DeepPavlov - User-Friendly NLP Framework Utilizing PyTorch and Transformers

Introduction to DeepPavlov 1.0

DeepPavlov 1.0 is an open-source framework designed to develop advanced Natural Language Processing (NLP) models. Built on the robust PyTorch library and complemented by the transformers library, DeepPavlov 1.0 facilitates creating modular and configuration-driven NLP solutions. This framework is ideal for practitioners who possess limited experience in NLP or machine learning.

Key Features

Modular Development: DeepPavlov supports the integration of various components, allowing users to customize and extend functionalities.
Configuration-Driven: With a focus on simplicity, users can manage model behaviors through straightforward configuration files.
Wide Range of Applications: It supports numerous NLP tasks such as sentiment analysis, named entity recognition, and more.

Quick Links to Resources

Demo: Experience live demonstrations of NLP models.
Documentation: Detailed guides on how to implement DeepPavlov and utilize its features.
Model List: Find available models tailored to various NLP tasks.
Contribution Guide: Guidelines for contributing to the project.
Issues: Report and track issues related to DeepPavlov.
Forum: Community-driven space for discussions and troubleshooting.
Blogs: Updates and insights about ongoing and future developments.
Extended Colab Tutorials: In-depth tutorials for various models.
Docker Hub: Access Docker images for quick deployment.
Feedback: Provide feedback to enhance DeepPavlov.

Installation Requirements

DeepPavlov is compatible with Linux, Windows 10+ (using WSL/WSL2), MacOS (Big Sur+), and requires Python versions 3.6 through 3.11. Depending on the model, RAM requirements range from 4 to 16 GB.

Steps to Install:

Create a Virtual Environment:

For Linux:

python -m venv env
source ./env/bin/activate

Install the Package:
```
pip install deeppavlov
```

QuickStart Guide

DeepPavlov offers a multitude of pre-trained NLP models listed in its documentation. Each model is associated with a specific configuration file, and users can interact with these models via a Command Line Interface (CLI) or within a Python script.

Command Line Interface (CLI)

Start Interactions:

python -m deeppavlov interact <config_path> [-d] [-i]

Train Models:

python -m deeppavlov train <config_path> [-d] [-i]

Additional Actions:

python -m deeppavlov <action> <config_path> [-d] [-i]

Python Interface

Interact with Models:

from deeppavlov import build_model

model = build_model(<config_path>, install=True, download=True)
model(['input_text1', 'input_text2'])

Train Models:

from deeppavlov import train_model

model = train_model(<config_path>, install=True, download=True)

Evaluate Models:

from deeppavlov import evaluate_model

model = evaluate_model(<config_path>, install=True, download=True)

Licensing

DeepPavlov is distributed under the Apache 2.0 license, ensuring that it remains freely accessible and modifiable by the community.