backprop

backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.

Solve a variety of tasks with pre-trained models or finetune them in one line for your own tasks.

Out of the box tasks you can solve with backprop:

• Conversational question answering in English
• Text Classification in 100+ languages
• Image Classification
• Text Vectorisation in 50+ languages
• Image Vectorisation
• Summarisation in English
• Emotion detection in English
• Text Generation

For more specific use cases, you can adapt a task with little data and a single line of code via finetuning.

⚡ Getting started	Installation, few minute introduction
Examples	Finetuning and usage examples
? Docs	In-depth documentation about task inference and finetuning
Models	Overview of available models

Getting started

Installation

Install backprop via PyPi:

pip install backprop

Basic task inference

Tasks act as interfaces that let you easily use a variety of supported models.

import backprop

context = "Take a look at the examples folder to see use cases!"

qa = backprop.QA()

# Start building!
answer = qa("Where can I see what to build?", context)

print(answer)
# Prints
"the examples folder"

You can run all tasks and models on your own machine, or in production with our inference API, simply by specifying your api_key.

See how to use all available tasks.

Basic finetuning and uploading

Each task implements finetuning that lets you adapt a model for your specific use case in a single line of code.

A finetuned model is easy to upload to production, letting you focus on building great applications.

import backprop

tg = backprop.TextGeneration("t5-small")

# Any text works as training data
inp = ["I really liked the service I received!", "Meh, it was not impressive."]
out = ["positive", "negative"]

# Finetune with a single line of code
tg.finetune({"input_text": inp, "output_text": out})

# Use your trained model
prediction = tg("I enjoyed it!")

print(prediction)
# Prints
"positive"

# Upload to backprop for production ready inference
# Describe your model
name = "t5-sentiment"
description = "Predicts positive and negative sentiment"

tg.upload(name=name, description=description, api_key="abc")

See finetuning for other tasks.

Why backprop?

1. No experience needed

   • Entrance to practical AI should be simple
   • Get state-of-the-art performance in your task without being an expert

2. Data is a bottleneck

   • Solve real world tasks without any data
   • With transfer learning, even a small amount of data can adapt a task to your niche requirements

3. There are an overwhelming amount of models

   • We offer a curated selection of the best open-source models and make them simple to use
   • A few general models can accomplish more with less optimisation

4. Deploying models cost effectively is hard work

   • If our models suit your use case, no deployment is needed: just call our API
   • Adapt and deploy your own model with just a few lines of code
   • Our API scales, is always available, and you only pay for usage

Examples

• Solve any text based task with Finetuning (Github, Colab)
• Search for images using text (Github)
• Finding answers from text (Github)
• More finetuning and task examples

Documentation

Check out our docs for in-depth task inference and finetuning.

Model Hub

Curated list of state-of-the-art models.

Demos

Zero-shot image classification with CLIP.

Credits

backprop relies on many great libraries to work, most notably:

• PyTorch
• PyTorch Lightning
• Transformers
• Sentence Transformers
• EfficientNet PyTorch
• CLIP

Feedback

Found a bug or have ideas for new tasks and models? Open an issue.