Update README.md

README.md

@@ -1,5 +1,6 @@
 ---
-language: []
+language:
+- ja
 library_name: sentence-transformers
 tags:
 - sentence-transformers
@@ -8,38 +9,13 @@ tags:
 base_model: tohoku-nlp/bert-base-japanese-v3
 widget: []
 pipeline_tag: sentence-similarity
+license: apache-2.0
+datasets:
+- cl-nagoya/ruri-dataset-ft
 ---
 
-# SentenceTransformer based on tohoku-nlp/bert-base-japanese-v3
+# Ruri: Japanese General Text Embeddings
 
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [tohoku-nlp/bert-base-japanese-v3](https://huggingface.co/tohoku-nlp/bert-base-japanese-v3). 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:** [tohoku-nlp/bert-base-japanese-v3](https://huggingface.co/tohoku-nlp/bert-base-japanese-v3) <!-- at revision 65243d6e5629b969c77309f217bd7b1a79d43c7e -->
-- **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: 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})
-)
-```
 
 ## Usage
 
@@ -53,64 +29,82 @@ pip install -U sentence-transformers
 
 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("sentence_transformers_model_id")
-# Run inference
+model = SentenceTransformer("cl-nagoya/ruri-pt-base")
+
+# Don't forget to add the prefix "クエリ: " for query-side or "文章: " for passage-side texts.
 sentences = [
-    'The weather is lovely today.',
-    "It's so sunny outside!",
-    'He drove to the stadium.',
+    "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)
-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>
+embeddings = model.encode(sentences, convert_to_tensor=True)
+print(embeddings.size())
+# [4, 1024]
 
-</details>
--->
-
-<!--
-### Downstream Usage (Sentence Transformers)
-
-You can finetune this model on your own dataset.
+similarities = F.cosine_similarity(embeddings.unsqueeze(0), embeddings.unsqueeze(1), dim=2)
+print(similarities)
+```
 
-<details><summary>Click to expand</summary>
+## 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-Base](https://huggingface.co/cl-nagoya/ruri-base)|111M|71.91|69.82|82.87|75.58|92.91|54.16|62.38|
+|[**Ruri-Large**](https://huggingface.co/cl-nagoya/ruri-large) (this model)|337M|73.31|73.02|83.13|77.43|92.99|51.82|62.29|
 
-</details>
--->
 
-<!--
-### Out-of-Scope Use
 
-*List how the model may foreseeably be misused and address what users ought not to do with the model.*
--->
+## Model Details
 
-<!--
-## Bias, Risks and Limitations
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [tohoku-nlp/bert-base-japanese-v3](https://huggingface.co/tohoku-nlp/bert-base-japanese-v3) 
+- **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
+<!-- - **Training Dataset:** Unknown -->
 
-*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
--->
+### Full Model Architecture
 
-<!--
-### Recommendations
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 1024, '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})
+)
+```
 
-*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
--->
 
 ## Training Details
 
+
 ### Framework Versions
 - Python: 3.10.13
 - Sentence Transformers: 3.0.0
@@ -120,24 +114,10 @@ You can finetune this model on your own dataset.
 - Datasets: 2.19.1
 - Tokenizers: 0.19.1
 
-## Citation
+<!-- ## 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.*
--->
-
-<!--
-## 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).