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backprop-0.1.3
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Backprop
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | backprop-0.1.3 |
| نام | backprop |
| نسخه کتابخانه | 0.1.3 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Backprop |
| ایمیل نویسنده | hello@backprop.co |
| آدرس صفحه اصلی | https://github.com/backprop-ai/backprop |
| آدرس اینترنتی | https://pypi.org/project/backprop/ |
| مجوز | - |
<h1 align="center">
<a href="https://backprop.co">
<img src=".github/header.png" width="300" alt="Backprop"/>
</a>
</h1>
<p align="center">
<a href="https://pypi.org/project/backprop/"><img src="https://img.shields.io/pypi/v/backprop"/></a> <img src="https://img.shields.io/pypi/pyversions/backprop"/> <a href="https://www.apache.org/licenses/LICENSE-2.0"><img src="https://img.shields.io/badge/License-Apache%202.0-blue.svg"/></a>
</p>
<p align="center">
Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.
</p>
<p align="center">
<img src=".github/example.png" width="600"/>
</p>
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](#getting-started) | Installation, few minute introduction |
| :---------------------------------------------------- | :-------------------------------------------------------- |
| 💡 [Examples](#examples) | Finetuning and usage examples |
| 📙 [Docs](https://backprop.readthedocs.io/en/latest/) | In-depth documentation about task inference and finetuning |
| ⚙️ [Models](https://backprop.co/hub) | Overview of available models |
## Getting started
### Installation
Install Backprop via PyPi:
```bash
pip install backprop
```
### Basic task inference
Tasks act as interfaces that let you easily use a variety of supported models.
```python
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](https://backprop.co), simply by specifying your `api_key`.
See how to use [all available tasks](https://backprop.readthedocs.io/en/latest/Tasks.html).
### 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.
```python
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
# 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](https://backprop.readthedocs.io/en/latest/Finetuning.html).
## 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](https://github.com/backprop-ai/backprop/blob/main/examples/Finetuning_GettingStarted.ipynb), [Colab](https://colab.research.google.com/github/backprop-ai/backprop/blob/main/examples/Finetuning_GettingStarted.ipynb))
- Search for images using text ([Github](https://github.com/backprop-ai/backprop/blob/main/examples/ImageVectorisation.ipynb))
- Finding answers from text ([Github](https://github.com/backprop-ai/backprop/blob/main/examples/Q%26A.ipynb))
- [More finetuning and task examples](https://github.com/backprop-ai/backprop/tree/main/examples)
## Documentation
Check out our [docs](https://backprop.readthedocs.io/en/latest/) for in-depth task inference and finetuning.
## Model Hub
Curated list of [state-of-the-art models](https://backprop.co/hub).
## Demos
Zero-shot image classification with [CLIP](https://clip.backprop.co).
## Credits
Backprop relies on many great libraries to work, most notably:
* [PyTorch](https://github.com/pytorch/pytorch)
* [PyTorch Lightning](https://github.com/PyTorchLightning/pytorch-lightning)
* [Transformers](https://github.com/huggingface/transformers)
* [Sentence Transformers](https://github.com/UKPLab/sentence-transformers)
* [EfficientNet PyTorch](https://github.com/lukemelas/EfficientNet-PyTorch)
* [CLIP](https://github.com/openai/CLIP)
## Feedback
Found a bug or have ideas for new tasks and models? Open an [issue](https://github.com/backprop-ai/backprop/issues).
نیازمندی
| مقدار | نام |
|---|---|
| - | dill |
| - | efficientnet-pytorch |
| - | ftfy |
| <1.3.0,>=1.2.0 | pytorch-lightning |
| >=0.4.1.2 | sentence-transformers |
| <1.8.0 | torch |
| <0.9.0 | torchtext |
| <0.9.0 | torchvision |
| <4.5.0,>=4.3.2 | transformers |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl backprop-0.1.3:
pip install backprop-0.1.3.whl
نصب پکیج tar.gz backprop-0.1.3:
pip install backprop-0.1.3.tar.gz