The model was presented in the paper VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification [arXiv]. **Abstract:** VLAI is a transformer-based model that predicts software vulnerability severity levels directly from text descriptions. Built on RoBERTa, VLAI is fine-tuned on over 600,000 real-world vulnerabilities and achieves over 82% accuracy in predicting severity categories, enabling faster and more consistent triage ahead of manual CVSS scoring. The model and dataset are open-source and integrated into the Vulnerability-Lookup service. > **Note (October 2026):** since VulnTrain 3.4.0, > the dataset is split so that records of the same vulnerability (for example a CVE and its > GitHub advisory) never appear in both train and test. Accuracy measured this way (**74%**) > reflects performance on vulnerabilities the model has never seen. Earlier figures, including > the one in the abstract above, were measured on a split where many test descriptions also > appeared in training. Details in the technical report > Improving vulnerability-scores: Leakage-Free Splits and Consistent CVSS 4.0 Scoring.
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Anima is a 2 billion parameter text-to-image model created via a collaboration between CircleStone Labs and Comfy Org. It is focused mainly on anime concepts, characters, and styles, but is also capable of generating a wide variety of other non-photorealistic content. The model is designed for making illustrations and artistic images, and will not work well at realism. It is trained on several million anime images and about 800k non-anime artistic images. No synthetic data was used for training. The knowledge cut-off date for the anime training data is September 2025. - Anima-Base - The pretrained, unrefined base model. Maximum flexibility, diversity, and style adherence. - LoRAs should be trained using this version. - Anima-Aesthetic - Fine-tuned for better consistency and a higher quality default art style. - v1.0b is an alternate version that is just an aesthetics full finetune, without additional style adjustment and stabilization loras merged in like 1.0. I personally think 1.0 is better. - Anima-Turbo - Distilled version for fast generations. - Use at CFG 1 and 8-12 steps. - The distillation process also increases stability and gives the model a strong default style, but reduces diversity.
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⚠️ This model has been superseded by cirimus/ultrabert-380M-go-emotions . 💡 It is part of the collection Frontier BERT Workhorses . This model was fine-tuned from ModernBERT-large on the GoEmotions dataset for multi-label classification. It predicts emotional states in text, with a total of 28 possible labels. Each input text can have one or more associated labels, reflecting the multi-label nature of the task. The GoEmotions dataset is a multi-label emotion classification dataset derived from Reddit comments. It contains 58,000 examples with 28 emotion labels (e.g., admiration, amusement, anger, etc.), and it is annotated for multi-label classification.
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**GlotLID** is a Fasttext language identification (LID) model that supports more than **2000 labels**. **Latest:** GlotLID is now updated to **V3**. V3 supports **2102 labels** (three-letter ISO codes with script). For more details on the supported languages and performance, as well as significant changes from previous versions, please refer to https://github.com/cisnlp/GlotLID/blob/main/languages-v3.md. - **Demo:** huggingface - **Repository:** github - **Paper:** paper (EMNLP 2023) - **Point of Contact:** amir@cis.lmu.de
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