sarashina-embedding

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

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。',
    'サラシナエンベディングは日本語言語モデルをベースにした日本語埋め込みモデルです。'
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1792]

# Get the similarity scores between 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: " to 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.

Datasets

Step2: Supervised Fine-tuning

To enable the model to learn a more accurate query-document similarity, we performed supervised fine-tuning using the following datasets.

Datasets

Evaluation Results with JMTEB