Benchmarking sentence similarity models

There are many semantic textual similarity models available, but it is highly recommended that you benchmark and understand their capabilities and differences using metrics. Papers With Code provides a list of these datasets at https://paperswithcode.com/task/semantic-textual-similarity.

Also, there are many model outputs in each dataset that are ranked by their results. These results have been taken from the aforementioned article.

General Language Understanding Evaluation (GLUE) provides most of these datasets and tests, but it is not only for semantic textual similarity. GLUE is a general benchmark for evaluating a model with different NLP characteristics. More details about the GLUE dataset and its usage were provided in Chapter 2. Let’s take a look at it before we move on:

1. To load the metrics and some datasets, we import datasets functions as follows:

   from datasets import load_metric, load_dataset

2. Let’s assume...