SentEval: An Evaluation Toolkit for Universal Sentence Representations
This provides a standardized evaluation framework for researchers working on sentence representations, though it is incremental as it consolidates existing tasks.
The authors tackled the problem of evaluating universal sentence representations by introducing SentEval, a toolkit that includes tasks like classification, natural language inference, and similarity, resulting in a centralized and easier evaluation process.
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.