DeepPavlov 1.0

DeepPavlov 1.0 is an open-source NLP framework built on PyTorch and transformers. DeepPavlov 1.0 is created for modular and configuration-driven development of state-of-the-art NLP models and supports a wide range of NLP model applications. DeepPavlov 1.0 is designed for practitioners with limited knowledge of NLP/ML.

Quick Links

name	Description
⭐️ Demo	Check out our NLP models in the online demo
📚 Documentation	How to use DeepPavlov 1.0 and its features
🚀 Model List	Find the NLP model you need in the list of available models
🪐 Contribution Guide	Please read the contribution guidelines before making a contribution
🎛 Issues	If you have an issue with DeepPavlov, please let us know
⏩ Forum	Please let us know if you have a problem with DeepPavlov
📦 Blogs	Read about our current development
🦙 Extended colab tutorials	Check out the code tutorials for our models
🌌 Docker Hub	Check out the Docker images for rapid deployment
👩‍🏫 Feedback	Please leave us your feedback to make DeepPavlov better

Installation

DeepPavlov supports Linux, Windows 10+ (through WSL/WSL2), MacOS (Big Sur+) platforms, Python 3.6, 3.7, 3.8, 3.9 and 3.10. Depending on the model used, you may need from 4 to 16 GB RAM.
Create and activate a virtual environment:
- Linux
```
python -m venv env
source ./env/bin/activate
```
Install the package inside the environment:
```
pip install deeppavlov
```

QuickStart

There is a bunch of great pre-trained NLP models in DeepPavlov. Each model is determined by its config file.

List of models is available on the doc page in the deeppavlov.configs (Python):

from deeppavlov import configs

When you're decided on the model (+ config file), there are two ways to train, evaluate and infer it:

via Command line interface (CLI) and
via Python.

GPU requirements

By default, DeepPavlov installs models requirements from PyPI. PyTorch from PyPI could not support your device CUDA capability. To run supported DeepPavlov models on GPU you should have CUDA compatible with used GPU and PyTorch version required by DeepPavlov models. See docs for details. GPU with Pascal or newer architecture and 4+ GB VRAM is recommended.

Command line interface (CLI)

To get predictions from a model interactively through CLI, run

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

-d downloads required data - pretrained model files and embeddings (optional).
-i installs model requirements (optional).

You can train it in the same simple way:

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

Dataset will be downloaded regardless of whether there was -d flag or not.

To train on your own data you need to modify dataset reader path in the train config doc. The data format is specified in the corresponding model doc page.

There are even more actions you can perform with configs:

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

<action> can be
- install to install model requirements (same as -i),
- download to download model's data (same as -d),
- train to train the model on the data specified in the config file,
- evaluate to calculate metrics on the same dataset,
- interact to interact via CLI,
- riseapi to run a REST API server (see doc),
- predict to get prediction for samples from stdin or from <file_path> if -f <file_path> is specified.
<config_path> specifies path (or name) of model's config file
-d downloads required data
-i installs model requirements

Python

To get predictions from a model interactively through Python, run

from deeppavlov import build_model

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

# get predictions for 'input_text1', 'input_text2'
model(['input_text1', 'input_text2'])

where

install=True installs model requirements (optional),
download=True downloads required data from web - pretrained model files and embeddings (optional),
<config_path> is model name (e.g. 'ner_ontonotes_bert_mult'), path to the chosen model's config file (e.g. "deeppavlov/configs/ner/ner_ontonotes_bert_mult.json"), or deeppavlov.configs attribute (e.g. deeppavlov.configs.ner.ner_ontonotes_bert_mult without quotation marks).

You can train it in the same simple way:

from deeppavlov import train_model 

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

To train on your own data you need to modify dataset reader path in the train config doc. The data format is specified in the corresponding model doc page.

You can also calculate metrics on the dataset specified in your config file:

from deeppavlov import evaluate_model 

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

DeepPavlov also allows to build a model from components for inference using Python.

License

DeepPavlov is Apache 2.0 - licensed.

Citation

@inproceedings{savkin-etal-2024-deeppavlov,
    title = "DeepPavlov 1.0: Your Gateway to Advanced NLP Models Backed by Transformers and Transfer Learning",
    author = "Savkin Maksim and Voznyuk Anastasia and Ignatov Fedor and Korzanova Anna and Karpov Dmitry and Popov Alexander and Konovalov Vasily"
    editor = "Hernandez Farias and Delia Irazu and Hope Tom and Li Manling",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.emnlp-demo.47",
    pages = "465--474",
    abstract = "We present DeepPavlov 1.0, an open-source framework for using Natural Language Processing (NLP) models by leveraging transfer learning techniques. DeepPavlov 1.0 is created for modular and configuration-driven development of state-of-the-art NLP models and supports a wide range of NLP model applications. DeepPavlov 1.0 is designed for practitioners with limited knowledge of NLP/ML. DeepPavlov is based on PyTorch and supports HuggingFace transformers. DeepPavlov is publicly released under the Apache 2.0 license and provides access to an online demo.",
}

deeppavlov/DeepPavlov