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 3.6,3.7,3.8,3.9and3.10. 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]
-ddownloads required data - pretrained model files and embeddings (optional).-iinstalls 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 beinstallto install model requirements (same as-i),downloadto download model's data (same as-d),trainto train the model on the data specified in the config file,evaluateto calculate metrics on the same dataset,interactto interact via CLI,riseapito run a REST API server (see doc),predictto 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-ddownloads required data-iinstalls 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=Trueinstalls model requirements (optional),download=Truedownloads 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"), ordeeppavlov.configsattribute (e.g.deeppavlov.configs.ner.ner_ontonotes_bert_multwithout 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.
1 maintainer
Release files for deeppavlov 1.7.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| deeppavlov-1.7.0.tar.gz | 288.5 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| deeppavlov-1.7.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size:781.1 kB
Release files / deeppavlov-1.7.0.tar.gz
| Download URL | deeppavlov-1.7.0.tar.gz |
|---|---|
| Size | 288.5 kB |
| Tags | Source |
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Release files / deeppavlov-1.7.0-py3-none-any.whl
| Download URL | deeppavlov-1.7.0-py3-none-any.whl |
|---|---|
| Size | 492.7 kB |
| Tags | Python 3 |
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