Update README.md

README.md

@@ -1,141 +1,166 @@
 ---
+language:
+- ja
 tags:
-- sentence-transformers
 - sentence-similarity
 - feature-extraction
 base_model: cl-nagoya/ruri-v3-pt-130m
+widget: []
 pipeline_tag: sentence-similarity
-library_name: sentence-transformers
+license: apache-2.0
+datasets:
+- cl-nagoya/ruri-v3-dataset-ft
 ---
 
-# SentenceTransformer based on cl-nagoya/ruri-v3-pt-130m
+# Ruri: Japanese General Text Embeddings
 
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [cl-nagoya/ruri-v3-pt-130m](https://huggingface.co/cl-nagoya/ruri-v3-pt-130m). It maps sentences & paragraphs to a 512-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+**Ruri v3** is a general-purpose Japanese text embedding model built on top of [**ModernBERT-Ja**](https://huggingface.co/collections/sbintuitions/modernbert-ja-67b68fe891132877cf67aa0a).
+Ruri v3 offers several key technical advantages:
+- **State-of-the-art performance** for Japanese text embedding tasks.
+- **Supports sequence lengths up to 8192 tokens**  
+  - Previous versions of Ruri (v1, v2) were limited to 512.
+- **Expanded vocabulary of 100K tokens**, compared to 32K in v1 and v2  
+  - The larger vocabulary make input sequences shorter, improving efficiency.
+- **Integrated FlashAttention**, following ModernBERT's architecture  
+  - Enables faster inference and fine-tuning.
+- **Tokenizer based solely on SentencePiece**  
+  - Unlike previous versions, which relied on Japanese-specific BERT tokenizers and required pre-tokenized input, Ruri v3 performs tokenization with SentencePiece only—no external word segmentation tool is required.
 
-## Model Details
 
-### Model Description
-- **Model Type:** Sentence Transformer
-- **Base model:** [cl-nagoya/ruri-v3-pt-130m](https://huggingface.co/cl-nagoya/ruri-v3-pt-130m) <!-- at revision 086588f9006908c33be10a4898cd0d6d0867b009 -->
-- **Maximum Sequence Length:** 8192 tokens
-- **Output Dimensionality:** 512 dimensions
-- **Similarity Function:** Cosine Similarity
-<!-- - **Training Dataset:** Unknown -->
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
+## Model Series
 
-### Model Sources
+We provide Ruri-v3 in several model sizes. Below is a summary of each model.
 
-- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
-- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
-- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
+|ID| #Param. | #Param.<br>w/o Emb.|Dim.|#Layers|Avg. JMTEB|
+|-|-|-|-|-|-|
+|[cl-nagoya/ruri-v3-30m](https://huggingface.co/cl-nagoya/ruri-v3-30m)|37M|10M|256|10|74.51|
+|[cl-nagoya/ruri-v3-70m](https://huggingface.co/cl-nagoya/ruri-v3-70m)|70M|31M|384|13|75.48|
+|[**cl-nagoya/ruri-v3-130m**](https://huggingface.co/cl-nagoya/ruri-v3-130m)|132M|80M|512|19|**76.55**|
+|[cl-nagoya/ruri-v3-310m](https://huggingface.co/cl-nagoya/ruri-v3-310m)|315M|236M|768|25|77.24|
 
-### Full Model Architecture
-
-```
-MySentenceTransformer(
-  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel 
-  (1): Pooling({'word_embedding_dimension': 512, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
-)
-```
 
 ## Usage
 
-### Direct Usage (Sentence Transformers)
-
-First install the Sentence Transformers library:
+You can use our models directly with the transformers library v4.48.0 or higher:
 
 ```bash
-pip install -U sentence-transformers
+pip install -U "transformers>=4.48.0"
+```
+
+Additionally, if your GPUs support Flash Attention 2, we recommend using our models with Flash Attention 2.
+
+```
+pip install flash-attn --no-build-isolation
 ```
 
