finetuner 0.0.3.dev51

Finetuner allows one to tune the weights of any deep neural network for better embedding on search tasks.

Finetuning any deep neural network for better embedding on neural search tasks

Finetuner allows one to tune the weights of any deep neural network for better embedding on search tasks. It accompanies Jina to deliver the last mile of performance-tuning for neural search applications.

🎛 Designed for finetuning: a machine learning-powered human-in-the-loop tool for leveling up your pretrained models in neural search applications.

🔱 Powerful yet intuitive: all you need is finetuner.fit() - a one-liner that unlocks rich features such as siamese/triplet network, interactive labeling, layer trimming, weights freezing, dimensionality reduction.

⚛️ Framework-agnostic: promise an identical API experience on Pytorch , Keras or PaddlePaddle deep learning backends.

🧈 Jina integration: buttery smooth integration with Jina, reducing the cost of context-switch between experimenting and production.

How does it work

Install

Make sure you have Python 3.7+ and one of Pytorch, Keras or PaddlePaddle installed on Linux/MacOS.

pip install finetuner

Documentation

Usage

🪄 Usage	Do you have an embedding model?
Yes	No
Do you have labeled data?	Yes	1️⃣	3️⃣
	No	2️⃣	4️⃣

1️⃣ Have embedding model and labeled data

Perfect! Now embed_model and labeled_data are given by you already, simply do:

import finetuner

finetuner.fit(
    embed_model,
    train_data=labeled_data
)

2️⃣ Have embedding model and unlabeled data

You have an embed_model to use, but no labeled data for finetuning this model. No worry, that's good enough already! You can use Finetuner to interactive label data and train embed_model as below:

import finetuner

finetuner.fit(
    embed_model,
    train_data=unlabeled_data,
    interactive=True
)

3️⃣ Have general model and labeled data

You have a general_model which does not output embeddings. Luckily you provide some labeled_data for training. No worry, Finetuner can convert your model into an embedding model and train it via:

import finetuner

finetuner.fit(
    general_model,
    train_data=labeled_data,
    to_embedding_model=True,
    output_dim=100
)

4️⃣ Have general model and unlabeled data

You have a general_model which is not for embeddings. Meanwhile, you don't have labeled data for training. But no worries, Finetuner can help you train an embedding model with interactive labeling on-the-fly:

import finetuner

finetuner.fit(
    general_model,
    train_data=unlabeled_data,
    interactive=True,
    to_embedding_model=True,
    output_dim=100
)