AI BriefingSentence Transformers Training Guide
Key point
This guide explains in detail how to fine-tune embedding models using the new training approach introduced in Sentence Transformers v3.0.
Details
Sentence Transformers v3.0 is the biggest update since the project began, introducing a new training approach. This guide covers how to fine-tune or train from scratch embedding models tailored to specific tasks such as RAG, semantic search, and sentence similarity measurement.
The training process consists of the following core components:
- Dataset: You can use data from the Hugging Face Hub or local data such as CSV, JSON, and Parquet.
- Loss Function: Measures the model's performance and determines the direction of optimization.
- Training Arguments: Sets parameters that affect training performance and debugging.
- Evaluator: Evaluates the model's performance during or after training.
- Trainer: The core engine that integrates the model, dataset, loss function, etc. to perform the actual training.
The guide also provides detailed instructions on multi-dataset training, which uses multiple datasets simultaneously, and Callbacks, which control the training process.
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