first commit

1_Pooling/config.json

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+{
+  "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
+}

LICENCE

@@ -0,0 +1,96 @@
+Sarashina Model NonCommercial License Agreement
+Released Date by SB Intuitions Corp.: November 8, 2024
+
+This Sarashina Model NonCommercial License Agreement (this "Agreement") is a legal agreement between Licensee (as defined in Article 1) and SB Intuitions Corp.  ("SB Intuitions"), and governs Licensee's use of the Models (as defined in Article 1) that SB Intuitions provides to Licensee under this Agreement.
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README.md

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+---
+language: 
+- ja
+license_name: sarahina-non-commercial-license
+license_link: LICENSE
+base_model:
+- sbintuitions/sarashina2.2-1b
+tags:
+- transformers
+- sentence-similarity
+- feature-extraction
+- sentence-transformers
+inference: false
+---
+
+# Sarashina-Embedding-v2-1B
+
+**[日本語のREADME/Japanese README](https://huggingface.co/sbintuitions/sarashina-embedding-v2-1b/blob/main/README_JA.md)**
+
+"Sarashina-Embedding-v2-1B" is a Japanese text embedding model, based on the Japanese LLM "[Sarashina2.2-1B](https://huggingface.co/sbintuitions/sarashina2.2-1b)".
+We trained this model with multi-stage contrastive learning. We achieved the state-of-the-art average score across 28 datasets in  [JMTEB](https://huggingface.co/datasets/sbintuitions/JMTEB) (Japanese Massive Text Embedding Benchmark).(Benchmarked on July 28,2025.)
+
+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 other applications.
+
+## Model Details
+
+### Model Description
+
+- **Model Type:** Sentence Transformer
+- **Base model:** [Sarashina2.2-1B](https://huggingface.co/sbintuitions/sarashina2.2-1b)
+- **Maximum Sequence Length:** 8,192 tokens
+- **Output Dimensionality:** 1,792 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Language:**  Japanese
+- **License:** [Sarashina Model NonCommercial License Agreement](https://huggingface.co/sbintuitions/sarashina-embedding-v2-1b/blob/main/LICENSE)
+
+### 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](https://github.com/UKPLab/sentence-transformers) library:
+
+```bash
+pip install sentence-transformers==4.0.2
+```
+
+Then you can load this model and run inference.
+
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sbintuitions/sarashina-embedding-v2-1b")
+# Run inference
+query = [
+      'task: クエリを与えるので、与えられたWeb検索クエリに答える関連文章を検索してください。\nquery: Sarashinaのテキスト埋め込みモデルはありますか?'
+  ]
+texts = [
+      'text: 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。',
+      'text: 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。',
+      'text: 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。'
+]
+query_embedding = model.encode(query)
+text_embeddings = model.encode(texts)
+# Get the similarity scores between the embeddings
+similarities = model.similarity(query_embedding, text_embeddings)
+print(similarities)
+# tensor([[0.7403, 0.8651, 0.8775]])
+```
+### How to add instructions and prefixes
+
+For both the query and document sides, use different prefix formats. On the query side, add the prefix `task:` followed by instructions. (*Only for STS tasks: The document side should also use the same format of instruction and prefix as the query side.)
+
+  - Query Side: ```task: {Instrcution}\nquery: {Query}```
+  - Document Side: ```text: {Document}```
+
+### Templates for instructions and prefixes
+
+The table below provides instruction and prefix templates for five main tasks.
+|Task|Query Side|Document Side|
+|:-:|:-|:-|
+|Retrieval<br>Reranking|task: 質問を与えるので、その質問に答えるのに役立つ関連文書を検索してください。\nquery: |text: |
+|Clustering|task: 与えられたドキュメントのトピックまたはテーマを特定してください。\nquery: |text: |
+|Classification|task: 与えられたレビューを適切な評価カテゴリに分類してください。\nquery: |text: |
+|STS|task: クエリを与えるので，もっともクエリに意味が似ている一節を探してください。\nquery: |task: クエリを与えるので，もっともクエリに意味が似ている一節を探してください。\nquery: |
+
+## Training
+
+Sarashina-Embedding-v2-1B is created through the following three-stage learning process:
+
+### Stage 1: Weakly-supervised Learning
+To build a general-purpose and high-performance embedding model for a wide range of domains, we employed contrastive learning using weak supervision data, which consists of our own web-crawled data and open datasets.
+
+### Step2: Supervised Fine-tuning
+To further train the model to better understand the similarity between queries and documents, we performed fine-tuning using higher-quality data than that used in Stage 1. Additionally, we trained multiple models by modifying parts of the data.
+
+### Stage 3: Model Merging
+To enhance performance, we merged the weights of the two models that yielded the highest JMTEB scores in Stage 2 through linear merging.
+
+## Evaluation Results with [JMTEB](https://huggingface.co/datasets/sbintuitions/JMTEB)
+
+|Model|Avg.|Retrieval|STS|Classfification|Reranking|Clustering|
+|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
+|Sarashina-Embedding-v2-1B(This model)|76.38|76.48|84.22|77.14|86.28|52.56|
+|[Ruri-v3-310m](https://huggingface.co/cl-nagoya/ruri-v3-310m)|75.85|76.03|81.59|77.65|85.84|50.52|
+|[Sarashina-Embedding-v1-1B](https://huggingface.co/sbintuitions/sarashina-embedding-v1-1b)|74.87|74.53|81.71|77.20|84.36|50.30|
+|text-embedding-3-large|73.86|71.95|82.52|77.27|83.06|51.82|
+
+(*) Evaluated on July 28, 2025.
+
+## License
+
+This model is licensed under [Sarashina Model NonCommercial License Agreement](https://huggingface.co/sbintuitions/sarashina-embedding-v2-1b/blob/main/LICENSE).
+
+**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).**

