finetuner

Task-oriented finetuning for better embeddings on neural search

Fine-tuning is an effective way to improve performance on neural search tasks. However, setting up and performing fine-tuning can be very time-consuming and resource-intensive.

Jina AI's Finetuner makes fine-tuning easier and faster by streamlining the workflow and handling all the complexity and infrastructure in the cloud. With Finetuner, you can easily enhance the performance of pre-trained models, making them production-ready without extensive labeling or expensive hardware.

🎏 Better embeddings: Create high-quality embeddings for semantic search, visual similarity search, cross-modal text<->image search, recommendation systems, clustering, duplication detection, anomaly detection, or other uses.

⏰ Low budget, high expectations: Bring considerable improvements to model performance, making the most out of as little as a few hundred training samples, and finish fine-tuning in as little as an hour.

📈 Performance promise: Enhance the performance of pre-trained models so that they deliver state-of-the-art performance on domain-specific applications.

🔱 Simple yet powerful: Easy access to 40+ mainstream loss functions, 10+ optimizers, layer pruning, weight freezing, dimensionality reduction, hard-negative mining, cross-modal models, and distributed training.

☁ All-in-cloud: Train using our GPU infrastructure, manage runs, experiments, and artifacts on Jina AI Cloud without worrying about resource availability, complex integration, or infrastructure costs.

Documentation

Pretrained Text Embedding Models

Benchmarks

_{^{All metrics were evaluated for k@20 after training for 5 epochs using the Adam optimizer with learning rates of 1e-4 for ResNet, 1e-7 for CLIP and 1e-5 for the BERT models, 5e-4 for PointNet++}}

Install

Make sure you have Python 3.8+ installed. Finetuner can be installed via pip by executing:

pip install -U finetuner

If you want to submit a fine-tuning job on the cloud, please use

pip install "finetuner[full]"

> ⚠️ Starting with version 0.5.0, Finetuner computing is performed on Jina AI Cloud. The last local version is 0.4.1. > This version is still available for installation via pip. See Finetuner git tags and releases.

Articles about Finetuner

Check out our published blogposts and tutorials to see Finetuner in action!

• Fine-tuning with Low Budget and High Expectations
• Hype and Hybrids: Search is more than Keywords and Vectors
• Improving Search Quality for Non-English Queries with Fine-tuned Multilingual CLIP Models
• How Much Do We Get by Finetuning CLIP?

If you find Jina Embeddings useful in your research, please cite the following paper:

@misc{günther2023jina,
      title={Jina Embeddings: A Novel Set of High-Performance Sentence Embedding Models}, 
      author={Michael Günther and Louis Milliken and Jonathan Geuter and Georgios Mastrapas and Bo Wang and Han Xiao},
      year={2023},
      eprint={2307.11224},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Support

• Use Discussions to talk about your use cases, questions, and support queries.
• Join our Discord community and chat with other community members about ideas.
• Join our Engineering All Hands meet-up to discuss your use case and learn Jina AI new features.
  • When? The second Tuesday of every month
  • Where? Zoom (see our public events calendar/.ical) and live stream on YouTube
• Subscribe to the latest video tutorials on our YouTube channel

Join Us

Finetuner is backed by Jina AI and licensed under Apache-2.0.

We are actively hiring AI engineers and solution engineers to build the next generation of open-source AI ecosystems.