 Then you can load this model and run inference.
 ```python
+import torch.nn.functional as F
 from sentence_transformers import SentenceTransformer
 
 # Download from the 🤗 Hub
-model = SentenceTransformer("hpprc/ruri-v3-130m-default14-1")
-# Run inference
+model = SentenceTransformer("cl-nagoya/ruri-v3-130m")
+
+# Ruri v3 employs a 1+3 prefix scheme to distinguish between different types of text inputs:
+# "" (empty string) is used for encoding semantic meaning.
+# "トピック: " is used for classification, clustering, and encoding topical information.
+# "検索クエリ: " is used for queries in retrieval tasks.
+# "検索文書: " is used for documents to be retrieved.
 sentences = [
-    'The weather is lovely today.',
-    "It's so sunny outside!",
-    'He drove to the stadium.',
+    "川べりでサーフボードを持った人たちがいます",
+    "サーファーたちが川べりに立っています",
+    "トピック: 瑠璃色のサーファー",
+    "検索クエリ: 瑠璃色はどんな色？",
+    "検索文書: 瑠璃色（る��いろ）は、紫みを帯びた濃い青。名は、半貴石の瑠璃（ラピスラズリ、英: lapis lazuli）による。JIS慣用色名では「こい紫みの青」（略号 dp-pB）と定義している[1][2]。",
 ]
-embeddings = model.encode(sentences)
-print(embeddings.shape)
-# [3, 512]
-
-# Get the similarity scores for the embeddings
-similarities = model.similarity(embeddings, embeddings)
-print(similarities.shape)
-# [3, 3]
-```
-
-<!--
-### Direct Usage (Transformers)
-
-<details><summary>Click to see the direct usage in Transformers</summary>
-
-</details>
--->
 
-<!--
-### Downstream Usage (Sentence Transformers)
-
-You can finetune this model on your own dataset.
-
-<details><summary>Click to expand</summary>
-
-</details>
--->
-
-<!--
-### Out-of-Scope Use
+embeddings = model.encode(sentences, convert_to_tensor=True)
+print(embeddings.size())
+# [5, 512]
+
+similarities = F.cosine_similarity(embeddings.unsqueeze(0), embeddings.unsqueeze(1), dim=2)
+print(similarities)
+# [[1.0000, 0.9564, 0.8183, 0.7000, 0.7108],
+#  [0.9564, 1.0000, 0.8112, 0.6994, 0.7117],
+#  [0.8183, 0.8112, 1.0000, 0.8788, 0.8514],
+#  [0.7000, 0.6994, 0.8788, 1.0000, 0.9448],
+#  [0.7108, 0.7117, 0.8514, 0.9448, 1.0000]]
+```
 
-*List how the model may foreseeably be misused and address what users ought not to do with the model.*
--->
+## Benchmarks
+
+### JMTEB
+Evaluated with [JMTEB](https://github.com/sbintuitions/JMTEB).
+
+|Model|#Param.|Avg.|Retrieval|STS|Classfification|Reranking|Clustering|PairClassification|
+|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
+||||||||||
+|[Ruri-v3-30m](https://huggingface.co/cl-nagoya/ruri-v3-30m)|37M|74.51|78.08|82.48|74.80|93.00|52.12|62.40|
+|[Ruri-v3-70m](https://huggingface.co/cl-nagoya/ruri-v3-70m)|70M|75.48|79.96|79.82|76.97|93.27|52.70|61.75|
+|[**Ruri-v3-130m**](https://huggingface.co/cl-nagoya/ruri-v3-130m)<br/>(this model)|**132M**|**76.55**|81.89|79.25|77.16|93.31|55.36|62.26|
+|[Ruri-v3-310m](https://huggingface.co/cl-nagoya/ruri-v3-310m)|315M|77.24|81.89|81.22|78.66|93.43|55.69|62.60|
+||||||||||
+|[sbintuitions/sarashina-embedding-v1-1b](https://huggingface.co/sbintuitions/sarashina-embedding-v1-1b)|1.22B|75.50|77.61|82.71|78.37|93.74|53.86|62.00|
+||||||||||
+|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|
+||||||||||
+|[pkshatech/GLuCoSE-base-ja](https://huggingface.co/pkshatech/GLuCoSE-base-ja)|133M|70.44|59.02|78.71|76.82|91.90|49.78|66.39|
