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.