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0.13.0
  • Installation
    • Docker Images
  • QuickStart
    • Command line interface (CLI)
    • Python
    • Using GPU
    • Pretrained models
    • Docker images
    • Out-of-the-box pretrained models
      • Text Question Answering
      • Name Entity Recognition
      • Insult Detection
      • Sentiment Analysis
      • Paraphrase Detection
  • General concepts
    • Key Concepts
  • Configuration file
    • Variables
    • Training
      • Train config
      • Train Parameters
        • Metrics
      • DatasetReader
      • DataLearningIterator and DataFittingIterator
    • Inference
    • Model Configuration
      • Preprocessors
      • Tokenizers
      • Embedders
      • Vectorizers
  • Choosing The Framework
    • Trainer
    • Text Classification on Keras or PyTorch
    • Other NLP-tasks on TensorFlow, Keras, or PyTorch
  • Models/Skills overview
    • Models
      • NER model [docs]
      • Slot filling models [docs]
      • Classification model [docs]
      • Automatic spelling correction model [docs]
      • Ranking model [docs]
      • TF-IDF Ranker model [docs]
      • Question Answering model [docs]
      • Morphological tagging model [docs]
      • Syntactic parsing model [docs]
      • Frequently Asked Questions (FAQ) model [docs]
    • Skills
      • Goal-oriented bot [docs]
      • Seq2seq goal-oriented bot [docs]
      • ODQA [docs]
    • AutoML
      • Hyperparameters optimization [docs]
    • Embeddings
      • Pre-trained embeddings [docs]
    • Examples of some models

Features

  • Pre-trained embeddings
    • BERT
      • License
      • Downloads
    • ELMo
      • License
      • Downloads
    • fastText
      • License
      • Downloads
      • Word vectors training parameters
  • AutoML
    • Cross-validation
      • Parameters
      • Special parameters in config
      • Results
    • Parameters evolution for DeepPavlov models
      • Example

Models

  • BERT-based models
    • BERT as Embedder
    • BERT for Classification
    • BERT for Named Entity Recognition (Sequence Tagging)
    • BERT for Morphological Tagging
    • BERT for Syntactic Parsing
    • BERT for Context Question Answering (SQuAD)
    • BERT for Ranking
    • BERT for Extractive Summarization
    • Using custom BERT in DeepPavlov
  • Multitask BERT
    • Train config
    • Inference config
  • Context Question Answering
    • Task definition
    • Models
      • BERT
      • R-Net
    • Configuration
    • Prerequisites
    • Model usage from Python
    • Model usage from CLI
      • Training
      • Interact mode
    • Pretrained models:
      • SQuAD
      • SQuAD with contexts without correct answers
      • SDSJ Task B
      • DRCD
  • Classification
    • Quick start
      • Command line
      • Python code
    • BERT models
    • Neural Networks on Keras
    • Neural Networks on PyTorch
    • Sklearn models
    • Pre-trained models
    • GLUE Benchmark
    • How to train on other datasets
    • Comparison
    • How to improve the performance
    • References
  • Entity Linking
    • Use the model
  • Morphological Tagger
    • Usage examples.
      • Python:
      • Advanced models (BERT and lemmatized models).
      • Command line:
      • Task description
        • Training data
        • Test data
      • Algorithm description
      • Model configuration.
        • Training configuration
  • Named Entity Recognition
    • Train and use the model
    • Multilingual BERT Zero-Shot Transfer
    • NER task
    • Training data
    • Few-shot Language-Model based
    • NER-based Model for Sentence Boundary Detection Task
    • Literature
  • Neural Ranking
    • Training and inference models on predifined datasets
      • BERT Ranking
      • Building your own response base for bert ranking
      • Ranking
      • Paraphrase identification
      • Paraphraser.ru dataset
      • Quora question pairs dataset
    • Training and inference on your own data
      • Ranking
      • Paraphrase identification
  • Slot filling
    • Configuration of the model
      • Dataset Reader
      • Dataset Iterator
      • Chainer
    • Usage of the model
    • Slotfilling without NER
  • Speech recognition and synthesis
    • Speech recognition
    • Speech synthesis
    • Audio encoding end decoding.
    • Quck Start
      • Preparation
      • Speech recognition
      • Speech synthesis
      • Speech to speech
    • Models training
  • Spelling Correction
    • Quick start
    • levenshtein_corrector
      • Component config parameters:
    • brillmoore
      • Component config parameters:
      • Training configuration
    • Language model
    • Comparison
  • Syntactic Parser
    • Model usage
    • Joint model usage
    • Model architecture
    • Model quality
  • TF-IDF Ranking
    • Quick Start
    • Configuration
    • Running the Ranker
      • Training
      • Interacting
    • Available Data and Pretrained Models
      • enwiki.db
      • enwiki_tfidf_matrix.npz
      • ruwiki.db
      • ruwiki_tfidf_matrix.npz
    • Comparison
    • References
  • Popularity Ranking
    • Quick Start
    • Configuration
    • Running the Ranker
      • Interacting
    • Available Data and Pretrained Models
    • References
  • Knowledge Base Question answering
    • Description
    • Use the model
    • Train models
    • Training of Query Prediction
    • Training of Entity Detection
    • Training of Relation and Path Ranking
    • Adding new SPARQL queries templates

