection with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use proventra/mdeberta-v3-base-prompt-injection with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="proventra/mdeberta-v3-base-prompt-injection")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("proventra/mdeberta-v3-base-prompt-injection") model = AutoModelForSequenceClassification.from_pretrained("proventra/mdeberta-v3-base-prompt-injection", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download config.json from proventra/mdeberta-v3-base-prompt-injection: direct link, hf CLI and curl.
- Browser
- Download file 1.06 kB
-
https://huggingface.co/proventra/mdeberta-v3-base-prompt-injection/resolve/main/config.json
- Command line
-
hf download hf://proventra/mdeberta-v3-base-prompt-injection/config.json
-
curl -L -o config.json https://huggingface.co/proventra/mdeberta-v3-base-prompt-injection/resolve/main/config.json
1.06 kB
| { | |
| "architectures": [ | |
| "DebertaV2ForSequenceClassification" | |
| ], | |
| "attention_probs_dropout_prob": 0.1, | |
| "class_weights": [ | |
| 1.0, | |
| 2.0 | |
| ], | |
| "hidden_act": "gelu", | |
| "hidden_dropout_prob": 0.1, | |
| "hidden_size": 768, | |
| "id2label": { | |
| "0": "SAFE", | |
| "1": "INJECTION" | |
| }, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 3072, | |
| "label2id": { | |
| "INJECTION": 1, | |
| "SAFE": 0 | |
| }, | |
| "layer_norm_eps": 1e-07, | |
| "legacy": true, | |
| "max_position_embeddings": 512, | |
| "max_relative_positions": -1, | |
| "model_type": "deberta-v2", | |
| "norm_rel_ebd": "layer_norm", | |
| "num_attention_heads": 12, | |
| "num_hidden_layers": 12, | |
| "pad_token_id": 0, | |
| "pooler_dropout": 0, | |
| "pooler_hidden_act": "gelu", | |
| "pooler_hidden_size": 768, | |
| "pos_att_type": [ | |
| "p2c", | |
| "c2p" | |
| ], | |
| "position_biased_input": false, | |
| "position_buckets": 256, | |
| "problem_type": "single_label_classification", | |
| "relative_attention": true, | |
| "share_att_key": true, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.51.3", | |
| "type_vocab_size": 0, | |
| "vocab_size": 251000 | |
| } | |