Parrotlet-e is an advanced multilingual medical embedding model optimized specifically for medical terms in various Indian languages. It is fine - tuned based on BAAI/bge - m3 and trained on over 18 million pairs of multilingual medical terms. It supports 12 Indian languages and English and has strong robustness against abbreviations, spelling variations, and colloquial expressions in clinical documents.

Natural Language ProcessingTransformers

This model is the GGUF quantized version of BAAI/bge-reranker-v2-m3, using the Q4_K_M quantization method. It is a multilingual reordering model mainly used for the result reordering task in text retrieval, which can improve the relevance of retrieval results.

Natural Language ProcessingSentence-transformersOther

BGE-M3 is a multi-functional text embedding model developed by BAAI, which supports multi-language, multi-granularity, and multi-functional text representation learning, and can handle multiple retrieval modes such as dense retrieval, sparse retrieval, and multi-vector retrieval simultaneously.

Natural Language ProcessingSentence-transformers

OpenMed-NER-ChemicalDetect-MultiMed-568M is a biomedical NLP model specifically designed for chemical entity recognition, capable of accurately identifying chemical compounds and substances in biomedical literature. This model is based on the bge-m3 architecture and was trained on the BC4CHEMD dataset, achieving an F1 score of 0.9459, providing strong support for biomedical research and clinical applications.

Natural Language ProcessingTransformersEnglish

This is a professional named entity recognition model focusing on cancer genetics, built on the bge-m3 architecture and having 568 million parameters. This model is trained on the BIONLP2013_CG dataset and can accurately identify cancer-related genetic entities, providing strong support for cancer research and clinical applications.

Natural Language ProcessingTransformersEnglish

OpenMed-NER-ProteinDetect-MultiMed-568M is an advanced model specifically designed for biomedical entity recognition. Based on the bge-m3 architecture, it has 568M parameters. This model is trained on the FSU dataset and can accurately identify various biomedical entities, including proteins, protein complexes, protein families, etc. It has extremely high practical value in clinical and research applications.

Natural Language ProcessingTransformersEnglish

A SentenceTransformer model based on bge-m3, fine-tuned for Korean financial texts, supporting multilingual processing and optimizing semantic similarity tasks in the financial domain.

Natural Language ProcessingSentence-transformersMultiple Languages

BGE-Code-v1 is an LLM-based code embedding model that supports code retrieval, text retrieval, and multilingual retrieval, excelling in code and text retrieval tasks.

Natural Language ProcessingTransformersMultiple Languages

This is a cross-encoder model fine-tuned from BAAI/bge-reranker-base, designed for scoring text pairs, suitable for text reordering and semantic search tasks.

Natural Language ProcessingSafetensors

BGE-M3 is a multilingual text embedding model developed by the Beijing Academy of Artificial Intelligence (BAAI). It supports three retrieval methods: dense retrieval, multi-vector retrieval, and sparse retrieval for over 100 languages. This model performs excellently in the MTEB benchmark test and can effectively handle multilingual text similarity calculation and retrieval tasks.

Natural Language ProcessingTransformersMultiple Languages

This is the 4-bit GPTQ quantized version of the BAAI/bge-multilingual-gemma2 model, supporting multilingual text embedding tasks.

Natural Language ProcessingTransformers

This is a sentence-transformers model fine-tuned from BAAI/bge-base-en-v1.5, which maps sentences and paragraphs to a 768-dimensional dense vector space.

Natural Language ProcessingSafetensors

This is a weighted/matrix quantized version of the BAAI/bge-large-zh-v1.5 model, offering multiple quantization options suitable for different scenarios.

Natural Language ProcessingTransformersChinese

BAAI/bge-large-zh-v1.5 is a Chinese sentence transformer model primarily used for feature extraction and sentence similarity calculation.

Natural Language ProcessingGgufChinese

GGUF format re-ranking model converted from BAAI/bge-reranker-base, supporting Chinese and English text sorting tasks

Natural Language ProcessingGgufMultiple Languages

This model is converted from BAAI/bge-reranker-v2-m3 into GGUF format using llama.cpp via ggml.ai's GGUF-my-repo space, primarily for text classification tasks.

Natural Language ProcessingGgufOther

BGE-m3 is a domain-specific text embedding model customized for legal applications. It can convert legal documents, sentences, and queries into dense vector representations, accurately capturing the subtle semantic relationships in legal language.

Natural Language ProcessingTransformersEnglish

This model is a GGUF format conversion of BAAI/bge-reranker-v2-m3, designed for text ranking tasks with multilingual support.

Natural Language ProcessingGgufOther

Vietnamese embedding model fine-tuned on BGE-M3, enhancing Vietnamese retrieval capabilities

Natural Language ProcessingSafetensorsOther

This is a sentence transformer model fine-tuned based on BAAI/bge-base-en-v1.5, specifically optimized for Q&A systems related to 3GPP technical documents, capable of mapping text to a 768-dimensional vector space.

Natural Language ProcessingSafetensorsEnglish