finetuner 0.5.1

Task-oriented finetuning for better embeddings on neural search.

Task-oriented finetuning for better embeddings on neural search

Fine-tuning is an effective way to improve the performance on neural search tasks. However, it is non-trivial for many deep learning engineers.

Finetuner makes fine-tuning easier, faster and performant by streamlining the workflow and handling all complexity and infrastructure on the cloud. With Finetuner, one can easily uplift pre-trained models to be more performant and production ready.

📈 Performance promise: uplift pretrained model and deliver SOTA performance on domain-specific neural search applications.

🔱 Simple yet powerful: easy access to 40+ mainstream losses, 10+ optimisers, layer pruning, weights freezing, dimensionality reduction, hard-negative mining, cross-modal model, distributed training.

☁ All-in-cloud: instant training with our free GPU (Apply here for free!); manage runs, experiments and artifacts on Jina Cloud without worrying about provisioning resources, integration complexity and infrastructure.

Documentation

Benchmark

Model	Task	Metric	Pretrained	Finetuned	Delta
BERT	Quora Question Answering	mRR	0.835	0.967	:arrow_up_small: 15.8%
		Recall	0.915	0.963	:arrow_up_small: 5.3%
ResNet	Visual similarity search on TLL	mAP	0.102	0.166	:arrow_up_small: 62.7%
		Recall	0.235	0.372	:arrow_up_small: 58.3%
CLIP	Deep Fashion text-to-image search	mRR	0.289	0.488	:arrow_up_small: 69.9%
		Recall	0.109	0.346	:arrow_up_small: 217.0%

_{^{[*] All metrics evaluation on k@20, trained 5 epochs using Adam optimizer with learning rate of 1e-5.}}

Install

pip install -U finetuner

From 0.5.0, Finetuner computing is hosted on Jina Cloud. THe last local version is 0.4.1, one can install it via pip or check out git tags/releases here.

Get Started

The following code snippet describes how to fine-tune ResNet50 on Totally Looks Like dataset, it can be run as-is:

import finetuner
from finetuner.callback import EvaluationCallback

finetuner.login()

run = finetuner.fit(
    model='resnet50',
    run_name='resnet50-tll-run',
    train_data='tll-train-da',
    callbacks=[EvaluationCallback(query_data='tll-eval-da')],
)

Fine-tuning might take 5 minute to finish. You can later re-connect your run with:

import finetuner

finetuner.login()

run = finetuner.get_run('resnet50-tll-run')
print(run.status())
print(run.logs())

run.save_artifact('resnet-tll')

Specifically, the code snippet describes the following steps:

• Login to Finetuner (Get free access here!)
• Select backbone model, training and evaluation data for your evaluation callback.
• Start the cloud run.
• Monitor the status: check the status and logs of the run.
• Save model for further use and integration.

Next steps

• Take the walkthrough and submit your first fine-tuning job.
• Try on different search tasks:
  • Text-to-Text Search via BERT
  • Image-to-Image Search via ResNet50
  • Text-to-Image Search via CLIP

Intrigued? That's only scratching the surface of what Finetuner is capable of. Read our docs to learn more.

Support

• Use Discussions to talk about your use cases, questions, and support queries.
• Join our Slack community and chat with other Jina AI 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, solution engineers to build the next neural search ecosystem in opensource.