The models were first trained on NLI data, then we fine-tuned them on the  STS benchmark dataset. This generate sentence embeddings that are especially suitable to measure the semantic similarity between sentence pairs.
Datasets
We use the training file from the  STS benchmark dataset.
For a training example, see:
- examples/training_stsbenchmark.py - Train directly on STS data
- examples/training_stsbenchmark_continue_training.py  - First train on NLI, than train on STS data.
Pre-trained models
We provide the following pre-trained models:
» Full List of STS Models
Performance Comparison
Here are the performances on the STS benchmark for other sentence embeddings methods. They were also computed by using cosine-similarity and Spearman rank correlation. Note, these models were not-fined on the STS benchmark.
