QuickStart¶

First, follow instructions on Installation page to install deeppavlov package for Python 3.6-3.11.

DeepPavlov contains a bunch of great pre-trained NLP models. Each model is determined by its config file. List of models is available on the doc page or in the deeppavlov.configs:

from deeppavlov import configs

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

via Command line interface (CLI) and
via Python.

Before making choice of an interface, install model’s package requirements (CLI):

python -m deeppavlov install <config_path>
where <config_path> is model name without .json extension (e.g. insults_kaggle_bert) or path to the chosen model’s config file (e.g. deeppavlov/configs/classifiers/insults_kaggle_bert.json)

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 section 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 docs),

risesocket to run a socket API server (see docs),

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 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)
download=True downloads pretrained model, therefore the pretrained model will be, first, loaded and then trained (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 the train section 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)

Using GPU¶

To run or train PyTorch-based DeepPavlov models on GPU you should have CUDA installed on your host machine, and install model’s package requirements. CUDA version should be compatible with DeepPavlov required PyTorch version. GPU with Pascal or newer architecture and 4+ GB VRAM is recommended.

Warning

If you use latest NVIDIA architecture, PyTorch installed from PyPI using DeepPavlov could not support your device CUDA capability. You will receive incompatible device warning after model initialization. You can install compatible package from download.pytorch.org. For example:

pip3 install torch==1.8.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html

If you want to run the code on GPU, just make the device visible for the script. If you want to use a particular device, you may set it in command line:

export CUDA_VISIBLE_DEVICES=3; python -m deeppavlov train <config_path>

or in Python script:

import os

os.environ["CUDA_VISIBLE_DEVICES"]="3"

In case you want to keep GPU visible but disable GPU acceleration for specific component, use device paramenter (available for TorchModel child classes): "device": "cpu".

Pretrained models¶

DeepPavlov provides a wide range of pretrained models. See features overview for more info. Please note that most of our models are trained on specific datasets for specific tasks and may require further training on your data. You can find a list of our out-of-the-box models below.

Docker images¶

You can run DeepPavlov models in riseapi mode or start Jupyter server via Docker without installing DeepPavlov. Both your CPU and GPU (we support NVIDIA graphic processors) can be utilised, please refer our Docker images run instructions.

Out-of-the-box pretrained models¶

While the best way to solve most of the NLP tasks lies through collecting datasets and training models according to the domain and an actual task itself, DeepPavlov offers several pretrained models, which can be strong baselines for a wide range of tasks.

You can run these models via Docker or in riseapi/risesocket mode to use in solutions. See riseapi and risesocket modes documentation for API details.

Text Question Answering¶

Text Question Answering component answers a question based on a given context (e.g, a paragraph of text), where the answer to the question is a segment of the context.

from deeppavlov import build_model

model = build_model('squad_bert', download=True, install=True)
contexts = ['DeepPavlov is a library for NLP and dialog systems.', 'All work and no play makes Jack a dull boy']
questions = ['What is DeepPavlov?', 'What makes Jack a dull boy?']
answer, answers_start_idx, score = model(contexts, questions)
print(answer)

['a library for NLP and dialog systems', 'All work and no play']

To get list of available models for Text Question Answering see documentation.

Open-Domain Question Answering¶

Open Domain Question Answering (ODQA) answers any question based on the document collection covering a wide range of topics. The ODQA task combines two challenges of document retrieval (finding the relevant articles) with that of machine comprehension of text (identifying the answer span from those articles). This component can be used to answer questions based on the company knowledge base.

from deeppavlov import build_model

model = build_model('en_odqa_infer_wiki', download=True, install=True)
questions = ["What is the name of Darth Vader's son?", 'Who was the first president of France?']
answer, answer_score, answer_place = model(questions)
print(answer)

['Luke Skywalker', 'Louis-Napoleon Bonaparte']

To get list of available models for Open-Domain Question Answering see documentation.