README_JA.md

@@ -0,0 +1,129 @@
+---
+language: 
+- ja
+license_name: sarahina-non-commercial-license
+license_link: LICENSE
+base_model:
+- sbintuitions/sarashina2.2-1b
+tags:
+- transformers
+- sentence-similarity
+- feature-extraction
+- sentence-transformers
+inference: false
+---
+
+# Sarashina-Embedding-v2-1B
+
+「Sarashina-Embedding-v2-1b」は、日本語LLM「[Sarashina2.2-1B](https://huggingface.co/sbintuitions/sarashina2.2-1b)」をベースにした日本語テキスト埋め込みモデルです。
+
+このモデルは、マルチステージの対照学習で訓練され、 [JMTEB](https://huggingface.co/datasets/sbintuitions/JMTEB) （Japanese Massive Text Embedding Benchmark）の28個のデータセットの平均で、最高水準の平均スコア(2025/07/28時点)を達成しました。
+
+このモデルは、文や文章を1792次元の高密度ベクトル空間にマッピングし、意味的テキスト類似度、意味的検索、paraphrase mining、テキスト分類、クラスタリングなどに使用できます。
+
+## モデル詳細
+
+### モデル説明
+
+- **モデルタイプ:** Sentence Transformer
+- **ベースモデル:** [Sarashina2.2-1B](https://huggingface.co/sbintuitions/sarashina2.2-1b)
+- **最大シーケンス長:** 8,192トークン
+- **出力次元数:** 1,792次元
+- **類似度関数:** コサイン類似度
+- **言語:** 日本語
+- **ライセンス:** [Sarashina Model NonCommercial License Agreement](https://huggingface.co/sbintuitions/sarashina-embedding-v2-1b/blob/main/LICENSE)
+
+### モデルアーキテクチャ
+
+```
+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})
+)
+```
+
+## 使用方法
+
+まず、[Sentence Transformers](https://github.com/UKPLab/sentence-transformers)ライブラリをインストールします。
+
+```bash
+pip install sentence-transformers==4.0.2
+```
+
+次に、このモデルをロードし、推論を実行します。
+
+```python
+from sentence_transformers import SentenceTransformer
+
+# 🤗 Hubからモデルをダウンロードする
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("sbintuitions/sarashina-embedding-v2-1b")
+# 推論を実行する
+query = [
+      'task: クエリを与えるので、与えられたWeb検索クエリに答える関連文章を検索してください。\nquery: Sarashinaのテキスト埋め込みモデルはありますか?'
+  ]
+texts = [
+      'text: 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。',
+      'text: 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。',
+      'text: 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。'
+  ]
+query_embedding = model.encode(query)
+text_embeddings = model.encode(texts)
+
+# 埋め込みの類似度スコアを取得する
+similarities = model.similarity(query_embedding, text_embeddings)
+print(similarities)
+# tensor([[0.7403, 0.8651, 0.8775]])
+```
+
+### instrcution,prefixの付け方
+
