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JA
Amber Large
A
Amber Large
retrieva-jpによって開発
modernbert-ja-310m をベースとした日英双语句子特徴抽出モデルで、文の類似度計算とテキスト分類タスクをサポート
ダウンロード数 239.28k
リリース時間 : 3/7/2025
モデル概要
このモデルは日英バイリンガルシナリオにおける文の埋め込み表現に特化しており、文の類似度計算、テキスト分類、クラスタリングタスクに使用可能。MTEBベンチマークテストでは分類とクラスタリングタスクで良好なパフォーマンスを示しています。
モデル特徴
日英バイリンガルサポート
日本語と英語のバイリンガルシナリオに最適化されており、両言語の文埋め込み表現を処理可能
マルチタスク適応
分類、クラスタリング、検索など様々な自然言語処理タスクをサポート
MTEB ベンチマーク検証
複数のMTEBベンチマークテストで良好な結果を示し、特に分類タスクでは73.34%の精度を達成
モデル能力
文特徴抽出
文類似度計算
テキスト分類
テキストクラスタリング
クロスランゲージテキスト処理
使用事例
電子商取引
製品レビュー分類
AmazonなどのECプラットフォームのユーザーレビューを分類
Amazonの反事実分類タスクで73.34%の精度を達成
学術研究
論文クラスタリング
arXiv学術論文の階層的クラスタリング
arXiv論文クラスタリングタスクでV-measure53.39を達成
情報検索
議論点検索
ディベートシナリオで関連する議論点を検索
ArguAnaタスクでNDCG@10が51.32を達成
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- mteb base_model: sbintuitions/modernbert-ja-310m language:
- ja
- en model-index:
- name: retrieva-jp/amber-large
results:
- dataset:
config: en
name: MTEB AmazonCounterfactualClassification (en)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy value: 73.3433
- type: f1 value: 67.2899
- type: f1_weighted value: 75.7948
- type: ap value: 36.123
- type: ap_weighted value: 36.123
- type: main_score value: 73.3433 task: type: Classification
- dataset:
config: default
name: MTEB ArXivHierarchicalClusteringP2P (default)
revision: 0bbdb47bcbe3a90093699aefeed338a0f28a7ee8
split: test
type: mteb/arxiv-clustering-p2p
metrics:
- type: v_measure value: 53.3936
- type: v_measure_std value: 3.9726999999999997
- type: main_score value: 53.3936 task: type: Clustering
- dataset:
config: default
name: MTEB ArXivHierarchicalClusteringS2S (default)
revision: b73bd54100e5abfa6e3a23dcafb46fe4d2438dc3
split: test
type: mteb/arxiv-clustering-s2s
metrics:
- type: v_measure value: 51.35999999999999
- type: v_measure_std value: 4.9623
- type: main_score value: 51.35999999999999 task: type: Clustering
- dataset:
config: default
name: MTEB ArguAna (default)
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
split: test
type: mteb/arguana
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- type: ndcg_at_5 value: 45.550000000000004
- type: ndcg_at_10 value: 51.317
- type: ndcg_at_20 value: 53.96300000000001
- type: ndcg_at_100 value: 55.358
- type: ndcg_at_1000 value: 55.596000000000004
- type: map_at_1 value: 26.743
- type: map_at_3 value: 37.162
- type: map_at_5 value: 39.964
- type: map_at_10 value: 42.355
- type: map_at_20 value: 43.1
- type: map_at_100 value: 43.313
- type: map_at_1000 value: 43.323
- type: recall_at_1 value: 26.743
- type: recall_at_3 value: 50.356
- type: recall_at_5 value: 62.376
- type: recall_at_10 value: 80.156
- type: recall_at_20 value: 90.469
- type: recall_at_100 value: 97.724
- type: recall_at_1000 value: 99.502
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- type: precision_at_3 value: 16.785
- type: precision_at_5 value: 12.475
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- type: precision_at_20 value: 4.523
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- type: mrr_at_5 value: 40.1102
- type: mrr_at_10 value: 42.493900000000004
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- dataset:
config: default
name: MTEB AskUbuntuDupQuestions (default)
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
split: test
type: mteb/askubuntudupquestions-reranking
metrics:
- type: map value: 58.0233
- type: mrr value: 70.5882
- type: nAUC_map_max value: 20.8533
- type: nAUC_map_std value: 12.612300000000001
- type: nAUC_map_diff1 value: 1.3859
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- type: nAUC_mrr_std value: 14.176400000000001
- type: nAUC_mrr_diff1 value: 14.2379
- type: main_score value: 58.0233 task: type: Reranking
- dataset:
config: default
name: MTEB BIOSSES (default)
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
split: test
type: mteb/biosses-sts
metrics:
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- type: spearman value: 78.7367
- type: cosine_pearson value: 83.4314
- type: cosine_spearman value: 78.7367
- type: manhattan_pearson value: 82.1388
- type: manhattan_spearman value: 78.747
- type: euclidean_pearson value: 82.1716
- type: euclidean_spearman value: 78.7367
- type: main_score value: 78.7367 task: type: STS
- dataset:
config: default
name: MTEB Banking77Classification (default)
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
split: test
type: mteb/banking77
metrics:
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- type: f1 value: 75.8746
- type: f1_weighted value: 75.8746
- type: main_score value: 76.8961 task: type: Classification
- dataset:
config: default
name: MTEB BiorxivClusteringP2P.v2 (default)
revision: f5dbc242e11dd8e24def4c4268607a49e02946dc
split: test
type: mteb/biorxiv-clustering-p2p
metrics:
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- type: v_measure_std value: 0.8959
- type: main_score value: 36.2676 task: type: Clustering
- dataset:
config: default
name: MTEB CQADupstackGamingRetrieval (default)
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
split: test
type: mteb/cqadupstack-gaming
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- type: ndcg_at_5 value: 44.915
