Metadata-Version: 2.1
Name: deeppavlov
Version: 0.10.0
Summary: An open source library for building end-to-end dialog systems and training chatbots.
Home-page: https://github.com/deepmipt/DeepPavlov
Author: Neural Networks and Deep Learning lab, MIPT
Author-email: info@deeppavlov.ai
License: Apache License, Version 2.0
Download-URL: https://github.com/deepmipt/DeepPavlov/archive/0.10.0.tar.gz
Description: [![License Apache 2.0](https://img.shields.io/badge/license-Apache%202.0-blue.svg)](https://github.com/deepmipt/DeepPavlov/blob/master/LICENSE)
        ![Python 3.6, 3.7](https://img.shields.io/badge/python-3.6%20%7C%203.7-green.svg)
        [![Downloads](https://pepy.tech/badge/deeppavlov)](https://pepy.tech/project/deeppavlov)
        <img align="right" height="27%" width="27%" src="https://raw.githubusercontent.com/deepmipt/DeepPavlov/master/docs/_static/ipavlov_logo.png"/>
        
        DeepPavlov is an open-source conversational AI library built on [TensorFlow](https://www.tensorflow.org/) and [Keras](https://keras.io/).
        
        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.
        
        ## Quick Links
        
        * Demo [*demo.deeppavlov.ai*](https://demo.deeppavlov.ai/)
        * Documentation [*docs.deeppavlov.ai*](http://docs.deeppavlov.ai/)
            * Model List [*docs:features/*](http://docs.deeppavlov.ai/en/master/features/overview.html)
            * Contribution Guide [*docs:contribution_guide/*](http://docs.deeppavlov.ai/en/master/devguides/contribution_guide.html)
        * Issues [*github/issues/*](https://github.com/deepmipt/DeepPavlov/issues)
        * Forum [*forum.deeppavlov.ai*](https://forum.deeppavlov.ai/)
        * Blogs [*medium.com/deeppavlov*](https://medium.com/deeppavlov)
        * Tutorials [*examples/*](https://github.com/deepmipt/DeepPavlov/tree/master/examples) and [extended colab tutorials](https://github.com/deepmipt/dp_tutorials)
        * Docker Hub [*hub.docker.com/u/deeppavlov/*](https://hub.docker.com/u/deeppavlov/) 
            * Docker Images Documentation [*docs:docker-images/*](http://docs.deeppavlov.ai/en/master/intro/installation.html#docker-images)
        
        Please leave us [your feedback](https://forms.gle/i64fowQmiVhMMC7f9) on how we can improve the DeepPavlov framework.
        
        **Models**
        
        [Named Entity Recognition](http://docs.deeppavlov.ai/en/master/features/models/ner.html) | [Slot filling](http://docs.deeppavlov.ai/en/master/features/models/slot_filling.html)
        
        [Intent/Sentence Classification](http://docs.deeppavlov.ai/en/master/features/models/classifiers.html) |  [Question Answering over Text (SQuAD)](http://docs.deeppavlov.ai/en/master/features/models/squad.html) 
        
        [Knowledge Base Question Answering](http://docs.deeppavlov.ai/en/master/features/models/kbqa.html)
        
        [Sentence Similarity/Ranking](http://docs.deeppavlov.ai/en/master/features/models/neural_ranking.html) | [TF-IDF Ranking](http://docs.deeppavlov.ai/en/master/features/models/tfidf_ranking.html) 
        
        [Morphological tagging](http://docs.deeppavlov.ai/en/master/features/models/morphotagger.html) | [Syntactic parsing](http://docs.deeppavlov.ai/en/master/features/models/syntaxparser.html)
        
        [Automatic Spelling Correction](http://docs.deeppavlov.ai/en/master/features/models/spelling_correction.html) | [ELMo training and fine-tuning](http://docs.deeppavlov.ai/en/master/apiref/models/elmo.html)
        
        [Speech recognition and synthesis (ASR and TTS)](http://docs.deeppavlov.ai/en/master/features/models/nemo.html) based on [NVIDIA NeMo](https://nvidia.github.io/NeMo/index.html)
        
