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bd33eac | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | # 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.
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