/backprop

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

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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

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:

Feedback

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