README.md

---
license: cc-by-sa-4.0
task_categories:
- text-classification
- question-answering
- zero-shot-classification
- sentence-similarity
language:
- ja
pretty_name: The Lightweight Version of JMTEB
size_categories:
- 100M<n<1B
---
# JMTEB-lite: The Lightweight Version of JMTEB

JMTEB-lite is a lightweight version of [JMTEB](https://huggingface.co/datasets/sbintuitions/JMTEB).
It makes agile evaluation possible by reaching an average of **5x** faster evaluation comparing with JMTEB.
The result of JMTEB-lite is proved to be highly relevant with that of JMTEB, making it a faithful preview of JMTEB.

## TL;DR

```python
from datasets import load_dataset

dataset = load_dataset("sbintuitions/JMTEB-lite", name="<dataset_name>", split="<split>")

SPLITS = ("train", "validation", "test", "corpus")
JMTEB_LITE_DATASET_NAMES = (
    'livedoor_news',
    'mewsc16_ja',
    'sib200_japanese_clustering',
    'amazon_review_classification',
    'amazon_counterfactual_classification',
    'massive_intent_classification',
    'massive_scenario_classification',
    'japanese_sentiment_classification',
    'sib200_japanese_classification',
    'wrime_classification',
    'jsts',
    'jsick',
    'jaqket-query',
    'jaqket-corpus',  # lightweight
    'mrtydi-query',
    'mrtydi-corpus',  # lightweight
    'jagovfaqs_22k-query',
    'jagovfaqs_22k-corpus',
    'nlp_journal_title_abs-query',
    'nlp_journal_title_abs-corpus',
    'nlp_journal_title_intro-query',
    'nlp_journal_title_intro-corpus',
    'nlp_journal_abs_intro-query',
    'nlp_journal_abs_intro-corpus',
    'nlp_journal_abs_article-query',
    'nlp_journal_abs_article-corpus',
    'jacwir-retrieval-query',
    'jacwir-retrieval-corpus',  # lightweight
    'miracl-retrieval-query',
    'miracl-retrieval-corpus',  # lightweight
    'mldr-retrieval-query',
    'mldr-retrieval-corpus',
    'mintaka-retrieval-query',
    'mintaka-retrieval-corpus',
    'esci-query',
    'esci-corpus',
    'jqara-query',
    'jqara-corpus',
    'jacwir-reranking-query',  # lightweight
    'jacwir-reranking-corpus',  # lightweight
    'miracl-reranking-query',  # lightweight
    'miracl-reranking-corpus',  # lightweight
    'mldr-reranking-query',
    'mldr-reranking-corpus',
)
```

## Introduction

We introduced [JMTEB](https://huggingface.co/datasets/sbintuitions/JMTEB) (Japanese Massive Text Embedding Benchmark), a comprehensive evaluation benchmark of Japanese text embedding models.
However, the massive size of JMTEB makes evaluation slow and resource demanding.
To address this, we now introduce **JMTEB-lite**, a lightweight version of JMTEB constructed by substaintially reducing corpus size in retrieval and reranking tasks.
We have also verified that JMTEB-lite significantly accelerates evaluation while maintaining high fidelity to the full JMTEB.

We recommand to use JMTEB-lite to obtain the preview evaluation results in agile development, and use JMTEB for full and final evaluation.

JMTEB-lite is compatible with the evaluation script of JMTEB: <https://github.com/sbintuitions/JMTEB>.

## Tasks and Datasets

Here is an overview of the tasks and datasets currently included in JMTEB-lite.

