Training and Fine-Tuning Multimodal Embedding & Reranker Models
Sentence Transformers has become an essential library for creating high-quality text and multimodal embeddings. The latest release extends support to multimodal models, allowing you to fine-tune both embedding and reranker models on data that includes images and text. This guide walks through the key steps to train and fine-tune these models effectively.
Prerequisites
To get started, ensure you have Sentence Transformers installed (v3.0 or later) and a dataset that pairs text with images or other modalities. The library provides flexible APIs for both training and evaluation.
Training Embedding Models
Fine-tuning a multimodal embedding model involves updating the encoder to project both text and images into a shared embedding space. Use the SentenceTransformer class with a loss function like ContrastiveLoss or TripletLoss. Example:
from sentence_transformers import SentenceTransformer, losses
from sentence_transformers.datasets import ParallelSentencesDataset
model = SentenceTransformer('clip-ViT-B-32-multilingual-v1')
train_data = ParallelSentencesDataset(model.tokenizer, image_processor, pairs)
train_loss = losses.ContrastiveLoss(model)
model.fit(train_objectives=[(train_data, train_loss)], epochs=5)
Fine-Tuning Reranker Models
For reranker models, which output a relevance score between a query and a passage (including images), use the CrossEncoder class with a custom dataset. Example:
from sentence_transformers import CrossEncoder
model = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
train_data = [(query, passage, label) for ...]
model.fit(train_data, epochs=3)
Evaluation and Deployment
After training, evaluate the model on a held-out set using metrics like accuracy or NDCG. Export the model with model.save('path') for later use in retrieval pipelines.
Note: Always check the official documentation for the latest API changes and best practices. Multimodal training requires substantial computational resources, so consider using a GPU for faster iteration.