Skills

  • Goal-Oriented Dialogue Bot
    • Intro
    • Quick Start: DSTC2
      • Building Goal-Oriented Bot Using DSTC-2
        • Requirements
        • Configs
        • Usage example
        • Config parameters
      • Datasets
        • DSTC2
        • Your data
    • Quick Start: RASA DSLs
      • Building Goal-Oriented Bot Using RASA DSLs
        • stories.md
        • nlu.md
        • domain.yml
    • Database (Optional)
      • Comparison
    • References
  • Open-Domain Question Answering
    • Task definition
    • Quick Start
    • Languages
    • Models
    • Running ODQA
      • Training
      • Interacting
    • Configuration
    • Comparison
    • References
  • Sequence-To-Sequence Dialogue Bot
    • Intro
    • Configs
    • Usage
    • Config parameters:
      • Comparison
    • References
  • Frequently Asked Questions Answering
    • Quick Start
      • Building
      • Inference
    • Config
      • Config Structure
      • Vectorizers
      • Classifiers for FAQ
    • Running FAQ
      • Training
      • Interacting
    • Available Data and Pretrained Models
  • AIML
    • Quick Start
      • Usage
  • Rasa
    • Quick Start
    • Dummy Rasa project
      • Usage without DeepPavlov configuration files
  • DSL
    • Quick Start
      • Usage

Integrations

  • REST API
    • API routes
      • /model
      • /probe
      • /api
      • /docs
      • /metrics
    • Advanced configuration
  • Socket API
    • Advanced configuration
    • Socket client example (Python)
  • DeepPavlov Agent RabbitMQ integration
    • Command line interface
    • Python interface
  • Telegram integration
    • Command line interface
    • Python
  • Yandex Alice integration
    • Command line interface
    • Python
  • Amazon Alexa integration
    • 1. Skill setup
    • 2. DeepPavlov skill/model REST service mounting
  • Microsoft Bot Framework integration
    • 1. Web App Bot setup
    • 2. DeepPavlov skill/model REST service mounting
  • Amazon AWS deployment
    • 1. AWS EC2 machine launch
    • 2. DeepPavlov ODQA deployment
    • 3. Accessing your ODQA API
  • DeepPavlov settings
    • 1. Settings files access and management
    • 2. Dialog logging
    • 3. Environment variables

Developer Guides

  • Contribution guide
  • Register your model

Package Reference

  • core
    • deeppavlov.core.commands
    • deeppavlov.core.common
    • deeppavlov.core.data
    • deeppavlov.core.models
    • deeppavlov.core.trainers
  • dataset_iterators
  • dataset_readers
  • metrics
  • models
    • deeppavlov.models.api_requester
    • deeppavlov.models.bert
    • deeppavlov.models.classifiers
    • deeppavlov.models.doc_retrieval
    • deeppavlov.models.elmo
    • deeppavlov.models.embedders
    • deeppavlov.models.entity_linking
    • deeppavlov.models.go_bot
    • deeppavlov.models.kbqa
    • deeppavlov.models.morpho_tagger
    • deeppavlov.models.multitask_bert
    • deeppavlov.models.nemo
    • deeppavlov.models.ner
    • deeppavlov.models.preprocessors
    • deeppavlov.models.ranking
    • deeppavlov.models.seq2seq_go_bot
    • deeppavlov.models.sklearn
    • deeppavlov.models.slotfill
    • deeppavlov.models.spelling_correction
    • deeppavlov.models.squad
    • deeppavlov.models.syntax_parser
    • deeppavlov.models.tokenizers
    • deeppavlov.models.torch_bert
    • deeppavlov.models.vectorizers
  • skills
    • deeppavlov.skills.aiml_skill
    • deeppavlov.skills.dsl_skill
    • deeppavlov.skills.rasa_skill
  • vocabs
DeepPavlov
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  • models
  • Edit on GitHub

modelsΒΆ

Concrete Model classes.

Models

  • deeppavlov.models.api_requester
  • deeppavlov.models.bert
  • deeppavlov.models.classifiers
  • deeppavlov.models.doc_retrieval
  • deeppavlov.models.elmo
  • deeppavlov.models.embedders
  • deeppavlov.models.entity_linking
  • deeppavlov.models.go_bot
  • deeppavlov.models.kbqa
  • deeppavlov.models.morpho_tagger
  • deeppavlov.models.multitask_bert
  • deeppavlov.models.nemo
  • deeppavlov.models.ner
  • deeppavlov.models.preprocessors
  • deeppavlov.models.ranking
  • deeppavlov.models.seq2seq_go_bot
  • deeppavlov.models.sklearn
  • deeppavlov.models.slotfill
  • deeppavlov.models.spelling_correction
  • deeppavlov.models.squad
  • deeppavlov.models.syntax_parser
  • deeppavlov.models.tokenizers
  • deeppavlov.models.torch_bert
  • deeppavlov.models.vectorizers
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