        **Skills**
        
        [Goal(Task)-oriented Bot](http://docs.deeppavlov.ai/en/master/features/skills/go_bot.html) | [Seq2seq Goal-Oriented bot](http://docs.deeppavlov.ai/en/master/features/skills/seq2seq_go_bot.html)
        
        [Open Domain Questions Answering](http://docs.deeppavlov.ai/en/master/features/skills/odqa.html) | [eCommerce Bot](http://docs.deeppavlov.ai/en/master/features/skills/ecommerce.html) 
        
        [Frequently Asked Questions Answering](http://docs.deeppavlov.ai/en/master/features/skills/faq.html) | [Pattern Matching](http://docs.deeppavlov.ai/en/master/features/skills/pattern_matching.html) 
        
        **Embeddings**
        
        [BERT embeddings for the Russian, Polish, Bulgarian, Czech, and informal English](http://docs.deeppavlov.ai/en/master/features/pretrained_vectors.html#bert)
        
        [ELMo embeddings for the Russian language](http://docs.deeppavlov.ai/en/master/features/pretrained_vectors.html#elmo)
        
        [FastText embeddings for the Russian language](http://docs.deeppavlov.ai/en/master/features/pretrained_vectors.html#fasttext)
        
        **Auto ML**
        
        [Tuning Models with Evolutionary Algorithm](http://docs.deeppavlov.ai/en/master/features/hypersearch.html)
        
        **Integrations**
        
        [REST API](http://docs.deeppavlov.ai/en/master/integrations/rest_api.html) | [Socket API](http://docs.deeppavlov.ai/en/master/integrations/socket_api.html) | [Yandex Alice](http://docs.deeppavlov.ai/en/master/integrations/yandex_alice.html)
        
        [Telegram](http://docs.deeppavlov.ai/en/master/integrations/telegram.html) | [Microsoft Bot Framework](http://docs.deeppavlov.ai/en/master/integrations/ms_bot.html)
        
        [Amazon Alexa](http://docs.deeppavlov.ai/en/master/integrations/amazon_alexa.html) | [Amazon AWS](http://docs.deeppavlov.ai/en/master/integrations/aws_ec2.html)
        
        ## Installation
        
        0. 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](https://git-scm.com/download/win)) and  `Visual Studio 2015/2017` with `C++` build tools installed!**
        
        1. Create and activate a virtual environment:
            * `Linux`
            ```
            python -m venv env
            source ./env/bin/activate
            ```
            * `Windows`
            ```
            python -m venv env
            .\env\Scripts\activate.bat
            ```
        2. 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](http://docs.deeppavlov.ai/en/master/features/overview.html) in
        the `deeppavlov.configs` (Python):
        
        ```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)](https://github.com/deepmipt/DeepPavlov/blob/master/#command-line-interface-cli) and
        * via [Python](https://github.com/deepmipt/DeepPavlov/blob/master/#python).
        
        #### GPU requirements
        
        To run supported DeepPavlov models on GPU you should have [CUDA](https://developer.nvidia.com/cuda-toolkit) 10.0
        installed on your host machine and TensorFlow with GPU support (`tensorflow-gpu`)
        installed in your python environment. Current supported TensorFlow version is 1.14.0.
        Run
        
        ```
        pip install tensorflow-gpu==1.14.0
        ```
        
        before installing model's package requirements to install supported `tensorflow-gpu` version.
        
        
        Before making choice of an interface, install model's package requirements
        (CLI):
        
        ```bash
        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
        
        ```bash
        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:
        
        ```bash
        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 config doc](http://docs.deeppavlov.ai/en/master/intro/config_description.html#train-config).
        The data format is specified in the corresponding model doc page. 
        