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config: en
name: MTEB MassiveScenarioClassification (en)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
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- type: main_score value: 71.1903 task: type: Classification
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config: default
name: MTEB MedrxivClusteringP2P.v2 (default)
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
split: test
type: mteb/medrxiv-clustering-p2p
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config: default
name: MTEB MedrxivClusteringS2S.v2 (default)
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
split: test
type: mteb/medrxiv-clustering-s2s
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config: default
name: MTEB MindSmallReranking (default)
revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
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type: mteb/mind_small
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config: default
name: MTEB SCIDOCS (default)
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
split: test
type: mteb/scidocs
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config: default
name: MTEB SICK-R (default)
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
split: test
type: mteb/sickr-sts
metrics:
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- type: cosine_spearman value: 80.3972
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config: default
name: MTEB STS12 (default)
revision: a0d554a64d88156834ff5ae9920b964011b16384
split: test
type: mteb/sts12-sts
metrics:
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- type: cosine_pearson value: 86.7684
- type: cosine_spearman value: 78.70899999999999
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- type: euclidean_spearman value: 78.70899999999999
- type: main_score value: 78.70899999999999 task: type: STS
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config: default
name: MTEB STS13 (default)
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
split: test
type: mteb/sts13-sts
metrics:
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- type: cosine_pearson value: 85.1773
- type: cosine_spearman value: 86.1602
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- type: manhattan_spearman value: 86.0645
- type: euclidean_pearson value: 84.8639
- type: euclidean_spearman value: 86.1602
- type: main_score value: 86.1602 task: type: STS
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config: default
name: MTEB STS14 (default)
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
split: test
type: mteb/sts14-sts
metrics:
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- type: spearman value: 81.2081
- type: cosine_pearson value: 82.87780000000001
- type: cosine_spearman value: 81.2081
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- type: manhattan_spearman value: 81.2182
- type: euclidean_pearson value: 81.917
- type: euclidean_spearman value: 81.2081
- type: main_score value: 81.2081 task: type: STS
- dataset:
config: default
name: MTEB STS15 (default)
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
split: test
type: mteb/sts15-sts
metrics:
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- type: spearman value: 87.5072
- type: cosine_pearson value: 86.9104
- type: cosine_spearman value: 87.5073
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- type: manhattan_spearman value: 87.4643
- type: euclidean_pearson value: 86.7938
- type: euclidean_spearman value: 87.5072
- type: main_score value: 87.5073 task: type: STS
- dataset:
config: en-en
name: MTEB STS17 (en-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
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- type: cosine_pearson value: 89.4941
- type: cosine_spearman value: 88.9712
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- type: manhattan_spearman value: 89.05720000000001
- type: euclidean_pearson value: 89.0296
- type: euclidean_spearman value: 88.9712
- type: main_score value: 88.9712 task: type: STS
- dataset:
config: en
name: MTEB STS22.v2 (en)
revision: d31f33a128469b20e357535c39b82fb3c3f6f2bd
split: test
type: mteb/sts22-crosslingual-sts
metrics:
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- type: cosine_pearson value: 66.6691
- type: cosine_spearman value: 65.5503
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- type: manhattan_spearman value: 65.2781
- type: euclidean_pearson value: 67.6466
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config: default
name: MTEB STSBenchmark (default)
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
split: test
type: mteb/stsbenchmark-sts
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- type: euclidean_spearman value: 86.40339999999999
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- dataset:
config: default
name: MTEB SprintDuplicateQuestions (default)
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
split: test
type: mteb/sprintduplicatequestions-pairclassification
metrics:
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- type: dot_f1_threshold value: 71.084
- type: dot_precision value: 91.134
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- type: max_precision value: 91.134
