cl-nagoya

--- language: - ja base_model: cl-nagoya/ruri-pt-base tags: - sentence-similarity - feature-extraction license: apache-2.0 datasets: - cl-nagoya/ruri-dataset-ft pipeline_tag: sentence-similarity ---

--- language: - ja tags: - sentence-similarity - feature-extraction base_model: cl-nagoya/ruri-v3-pt-310m widget: [] pipeline_tag: sentence-similarity license: apache-2.0 datasets: - cl-nagoya/ruri-v3-dataset-ft ---

Notes: v3 models are out! We recommend using the following v3 models going forward. |ID| #Param.|Max Len.|Avg. JMTEB| |-|-|-|-| |cl-nagoya/ruri-v3-30m|37M|8192|74.51| |cl-nagoya/ruri-v3-70m|70M|8192|75.48| |cl-nagoya/ruri-v3-130m|132M|8192|76.55| |cl-nagoya/ruri-v3-310m|315M|8192|77.24| |Model|#Param.|Avg.|Retrieval|STS|Classfification|Reranking|Clustering|PairClassification| |:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:| |cl-nagoya/sup-simcse-ja-base|111M|68.56|49.64|82.05|73.47|91.83|51.79|62.57| |cl-nagoya/sup-simcse-ja-large|337M|66.51|37.62|83.18|73.73|91.48|50.56|62.51| |cl-nagoya/unsup-simcse-ja-base|111M|65.07|40.23|78.72|73.07|91.16|44.77|62.44| |cl-nagoya/unsup-simcse-ja-large|337M|66.27|40.53|80.56|74.66|90.95|48.41|62.49| |pkshatech/GLuCoSE-base-ja|133M|70.44|59.02|78.71|76.82|91.90|49.78|66.39| |||||||||| |sentence-transformers/LaBSE|472M|64.70|40.12|76.56|72.66|91.63|44.88|62.33| |intfloat/multilingual-e5-small|118M|69.52|67.27|80.07|67.62|93.03|46.91|62.19| |intfloat/multilingual-e5-base|278M|70.12|68.21|79.84|69.30|92.85|48.26|62.26| |intfloat/multilingual-e5-large|560M|71.65|70.98|79.70|72.89|92.96|51.24|62.15| |||||||||| |OpenAI/text-embedding-ada-002|-|69.48|64.38|79.02|69.75|93.04|48.30|62.40| |OpenAI/text-embedding-3-small|-|70.86|66.39|79.46|73.06|92.92|51.06|62.27| |OpenAI/text-embedding-3-large|-|73.97|74.48|82.52|77.58|93.58|53.32|62.35| |||||||||| |Ruri-Small|68M|71.53|69.41|82.79|76.22|93.00|51.19|62.11| |Ruri-Small v2|68M|73.30|73.94|82.91|76.17|93.20|51.58|62.32| |Ruri-Base|111M|71.91|69.82|82.87|75.58|92.91|54.16|62.38| |Ruri-Base v2|111M|72.48|72.33|83.03|75.34|93.17|51.38|62.35| |Ruri-Large|337M|73.31|73.02|83.13|77.43|92.99|51.82|62.29| |Ruri-Large v2 (this model)|337M|74.55|76.34|83.17|77.18|93.21|52.14|62.27| Model Description - Model Type: Sentence Transformer - Base model: cl-nagoya/ruri-pt-large-v2 - Maximum Sequence Length: 512 tokens - Output Dimensionality: 1024 - Similarity Function: Cosine Similarity - Language: Japanese - License: Apache 2.0 - Paper: https://arxiv.org/abs/2409.07737 Framework Versions - Python: 3.10.13 - Sentence Transformers: 3.0.0 - Transformers: 4.41.2 - PyTorch: 2.3.1+cu118 - Accelerate: 0.30.1 - Datasets: 2.19.1 - Tokenizers: 0.19.1 License This model is published under the Apache License, Version 2.0.

⚠️Notes: This model is a pretrained version and has not been fine-tuned. For the fine-tuned version, please use cl-nagoya/ruri-v3-30m! Ruri v3 is a general-purpose Japanese text embedding model built on top of ModernBERT-Ja. We provide Ruri-v3 in several model sizes. Below is a summary of each model. |ID| #Param. | #Param. w/o Emb.|Dim.|#Layers|Avg. JMTEB| |-|-|-|-|-|-| |cl-nagoya/ruri-v3-30m|37M|10M|256|10|74.51| |cl-nagoya/ruri-v3-70m|70M|31M|384|13|75.48| |cl-nagoya/ruri-v3-130m|132M|80M|512|19|76.55| |cl-nagoya/ruri-v3-310m|315M|236M|768|25|77.24| You can use our models directly with the transformers library v4.48.0 or higher: Additionally, if your GPUs support Flash Attention 2, we recommend using our models with Flash Attention 2. License This model is published under the Apache License, Version 2.0.