Knowledge Base Question Answering¶

Knowledge Base Question Answering (KBQA) answers any question based on Knowledge Base (Knowledge Graph) - a comprehensive repository of information about a given domain or a number of domains that reflects the ways we model knowledge about a given subject or subjects, in terms of concepts, entities, properties, and relationships. KBQA models validate questions against a preconfigured list of question templates, disambiguate entities using Entity Linking, and answer questions asked in natural language.

from deeppavlov import build_model

model = build_model('kbqa_cq_en', download=True, install=True)
questions = ['What is the currency of Sweden?', 'When did the Korean War end?']
answers, answer_ids, query = model(questions)
print(answers)

['Swedish krona', '27 July 1953']

To get list of available models for Knowledge Base Question Answering see documentation.

Classification (insult and paraphrase detection, sentiment analysis, topic classification)¶

Insult detection predicts whether a text (e.g, post or speech in some public discussion) is considered insulting to one of the persons it is related to.

Sentiment analysis is a task of classifying the polarity of the the given sequence.

The models trained for the paraphrase detection task identify whether two sentences expressed with different words convey the same meaning.

Topic classification refers to the task of classifying an utterance by the topic which belongs to the conversational domain.

from deeppavlov import build_model

model = build_model('insults_kaggle_bert', download=True, install=True)
phrases = ['You are kind of stupid', 'You are a wonderful person!']
labels = model(phrases)
print(labels)

['Insult', 'Not Insult']

To get list of available models for Classification see documentation.

Name Entity Recognition¶

Named Entity Recognition (NER) classifies tokens in text into predefined categories (tags), such as person names, quantity expressions, percentage expressions, names of locations, organizations, as well as expression of time, currency and others.

from deeppavlov import build_model

model = build_model('ner_ontonotes_bert', download=True, install=True)
phrases = ['Bob Ross lived in Florida', 'Elon Musk founded Tesla']
tokens, tags = model(phrases)
print(tokens, tags, sep='\n')

[['Bob', 'Ross', 'lived', 'in', 'Florida'], ['Elon', 'Musk', 'founded', 'Tesla']]
[['B-PERSON', 'I-PERSON', 'O', 'O', 'B-GPE'], ['B-PERSON', 'I-PERSON', 'O', 'B-ORG']]

To get list of available models for Name Entity Recognition see documentation.

Entity Extraction¶

Entity Detection is the task of identifying entity mentions in text with corresponding entity types. Entity Linking is the task of finding knowledge base entity ids for entity mentions in text. Entity Extraction configs perform subsequent Entity Detection and Entity Linking of extracted entity mentions.

from deeppavlov import build_model

model = build_model('entity_extraction_en', download=True, install=True)
phrases = ['Forrest Gump is a comedy-drama film directed by Robert Zemeckis and written by Eric Roth.']
entity_substr, tags, entity_offsets, entity_ids, entity_conf, entity_pages, entity_labels = model(phrases)
print(entity_substr, tags, entity_ids, entity_labels, sep='\n')

[['forrest gump', 'robert zemeckis', 'eric roth']]
[['WORK_OF_ART', 'PERSON', 'PERSON']]
[[['Q134773', 'Q552213', 'Q12016774'], ['Q187364', 'Q36951156'], ['Q942932', 'Q89320386', 'Q89909683']]]
[[['Forrest Gump', 'Forrest Gump', 'Forrest Gump'], ['Robert Zemeckis', 'Welcome to Marwen'], ['Eric Roth', 'Eric Roth', 'Eric W Roth']]]

To get list of available models for Entity Extraction see documentation.

Spelling Correction¶

Spelling Correction models detect and correct spelling errors in texts.

from deeppavlov import build_model

model = build_model('brillmoore_wikitypos_en', download=True, install=True)
phrases_w_typos = ['I think this is the begining of a beautifull frendship.', "I'll be bak"]
correct_phrases = model(phrases_w_typos)
print(correct_phrases)

['i think this is the beginning of a beautiful friendship.', "i'll be back"]

To get list of available models for Spelling Correction see documentation.