- type: max_recall value: 90.7
- type: max_ap value: 94.32199999999999
- type: main_score value: 94.32199999999999 task: type: PairClassification
- dataset:
config: default
name: MTEB StackExchangeClustering.v2 (default)
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
split: test
type: mteb/stackexchange-clustering
metrics:
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- type: v_measure_std value: 0.6015
- type: main_score value: 53.5198 task: type: Clustering
- dataset:
config: default
name: MTEB StackExchangeClusteringP2P.v2 (default)
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
split: test
type: mteb/stackexchange-clustering-p2p
metrics:
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- dataset:
config: default
name: MTEB SummEvalSummarization.v2 (default)
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
split: test
type: mteb/summeval
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config: default
name: MTEB TRECCOVID (default)
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
split: test
type: mteb/trec-covid
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config: default
name: MTEB Touche2020Retrieval.v3 (default)
revision: 431886eaecc48f067a3975b70d0949ea2862463c
split: test
type: mteb/webis-touche2020-v3
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- dataset:
config: default
name: MTEB ToxicConversationsClassification (default)
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
split: test
type: mteb/toxic_conversations_50k
metrics:
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- type: main_score value: 65.65429999999999 task: type: Classification
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config: default
name: MTEB TweetSentimentExtractionClassification (default)
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
split: test
type: mteb/tweet_sentiment_extraction
metrics:
- type: accuracy value: 61.7119
- type: f1 value: 61.8672
- type: f1_weighted value: 60.762499999999996
- type: main_score value: 61.7119 task: type: Classification
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config: default
name: MTEB TwentyNewsgroupsClustering.v2 (default)
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
split: test
type: mteb/twentynewsgroups-clustering
metrics:
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- type: main_score value: 37.4338 task: type: Clustering
- dataset:
config: default
name: MTEB TwitterSemEval2015 (default)
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
split: test
type: mteb/twittersemeval2015-pairclassification
metrics:
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- type: similarity_ap value: 63.522
- type: cosine_accuracy value: 82.8873
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- type: cosine_f1 value: 60.3641
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- type: cosine_ap value: 63.522
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- type: euclidean_f1_threshold value: 88.7989
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- type: euclidean_ap value: 63.522
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- type: dot_f1 value: 60.3641
- type: dot_f1_threshold value: 60.5738
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- type: dot_recall value: 65.62010000000001
- type: dot_ap value: 63.522
- type: max_accuracy value: 82.8873
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- type: max_precision value: 55.887600000000006
- type: max_recall value: 67.0976
- type: max_ap value: 63.522
- type: main_score value: 63.522 task: type: PairClassification
- dataset:
config: default
name: MTEB TwitterURLCorpus (default)
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
split: test
type: mteb/twitterurlcorpus-pairclassification
metrics:
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- type: similarity_f1 value: 77.8938
- type: similarity_f1_threshold value: 59.013400000000004
- type: similarity_precision value: 74.31309999999999
- type: similarity_recall value: 81.83709999999999
- type: similarity_ap value: 85.1691
- type: cosine_accuracy value: 88.7337
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- type: cosine_f1 value: 77.8938
- type: cosine_f1_threshold value: 59.013400000000004
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- type: cosine_recall value: 81.83709999999999
- type: cosine_ap value: 85.1691
- type: manhattan_accuracy value: 88.689
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- type: dot_recall value: 81.83709999999999
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- type: max_precision value: 74.6414
- type: max_recall value: 81.83709999999999
- type: max_ap value: 85.1691
- type: main_score value: 85.1691 task: type: PairClassification license: apache-2.0
- dataset:
config: en
name: MTEB AmazonCounterfactualClassification (en)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
RetrievaEmbedding-01: AMBER
The AMBER (Adaptive Multitask Bilingual Embedding Representations) is a text embedding model trained by Retrieva, Inc. This model is primarily designed for Japanese, but it also supports English. We trained this model on various datasets related to Japanese and English.
