?? Unlock the Power of Sentence Transformers: Training and Fine-Tuning Guide ???

?? Introduction

Sentence Transformers is a widely recognized Python module for training or fine-tuning state-of-the-art text embedding models. In the realm of large language models (LLMs), embedding plays a crucial role, significantly enhancing the performance of tasks such as similarity search when tailored to specific datasets.

?? Recently, Hugging Face released version 3.0.0 of Sentence Transformers, simplifying training, logging, and evaluation processes. In this guide, we explore how to train and fine-tune a Sentence Transformer model using your data.

?? Embeddings for Similarity Search

Embedding converts text into fixed-size vector representations (floating-point numbers) that capture the semantic meaning of the text. For similarity search, embed queries into a vector database, then find similar queries by comparing embeddings using algorithms like Cosine Similarity, Manhattan Distance, or Euclidean Distance.

?? Steps for Similarity Search

1. Convert all textual data into fixed-size vector embeddings and store them in a vector database.

2. Accept a query from the user and convert it into an embedding.

3. Find similar search terms or keywords in the vector database by retrieving the closest embeddings.

?? What is SBERT?

SBERT (Sentence-BERT) is a specialized type of sentence transformer model tailored for efficient sentence processing and comparison. It employs a Siamese network architecture, utilizing identical BERT models to process sentence pairs independently, and uses mean pooling to generate high-quality sentence embeddings.

?? Training Components Breakdown

- Accelerator: Determines the number of GPUs available.

- Sentence Transformers Model: Load from the HuggingFace repository, extract the word embedding dimension, and add a mean pooling layer.

- Loss Function: CoSENTLoss to calculate the model’s loss based on float similarity scores.

- Evaluator: EmbeddingSimilarityEvaluator to calculate the evaluation loss during training and obtain specific metrics.

- Training Arguments: Define parameters like output directory, batch size, number of epochs, learning rate, precision, evaluation steps, etc.

- Training: Use SentenceTransformerTrainer to define training and validation data, optionally including an evaluator, and initiate training.

?? Conclusion

Using SentenceTransformer 3.0.0 makes training or fine-tuning embedding models straightforward. The new version supports multi-GPU utilization via the DDP method and introduces logging and experimentation features through Weights & Biases. By encapsulating code within a single main function and executing it with a single command, developers can streamline their workflow significantly.

The Evaluator functionality aids in evaluating models during the training phase, catering to tasks like Embedding Similarity Search. Upon loading the model for inference, it delivers as anticipated, yielding satisfactory similarity scores.

This process harnesses the potential of vector embeddings to enhance search results, leveraging user queries and database interactions effectively.

To View training code in Github: Code

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