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.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
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+# Image files - uncompressed
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+*.jpg filter=lfs diff=lfs merge=lfs -text
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+Retrieval-lite-data/jaqket-lite-corpus.jsonl filter=lfs diff=lfs merge=lfs -text
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+Retrieval-lite-data/mrtydi-lite-corpus.jsonl filter=lfs diff=lfs merge=lfs -text
+Reranking-lite-data/jacwir-reranking-lite-query-test.jsonl filter=lfs diff=lfs merge=lfs -text
+Reranking-lite-data/jacwir-reranking-lite-query-validation.jsonl filter=lfs diff=lfs merge=lfs -text
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.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
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+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
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+*.pot
+
+# Django stuff:
+*.log
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+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
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+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

JMTEB-lite.py

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+import json
+
+import datasets
+
+
+class JMTEBlite(datasets.GeneratorBasedBuilder):
+    # Define builder configs directly in the class
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name=name,
+            description=f"JMTEB-lite dataset configuration for {name}",
+        )
+        for name in [
+            "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",  # lightweight
+            "jacwir-reranking-query",
+            "jacwir-reranking-corpus",  # lightweight
+            "miracl-reranking-query",
+            "miracl-reranking-corpus",
+            "mldr-reranking-query",
+            "mldr-reranking-corpus",
+        ]
+    ]
+
+    def _info(self):
+        builder = datasets.load_dataset_builder(
+            "sbintuitions/JMTEB",
+            self.config.name,
+            trust_remote_code=True,
+        )
+        return datasets.DatasetInfo(
+            description=f"{self.config.name} in JMTEB-lite:" + builder.info.description,
+            citation=builder.info.citation,
+            homepage=builder.info.homepage,
+            license=builder.info.license,
+            features=builder.info.features,
+        )
+
+    def _split_generators(self, dl_manager):
+        if self.config.name in [
+            "jacwir-retrieval-corpus",
+            "jaqket-corpus",
+            "miracl-retrieval-corpus",
+            "mrtydi-corpus",
+        ]:
+            filename = self.config.name.replace("corpus", "lite-corpus")
+            data_file = dl_manager.download(f"Retrieval-lite-data/{filename}.jsonl")
+            return [
+                datasets.SplitGenerator(
+                    name="corpus", gen_kwargs={"filepath": data_file, "split": "corpus"}
+                )
+            ]
+
+        if self.config.name in ["jacwir-reranking-corpus", "jqara-corpus"]:
+            filename = self.config.name.replace("corpus", "lite-corpus")
+            data_file = dl_manager.download(f"Reranking-lite-data/{filename}.jsonl")
+            return [
+                datasets.SplitGenerator(
+                    name="corpus", gen_kwargs={"filepath": data_file, "split": "corpus"}
+                )
+            ]
+
+        if self.config.name in ["jacwir-reranking-query", "jqara-query"]:
+            filename = self.config.name.replace("query", "lite-query-{split}.jsonl")
+            return [
+                datasets.SplitGenerator(
+                    name=split,
+                    gen_kwargs={
+                        "filepath": dl_manager.download(
+                            f"Reranking-lite-data/{filename.format(split=split)}"
+                        ),
+                        "split": split,
+                    },
+                )
+                for split in ["validation", "test"]
+            ]
+
+        original = datasets.load_dataset(
+            "sbintuitions/JMTEB", self.config.name, trust_remote_code=True
+        )
+        return [
+            datasets.SplitGenerator(name=split, gen_kwargs={"split": split})
+            for split in original.keys()
+        ]
+
+    def _generate_examples(self, split, filepath=None):
+        if filepath:
+            with open(filepath, "r", encoding="utf-8") as f:
+                for i, line in enumerate(f):
+                    example = json.loads(line.strip())
+                    yield i, example
+        else:
+            original_split = datasets.load_dataset(
+                "sbintuitions/JMTEB",
+                self.config.name,
+                split=split,
+                trust_remote_code=True,
+            )
+            for i, example in enumerate(original_split):
+                yield i, example

README.md

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+---
+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.

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