tags:

• sentence-transformers
• sentence-similarity
• feature-extraction
• mteb base_model: sbintuitions/modernbert-ja-130m language:
• ja
• en model-index:
• name: retrieva-jp/amber-base results:
  • dataset: config: en name: MTEB AmazonCounterfactualClassification (en) revision: e8379541af4e31359cca9fbcf4b00f2671dba205 split: test type: mteb/amazon_counterfactual metrics:
    • type: accuracy value: 68.1642
    • type: f1 value: 61.9811
    • type: f1_weighted value: 71.2157
    • type: ap value: 30.6541
    • type: ap_weighted value: 30.6541
    • type: main_score value: 68.1642 task: type: Classification
  • dataset: config: default name: MTEB ArXivHierarchicalClusteringP2P (default) revision: 0bbdb47bcbe3a90093699aefeed338a0f28a7ee8 split: test type: mteb/arxiv-clustering-p2p metrics:
    • type: v_measure value: 55.655100000000004
    • type: v_measure_std value: 3.2918999999999996
    • type: main_score value: 55.655100000000004 task: type: Clustering
  • dataset: config: default name: MTEB ArXivHierarchicalClusteringS2S (default) revision: b73bd54100e5abfa6e3a23dcafb46fe4d2438dc3 split: test type: mteb/arxiv-clustering-s2s metrics:
    • type: v_measure value: 53.6493
    • type: v_measure_std value: 3.2359
    • type: main_score value: 53.6493 task: type: Clustering
  • dataset: config: default name: MTEB ArguAna (default) revision: c22ab2a51041ffd869aaddef7af8d8215647e41a split: test type: mteb/arguana metrics:
    • type: ndcg_at_1 value: 25.249
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    • type: ndcg_at_5 value: 43.124
    • type: ndcg_at_10 value: 48.068
    • type: ndcg_at_20 value: 51.461
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    • type: ndcg_at_1000 value: 53.38
    • type: map_at_1 value: 25.249
    • type: map_at_3 value: 34.803
    • type: map_at_5 value: 37.598
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    • type: map_at_20 value: 40.569
    • type: map_at_100 value: 40.821000000000005
    • type: map_at_1000 value: 40.83
    • type: recall_at_1 value: 25.249
    • type: recall_at_3 value: 47.510999999999996
    • type: recall_at_5 value: 59.885999999999996
    • type: recall_at_10 value: 75.32
    • type: recall_at_20 value: 88.549
    • type: recall_at_100 value: 97.44
    • type: recall_at_1000 value: 99.14699999999999
    • type: precision_at_1 value: 25.249
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    • type: precision_at_20 value: 4.427
    • type: precision_at_100 value: 0.9740000000000001
    • type: precision_at_1000 value: 0.099
    • type: mrr_at_1 value: 25.817899999999998
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    • type: main_score value: 48.068 task: type: Retrieval
  • dataset: config: default name: MTEB AskUbuntuDupQuestions (default) revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 split: test type: mteb/askubuntudupquestions-reranking metrics:
    • type: map value: 56.5225
    • type: mrr value: 70.5146
    • type: nAUC_map_max value: 18.224
    • type: nAUC_map_std value: 12.5352
    • type: nAUC_map_diff1 value: 14.0464
    • type: nAUC_mrr_max value: 28.619699999999998
    • type: nAUC_mrr_std value: 21.69
    • type: nAUC_mrr_diff1 value: 15.8021
    • type: main_score value: 56.5225 task: type: Reranking
  • dataset: config: default name: MTEB BIOSSES (default) revision: d3fb88f8f02e40887cd149695127462bbcf29b4a split: test type: mteb/biosses-sts metrics:
    • type: pearson value: 86.6855
    • type: spearman value: 83.17360000000001
    • type: cosine_pearson value: 86.6855
    • type: cosine_spearman value: 83.17360000000001
    • type: manhattan_pearson value: 85.5442
    • type: manhattan_spearman value: 83.9501
    • type: euclidean_pearson value: 85.0403
    • type: euclidean_spearman value: 83.17360000000001
    • type: main_score value: 83.17360000000001 task: type: STS
  • dataset: config: default name: MTEB Banking77Classification (default) revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 split: test type: mteb/banking77 metrics:
    • type: accuracy value: 76.3312
    • type: f1 value: 75.4609
    • type: f1_weighted value: 75.4609
    • type: main_score value: 76.3312 task: type: Classification
  • dataset: config: default name: MTEB BiorxivClusteringP2P.v2 (default) revision: f5dbc242e11dd8e24def4c4268607a49e02946dc split: test type: mteb/biorxiv-clustering-p2p metrics:
    • type: v_measure value: 33.6692
    • type: v_measure_std value: 0.769
    • type: main_score value: 33.6692 task: type: Clustering
  • dataset: config: default name: MTEB CQADupstackGamingRetrieval (default) revision: 4885aa143210c98657558c04aaf3dc47cfb54340 split: test type: mteb/cqadupstack-gaming metrics:
    • type: ndcg_at_1 value: 30.345
    • type: ndcg_at_3 value: 37.726
    • type: ndcg_at_5 value: 39.999
    • type: ndcg_at_10 value: 42.732
    • type: ndcg_at_20 value: 44.696000000000005
    • type: ndcg_at_100 value: 47.461
    • type: ndcg_at_1000 value: 49.341
    • type: map_at_1 value: 26.484999999999996
    • type: map_at_3 value: 34.474
