Great article. I've been working in and around this space since 2014, and I think similarity learning, vector search, and embedding management will be a core part of future applications that leverage ML.
I recently built a similarity search application that recommends new Pinterest users channels to follow based on liked images using Milvus (https://github.com/milvus-io/milvus) as a backend. Similarity learning is a huge part of it, and I'm glad more and more tools like Quaterion are being released to help make this kind of tech ubiquitous.
The idea is to fine-tune model, which can be used to produce embeddings required for Qdrant. Our design approach is to make things as modular as possible, so you can use framework and engine independently.
But we are working on integrating components as well
I’m familiar with metric learning within the Mahalanobis family for kNN oriented applications . I’m not getting what use cases this framework targets? Is it custom image search type stuff which may benefit from fine tuning?
What is a realistic minimum viable dataset for an approach like this? When is it not advisable? How does it compare to other more basic approaches?
The main idea is to train a deep learning model to encode a high-dimensional sample to a low-dimensional vector in a latent space. Then it can be used in various downstream tasks such as KNN applications, semantic search, multimodal retrieval, recommendation systems, anomaly detection etc. It's not limited to the image domain --it can be also audios, texts, videos, or more specific entities such as authors, soccer players, songs etc. The size of the dataset can be thought of being similar to other deep learning methods, but you can make a choice among various similarity learning methods based on the size of your dataset or according to whether it's labeled or not. A common approach is (1) to train a base model by using a self-supervised method with a bulk amount of unlabeled data and (2) to finetune it on a more specific domain or task with a smaller labeled dataset. If you can start with a pretrained model such as ResNet or BERT, you can skip the first step.
I’d be surprised if it’d be useful for something like cars or soccer players, or really anything that may not have a continuous mapping. I guess more generally whenever the underlying “true” similarity function is not differentiable — categorical data springs to mind (cars, football players…).
I could see it making sense for complex unstructured data — Qdrant seems to point in that direction.
I'm one of the authors of Quaterion and also a contributor of TF Similarity. First of all, Quaterion is based on PyTorch. From a more technical perspective, TF Similarity is currently stronger on the self-supervised methods for images and lacks some more generic losses while Quaterion puts a bigger emphasis on finetuning models from any domain efficiently for practical purposes. For example, Quaterion has a intermediate caching mechanism that accelerates finetuning considerably. Overall, They both do a good job for their own use cases.
Basically, you can collect text from different authors, then you can use authors names as labels to train a similarity learning with it. My suggestion would be finetune a Transformer model with a specific head and an ArcFace loss.
It is definitely possible to do, if you have a proper training set. You would need to somehow give model a signal, that you are interesting in e.g. arguing style specifically, and not the topic of the text.
Starting from 0.5.0 finetuner is no longer an open-source.
> 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.
But there are some cool ideas implemented there as well, I encourage you to try both!
Unfortunately, it's no longer open source, but requires using their cloud.
"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."
Readit News
Disclaimer: I've made some contributions to it.
I recently built a similarity search application that recommends new Pinterest users channels to follow based on liked images using Milvus (https://github.com/milvus-io/milvus) as a backend. Similarity learning is a huge part of it, and I'm glad more and more tools like Quaterion are being released to help make this kind of tech ubiquitous.
What is a realistic minimum viable dataset for an approach like this? When is it not advisable? How does it compare to other more basic approaches?
I could see it making sense for complex unstructured data — Qdrant seems to point in that direction.
https://qdrant.tech/
More specifically, I'm interested in deriving distances between writing style, arguing style, etc.
Basically, you can collect text from different authors, then you can use authors names as labels to train a similarity learning with it. My suggestion would be finetune a Transformer model with a specific head and an ArcFace loss.
> 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.
But there are some cool ideas implemented there as well, I encourage you to try both!
"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."