SentEval is a standardized toolkit for evaluating sentence embeddings across a wide spectrum of downstream tasks and probing tests. It defines a simple interface—provide an encoder function from sentences to vectors—and then runs consistent training/evaluation loops for tasks like sentiment, entailment, paraphrase, and semantic textual similarity. The suite also contains linguistic probing tasks that illuminate what properties embeddings capture, such as tense, word order, or syntactic structure. Datasets are wrapped with unified preprocessing and metrics so results are comparable across papers and implementations. Because the interface is minimal, researchers can plug in encoders from any framework or language model and obtain a broad evaluation with little glue code. SentEval helped establish common baselines and reporting conventions in the sentence-representation community, reducing friction when comparing new methods.

Features

  • Plug-and-play API that evaluates any sentence-to-vector encoder
  • Broad coverage of classification, similarity, and entailment tasks
  • Probing tests to analyze linguistic properties captured by embeddings
  • Unified preprocessing and metrics for apples-to-apples comparisons
  • Ready scripts to download, cache, and run standardized benchmarks
  • Clear result reporting to support fair, reproducible research comparisons

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Python

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4 days ago