Add dataset card

README.md

@@ -5,9 +5,12 @@ language:
 - jpn
 license: cc-by-4.0
 multilinguality: monolingual
+source_datasets:
+- sbintuitions/JMTEB
 task_categories:
 - text-retrieval
-task_ids: []
+task_ids:
+- document-retrieval
 dataset_info:
 - config_name: corpus
   features:
@@ -74,13 +77,16 @@ tags:
   <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
 </div>
 
-This dataset was created from the Japanese NLP Journal LaTeX Corpus. The titles, abstracts and introductions of the academic papers were shuffled. The goal is to find the corresponding introduction with the given title.
+This dataset was created from the Japanese NLP Journal LaTeX Corpus. The titles, abstracts and introductions of the academic papers were shuffled. The goal is to find the corresponding introduction with the given title. This is the V1 dataset (last updated 2020-06-15).
 
 |               |                                             |
 |---------------|---------------------------------------------|
 | Task category | t2t                              |
 | Domains       | Academic, Written                               |
-| Reference     | https://github.com/sbintuitions/JMTEB |
+| Reference     | https://huggingface.co/datasets/sbintuitions/JMTEB |
+
+Source datasets:
+- [sbintuitions/JMTEB](https://huggingface.co/datasets/sbintuitions/JMTEB)
 
 
 ## How to evaluate on this task
@@ -90,15 +96,15 @@ You can evaluate an embedding model on this dataset using the following code:
 ```python
 import mteb
 
-task = mteb.get_tasks(["NLPJournalTitleIntroRetrieval"])
-evaluator = mteb.MTEB(task)
+task = mteb.get_task("NLPJournalTitleIntroRetrieval")
+evaluator = mteb.MTEB([task])
 
 model = mteb.get_model(YOUR_MODEL)
 evaluator.run(model)
 ```
 
 <!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
-To learn more about how to run models on `mteb` task check out the [GitHub repitory](https://github.com/embeddings-benchmark/mteb). 
+To learn more about how to run models on `mteb` task check out the [GitHub repository](https://github.com/embeddings-benchmark/mteb).
 
 ## Citation
 
@@ -106,6 +112,13 @@ If you use this dataset, please cite the dataset as well as [mteb](https://githu
 
 ```bibtex
 
+@misc{jmteb,
+  author = {Li, Shengzhe and Ohagi, Masaya and Ri, Ryokan},
+  howpublished = {\url{https://huggingface.co/datasets/sbintuitions/JMTEB}},
+  title = {{J}{M}{T}{E}{B}: {J}apanese {M}assive {T}ext {E}mbedding {B}enchmark},
+  year = {2024},
+}
+
 
 @article{enevoldsen2025mmtebmassivemultilingualtext,
   title={MMTEB: Massive Multilingual Text Embedding Benchmark},
@@ -118,7 +131,7 @@ If you use this dataset, please cite the dataset as well as [mteb](https://githu
 }
 
 @article{muennighoff2022mteb,
-  author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Lo{\"\i}c and Reimers, Nils},
+  author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Loïc and Reimers, Nils},
   title = {MTEB: Massive Text Embedding Benchmark},
   publisher = {arXiv},
   journal={arXiv preprint arXiv:2210.07316},