+|[pkshatech/GLuCoSE-base-ja-v2](https://huggingface.co/pkshatech/GLuCoSE-base-ja-v2)|133M|72.23|73.36|82.96|74.21|93.01|48.65|62.37|
+|[retrieva-jp/amber-base](https://huggingface.co/retrieva-jp/amber-base)|130M|72.12|73.40|77.81|76.14|93.27|48.05|64.03|
+|[retrieva-jp/amber-large](https://huggingface.co/retrieva-jp/amber-large)|315M|73.22|75.40|79.32|77.14|93.54|48.73|60.97|
+||||||||||
+|[sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE)|472M|64.70|40.12|76.56|72.66|91.63|44.88|62.33|
+|[intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small)|118M|69.52|67.27|80.07|67.62|93.03|46.91|62.19|
+|[intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base)|278M|70.12|68.21|79.84|69.30|92.85|48.26|62.26|
+|[intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large)|560M|71.65|70.98|79.70|72.89|92.96|51.24|62.15|
+||||||||||
+|[Ruri-Small](https://huggingface.co/cl-nagoya/ruri-small)|68M|71.53|69.41|82.79|76.22|93.00|51.19|62.11|
+|[Ruri-Small v2](https://huggingface.co/cl-nagoya/ruri-small-v2)|68M|73.30|73.94|82.91|76.17|93.20|51.58|62.32|
+|[Ruri-Base](https://huggingface.co/cl-nagoya/ruri-base)|111M|71.91|69.82|82.87|75.58|92.91|54.16|62.38|
+|[Ruri-Base v2](https://huggingface.co/cl-nagoya/ruri-base-v2)|111M|72.48|72.33|83.03|75.34|93.17|51.38|62.35|
+|[Ruri-Large](https://huggingface.co/cl-nagoya/ruri-large)|337M|73.31|73.02|83.13|77.43|92.99|51.82|62.29|
+|[Ruri-Large v2](https://huggingface.co/cl-nagoya/ruri-large-v2)|337M|74.55|76.34|83.17|77.18|93.21|52.14|62.27|
 
-<!--
-## Bias, Risks and Limitations
 
-*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
--->
+## Model Details
 
-<!--
-### Recommendations
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [cl-nagoya/ruri-v3-pt-130m](https://huggingface.co/cl-nagoya/ruri-v3-pt-130m) 
+- **Maximum Sequence Length:** 8192 tokens
+- **Output Dimensionality:** 512
+- **Similarity Function:** Cosine Similarity
+- **Language:** Japanese
+- **License:** Apache 2.0
+- **Paper:** https://arxiv.org/abs/2409.07737
 
-*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
--->
 
-## Training Details
+### Full Model Architecture
 
-### Framework Versions
-- Python: 3.10.13
-- Sentence Transformers: 3.4.1
-- Transformers: 4.48.3
-- PyTorch: 2.5.1+cu124
-- Accelerate: 1.3.0
-- Datasets: 3.3.0
-- Tokenizers: 0.21.0
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: ModernBertModel 
+  (1): Pooling({'word_embedding_dimension': 512, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
 
 ## Citation
 
-### BibTeX
-
-<!--
-## Glossary
-
-*Clearly define terms in order to be accessible across audiences.*
--->
-
-<!--
-## Model Card Authors
-
-*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
--->
+```bibtex
+@misc{
+  Ruri,
+  title={{Ruri: Japanese General Text Embeddings}}, 
+  author={Hayato Tsukagoshi and Ryohei Sasano},
+  year={2024},
+  eprint={2409.07737},
+  archivePrefix={arXiv},
+  primaryClass={cs.CL},
+  url={https://arxiv.org/abs/2409.07737}, 
+}
+```
 
-<!--
-## Model Card Contact
 
-*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--->
+## License
+This model is published under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).