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SentEval: An Evaluation Toolkit for Universal Sentence Representations

arXiv 1803.05449 ↗First posted 14 Mar 2018cs.CLcited by 294

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Abstract (arXiv)

We introduce SentEval, a toolkit for evaluating the quality of universal sentence representations. SentEval encompasses a variety of tasks, including binary and multi-class classification, natural language inference and sentence similarity. The set of tasks was selected based on what appears to be the community consensus regarding the appropriate evaluations for universal sentence representations. The toolkit comes with scripts to download and preprocess datasets, and an easy interface to evaluate sentence encoders. The aim is to provide a fairer, less cumbersome and more centralized way for evaluating sentence representations.

Key facts. Cited by 294 papers in the map (data through 7 Oct 2026). First posted on arXiv 14 Mar 2018.

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Citing papers per year: 2021: 60; 2022: 70; 2023: 75; 2024: 55; 2025: 20; 2026: 14.

294 papers.

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  1. 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,168
  2. 2022Text Embeddings by Weakly-Supervised Contrastive Pre-trainingcited by 1,216
  3. 2023AGIEval: A Human-Centric Benchmark for Evaluating Foundation Modelscited by 701
  4. 2022MTEB: Massive Text Embedding Benchmarkcited by 623
  5. 2023C-Pack: Packed Resources For General Chinese Embeddingscited by 556
  6. 2021Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Modelscited by 462
  7. 2021Dynabench: Rethinking Benchmarking in NLPcited by 447
  8. 2023Larger language models do in-context learning differentlycited by 386
  9. 2022Text and Code Embeddings by Contrastive Pre-Trainingcited by 358
  10. 2021AI and the Everything in the Whole Wide World Benchmarkcited by 340
  11. 2022One Embedder, Any Task: Instruction-Finetuned Text Embeddingscited by 329
  12. 2021LexGLUE: A Benchmark Dataset for Legal Language Understanding in Englishcited by 302
38 more
  1. 2021Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworkscited by 278
  2. 2021ConSERT: A Contrastive Framework for Self-Supervised Sentence Representation Transfercited by 231
  3. 2024Generative Representational Instruction Tuningcited by 227
  4. 2021Masked Language Modeling and the Distributional Hypothesis: Order Word Matters Pre-training for Littlecited by 176
  5. 2021Condenser: a Pre-training Architecture for Dense Retrievalcited by 162
  6. 2022Efficient Few-Shot Learning Without Promptscited by 135
  7. 2022DiffCSE: Difference-based Contrastive Learning for Sentence Embeddingscited by 131
  8. 2021BERT: A Review of Applications in Natural Language Processing and Understandingcited by 109
  9. 2021Self-Guided Contrastive Learning for BERT Sentence Representationscited by 103
  10. 2021CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmarkcited by 99
  11. 2023AnglE-optimized Text Embeddingscited by 98
  12. 2021VALUE: A Multi-Task Benchmark for Video-and-Language Understanding Evaluationcited by 95
  13. 2022Same Author or Just Same Topic? Towards Content-Independent Style Representationscited by 83
  14. 2023Scaling Sentence Embeddings with Large Language Modelscited by 83
  15. 2022Continual Pre-Training Mitigates Forgetting in Language and Visioncited by 82
  16. 2023Sentence Embedding Leaks More Information than You Expect: Generative Embedding Inversion Attack to Recover the Whole Sentencecited by 81
  17. 2022PromptBERT: Improving BERT Sentence Embeddings with Promptscited by 75
  18. 2023Symbol tuning improves in-context learning in language modelscited by 72
  19. 2023What Do Self-Supervised Speech Models Know About Words?cited by 62
  20. 2021BERT Learns to Teach: Knowledge Distillation with Meta Learningcited by 61
  21. 2022Debiased Contrastive Learning of Unsupervised Sentence Representationscited by 56
  22. 2022Parameter-Efficient and Student-Friendly Knowledge Distillationcited by 47
  23. 2021Towards Efficient NLP: A Standard Evaluation and A Strong Baselinecited by 44
  24. 2024Why Larger Language Models Do In-context Learning Differently?cited by 44
  25. 2021On the proper role of linguistically-oriented deep net analysis in linguistic theorizingcited by 43
  26. 2021Adversarial Training Helps Transfer Learning via Better Representationscited by 36
  27. 2023How Predictable Are Large Language Model Capabilities? A Case Study on BIG-benchcited by 34
  28. 2021STraTA: Self-Training with Task Augmentation for Better Few-shot Learningcited by 33
  29. 2021The Inductive Bias of In-Context Learning: Rethinking Pretraining Example Designcited by 33
  30. 2022A Data-Based Perspective on Transfer Learningcited by 32
  31. 2023BeLLM: Backward Dependency Enhanced Large Language Model for Sentence Embeddingscited by 32
  32. 2021Pairwise Supervised Contrastive Learning of Sentence Representationscited by 31
  33. 2022MABEL: Attenuating Gender Bias using Textual Entailment Datacited by 31
  34. 2022Improved Universal Sentence Embeddings with Prompt-based Contrastive Learning and Energy-based Learningcited by 30
  35. 2023Instruction-following Evaluation through Verbalizer Manipulationcited by 30
  36. 2021Exploring the Role of BERT Token Representations to Explain Sentence Probing Resultscited by 29
  37. 2022PCL: Peer-Contrastive Learning with Diverse Augmentations for Unsupervised Sentence Embeddingscited by 29
  38. 2023Improving Contrastive Learning of Sentence Embeddings from AI Feedbackcited by 29

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