backprop 0.1.0

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.

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

Your finetuned models can be deployed in one line of code.

⚡ Getting started	Installation, few minute introduction
💡 Examples	Finetuning and usage examples
📙 Docs	In-depth documentation about our tasks and models

Getting started

Installation

Install Backprop via PyPi:

pip install backprop

Basic task solving

from backprop import QA

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

qa = QA()

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

print(answer)
# Prints
"the examples folder"

See all available tasks.

Basic finetuning and uploading

from backprop.models import T5
from backprop import TextGeneration

tg = TextGeneration(T5)

# 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

model = tg.model
# Describe your model
model.name = "t5-sentiment"
model.description = "Predicts positive and negative sentiment"

backprop.upload(model, 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

   • Use AI without needing access to "big 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 implement the best open-source 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

• Getting started with Finetuning (Github, Colab)
• Finetuning and task examples

Documentation

Check out our docs.

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.