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 or 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:

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),

    • interactbot to run as a Telegram bot (see docs),

    • interactmsbot 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") 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 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.