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AIモデルを探す 2043

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Multilingual E5 Text Embeddings: A Technical Report. Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024 This model is initialized from xlm-roberta-base and continually trained on a mixture of multilingual datasets. It supports 100 languages from xlm-roberta, but low-resource languages may see performance degradation. Here are some rules of thumb: - Use "query: " and "passage: " correspondingly for asymmetric tasks such as passage retrieval in open QA, ad-hoc information retrieval.

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We introduce **Instructor**👨‍🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, without any finetuning***. Instructor👨‍ achieves sota on 70 diverse embedding tasks! The model is easy to use with **our customized** `sentence-transformer` library. For more details, check out our paper and project page! * 01/21: We released a new checkpoint trained with hard negatives, which gives better performance. * 12/21: We released our paper, code, checkpoint and project page! Check them out! Then you can use the model like this to calculate domain-specific and task-aware embeddings:

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埋め込みと類似度
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We introduce **Instructor**👨‍🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, without any finetuning***. Instructor👨‍ achieves sota on 70 diverse embedding tasks (MTEB leaderboard)! The model is easy to use with **our customized** `sentence-transformer` library. For more details, check out our paper and project page! * 12/28: We released a new checkpoint trained with hard negatives, which gives better performance. * 12/21: We released our paper, code, checkpoint and project page! Check them out! Then you can use the model like this to calculate domain-specific and task-aware embeddings:

翻訳待ち・原文を表示

埋め込みと類似度
入力 テキスト→ 出力 埋め込み
料金未確認マルチモーダル・チャネル料金は個別に確認
詳細を見る ↗
出典・文書 ↗

We introduce **Instructor**👨‍🏫, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) ***by simply providing the task instruction, without any finetuning***. Instructor👨‍ achieves sota on 70 diverse embedding tasks! The model is easy to use with **our customized** `sentence-transformer` library. For more details, check out our paper and project page! If you want to calculate customized embeddings for specific sentences, you may follow the unified template to write instructions:                           Represent the `domain` `text_type` for `task_objective`: * `domain` is optional, and it specifies the domain of the text, e.g., science, finance, medicine, etc. * `text_type` is required, and it specifies the encoding unit, e.g., sentence, document, paragraph, etc. * `task_objective` is optional, and it specifies the objective of embedding, e.g., retrieve a document, classify the sentence, etc.

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xcodec2-hf

HKUSTAudio

The X-Codec2 model was proposed in Llasa: Scaling Train-Time and Inference-Time Compute for Llama-based Speech Synthesis. X-Codec2 is a neural audio codec designed to improve speech synthesis and general audio generation for large language model (LLM) pipelines. It extends the original X-Codec by refining how semantic and acoustic information is integrated and tokenized, enabling efficient and high-fidelity audio representation. About its architecture: - **Unified Semantic-Acoustic Tokenization**: X-Codec2 fuses outputs from a semantic encoder (e.g., Wav2Vec2-BERT) and an acoustic encoder into a single embedding, capturing both high-level meaning (e.g., text content, emotion) and low-level audio details (e.g., timbre). - **Single-Stage Feature Scalar Quantization (FSQ)**: Unlike the multi-layer residual VQ in most approaches (e.g., DAC, EnCodec, X-Codec, Mimi), X-Codec2 uses a single-layer of Feature Scalar Quantization (FSQ) for stability and compatibility with causal, autoregressive LLMs. - **Transformer-Friendly Design**: The 1D token structure of X-Codec2 naturally aligns with the autoregressive modeling in LLMs like LLaMA, improving training efficiency and downstream compatibility.

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This is `bge-m3-onnx-o4` weights of the original `BAAI/bge-m3`. Why is this model cool? - [x] Multi-Functionality: It can simultaneously perform the three common retrieval functionalities of embedding model: dense retrieval, multi-vector retrieval, and sparse retrieval. - [x] Multi-Linguality: It can support more than **100** working languages. - [x] Multi-Granularity: It is able to process inputs of different granularities, spanning from short sentences to long documents of up to **8192** tokens. 1. **Download** the checkpoint: For some reason you cannot directly load from this online version (you will get an exception). Please download this repo as below:

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bekko-embedding-v1-a25m is an ultra-compact multilingual text embedding model. It has just 25M active parameters — light enough to run comfortably on modest CPUs — yet its retrieval quality is comparable to models with 3–10x more active parameters. You can also try bekko right in your browser: the bekko-embedding-web demo runs the model fully client-side with Transformers.js — no server involved. > [!NOTE] > For a guided overview of the models, training recipe, and results, read Bekko Embedding: how small can a multilingual retrieval model be?.

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bekko-embedding-v1-a8m is an ultra-compact multilingual text embedding model. It has just 8M active parameters — light enough to run comfortably even on low-spec CPUs — yet its retrieval quality is comparable to models with 3–10x more active parameters. You can also try bekko right in your browser: the bekko-embedding-web demo runs the model fully client-side with Transformers.js — no server involved. > [!NOTE] > For a guided overview of the models, training recipe, and results, read Bekko Embedding: how small can a multilingual retrieval model be?.

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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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**Model Summary:** Granite-Embedding-30m-English is a 30M parameter dense bi-encoder embedding model from the Granite Embeddings suite that can be used to generate high quality text embeddings. This model produces embedding vectors of size 384 and is trained using a combination of open source relevance-pair datasets with permissive, enterprise-friendly license, and IBM collected and generated datasets. While maintaining competitive scores on academic benchmarks such as BEIR, this model also performs well on many enterprise use cases. This model is developed using retrieval oriented pre-training, contrastive fine-tuning, knowledge distillation and model merging for improved performance. ***Granite-embedding-30m-r1.1*** was specifically designed to support multi-turn information retrieval and is designed to handle contextual document retrieval in multi-turn conversational information retrieval. Granite-embedding-30m-r1.1 was trained on data tailored for multi-turn conversational information retrieval and uses multi-teacher distillation over granite-embedding-30m-english (https://huggingface.co/ibm-granite/granite-embedding-30m-english) - **Developers:** Granite Embedding Team, IBM - **GitHub Repository:** ibm-granite/granite-embedding-models - **Website**: Granite Docs - **Paper:** Technical Report - **Release Date**: August 29, 2025 (granite-embedding-30m-english-r1.1) - **License:** Apache 2.0

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