    • type: map_at_5 value: 35.94
    • type: map_at_10 value: 37.24
    • type: map_at_20 value: 37.852999999999994
    • type: map_at_100 value: 38.286
    • type: map_at_1000 value: 38.369
    • type: recall_at_1 value: 26.484999999999996
    • type: recall_at_3 value: 42.857
    • type: recall_at_5 value: 48.501
    • type: recall_at_10 value: 56.48
    • type: recall_at_20 value: 63.81099999999999
    • type: recall_at_100 value: 77.518
    • type: recall_at_1000 value: 90.89
    • type: precision_at_1 value: 30.345
    • type: precision_at_3 value: 17.241
    • type: precision_at_5 value: 11.962
    • type: precision_at_10 value: 7.204000000000001
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    • type: precision_at_100 value: 1.0330000000000001
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  • dataset: config: default name: MTEB CQADupstackUnixRetrieval (default) revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 split: test type: mteb/cqadupstack-unix metrics:
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    • type: recall_at_20 value: 42.714
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  • dataset: config: default name: MTEB ImdbClassification (default) revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 split: test type: mteb/imdb metrics:
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  • dataset: config: en name: MTEB MTOPDomainClassification (en) revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf split: test type: mteb/mtop_domain metrics:
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  • dataset: config: en name: MTEB MassiveIntentClassification (en) revision: 4672e20407010da34463acc759c162ca9734bca6 split: test type: mteb/amazon_massive_intent metrics:
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  • dataset: config: en name: MTEB MassiveScenarioClassification (en) revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8 split: test type: mteb/amazon_massive_scenario metrics:
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  • dataset: config: default name: MTEB MedrxivClusteringS2S.v2 (default) revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 split: test type: mteb/medrxiv-clustering-s2s metrics:
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  • dataset: config: default name: MTEB SICK-R (default) revision: 20a6d6f312dd54037fe07a32d58e5e168867909d split: test type: mteb/sickr-sts metrics:
    • type: pearson value: 84.5347
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    • type: cosine_pearson value: 84.5347
    • type: cosine_spearman value: 79.80850000000001
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  • dataset: config: default name: MTEB STS12 (default) revision: a0d554a64d88156834ff5ae9920b964011b16384 split: test type: mteb/sts12-sts metrics:
    • type: pearson value: 86.88
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    • type: cosine_pearson value: 86.88
    • type: cosine_spearman value: 78.1052
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    • type: manhattan_spearman value: 78.0898
    • type: euclidean_pearson value: 83.3731
    • type: euclidean_spearman value: 78.1052
    • type: main_score value: 78.1052 task: type: STS
  • dataset: config: default name: MTEB STS13 (default) revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca split: test type: mteb/sts13-sts metrics:
    • type: pearson value: 83.5938
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    • type: cosine_pearson value: 83.5938
    • type: cosine_spearman value: 84.2951
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    • type: manhattan_spearman value: 83.8292
    • type: euclidean_pearson value: 83.69640000000001
    • type: euclidean_spearman value: 84.2951
    • type: main_score value: 84.2951 task: type: STS
  • dataset: config: default name: MTEB STS14 (default) revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 split: test type: mteb/sts14-sts metrics:
    • type: pearson value: 82.6003
    • type: spearman value: 81.3569
    • type: cosine_pearson value: 82.6003
    • type: cosine_spearman value: 81.357
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    • type: manhattan_spearman value: 81.17229999999999
    • type: euclidean_pearson value: 81.7147
    • type: euclidean_spearman value: 81.3569
    • type: main_score value: 81.357 task: type: STS
  • dataset: config: default name: MTEB STS15 (default) revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 split: test type: mteb/sts15-sts metrics:
    • type: pearson value: 86.4161
    • type: spearman value: 87.0039
    • type: cosine_pearson value: 86.4161
    • type: cosine_spearman value: 87.0039
    • type: manhattan_pearson value: 86.2482
    • type: manhattan_spearman value: 86.934
    • type: euclidean_pearson value: 86.3344
    • type: euclidean_spearman value: 87.0039
    • type: main_score value: 87.0039 task: type: STS
  • dataset: config: en-en name: MTEB STS17 (en-en) revision: faeb762787bd10488a50c8b5be4a3b82e411949c split: test type: mteb/sts17-crosslingual-sts metrics:
