| # STS Models | |
| The models were first trained on [NLI data](nli-models.md), then we fine-tuned them on the [STS benchmark dataset](http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark). 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](http://ixa2.si.ehu.es/stswiki/index.php/STSbenchmark). | |
| For a training example, see: | |
| - [examples/training_stsbenchmark.py](https://github.com/UKPLab/sentence-transformers/blob/master/examples/training_transformers/training_stsbenchmark.py) - Train directly on STS data | |
| - [examples/training_stsbenchmark_continue_training.py ](https://github.com/UKPLab/sentence-transformers/blob/master/examples/training_transformers/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](https://docs.google.com/spreadsheets/d/14QplCdTCDwEmTqrn1LH4yrbKvdogK4oQvYO1K1aPR5M/edit#gid=0) | |
| # 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 | |