| # Training | |
| This folder contains various examples to fine-tune `SparseEncoder` models for specific tasks. | |
| For the beginning, I can recommend to have a look at the [MS MARCO](ms_marco/) examples. | |
| For the documentation how to train your own models, see [Training Overview](http://www.sbert.net/docs/sparse_encoder/training_overview.html). | |
| ## Training Examples | |
| - [distillation](distillation/) - Examples to make models smaller, faster and lighter. | |
| - [ms_marco](ms_marco/) - Example training scripts for training on the MS MARCO information retrieval dataset. | |
| - [nli](nli/) - Natural Language Inference (NLI) data can be quite helpful to pre-train and fine-tune models to create meaningful sparse embeddings. | |
| - [quora_duplicate_questions](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. | |
| - [retrievers](retrievers/) - Example training scripts for training on generic information retrieval datasets. | |
| - [sts](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. | |