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--- |
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language: |
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- ja |
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- en |
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license_name: sarahina-non-commercial-license |
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license_link: LICENSE |
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tags: |
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- transformers |
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- sentence-similarity |
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- feature-extraction |
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- sentence-transformers |
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pipeline_tag: sentence-similarity |
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inference: false |
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datasets: |
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- hpprc/emb |
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- cl-nagoya/auto-wiki-qa |
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- cl-nagoya/ruri-dataset-ft |
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- hpprc/mqa-ja |
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- izumi-lab/llm-japanese-dataset |
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- sentence-transformers/NQ-retrieval |
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- sbintuitions/JSQuAD |
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- SkelterLabsInc/JaQuAD |
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--- |
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# Sarashina-Embedding-v1-1B |
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**[日本語のREADME/Japanese README](https://huggingface.co/sbintuitions/sarashina-embedding-v1-1b/blob/main/README_JA.md)** |
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"Sarashina-Embedding-v1-1B" is a Japanese text embedding model, based on the 1.2B-parameter Japansese LLM "[Sarashina2.1-1B](https://huggingface.co/sbintuitions/sarashina2.1-1b)". |
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We trained this model with multi-stage contrastive learning. We achieved the state-of-the-art average score in the average of 16 datasets in [JMTEB](https://huggingface.co/datasets/sbintuitions/JMTEB) (Japanese Massive Text Embedding Benchmark). |
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This model maps sentences & paragraphs to a 1792-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [Sarashina2.1-1B](https://huggingface.co/sbintuitions/sarashina2.1-1b) |
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- **Maximum Sequence Length:** 8,192 tokens |
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- **Output Dimensionality:** 1,792 dimensions |
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- **Similarity Function:** Cosine Similarity |
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- **Language:** Japanese |
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- **License:** [Sarashina Model NonCommercial License Agreement](https://huggingface.co/sbintuitions/sarashina-embedding-v1-1b/blob/main/LICENSE) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: LlamaModel |
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(1): Pooling({'word_embedding_dimension': 1792, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': True, 'include_prompt': False}) |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("sbintuitions/sarashina-embedding-v1-1b") |
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# Run inference |
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sentences = [ |
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'更級日記は、平安時代中期に菅原孝標女によって書かれた回想録です。', |
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'Sarashinaは、SB Intuitionsが開発した日本語大規模言語モデルです。これまでに7B, 13B, 70B, 8x70Bのモデルが公開されています。', |
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'更科蕎麦とはなんですか?' |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 1792] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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**Note** |
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- You do not need to add prefixes such as "Query: " and "Document: " at the beginning of the input sentence. |
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- This model is licensed under the [Sarashina Model NonCommercial License Agreement](https://huggingface.co/sbintuitions/sarashina-embedding-v1-1b/blob/main/LICENSE), which has restrictions on commercial use. If you are interested in utilizing this model for your business, please feel free to contact us through our [contact page](https://www.sbintuitions.co.jp/#contact). |
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## Training |
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"Sarashina-Embedding-v1-1B" is created through the following two-stage learning process: |
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### Stage 1: Weakly-supervised Learning |
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To achieve generic text embedding performance across a wide range of domains, we performed contrastive training on weakly-supervised data consisting of our own web-crawled data and open data. |
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#### Datasets |
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|dataset|counts| |
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|:-:|:-:| |
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|AutoWikiQA|50,521,135| |
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|web-crawled data|47,370,649| |
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|MQA|12,941,472| |
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|llm-japanese-dataset|9,074,340| |
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|wikipedia|5,555,212| |
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|Quiz dataset|988,478| |
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|Natural Questions|132,796| |
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|JSQuAD|62,859| |
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|snow|62,758| |
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|JaQuAD|31,746| |
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|mkqa|3,318| |
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|**total**|**126,744,763**| |
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### Step2: Supervised Fine-tuning |
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To enable the model to learn a more accurate query-document similarity, we performed supervised fine-tuning using the following datasets. |
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#### Datasets |
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|dataset|counts| |
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|:-:|:-:| |
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|JSNLI|141,388 | |
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|NU-MNLI|67,987| |
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|Mr. TyDi (only Japanese subset)| 3,697 | |
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|Natural Question (sampled)| 20,000| |
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|**total**|**233,072**| |
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# Evaluation Results with [JMTEB](https://huggingface.co/datasets/sbintuitions/JMTEB) |
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Model |Max Tokens|Avg. | Retrieval | STS | Classification | Reranking | Clustering | PairClassification | |
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|:----------------------------------------------|:----------|:----------|:------------|:----------|:-----------------|:------------|:-------------|:---------------------| |
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| OpenAI/text-embedding-3-large | 8191 |74.05 | 74.48 | 82.52 | 77.58 | 93.58 | 53.32 | 62.35 | |
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| [cl-nagoya/ruri-large](https://huggingface.co/intfloat/multilingual-e5-large) | 512 |73.31 | 73.02 | **83.13** | 77.43 | 92.99 | 51.82 | 62.29 | |
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| [pkshatech/GLuCoSE-base-ja-v2](https://huggingface.co/pkshatech/GLuCoSE-base-ja-v2) | 512 |72.23 | 73.36 | 82.96 | 74.21 | 93.01 | 48.65 | **62.37** | |
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| [pkshatech/RoSEtta-base-ja](https://huggingface.co/pkshatech/RoSEtta-base-ja) |1024 |72.04 | 73.21 | 81.39 | 72.41 | 92.69 | 53.23 | 61.74 | |
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| [intfloat/multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) | 512|70.90 | 70.98 | 79.70 | 72.89 | 92.96 | 51.24 | 62.15 | |
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|[**sarashina-embedding-v1-1b**](https://huggingface.co/sbintuitions/sarashina-embedding-v1-1b)(This model)|**8192**|**75.50**|**77.61**|82.71|**78.37**|**93.74**|**53.86**|62.00| |
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## License |
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This model is licensed under [Sarashina Model NonCommercial License Agreement](https://huggingface.co/sbintuitions/sarashina-embedding-v1-1b/blob/main/LICENSE). |
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**If you are interested in using this model for commercial purposes, please feel free to contact us through our [contact page](https://www.sbintuitions.co.jp/#contact).** |
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