This model size is 315M parameters (large size).
Model Details
Model Description
The AMBER model is a text embedding model based on the sbintuitions/modernbert-ja-310m architecture, designed for Japanese text. This model was trained on a variety of datasets related to Japanese, and also includes English datasets. The model can be used for English text as well. During training, prompts (instructions) in natural language were included, allowing the model to generate embeddings tailored to specific tasks.
- Developed by: Retrieva, Inc.
- Model type: Based on the ModernBERT Architecture.
- Language(s) (NLP): Primarily Japanese (optional support for English).
- License: Apache 2.0
- Finetuned from model:
sbintuitions/modernbert-ja-310m - Model Type: Sentence Transformer
- Maximum Sequence Length: 512 tokens
- Output Dimensionality: 768 dimensions
- Similarity Function: Cosine Similarity
Uses
How to Get Started with the Model
Install Library
First install the python library using pip:
pip install sentence-transformers sentencepiece
Run Inference
Then you can load this model and run inference.
You can specify the prompt at inference time by adding an argument called prompt to model.encode.
The prompts used in the Japanese benchmark are described in jmteb/tasks, and the prompts used in the English benchmark are described in mteb/models/retrieva_en.py.
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("retrieva-jp/amber-large")
# Run inference
queries = [
"自然言語処理とはなんですか？",
"株式会社レトリバについて教えて",
]
documents = [
"自然言語処理（しぜんげんごしょり、英語: Natural language processing、略称：NLP）は、人間が日常的に使っている自然言語をコンピュータに処理させる一連の技術であり、人工知能と言語学の一分野である。",
"株式会社レトリバは、自然言語処理と機械学習を核としたAI技術で組織の課題解決を支援するテクノロジー企業である。",
]
queries_embeddings = model.encode(queries, prompt_name="Retrieval-query")
documents_embeddings = model.encode(documents, prompt_name="Retrieval-passage")
similarities = model.similarity(queries_embeddings, documents_embeddings)
print(similarities.shape)
Training Details
Training Data
We used multiple datasets to train this model. We selected datasets from llm-jp-eval, llm-japanese-dataset, and hpprc/emb for Japanese datasets. For English datasets, we mainly used some of the datasets utilized in Asai et al. (2023). Additionally, we partially used the English datasets at the sentence-transformers repository and kilt-tasks. To consider cross-lingual between Japanese and English, we also used translation datasets between Japanese and English.
For Japanese, we used synthetic data created by LLM to prepare a sufficient amount of training data.
Evaluation
We evaluated the model on the following benchmarks:
- Japanese Benchmark: JMTEB
- Japanese Retrieval Tasks: JQaRA, JaCWIR, MLDR Japanese Subset
- English Benchmark: MTEB(eng, v2).
The scores in the table are all calculated by us unless otherwise noted.
Japanese Benchmark: JMTEB
Note that the Mean (TaskType) in the following leaderboard is the same as the Avg. in the original JMTEB leaderboard.
