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**CAMeLBERT-DA SA Model** is a Sentiment Analysis (SA) model that was built by fine-tuning the CAMeLBERT Dialectal Arabic (DA) model. For the fine-tuning, we used the ASTD, ArSAS, and SemEval datasets. Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models." * Our fine-tuning code can be found here. You can use the CAMeLBERT-DA SA model directly as part of our CAMeL Tools SA component (*recommended*) or as part of the transformers pipeline. To use the model with the CAMeL Tools SA component: *Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually.

Text analysis
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**CAMeLBERT** is a collection of BERT models pre-trained on Arabic texts with different sizes and variants. We release pre-trained language models for Modern Standard Arabic (MSA), dialectal Arabic (DA), and classical Arabic (CA), in addition to a model pre-trained on a mix of the three. We also provide additional models that are pre-trained on a scaled-down set of the MSA variant (half, quarter, eighth, and sixteenth). The details are described in the paper *"The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models."* This model card describes **CAMeLBERT-Mix** (`bert-base-arabic-camelbert-mix`), a model pre-trained on a mixture of these variants: MSA, DA, and CA. You can use the released model for either masked language modeling or next sentence prediction. However, it is mostly intended to be fine-tuned on an NLP task, such as NER, POS tagging, sentiment analysis, dialect identification, and poetry classification. We release our fine-tuninig code here.

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**CAMeLBERT-Mix NER Model** is a Named Entity Recognition (NER) model that was built by fine-tuning the CAMeLBERT Mix model. For the fine-tuning, we used the ANERcorp dataset. Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models. "* Our fine-tuning code can be found here. You can use the CAMeLBERT-Mix NER model directly as part of our CAMeL Tools NER component (*recommended*) or as part of the transformers pipeline. *Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually.

Text analysis
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**CAMeLBERT Mix SA Model** is a Sentiment Analysis (SA) model that was built by fine-tuning the CAMeLBERT Mix model. For the fine-tuning, we used the ASTD, ArSAS, and SemEval datasets. Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models."* Our fine-tuning code can be found here. You can use the CAMeLBERT Mix SA model directly as part of our CAMeL Tools SA component (*recommended*) or as part of the transformers pipeline. To use the model with the CAMeL Tools SA component: *Note*: to download our models, you would need `transformers>=3.5.0`. Otherwise, you could download the models manually.

Text analysis
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**CAMeLBERT** is a collection of BERT models pre-trained on Arabic texts with different sizes and variants. We release pre-trained language models for Modern Standard Arabic (MSA), dialectal Arabic (DA), and classical Arabic (CA), in addition to a model pre-trained on a mix of the three. We also provide additional models that are pre-trained on a scaled-down set of the MSA variant (half, quarter, eighth, and sixteenth). The details are described in the paper *"The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models."* This model card describes **CAMeLBERT-MSA** (`bert-base-arabic-camelbert-msa`), a model pre-trained on the entire MSA dataset. You can use the released model for either masked language modeling or next sentence prediction. However, it is mostly intended to be fine-tuned on an NLP task, such as NER, POS tagging, sentiment analysis, dialect identification, and poetry classification. We release our fine-tuninig code here.

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**CAMeLBERT MSA NER Model** is a Named Entity Recognition (NER) model that was built by fine-tuning the CAMeLBERT Modern Standard Arabic (MSA) model. For the fine-tuning, we used the ANERcorp dataset. Our fine-tuning procedure and the hyperparameters we used can be found in our paper *"The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models. "* Our fine-tuning code can be found here. You can use the CAMeLBERT MSA NER model directly as part of our CAMeL Tools NER component (*recommended*) or as part of the transformers pipeline. To use the model with the CAMeL Tools NER component:

Text analysis
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AnimateDiff

camenduru

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DINOv3 is a family of versatile vision foundation models that outperforms the specialized state of the art across a broad range of settings, without fine-tuning. DINOv3 produces high-quality dense features that achieve outstanding performance on various vision tasks, significantly surpassing previous self- and weakly-supervised foundation models. These are Vision Transformer and ConvNeXt models trained following the method described in the DINOv3 paper. 12 models are provided: - 10 models pretrained on web data (LVD-1689M dataset) - 1 ViT-7B trained from scratch, - 5 ViT-S/S+/B/L/H+ models distilled from the ViT-7B, - 4 ConvNeXt-{T/S/B/L} models distilled from the ViT-7B, - 2 models pretrained on satellite data (SAT-493M dataset) - 1 ViT-7B trained from scratch - 1 ViT-L distilled from the ViT-7B

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IDM-VTON-F16

camenduru

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Image editing
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TRELLIS.2-4B

camenduru

**TRELLIS.2** is a state-of-the-art large 3D generative model designed for high-fidelity **image-to-3D** generation. It leverages a novel "field-free" sparse voxel structure termed **O-Voxel** and a large-scale flow-matching transformer (4 Billion parameters). Unlike previous methods that rely on iso-surface fields (e.g., SDF, Flexicubes) which struggle with open surfaces or non-manifold geometry, TRELLIS can reconstruct and generate **arbitrary 3D assets** with complex topologies, sharp features, and full Physical-Based Rendering (PBR) materials—including transparency/translucency. *Note: Please refer to the official GitHub Repository for installation instructions and dependencies.*

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Poseidon-B

camlab-ethz

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