Note that only datasets in **bold** are lightweight, and the rest are exactly the same with the counterparts in JMTEB.

|Task|Dataset|Train|Dev|Test|Document (Retrieval)|
|----|-------|----:|--:|---:|--:|
|Clustering|Livedoor-News|5,163|1,106|1,107|-|
||MewsC-16-ja|-|992|992|-|
||SIB200 Japanese Clustering|701|99|204|-|
|Classification|AmazonCounterfactualClassification|5,600|466|934|-|
||AmazonReviewClassification|200,000|5,000|5,000|-|
||MassiveIntentClassification|11,514|2,033|2,974|-|
||MassiveScenarioClassification|11,514|2,033|2,974|-|
||Japanese Sentiment Classification|9,831|1,677|2,552|-|
||SIB200 Japanese Classification|701|99|204|-|
||WRIME Classification|30,000|2,500|2,500|-|
|STS|JSTS|12,451|-|1,457|-|
||JSICK|5,956|1,985|1,986|-|
|Retrieval|**JAQKET**|13,061|995|997|**65,802**|
||**Mr.TyDi-ja**|3,697|928|720|**93,382**|
||NLP Journal title-abs|-|127|510|637|
||NLP Journal title-intro|-|127|510|637|
||NLP Journal abs-intro|-|127|510|637|
||NLP Journal abs-abstract|-|127|510|637|
||JaGovFaqs-22k|15,955|3,419|3,420|22,794|
||**JaCWIR-Retrieval**|-|1,000|4,000|**302,638**|
||**MIRACL-Retrieval**|2,433|1,044|860|**105,064**|
||MLDR-Retrieval|2,262|200|200|10,000|
||Mintaka-Retrieval|-|2,313[^1]|2,313|2,313|
|Reranking|Esci|10,141|1,790|4,206|149,999|
||**JaCWIR-Reranking**|-|1,000|4,000|**188,033**|
||**JQaRA**|498|1,737|1,667|**172,897**|
||MIRACL-Reranking|2,433|1,044|860|37,124|
||MLDR-Reranking|2,262|200|200|5,339|

[^1]: To keep consistent with [MTEB](https://github.com/embeddings-benchmark/mteb/blob/5a8ccec9017742f6c3246519d2a92bd03f218a6d/mteb/tasks/Retrieval/multilingual/MintakaRetrieval.py) where Mintaka-Retrieval doesn't have a validation set, we set our validation set the same as the test set.

## Construction Process

For the 4 retrieval datasets (JAQKET, Mr.TyDi, JaCWIR-Retrieval, MIRACL-Retrieval), we use 5 highly performant models to predict hard negative documents for each query (the query's most 50 semantically similar documents in the corpus), and merge these hard negatives along with golden documents.

For the 2 reranking datasets (JQaRA, JaCWIR-Reranking), we use 5 highly performant models to rerank the documents for each query, and retain top-50 hard negative documents for each query. Then we merge these hard negatives with golden documents.

For the rest, they are kept exactly the same with their counterparts in JMTEB.

## Reference

```
@misc{jmteb_lite,
	author = {Li, Shengzhe and Ohagi, Masaya and Ri, Ryokan and Fukuchi, Akihiko and Shibata, Tomohide and Kawahara, Daisuke},
	title = {{J}{M}{T}{E}{B}-lite: {T}he {L}ightweight {V}ersion of {JMTEB}},
	howpublished = {\url{https://huggingface.co/datasets/sbintuitions/JMTEB-lite}},
	year = {2025},
}

@techreport{jmteb_lite,
	author = {Li, Shengzhe and Ohagi, Masaya and Ri, Ryokan and Fukuchi, Akihiko and Shibata, Tomohide and Kawahara, Daisuke},
	title = {{JMTEB} and {JMTEB}-lite: {J}apanese {M}assive {T}ext {E}mbedding {B}enchmark and {I}ts {L}ightweight {V}ersion},
    institution = {SB Intuitions / Waseda University},
	number = {IPSJ SIG Technical Reports Vol.2025-NL-265 No.3},
	year = {2025},
    month = {07},
}
```

## License

Our code is licensed under the [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/).

<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a><br />

Regarding the license information of datasets, please refer to the individual datasets.