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  1. README.md +1064 -0
  2. afr/test-00000-of-00001.parquet +3 -0
  3. afr/train-00000-of-00001.parquet +3 -0
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  17. deu/test-00000-of-00001.parquet +3 -0
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+ - config_name: zul
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+ - split: validation
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+ - split: test
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+ path: zul/test-*
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+ - split: train
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+ path: zul/train-*
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+ tags:
826
+ - mteb
827
+ - text
828
+ ---
829
+ <!-- adapted from https://github.com/huggingface/huggingface_hub/blob/v0.30.2/src/huggingface_hub/templates/datasetcard_template.md -->
830
+
831
+ <div align="center" style="padding: 40px 20px; background-color: white; border-radius: 12px; box-shadow: 0 2px 10px rgba(0, 0, 0, 0.05); max-width: 600px; margin: 0 auto;">
832
+ <h1 style="font-size: 3.5rem; color: #1a1a1a; margin: 0 0 20px 0; letter-spacing: 2px; font-weight: 700;">EmotionAnalysisPlus</h1>
833
+ <div style="font-size: 1.5rem; color: #4a4a4a; margin-bottom: 5px; font-weight: 300;">An <a href="https://github.com/embeddings-benchmark/mteb" style="color: #2c5282; font-weight: 600; text-decoration: none;" onmouseover="this.style.textDecoration='underline'" onmouseout="this.style.textDecoration='none'">MTEB</a> dataset</div>
834
+ <div style="font-size: 0.9rem; color: #2c5282; margin-top: 10px;">Massive Text Embedding Benchmark</div>
835
+ </div>
836
+
837
+ Multi-label emotion classification dataset for 28 languages released with the BRIGHTER project and SemEval-2025 Task 11.
838
+
839
+ | | |
840
+ |---------------|---------------------------------------------|
841
+ | Task category | t2c |
842
+ | Domains | Social, Written |
843
+ | Reference | https://github.com/emotion-analysis-project/SemEval2025-Task11 |
844
+
845
+ Source datasets:
846
+ - [llama-lang-adapt/EmotionAnalysisFinal](https://huggingface.co/datasets/llama-lang-adapt/EmotionAnalysisFinal)
847
+
848
+
849
+ ## Dataset Preparation in MTEB
850
+
851
+ This repository is a staging copy of `llama-lang-adapt/EmotionAnalysisFinal` for MTEB. The intended long-term canonical benchmark copy is `mteb/EmotionAnalysis`.
852
+
853
+ ### Transformations
854
+ - Converted per-emotion indicator columns into the MTEB multi-label format: `label: list[int]`
855
+ - Preserved the task-facing subset names, including `gaz` and `swh`, while sourcing from the original Hub configs
856
+ - Backfilled a `train` split where missing for benchmark compatibility before cleaning
857
+ - Applied dataset cleaning before upload to reduce duplicates and copied-train overlap in the staging copy
858
+
859
+ ### Label Schema
860
+ - `0`: anger
861
+ - `1`: disgust
862
+ - `2`: fear
863
+ - `3`: joy
864
+ - `4`: sadness
865
+ - `5`: surprise
866
+
867
+ ### Splits and subsets
868
+ - Language-specific configs from the benchmark task are preserved
869
+ - The staged copy includes the cleaned train/validation/test-style splits used by `EmotionAnalysisPlus`
870
+
871
+ ## How to evaluate on this task
872
+
873
+ You can evaluate an embedding model on this dataset using the following code:
874
+
875
+ ```python
876
+ import mteb
877
+
878
+ task = mteb.get_task("EmotionAnalysisPlus")
879
+ evaluator = mteb.MTEB([task])
880
+
881
+ model = mteb.get_model(YOUR_MODEL)
882
+ evaluator.run(model)
883
+ ```
884
+
885
+ <!-- Datasets want link to arxiv in readme to autolink dataset with paper -->
886
+ To learn more about how to run models on `mteb` task check out the [GitHub repository](https://github.com/embeddings-benchmark/mteb).
887
+
888
+ ## Citation
889
+
890
+ If you use this dataset, please cite the dataset as well as [mteb](https://github.com/embeddings-benchmark/mteb), as this dataset likely includes additional processing as a part of the [MMTEB Contribution](https://github.com/embeddings-benchmark/mteb/tree/main/docs/mmteb).
