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Multilingual Representations for Indian Languages : A BERT Large (24L) model pre-trained on 17 Indian languages, and their transliterated counterparts. This model uses a BERT large architecture [1] pretrained from scratch using the Wikipedia [2], Common Crawl [3], PMINDIA [4] and Dakshina [5] corpora for 17 [6] Indian languages. We use a training paradigm similar to multilingual bert, with a few modifications as listed:

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This model outputs dense, sparse and ColBERT embedding representations all at once. The output is a list of numpy arrays in previously mentioned order of representations. Note: dense and ColBERT embeddings are normalized like the default behavior in the original FlagEmbedding library, if you want unnormalized outputs you can modify the code in `export_onnx_int8.py` and re-run the script. This model also has "O2" level graph optimizations applied, you can read more about optimization levels here. If you want ONNX model with different optimization or without optimizations, you can re-run the ONNX export script `export_onnx_int8.py` with appropriate optimization argument.

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bge-m3-GGUF

gpustack

**Model creator**: BAAI **Original model**: bge-m3 **GGUF quantization**: based on llama.cpp release 61408e7f For more details please refer to our github repo: https://github.com/FlagOpen/FlagEmbedding In this project, we introduce BGE-M3, which is distinguished for its versatility in Multi-Functionality, Multi-Linguality, and Multi-Granularity. - Multi-Functionality: It can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector retrieval, and sparse retrieval. - Multi-Linguality: It can support more than 100 working languages. - Multi-Granularity: It is able to process inputs of different granularities, spanning from short sentences to long documents of up to 8192 tokens.

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This is a sentence-transformers model trained for semantic text understanding. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, classification, clustering, and more.

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