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- Model description - Intended uses and limitations - How to use - Limitations and bias - Training - Training data - Training procedure - Evaluation - Variable and metrics - Evaluation results - Additional information - Author - Contact information - Copyright - Licensing information - Funding - Citing information - Disclaimer The **roberta-base-ca-v2-cased-ner** is a Named Entity Recognition (NER) model for the Catalan language fine-tuned from the roberta-base-ca-v2 model, a RoBERTa base model pre-trained on a medium-size corpus collected from publicly available corpora and crawlers (check the roberta-base-ca-v2 model card for more details). **roberta-base-ca-v2-cased-ner** model can be used to recognize Named Entities in the provided text. The model is limited by its training dataset and may not generalize well for all use cases.

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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 **THIS PROJECT HAS BEEN ARCHIVED.** 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.

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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 **THIS PROJECT HAS BEEN ARCHIVED.** 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.

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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 **THIS PROJECT HAS BEEN ARCHIVED.** 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 **There is a newer version of the model - protectai/deberta-v3-base-prompt-injection-v2.** This model is a fine-tuned version of microsoft/deberta-v3-base on multiple combined datasets of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.

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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.

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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.

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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.

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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.

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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.

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This model is a fine-tuned version of google/electra-small-discriminator on the GLUE COLA dataset. It achieves the following results on the evaluation set: - Loss: 0.4403 - Matthews Correlation: 0.5510 The following hyperparameters were used during training: - learning_rate: 8e-05 - train_batch_size: 512 - eval_batch_size: 16 - seed: 32754 - distributed_type: multi-GPU - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 8.0 - mixed_precision_training: Native AMP

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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - **Developed by:** [More Information Needed] - **Funded by [optional]:** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [optional]:** [More Information Needed] - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed]

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