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An open source library for deep learning end-to-end dialog systems and chatbots.
DeepPavlov is an open-source conversational AI library built on TensorFlow, Kerasand PyTorch.
DeepPavlov is designed for * development of production ready chat-bots and complex conversational systems, * research in the area of NLP and, particularly, of dialog systems.
Demo demo.deeppavlov.ai
Documentation docs.deeppavlov.ai
Model List docs:features/
Contribution Guide docs:contribution_guide/
Issues github/issues/
Forum forum.deeppavlov.ai
Blogs medium.com/deeppavlov
Tutorials examples/ and extended colab tutorials
Docker Hub hub.docker.com/u/deeppavlov/
Docker Images Documentation docs:docker-images/
Please leave us your feedback on how we can improve the DeepPavlov framework.
Models
Named Entity Recognition | Slot filling
Intent/Sentence Classification | Question Answering over Text (SQuAD)
Knowledge Base Question Answering
Sentence Similarity/Ranking | TF-IDF Ranking
Morphological tagging | Syntactic parsing
Automatic Spelling Correction | ELMo training and fine-tuning
Speech recognition and synthesis (ASR and TTS) based on NVIDIA NeMo
Entity Linking | Multitask BERT
Skills
Goal(Task)-oriented Bot | Seq2seq Goal-Oriented bot
Open Domain Questions Answering | eCommerce Bot
Frequently Asked Questions Answering | Pattern Matching
Embeddings
BERT embeddings for the Russian, Polish, Bulgarian, Czech, and informal English
ELMo embeddings for the Russian language
FastText embeddings for the Russian language
Auto ML
Tuning Models with Evolutionary Algorithm
Integrations
REST API | Socket API | Yandex Alice
Telegram | Microsoft Bot Framework
We support Linux and Windows platforms, Python 3.6 and Python 3.7
Python 3.5 is not supported!
installation for Windows requires Git(for example, git) and Visual Studio 2015/2017 with C++ build tools installed!
Create and activate a virtual environment:
Linuxpython -m venv env
source ./env/bin/activate
Windowspython -m venv env
.\env\Scripts\activate.bat
Install the package inside the environment:pip install deeppavlov
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 onthe 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.
To run supported DeepPavlov models on GPU you should have CUDA 10.0
installed on your host machine and TensorFlow with GPU support (tensorflow-gpu)
installed in your python environment. Current supported TensorFlow version is 1.15.2.
Run
pip install tensorflow-gpu==1.15.2
before installing model's package requirements to install supported tensorflow-gpu version.
Before making choice of an interface, install model's package requirements (CLI):
python -m deeppavlov install <config_path>
where <config_path> is path to the chosen model's config file (e.g. deeppavlov/configs/ner/slotfill_dstc2.json) or just name without .json extension (e.g. slotfill_dstc2)
To get predictions from a model interactively through CLI, run
python -m deeppavlov interact <config_path> [-d]
-d downloads required data -- pretrained model files and embeddings
(optional).
You can train it in the same simple way:
python -m deeppavlov train <config_path> [-d]
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 thetrain 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]
<action> can be
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 (seedoc),
telegram to run as a Telegram bot (seedoc),
msbot to run a Miscrosoft Bot Framework server (seedoc),
predict to get prediction for samples from stdin or from -f <file_path> is specified.
<config_path> specifies path (or name) of model's config file
-d downloads required data
To get predictions from a model interactively through Python, run
from deeppavlov import build_model model = build_model(<config_path>, download=True) # get predictions for 'input_text1', 'input_text2' model(['input_text1', 'input_text2'])
where download=True downloads required data from web -- pretrained model
files and embeddings (optional),
<config_path> is 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>, download=True)
download=True downloads pretrained model, therefore the pretrained
model will be, first, loaded and then train (optional).
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 thetrain 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>, download=True)
There are also available integrations with various messengers, seeTelegram Bot doc pageand others in the Integrations section for more info.
Breaking changes in version 0.7.0- in dialog logger config file dialog_logger_config.json agent_name parameter was renamed to logger_name,
the default value was changed
- Agent, Skill, eCommerce Bot and Pattern Matching classes were moved to deeppavlov.deprecated- AIML Skill, RASA Skill, Yandex Alice, Amazon Alexa, Microsoft Bot Framework and Telegram integration interfaces were changed
- /start and /help Telegram messages were moved from models_info.json to server_config.json- risesocket request and response format was changed
- riseapi and risesocket model-specific
properties parametrization was changed
Breaking changes in version 0.6.0- REST API:
- all models default endpoints were renamed to /model
- by default model arguments names are taken from chainer.inconfiguration parameter instead of pre-set names
from a settings file
- swagger api endpoint moved from /apidocs to /docs- when using "max_proba": true in
a proba2labels component for classification,
it will return single label for every batch element instead of a list. One can set "top_n": 1
to get batches of single item lists as before
Breaking changes in version 0.5.0- dependencies have to be reinstalled for most pipeline configurations
- models depending on tensorflow require CUDA 10.0 to run on GPU instead of CUDA 9.0- scikit-learn models have to be redownloaded or retrained
Breaking changes in version 0.4.0!- default target variable name for neural evolutionwas changed from MODELS_PATH to MODEL_PATH.
Breaking changes in version 0.3.0!- component option fit_on_batch in configuration files was removed and replaced with adaptive usage of the fit_on parameter.
Breaking changes in version 0.2.0!- utils module was moved from repository root in to deeppavlov module
- ms_bot_framework_utils,server_utils, telegram utils modules was renamed to ms_bot_framework, server and telegram correspondingly
- rename metric functions exact_match to squad_v2_em and squad_f1 to squad_v2_f1- replace dashes in configs name with underscores
Breaking changes in version 0.1.0!- As of version 0.1.0 all models, embeddings and other downloaded data for provided configurations are
by default downloaded to the .deeppavlov directory in current user's home directory.
This can be changed on per-model basis by modifying
a ROOT_PATH variable
or related fields one by one in model's configuration file.
In configuration files, for all features/models, dataset readers and iterators "name" and "class" fields are combined
into the "class_name" field.
deeppavlov.core.commands.infer.build_model_from_config() was renamed to build_model and can be imported from the deeppavlov module directly.
The way arguments are passed to metrics functions during training and evaluation was changed and documented.
DeepPavlov is Apache 2.0 - licensed.
DeepPavlov is built and maintained by Neural Networks and Deep Learning Labat MIPT.
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