SentEval is 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.
Source: SentEval: An Evaluation Toolkit for Universal Sentence Representations
Variants: SentEval
This dataset is used in 1 benchmark:
Task | Model | Paper | Date |
---|---|---|---|
Semantic Textual Similarity | XLNet-Large | XLNet: Generalized Autoregressive Pretraining for … | 2019-06-19 |
Semantic Textual Similarity | MT-DNN-ensemble | Improving Multi-Task Deep Neural Networks … | 2019-04-20 |
Semantic Textual Similarity | Snorkel MeTaL(ensemble) | Training Complex Models with Multi-Task … | 2018-10-05 |
Semantic Textual Similarity | GenSen | Learning General Purpose Distributed Sentence … | 2018-03-30 |
Semantic Textual Similarity | InferSent | Supervised Learning of Universal Sentence … | 2017-05-05 |
Recent papers with results on this dataset: