language:
- ja
- en
license_name: sarahina-non-commercial-license
license_link: LICENSE
tags:
- transformers
- sentence-similarity
- feature-extraction
- sentence-transformers
pipeline_tag: sentence-similarity
inference: false
datasets:
- hpprc/emb
- cl-nagoya/auto-wiki-qa
- cl-nagoya/ruri-dataset-ft
- hpprc/mqa-ja
- izumi-lab/llm-japanese-dataset
- sentence-transformers/NQ-retrieval
- sbintuitions/JSQuAD
- SkelterLabsInc/JaQuAD
Sarashina-Embedding-v1-1B
"Sarashina-Embedding-v1-1B" is a Japanese text embedding model, based on the 1.2B-parameter Japansese LLM "Sarashina2.1-1B". 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 (Japanese Massive Text Embedding Benchmark).
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.
Model Details
Model Description
- Model Type: Sentence Transformer
- Base model: Sarashina2.1-1B
- Maximum Sequence Length: 8,192 tokens
- Output Dimensionality: 1,792 dimensions
- Similarity Function: Cosine Similarity
- Language: Japanese
- License: Sarashina Model NonCommercial License Agreement
Full Model Architecture
SentenceTransformer(
(0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: LlamaModel
(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})
)
Usage
Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
pip install -U sentence-transformers
Then you can load this model and run inference.
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sbintuitions/sarashina-embedding-v1-1b")
# Run inference
sentences = [
'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。',
'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。',
'更科蕎麦とはなんですか?'
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1792]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
Note
- You do not need to add prefixes such as "Query: " and "Document: " at the beginning of the input sentence.
- This model is licensed under the Sarashina Model NonCommercial License Agreement, 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.
Training
"Sarashina-Embedding-v1-1B" is created through the following two-stage learning process:
Stage 1: Weakly-supervised Learning
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.
Dataset
| dataset | counts |
|---|---|
| AutoWikiQA | 50,521,135 |
| web-crawled data | 47,370,649 |
| MQA | 12,941,472 |
| llm-japanese-dataset | 9,074,340 |
| wikipedia | 5,555,212 |
| Quiz dataset | 988,478 |
| Natural Questions | 132,796 |
| JSQuAD | 62,859 |
| snow | 62,758 |
| JaQuAD | 31,746 |
| mkqa | 3,318 |
| total | 126,744,763 |
Step2: Supervised Fine-tuning
To enable the model to learn a more accurate query-document similarity, we performed supervised fine-tuning using the following dataset.
Dataset
| dataset | counts |
|---|---|
| JSNLI | 141,388 |
| NU-MNLI | 67,987 |
| Mr. TyDi (only Japanese subset) | 3,697 |
| Natural Question (sampled) | 20,000 |
| total | 233,072 |
Evaluation Results with JMTEB
| Model | Max Tokens | Avg. | Retrieval | STS | Classification | Reranking | Clustering | PairClassification |
|---|---|---|---|---|---|---|---|---|
| OpenAI/text-embedding-3-large | 8191 | 74.05 | 74.48 | 82.52 | 77.58 | 93.58 | 53.32 | 62.35 |
| cl-nagoya/ruri-large | 512 | 73.31 | 73.02 | 83.13 | 77.43 | 92.99 | 51.82 | 62.29 |
| pkshatech/GLuCoSE-base-ja-v2 | 512 | 72.23 | 73.36 | 82.96 | 74.21 | 93.01 | 48.65 | 62.37 |
| pkshatech/RoSEtta-base-ja | 1024 | 72.04 | 73.21 | 81.39 | 72.41 | 92.69 | 53.23 | 61.74 |
| intfloat/multilingual-e5-large | 512 | 70.90 | 70.98 | 79.70 | 72.89 | 92.96 | 51.24 | 62.15 |
| sarashina-embedding-v1-1b(This model) | 8192 | 75.50 | 77.61 | 82.71 | 78.37 | 93.74 | 53.86 | 62.00 |
License
This model is licensed under Sarashina Model NonCommercial License Agreement.
If you are interested in using this model for commercial purposes, please feel free to contact us through our contact page.