+クエリ側とドキュメント側で形式の異なるprefixをつけ、クエリ側には```task:```というprefixの後に指示を付与します。(*ただしSTSタスクのみドキュメント側にもクエリ側と同じ形式のinstrcutionとprefixを使用します)
+  - クエリ側: ```task: {インストラクション}\nquery: {クエリ}```
+  - ドキュメント側: ```text: {ドキュメント}```
+
+
+### instrcution,prefixのテンプレート
+
+テキスト埋め込みモデルが用いられる5つの主なタスクで使用できるinstrcutionとprefixのテンプレートを下記の表に示します。
+
+|タスク|クエリ側|ドキュメント側|
+|:-:|:-|:-|
+|Retrieval<br>Reranking|task: 質問を与えるので、その質問に答えるのに役立つ関連文書を検索してください。\nquery: |text: |
+|Clustering|task: 与えられたドキュメントのトピックまたはテーマを特定してください。\nquery: |text: |
+|Classification|task: 与えられたレビューを適切な評価カテゴリに分類してください。\nquery: |text: |
+|STS|task: クエリを与えるので，もっともクエリに意味が似ている一節を探してください。\nquery: |task: クエリを与えるので，もっともクエリに意味が似ている一節を探してください。\nquery: |
+
+## 学習
+
+Sarashina-Embedding-v2-1Bは、以下の3段階の学習ステージによって学習されています。
+
+### Stage 1: 弱教師あり学習
+
+幅広い���メインに対して汎用的かつ高い性能を持つ埋め込みモデルを構築するため、独自のwebクロールデータとオープンデータで構成された弱教師データによる対照学習を行いました。
+
+### Stage 2: ファインチューニング
+
+より正確なクエリ-ドキュメント間の類似度をモデルに学習させるために、Stage 1で使用したデータよりも高品質なデータを用いてファインチューニングを行いました。
+またデータの一部を変更して複数のモデルを学習しました。
+
+### Stage 3: モデルマージ
+Stage 2においてJMTEBのスコアが最も高かった2つのモデルの重みを線形マージすることで性能向上を図りました。
+
+## [JMTEB](https://huggingface.co/datasets/sbintuitions/JMTEB)による評価
+
+|Model|Avg.|Retrieval|STS|Classfification|Reranking|Clustering|
+|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
+|Sarashina-Embedding-v2-1B(This model)|76.38|76.48|84.22|77.14|86.28|52.56|
+|[Ruri-v3-310m](https://huggingface.co/cl-nagoya/ruri-v3-310m)|75.85|76.03|81.59|77.65|85.84|50.52|
+|[Sarashina-Embedding-v1-1B](https://huggingface.co/sbintuitions/sarashina-embedding-v1-1b)|74.87|74.53|81.71|77.20|84.36|50.30|
+|text-embedding-3-large|73.86|71.95|82.52|77.27|83.06|51.82|
+
+(*) 評価日:2025.07.28
+
+## ライセンス
+
+このモデルは[Sarashina Model NonCommercial License Agreement](https://huggingface.co/sbintuitions/sarashina-embedding-v2-1b/blob/main/LICENSE)に基づいて公開されています。
+
+**もしこのモデルの商用利用にご興味がある場合は、お気軽に[コンタクトページ](https://www.sbintuitions.co.jp/#contact)へご連絡ください。**

config.json

@@ -0,0 +1,29 @@
+{
+  "architectures": [
+    "LlamaModel"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "head_dim": 112,
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modules.json

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