891
+
892
+ ```bibtex
893
+
894
+
895
+ @article{enevoldsen2025mmtebmassivemultilingualtext,
896
+ title={MMTEB: Massive Multilingual Text Embedding Benchmark},
897
+ author={Kenneth Enevoldsen and Isaac Chung and Imene Kerboua and Márton Kardos and Ashwin Mathur and David Stap and Jay Gala and Wissam Siblini and Dominik Krzemiński and Genta Indra Winata and Saba Sturua and Saiteja Utpala and Mathieu Ciancone and Marion Schaeffer and Gabriel Sequeira and Diganta Misra and Shreeya Dhakal and Jonathan Rystrøm and Roman Solomatin and Ömer Çağatan and Akash Kundu and Martin Bernstorff and Shitao Xiao and Akshita Sukhlecha and Bhavish Pahwa and Rafał Poświata and Kranthi Kiran GV and Shawon Ashraf and Daniel Auras and Björn Plüster and Jan Philipp Harries and Loïc Magne and Isabelle Mohr and Mariya Hendriksen and Dawei Zhu and Hippolyte Gisserot-Boukhlef and Tom Aarsen and Jan Kostkan and Konrad Wojtasik and Taemin Lee and Marek Šuppa and Crystina Zhang and Roberta Rocca and Mohammed Hamdy and Andrianos Michail and John Yang and Manuel Faysse and Aleksei Vatolin and Nandan Thakur and Manan Dey and Dipam Vasani and Pranjal Chitale and Simone Tedeschi and Nguyen Tai and Artem Snegirev and Michael Günther and Mengzhou Xia and Weijia Shi and Xing Han Lù and Jordan Clive and Gayatri Krishnakumar and Anna Maksimova and Silvan Wehrli and Maria Tikhonova and Henil Panchal and Aleksandr Abramov and Malte Ostendorff and Zheng Liu and Simon Clematide and Lester James Miranda and Alena Fenogenova and Guangyu Song and Ruqiya Bin Safi and Wen-Ding Li and Alessia Borghini and Federico Cassano and Hongjin Su and Jimmy Lin and Howard Yen and Lasse Hansen and Sara Hooker and Chenghao Xiao and Vaibhav Adlakha and Orion Weller and Siva Reddy and Niklas Muennighoff},
898
+ publisher = {arXiv},
899
+ journal={arXiv preprint arXiv:2502.13595},
900
+ year={2025},
901
+ url={https://arxiv.org/abs/2502.13595},
902
+ doi = {10.48550/arXiv.2502.13595},
903
+ }
904
+
905
+ @article{muennighoff2022mteb,
906
+ author = {Muennighoff, Niklas and Tazi, Nouamane and Magne, Loïc and Reimers, Nils},
907
+ title = {MTEB: Massive Text Embedding Benchmark},
908
+ publisher = {arXiv},
909
+ journal={arXiv preprint arXiv:2210.07316},
910
+ year = {2022}
911
+ url = {https://arxiv.org/abs/2210.07316},
912
+ doi = {10.48550/ARXIV.2210.07316},
913
+ }
914
+ ```
915
+
916
+ # Dataset Statistics
917
+ <details>
918
+ <summary> Dataset Statistics</summary>
919
+
920
+ The following code contains the descriptive statistics from the task. These can also be obtained using:
921
+
922
+ ```python
923
+ import mteb
924
+
925
+ task = mteb.get_task("EmotionAnalysisPlus")
926
+
927
+ desc_stats = task.metadata.descriptive_stats
928
+ ```
929
+
930
+ ```json
931
+ {
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+ "validation": {
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+ "num_samples": 9913,
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+ "number_texts_intersect_with_train": 9912,
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+ "min_text_length": 6,
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+ "average_text_length": 92.15968929688287,
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+ "max_text_length": 2022,
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+ "unique_texts": 9912
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+ },
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+ "image_statistics": null,
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+ },
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+ "text_statistics": {
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+ "average_text_length": 94.02727769928444,
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+ "max_text_length": 3779,
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+ "unique_texts": 43848
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+ },
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+ "image_statistics": null,
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+ "label_statistics": {
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+ "min_labels_per_text": 0,
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+ "labels": {
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+ "3": {
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+ "None": {
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+ },
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+ "5": {
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+ "count": 6020
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+ }
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+ }
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+ }
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+ },
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+ "train": {
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+ "num_samples": 9913,
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+ "number_texts_intersect_with_train": null,
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+ "text_statistics": {
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+ "total_text_length": 913579,
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+ "min_text_length": 6,
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+ "average_text_length": 92.15968929688287,
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+ "max_text_length": 2022,
1024
+ "unique_texts": 9912
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+ },
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+ "image_statistics": null,
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+ "audio_statistics": null,
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+ "label_statistics": {
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+ "min_labels_per_text": 0,
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+ "average_label_per_text": 0.9135478664380107,
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+ "max_labels_per_text": 5,
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+ "unique_labels": 7,
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+ "labels": {
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+ "3": {
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+ "count": 1956
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+ },
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+ "None": {
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+ "count": 2679
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+ },
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+ "2": {
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+ "count": 721
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+ },
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+ "0": {
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+ "count": 1509
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+ "1": {
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+ "count": 1584
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+ },
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+ "4": {
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+ "count": 2091
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+ },
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+ "5": {
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+ "count": 1195
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+ }
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+ }
1056
+ }
1057
+ }
1058
+ }
1059
+ ```
1060
+
1061
+ </details>
1062
+
1063
+ ---
1064
+ *This dataset card was automatically generated using [MTEB](https://github.com/embeddings-benchmark/mteb)*
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