QuickStart¶
First, follow instructions on Installation page
to install deeppavlov
package for Python 3.6/3.7.
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 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
)
Command line interface (CLI)¶
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 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]
<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 (see docs),
risesocket
to run a socket API server (see docs),
telegram
to run as a Telegram bot (see docs),
msbot
to run a Miscrosoft Bot Framework 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
Python¶
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"
) ordeeppavlov.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 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>, download=True)
There are also available integrations with various messengers, see Telegram Bot doc page and others in the Integrations section for more info.
Using GPU¶
To run or train TensorFlow-based 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.
To run or train PyTorch-based DeepPavlov models on GPU you should also have CUDA 9.0 or 10.0 installed on your host machine, and install model’s package requirements. 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 one wants to left the GPU device visible but use CPU, one can set directly in the configuration file in dictionary with model parameters “device”: “cpu”.
Pretrained models¶
DeepPavlov provides a wide range of pretrained models and skills. 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 via Docker without installing DP. Both your CPU and GPU (we support NVIDIA graphic processors) can be utilised, please refer our CPU and GPU 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.
Language |
DeepPavlov config |
Demo |
---|---|---|
Multi |
||
En |
||
Ru |
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.
Language |
DeepPavlov config |
Demo |
---|---|---|
Multi |
||
En |
||
Ru |
Insult Detection¶
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.
Language |
DeepPavlov config |
Demo |
---|---|---|
En |
Sentiment Analysis¶
Classify text according to a prevailing emotion (positive, negative, etc.) in it.
Language |
DeepPavlov config |
Demo |
---|---|---|
Ru |
Paraphrase Detection¶
Detect if two given texts have the same meaning.
Language |
DeepPavlov config |
Demo |
---|---|---|
En |
None |
|
Ru |
None |