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NeoMME-260M

Hcompany

NeoMME is an efficient Multilingual and Multimodal-native foundational Encoder. Text tokens and raw image patches pass through one shared Transformer encoder. NeoMME does not use a separately pretrained vision encoder or a causal language model. NeoMME-260M is a pretrained encoder backbone and cannot be used on its own for a downstream task. It returns contextual token representations, so users should fine-tune a task-specific head for retrieval, classification, extraction, or another downstream task. For document retrieval, use NeoMME-260M-Retriever. Parameters 263M Vocabulary 131,072 tokens Context length 16,384 tokens Hidden size 1,024 Image patches 32 × 32 pixels, up to 2,048 pixels on the longest side (default)

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This model is a fine-tuned version of xlm-roberta-base, specifically trained on a massive corpus of Persian data to create high-quality contextual embeddings for Persian sentences and paragraphs. It is designed to perform exceptionally well on tasks such as semantic search, clustering, and contextual similarity for Persian text, while also supporting multilingual tasks in English and Persian. The fine-tuning process focused on adapting the pre-trained multilingual XLM-RoBERTa model to better capture Persian linguistic nuances, making it highly effective for tasks requiring embeddings tailored to the Persian language. 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 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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