cl-nagoya
/

ruri-small-v2

@@ -1,141 +1,126 @@
 ---
-base_model: hpprc/ruri-v2-pt-small-m
-library_name: sentence-transformers
-pipeline_tag: sentence-similarity
 tags:
-- sentence-transformers
 - sentence-similarity
 - feature-extraction
 ---
-# SentenceTransformer based on hpprc/ruri-v2-pt-small-m
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [hpprc/ruri-v2-pt-small-m](https://huggingface.co/hpprc/ruri-v2-pt-small-m). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
-## Model Details
-### Model Description
-- **Model Type:** Sentence Transformer
-- **Base model:** [hpprc/ruri-v2-pt-small-m](https://huggingface.co/hpprc/ruri-v2-pt-small-m) <!-- at revision c64c90af5641f0e60d2a384240f29f8cf2cd6167 -->
-- **Maximum Sequence Length:** 512 tokens
-- **Output Dimensionality:** 768 tokens
-- **Similarity Function:** Cosine Similarity
-<!-- - **Training Dataset:** Unknown -->
-<!-- - **Language:** Unknown -->
-<!-- - **License:** Unknown -->
-### Model Sources
-- **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)
-### Full Model Architecture
-```
-MySentenceTransformer(
-  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
-  (1): Pooling({'word_embedding_dimension': 768, '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:
 ```bash
-pip install -U sentence-transformers
 ```
 Then you can load this model and run inference.
 ```python
 from sentence_transformers import SentenceTransformer
 # Download from the 🤗 Hub
-model = SentenceTransformer("hpprc/ruri-v2-small15")
-# Run inference
 sentences = [
-    'The weather is lovely today.',
-    "It's so sunny outside!",
-    'He drove to the stadium.',
 ]
-embeddings = model.encode(sentences)
-print(embeddings.shape)
-# [3, 768]
-# 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
-*List how the model may foreseeably be misused and address what users ought not to do with the model.*
--->
-<!--
-## 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.*
--->
-<!--
-### Recommendations
-*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
--->
-## Training Details
 ### Framework Versions
 - Python: 3.10.13
-- Sentence Transformers: 3.1.1
-- Transformers: 4.45.1
-- PyTorch: 2.4.1+cu124
-- Accelerate: 0.34.2
 - Datasets: 2.19.1
-- Tokenizers: 0.20.0
 ## 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.*
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-## Model Card Contact
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--->

 ---
+language:
+- ja
 tags:
 - sentence-similarity
 - feature-extraction
+base_model: cl-nagoya/ruri-pt-small-v2
+widget: []
+pipeline_tag: sentence-similarity
+license: apache-2.0
 ---
+# Ruri: Japanese General Text Embeddings
 ## Usage
 First install the Sentence Transformers library:
 ```bash
+pip install -U sentence-transformers fugashi sentencepiece unidic-lite
 ```
 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("cl-nagoya/ruri-small-v2")
+# Don't forget to add the prefix "クエリ: " for query-side or "文章: " for passage-side texts.
 sentences = [
+    "クエリ: 瑠璃色はどんな色？",
+    "文章: 瑠璃色（るりいろ）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]。",
+    "クエリ: ワシやタカのように、鋭いくちばしと爪を持った大型の鳥類を総称して「何類」というでしょう?",
+    "文章: ワシ、タカ、ハゲワシ、ハヤブサ、コンドル、フクロウが代表的である。これらの猛禽類はリンネ前後の時代(17~18世紀)には鷲類・鷹類・隼類及び梟類に分類された。ちなみにリンネは狩りをする鳥を単一の目(もく)にまとめ、vultur(コンドル、ハゲワシ)、falco(ワシ、タカ、ハヤブサなど)、strix(フクロウ)、lanius(モズ)の4属を含めている。",
 ]
+embeddings = model.encode(sentences, convert_to_tensor=True)
+print(embeddings.size())
+# [4, 768]
+similarities = F.cosine_similarity(embeddings.unsqueeze(0), embeddings.unsqueeze(1), dim=2)
+print(similarities)
+```
+## Benchmarks
+### JMTEB
+Evaluated with [JMTEB](https://github.com/sbintuitions/JMTEB).
+|Model|#Param.|Avg.|Retrieval|STS|Classfification|Reranking|Clustering|PairClassification|
+|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
+|[cl-nagoya/sup-simcse-ja-base](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/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](https://huggingface.co/pkshatech/GLuCoSE-base-ja)|133M|70.44|59.02|78.71|76.82|91.90|49.78|66.39|
+||||||||||
+|[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|
+||||||||||
+|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](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) (this model)|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|
+## Model Details
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [cl-nagoya/ruri-pt-small-v2](https://huggingface.co/cl-nagoya/ruri-pt-small-v2)
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768
+- **Similarity Function:** Cosine Similarity
+- **Language:** Japanese
+- **License:** Apache 2.0
+- **Paper:** https://arxiv.org/abs/2409.07737
+### Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
+  (1): Pooling({'word_embedding_dimension': 768, '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})
+)
+```
 ### 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
 ## Citation
+```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},
+}
+```
+## License
+This model is published under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).