This model is a fine-tuned version of microsoft/deberta-v3-base specifically developed to detect and classify prompt injection attacks which can manipulate language models into producing unintended outputs. Prompt injection attacks manipulate language models by inserting or altering prompts to trigger harmful or unintended responses. The deberta-v3-base-prompt-injection-v2 model is designed to enhance security in language model applications by detecting these malicious interventions. This model classifies inputs into benign (0) and injection-detected (1). deberta-v3-base-prompt-injection-v2 is highly accurate in identifying prompt injections in English. It does not detect jailbreak attacks…
Open weights
apache-2.0
184M parameters
512 tokens
transformers
This model is a conversion of unitary/unbiased-toxic-roberta to ONNX format using the Optimum library. Trained models & code to predict toxic comments on 3 Jigsaw challenges: Toxic comment classification, Unintended Bias in Toxic comments, Multilingual toxic comment classification. Built by Laura Hanu at Unitary. The huggingface models currently give different results to the detoxify library (see issue here). All challenges have a toxicity label. The toxicity labels represent the aggregate ratings of up to 10 annotators according the following schema: - Very Toxic (a very hateful, aggressive, or disrespectful comment that is very likely to make you leave a discussion or give up on sharing…
Open weights
apache-2.0
514 tokens
transformers
This model is a conversion of MoritzLaurer/roberta-base-zeroshot-v2.0-c to ONNX format using the Optimum library.
Open weights
apache-2.0
514 tokens
transformers
