Training

This folder contains various examples to fine-tune SentenceTransformers for specific tasks.

For the beginning, I can recommend to have a look at the Semantic Textual Similarity (STS) or the Natural Language Inference (NLI) examples.

For the documentation how to train your own models, see Training Overview.

Training Examples

• avg_word_embeddings - This folder contains examples to train models based on classical word embeddings like GloVe. These models are extremely fast, but are a more inaccuracte than transformers based models.

• distillation - Examples to make models smaller, faster and lighter.

• multilingual - Existent monolingual models can be extend to various languages (paper). This folder contains a step-by-step guide to extend existent models to new languages.

• nli - Natural Language Inference (NLI) data can be quite helpful to pre-train and fine-tune models to create meaningful sentence embeddings.

• quora_duplicate_questions - Quora Duplicate Questions is large set corpus with duplicate questions from the Quora community. The folder contains examples how to train models for duplicate questions mining and for semantic search.

• sts - The most basic method to train models is using Semantic Textual Similarity (STS) data. Here, we have a sentence pair and a score indicating the semantic similarity.

• other - Various tiny examples for show-casing one specific training case.