The files used for evaluation are stored in the jmteb directory.
| Model | # Parameters | Mean (TaskType) | Mean (Task) | Retrieval | STS | Classification | Reranking | Clustering | PairClassification |
|---|---|---|---|---|---|---|---|---|---|
| base models | < 300M | ||||||||
| cl-nagoya/ruri-base | 111M | 72.60 | 71.56 | 69.53 | 82.87 | 75.49 | 92.91 | 52.40 | 62.38 |
| AMBER-base | 130M | 72.12 | 72.12 | 73.40 | 77.81 | 76.14 | 93.27 | 48.05 | 64.03 |
| pkshatech/GLuCoSE-base-ja-v2 | 133M | 72.89 | 72.47 | 73.03 | 82.96 | 74.02 | 93.01 | 51.96 | 62.37 |
| pkshatech/RoSEtta-base-ja | 190M | 72.49 | 72.05 | 73.14 | 81.39 | 72.37 | 92.69 | 53.60 | 61.74 |
| intfloat/multilingual-e5-base | 278M | 71.11 | 69.72 | 69.45 | 80.45 | 69.86 | 92.90 | 51.62 | 62.35 |
| large models | 300M < | ||||||||
| AMBER-large (this model) |
315M | 72.52 | 73.22 | 75.40 | 79.32 | 77.14 | 93.54 | 48.73 | 60.97 |
| cl-nagoya/ruri-large | 337M | 73.20 | 73.06 | 72.86 | 83.14 | 77.15 | 93.00 | 50.78 | 62.29 |
| intfloat/multilingual-e5-large | 560M | 72.06 | 71.29 | 71.71 | 80.87 | 72.45 | 93.29 | 51.59 | 62.42 |
Japanese Retrieval Tasks: JQaRA, JaCWIR, MLDR Japanese Subset
The files used for MLDR are stored in the mldr directory.
The prompts used in JQaRA and JaCWIR are Retrieval-query and Retrieval-passage described in config_sentence_transformers.json.
| Model | # Parameters | JQaRA (nDCG@10) | JaCWIR (MAP@10) | MLDR Japanese Subset (nDCG@10) |
|---|---|---|---|---|
| base models | < 300M | |||
| cl-nagoya/ruri-base | 111M | 58.4 | 83.3 | 32.77 |
| AMBER-base | 130M | 57.1 | 81.6 | 35.69 |
| pkshatech/GLuCoSE-base-ja-v2 | 133M | 60.6 | 85.3 | 33.99 |
| intfloat/multilingual-e5-base | 278M | 47.1 | 85.3 | 25.46 |
| large models | 300M < | |||
| AMBER-large (this model) |
315M | 62.5 | 82.4 | 34.57 |
| cl-nagoya/ruri-large | 337M | 62.8 | 82.5 | 34.78 |
| intfloat/multilingual-e5-large | 560M | 55.4 | 87.3 | 29.95 |
English Benchmark: MTEB(eng, v2)
The files used for evaluation are stored in the mteb directory.
| Model | # Parameters | Mean (TaskType) | Mean (Task) | Retrieval | STS | Classification | Reranking | Clustering | PairClassification | Summarization |
|---|---|---|---|---|---|---|---|---|---|---|
| base models | < 300M | |||||||||
| AMBER-base | 130M | 54.75 | 58.20 | 40.11 | 81.29 | 70.39 | 42.98 | 42.27 | 80.12 | 26.08 |
| intfloat/multilingual-e5-base | 278M | 56.21 | 59.75 | 43.22 | 80.50 | 73.84 | 43.87 | 42.19 | 83.74 | 26.10 |
| large models | 300M < | |||||||||
| AMBER-large (this model) |
315M | 56.08 | 59.13 | 41.04 | 81.52 | 72.23 | 43.83 | 42.71 | 81.00 | 30.21 |
| intfloat/multilingual-e5-large | 560M | 57.06 | 60.84 | 46.17 | 81.11 | 74.88 | 44.31 | 41.91 | 84.33 | 26.67 |
Citation
BibTeX:
@inproceedings{amber2025,
title = {インストラクションと複数タスクを利用した日本語向け分散表現モデルの構築},
author = {勝又智 and 木村大翼 and 西鳥羽二郎},
booktitle = {言語処理学会第31回年次大会発表論文集},
year = {2025},
}
More Information
https://note.com/retrieva/n/n4ee9d304f44d (in Japanese)
Model Card Authors
Satoru Katsumata, Daisuke Kimura, Jiro Nishitoba
Model Card Contact
pr[at]retrieva.jp
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