        There are even more actions you can perform with configs:
        
        ```bash
        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
            [doc](http://docs.deeppavlov.ai/en/master/integrations/rest_api.html)),
            * `telegram` to run as a Telegram bot (see
            [doc](http://docs.deeppavlov.ai/en/master/integrations/telegram.html)),
            * `msbot` to run a Miscrosoft Bot Framework server (see
            [doc](http://docs.deeppavlov.ai/en/master/integrations/ms_bot.html)),
            * `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
        
        ```python
        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:
        
        ```python
        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 config doc](http://docs.deeppavlov.ai/en/master/intro/config_description.html#train-config).
        The data format is specified in the corresponding model doc page. 
        
        You can also calculate metrics on the dataset specified in your config file:
        
        ```python
        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](http://docs.deeppavlov.ai/en/master/integrations/telegram.html)
        and others in the Integrations section for more info.
        
        
        ## Breaking Changes
        
        **Breaking changes in version 0.7.0**
        - in dialog logger config file [dialog_logger_config.json](https://github.com/deepmipt/DeepPavlov/blob/master/deeppavlov/utils/settings/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](https://github.com/deepmipt/DeepPavlov/blob/master/deeppavlov/deprecated)
        - [AIML Skill](http://docs.deeppavlov.ai/en/0.7.0/features/skills/aiml_skill.html),
          [RASA Skill](http://docs.deeppavlov.ai/en/0.7.0/features/skills/rasa_skill.html),
          [Yandex Alice](http://docs.deeppavlov.ai/en/0.7.0/integrations/yandex_alice.html),
          [Amazon Alexa](http://docs.deeppavlov.ai/en/0.7.0/integrations/amazon_alexa.html),
          [Microsoft Bot Framework](http://docs.deeppavlov.ai/en/0.7.0/integrations/ms_bot.html) and
          [Telegram integration](http://docs.deeppavlov.ai/en/0.7.0/integrations/telegram.html) interfaces were changed
        - `/start` and `/help` Telegram messages were moved from `models_info.json` to [server_config.json](https://github.com/deepmipt/DeepPavlov/blob/master/deeppavlov/utils/settings/server_config.json)
        - [risesocket](http://docs.deeppavlov.ai/en/0.7.0/integrations/socket_api.html) request and response format was changed
        - [riseapi](http://docs.deeppavlov.ai/en/0.7.0/integrations/rest_api.html#advanced-configuration) and
          [risesocket](http://docs.deeppavlov.ai/en/0.7.0/integrations/socket_api.html#advanced-configuration) model-specific
          properties parametrization was changed
        
        **Breaking changes in version 0.6.0**
        - [REST API](http://docs.deeppavlov.ai/en/0.6.0/integrations/rest_api.html):
          - all models default endpoints were renamed to `/model`
          - by default model arguments names are taken from `chainer.in`
            [configuration parameter](http://docs.deeppavlov.ai/en/0.6.0/intro/configuration.html) instead of pre-set names
            from a [settings file](http://docs.deeppavlov.ai/en/0.6.0/integrations/settings.html)
          - swagger api endpoint moved from `/apidocs` to `/docs`
        - when using `"max_proba": true` in
          a [`proba2labels` component](http://docs.deeppavlov.ai/en/0.6.0/apiref/models/classifiers.html) 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 evolution](https://docs.deeppavlov.ai/en/0.4.0/intro/hypersearch.html#parameters-evolution-for-deeppavlov-models)
        was 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](http://docs.deeppavlov.ai/en/master/intro/configuration.html#variables)
         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](http://docs.deeppavlov.ai/en/0.4.0/intro/config_description.html#metrics).
        
        ## License
        
        DeepPavlov is Apache 2.0 - licensed.
        
        ## The Team
        
        DeepPavlov is built and maintained by [Neural Networks and Deep Learning Lab](https://www.facebook.com/deepmipt/)
        at [MIPT](https://mipt.ru/english/).
        
        <p align="center">
        <img src="https://raw.githubusercontent.com/deepmipt/DeepPavlov/master/docs/_static/ipavlov_footer.png" width="50%" height="50%"/>
        </p>
        
Keywords: NLP,NER,SQUAD,Intents,Chatbot
Platform: UNKNOWN
Description-Content-Type: text/markdown
Provides-Extra: tests
Provides-Extra: docs