    • type: pearson value: 88.6011
    • type: spearman value: 88.1023
    • type: cosine_pearson value: 88.6011
    • type: cosine_spearman value: 88.1023
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    • type: manhattan_spearman value: 88.55380000000001
    • type: euclidean_pearson value: 88.011
    • type: euclidean_spearman value: 88.1023
    • type: main_score value: 88.1023 task: type: STS
  • dataset: config: en name: MTEB STS22.v2 (en) revision: d31f33a128469b20e357535c39b82fb3c3f6f2bd split: test type: mteb/sts22-crosslingual-sts metrics:
    • type: pearson value: 65.7746
    • type: spearman value: 64.7997
    • type: cosine_pearson value: 65.7746
    • type: cosine_spearman value: 64.7997
    • type: manhattan_pearson value: 67.5417
    • type: manhattan_spearman value: 65.27629999999999
    • type: euclidean_pearson value: 67.2574
    • type: euclidean_spearman value: 64.7997
    • type: main_score value: 64.7997 task: type: STS
  • dataset: config: default name: MTEB STSBenchmark (default) revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 split: test type: mteb/stsbenchmark-sts metrics:
    • type: pearson value: 84.4276
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    • type: cosine_pearson value: 84.4276
    • type: cosine_spearman value: 84.9631
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    • type: euclidean_pearson value: 84.6058
    • type: euclidean_spearman value: 84.9631
    • type: main_score value: 84.9631 task: type: STS
  • dataset: config: default name: MTEB SprintDuplicateQuestions (default) revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 split: test type: mteb/sprintduplicatequestions-pairclassification metrics:
    • type: similarity_accuracy value: 99.7931
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    • type: similarity_f1 value: 89.4293
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    • type: cosine_accuracy value: 99.7931
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    • type: manhattan_accuracy_threshold value: 1354.3922
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  • dataset: config: default name: MTEB StackExchangeClustering.v2 (default) revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 split: test type: mteb/stackexchange-clustering metrics:
    • type: v_measure value: 53.9397
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  • 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 metrics:
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  • dataset: config: default name: MTEB TRECCOVID (default) revision: bb9466bac8153a0349341eb1b22e06409e78ef4e split: test type: mteb/trec-covid metrics:
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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: f1_weighted value: 71.6158
    • type: ap value: 10.9541
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  • dataset: config: default name: MTEB TweetSentimentExtractionClassification (default) revision: d604517c81ca91fe16a244d1248fc021f9ecee7a split: test type: mteb/tweet_sentiment_extraction metrics:
    • type: accuracy value: 60.506499999999996
    • type: f1 value: 60.711499999999994
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    • type: main_score value: 60.506499999999996 task: type: Classification
  • dataset: config: default name: MTEB TwentyNewsgroupsClustering.v2 (default) revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 split: test type: mteb/twentynewsgroups-clustering metrics:
    • type: v_measure value: 33.5462
    • type: v_measure_std value: 1.3361
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  • dataset: config: default name: MTEB TwitterSemEval2015 (default) revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 split: test type: mteb/twittersemeval2015-pairclassification metrics:
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  • dataset: config: default name: MTEB TwitterURLCorpus (default) revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf split: test type: mteb/twitterurlcorpus-pairclassification metrics:
    • type: similarity_accuracy value: 87.84880000000001
    • type: similarity_accuracy_threshold value: 62.77890000000001
    • type: similarity_f1 value: 75.968
    • type: similarity_f1_threshold value: 57.5925
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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 132M parameters (base size).

Model Details

Model Description

The AMBER model is a text embedding model based on the sbintuitions/modernbert-ja-130m 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-130m
• Model Type: Sentence Transformer
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 512 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-base")
# 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.

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.

English Benchmark: MTEB(eng, v2)

The files used for evaluation are stored in the mteb directory.

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