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> [!WARNING] > **THIS PROJECT HAS BEEN ARCHIVED.** > > This project and its associated code on GitHub are no longer under active development or maintained. `bert-base-NER` is a fine-tuned BERT model that is ready to use for Named Entity Recognition and achieves state-of-the-art performance for the NER task. It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PER) and Miscellaneous (MISC). Specifically, this model is a `bert-base-cased` model that was fine-tuned on the English version of the standard `CoNLL-2003 Named Entity Recognition` dataset.

Text analysis
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> [!WARNING] > **THIS PROJECT HAS BEEN ARCHIVED.** > > This project and its associated code on GitHub are no longer under active development or maintained. **This model is a conversion of DunnBC22/codebert-base-Malicious_URLs to ONNX** format. It's based on the CodeBERT architecture, tailored for the specific task of identifying URLs that may pose security threats. The model was converted to ONNX using the 🤗 Optimum library. **Modifications**: Details of any modifications or fine-tuning done to tailor the model for malicious URL detection.

Text analysis
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> [!WARNING] > **THIS PROJECT HAS BEEN ARCHIVED.** > > This project and its associated code on GitHub are no longer under active development or maintained. **There is a newer version of the model - protectai/deberta-v3-base-prompt-injection-v2.** This model is a fine-tuned version of microsoft/deberta-v3-base on multiple combined datasets of prompt injections and normal prompts.

Text analysis
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> [!WARNING] > **THIS PROJECT HAS BEEN ARCHIVED.** > > This project and its associated code on GitHub are no longer under active development or maintained. 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.

Text generationOpen weights
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> [!WARNING] > **THIS PROJECT HAS BEEN ARCHIVED.** > > This project and its associated code on GitHub are no longer under active development or maintained. This model is a fine-tuned version of distilroberta-base on multiple combined datasets of rejections from different LLMs and normal responses from RLHF datasets. It aims to identify rejections in LLMs when the prompt doesn't pass content moderation, classifying inputs into two categories: `0` for normal outputs and `1` for rejection detected.

Text analysis
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> [!WARNING] > **THIS PROJECT HAS BEEN ARCHIVED.** > > This project and its associated code on GitHub are no longer under active development or maintained. **This model is a conversion of valurank/distilroberta-bias to ONNX** format. It is designed to detect biases in text using the distilled version of the RoBERTa model. The model was converted to ONNX using the 🤗 Optimum library. **Base Model**: DistilRoBERTa, a distilled version of the RoBERTa model that is optimized for faster performance while maintaining similar accuracy.

Text analysis
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> [!WARNING] > **THIS PROJECT HAS BEEN ARCHIVED.** > > This project and its associated code on GitHub are no longer under active development or maintained. **This model is a conversion of MoritzLaurer/roberta-base-zeroshot-v2.0-c to ONNX** format using the 🤗 Optimum library.

Category pending
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> [!WARNING] > **THIS PROJECT HAS BEEN ARCHIVED.** > > This project and its associated code on GitHub are no longer under active development or maintained. **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.

Text analysis
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> [!WARNING] > **THIS PROJECT HAS BEEN ARCHIVED.** > > This project and its associated code on GitHub are no longer under active development or maintained. **This model is a conversion of vishnun/codenlbert-sm to ONNX** format using the 🤗 Optimum library.

Text analysis
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> [!WARNING] > **THIS PROJECT HAS BEEN ARCHIVED.** > > This project and its associated code on GitHub are no longer under active development or maintained. **This model is a conversion of papluca/xlm-roberta-base-language-detection to ONNX** format using the 🤗 Optimum library. This model is a fine-tuned version of xlm-roberta-base on the Language Identification dataset.

Text analysis
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