train-sentence-transformers

To fine-tune sentence-transformers models, you load example scripts and references, identify your model type, select the appropriate loss function, and follow the training script structure to proceed with the training process.

Choosing the right loss function for sentence-transformers retrieval depends on your specific task, and this Skill provides guidance on loss selection to match your use case, such as retrieval, clustering, or paraphrase mining.

Yes, you can train sentence-transformers for multimodal tasks using PyTorch and the Hugging Face transformers library, as these are listed dependencies required for the training and evaluation process.

The best way to evaluate sentence-transformers models during fine-tuning is by using built-in evaluators, and this Skill offers guidance on selecting and using evaluators for accurate model assessment.

Yes, you need the trackio and datasets libraries along with sentence-transformers, transformers, and torch, as these dependencies are required for training and evaluating your models.

Besides semantic similarity, you can train sentence-transformers models for retrieval, clustering, classification, paraphrase mining, deduplication, and multimodal tasks using the provided training scripts.