Update README.md

README.md

@@ -12,36 +12,8 @@ pipeline_tag: sentence-similarity
 license: apache-2.0
 ---
 
-# SentenceTransformer based on cl-nagoya/ruri-small-pt
+# Ruri: Japanese General Text Embeddings
 
-This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [cl-nagoya/ruri-small-pt](https://huggingface.co/cl-nagoya/ruri-small-pt). 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:** [cl-nagoya/ruri-small-pt](https://huggingface.co/cl-nagoya/ruri-small-pt) <!-- at revision 7fc406373e1b317cddbf9962bb2d55270dca7ea8 -->
-- **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
 
@@ -55,64 +27,86 @@ 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("cl-nagoya/ruri-small-55-alpha0.0-0")
-# Run inference
+model = SentenceTransformer("cl-nagoya/ruri-small", trust_remote_code=True)
+
+# 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, 768]
 
-</details>
--->
-
-<!--
-### Downstream Usage (Sentence Transformers)
+similarities = F.cosine_similarity(embeddings.unsqueeze(0), embeddings.unsqueeze(1), dim=2)
+print(similarities)
+# [[1.0000, 0.9453, 0.6860, 0.7225],
+#  [0.9453, 1.0000, 0.6852, 0.7005],
+#  [0.6860, 0.6852, 1.0000, 0.8567],
+#  [0.7225, 0.7005, 0.8567, 1.0000]]
+```
 
-You can finetune this model on your own dataset.
+## Benchmarks
+
+### JMTEB
+Evaluated with [JMTEB](https://github.com/sbintuitions/JMTEB).
+
+|Model|#Param.|Retrieval|STS|Classfification|Reranking|Clustering|PairClassification|Avg.|
+|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
+|[cl-nagoya/sup-simcse-ja-base](https://huggingface.co/cl-nagoya/sup-simcse-ja-base)|111M|49.64|82.05|73.47|91.83|51.79|62.57|68.56|
+|[cl-nagoya/sup-simcse-ja-large](https://huggingface.co/cl-nagoya/sup-simcse-ja-large)|337M|37.62|83.18|73.73|91.48|50.56|62.51|66.51|
+|[cl-nagoya/unsup-simcse-ja-base](https://huggingface.co/cl-nagoya/unsup-simcse-ja-base)|111M|40.23|78.72|73.07|91.16|44.77|62.44|65.07|
+|[cl-nagoya/unsup-simcse-ja-large](https://huggingface.co/cl-nagoya/unsup-simcse-ja-large)|337M|40.53|80.56|74.66|90.95|48.41|62.49|66.27|
+|[pkshatech/GLuCoSE-base-ja](https://huggingface.co/pkshatech/GLuCoSE-base-ja)|133M|59.02|78.71|76.82|91.90|49.78|66.39|70.44|
+||||||||||
+|[sentence-transformers/LaBSE](https://huggingface.co/sentence-transformers/LaBSE)|472M|40.12|76.56|72.66|91.63|44.88|62.33|64.70|
+|[intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small)|118M|67.27|80.07|67.62|93.03|46.91|62.19|69.52|
+|[intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base)|278M|68.21|79.84|69.30|92.85|48.26|62.26|70.12|
+|[intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large)|560M|70.98|79.70|72.89|92.96|51.24|62.15|71.65|
+||||||||||
+|OpenAI/text-embedding-ada-002|-|64.38|79.02|69.75|93.04|48.30|62.40|69.48|
+|OpenAI/text-embedding-3-small|-|66.39|79.46|73.06|92.92|51.06|62.27|70.86|
+|OpenAI/text-embedding-3-large|-|74.48|82.52|77.58|93.58|53.32|62.35|73.97|
+||||||||||
+|**[Ruri-Small](https://huggingface.co/cl-nagoya/ruri-small)**|68M|69.41|82.79|76.22|93.00|51.19|62.11|71.53|
+|[Ruri-Base](https://huggingface.co/cl-nagoya/ruri-base)|111M|69.82|82.87|75.58|92.91|54.16|62.38|71.91|
+|[Ruri-Large](https://huggingface.co/cl-nagoya/ruri-large)|337M|73.02|83.13|77.43|92.99|51.82|62.29|73.31|
 
-<details><summary>Click to expand</summary>
 
-</details>
--->
 
-<!--
-### Out-of-Scope Use
+## Model Details
 
-*List how the model may foreseeably be misused and address what users ought not to do with the model.*
--->
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [cl-nagoya/ruri-pt-small](https://huggingface.co/cl-nagoya/ruri-pt-small) 
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768
+- **Similarity Function:** Cosine Similarity
+- **Language:** Japanese
+- **License:** Apache 2.0
+<!-- - **Training Dataset:** Unknown -->
 
-<!--
-## 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.*
--->
+### Full Model Architecture
 
-<!--
-### Recommendations
+```
+SentenceTransformer(
+  (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})
+)
+```
 
-*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
@@ -122,24 +116,10 @@ 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).