STS Models

The models were first trained on NLI data, then we fine-tuned them on the STS benchmark dataset (docs, 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/sentence_transformer/training_stsbenchmark.py - Train directly on STS data
• examples/sentence_transformer/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.

• Avg. GloVe embeddings: 58.02
• BERT-as-a-service avg. embeddings: 46.35
• BERT-as-a-service CLS-vector: 16.50
• InferSent - GloVe: 68.03
• Universal Sentence Encoder: 74.92