Theia is a vision foundation model for robot learning that distills multiple off-the-shelf vision foundation models trained on varied vision tasks. Theia’s rich visual representations encode diverse visual knowledge, enhancing downstream robot learning. It was introduced in the paper Theia: Distilling Diverse Vision Foundation Models for Robot Learning, which also includes experiments demonstrating that Theia outperforms its teacher models and prior robot learning models using less training data and smaller model sizes. Demo videos can be found on the project page. The `theia-tiny-patch16-224-cddsv` model, uses DeiT-Tiny as a backbone, and simulatenously distills CLIP, Depth Anything, DINOv2, Segment Anything and ViT. For more information on usage, please visit the Theia repository. The pre-trained model weights and code released with Theia are available for use under The AI Institute License, reproduced in full below:
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Theia is a vision foundation model for robot learning that distills multiple off-the-shelf vision foundation models trained on varied vision tasks. Theia’s rich visual representations encode diverse visual knowledge, enhancing downstream robot learning. It was introduced in the paper Theia: Distilling Diverse Vision Foundation Models for Robot Learning, which also includes experiments demonstrating that Theia outperforms its teacher models and prior robot learning models using less training data and smaller model sizes. Demo videos can be found on the project page. The `theia-tiny-patch16-224-cddsv` model, uses DeiT-Tiny as a backbone, and simulatenously distills CLIP, Depth Anything, DINOv2, Segment Anything and ViT. For more information on usage, please visit the Theia repository. The pre-trained model weights and code released with Theia are available for use under The AI Institute License, reproduced in full below:
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General Text Embeddings (GTE) model. Towards General Text Embeddings with Multi-stage Contrastive Learning The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including GTE-large, GTE-base, and GTE-small. The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc. We compared the performance of the GTE models with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the MTEB leaderboard.
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General Text Embeddings (GTE) model. Towards General Text Embeddings with Multi-stage Contrastive Learning The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including GTE-large, GTE-base, and GTE-small. The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc. We compared the performance of the GTE models with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the MTEB leaderboard.
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General Text Embeddings (GTE) model. Towards General Text Embeddings with Multi-stage Contrastive Learning The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer different sizes of models for both Chinese and English Languages. The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc. We compared the performance of the GTE models with other popular text embedding models on the MTEB (CMTEB for Chinese language) benchmark. For more detailed comparison results, please refer to the MTEB leaderboard.
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This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed: Without sentence-transformers, you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
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This is an encoder model from **Tochka AI** based on the **RoPEBert** architecture, using the cloning method described in our article on Habr. CulturaX dataset was used for model training. The **hivaze/ru-e5-base** (only english and russian embeddings of **intfloat/multilingual-e5-base**) model was used as the original; this model surpasses it and all other models in quality (at the time of creation), according to the `S+W` score of encodechka benchmark. The model is trained on contexts **up to 2048 tokens** in length, but can be used on larger contexts.
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