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1.7.0
  • Installation
    • Install with pip
    • Install from source
    • Editable install
    • Docker Images
  • QuickStart
    • Command line interface (CLI)
    • Python
    • Using GPU
    • Pretrained models
    • Docker images
    • Out-of-the-box pretrained models
      • Text Question Answering
      • Open-Domain Question Answering
      • Knowledge Base Question Answering
      • Classification (insult and paraphrase detection, sentiment analysis, topic classification)
      • Name Entity Recognition
      • Entity Extraction
      • Spelling Correction
  • General concepts
    • Key Concepts
  • Configuration file
    • Nested configuration files
    • Variables
    • Training
      • Train config
      • Train Parameters
        • Metrics
      • DatasetReader
      • DataLearningIterator and DataFittingIterator
    • Inference
    • Model Configuration
      • Preprocessors
      • Tokenizers
      • Embedders
      • Vectorizers
  • Python pipelines
  • Models overview
    • Models
      • NER model [docs]
      • Classification model [docs]
      • Automatic spelling correction model [docs]
      • Ranking model [docs]
      • TF-IDF Ranker model [docs]
      • Question Answering model [docs]
      • ODQA [docs]
    • AutoML
      • Hyperparameters optimization [docs]
    • Embeddings
      • Pre-trained embeddings [docs]
    • Examples of some models

Features

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

Models

  • Multitask BERT
    • Train config
  • Context Question Answering
    • Table of contents
    • 1. Introduction to the task
    • 2. Get started with the model
    • 3. Models list
    • 4. Use the model for prediction
      • 4.1 Predict using Python
      • 4.2 Predict using CLI
    • 5. Train the model on your data
      • 5.1 Train your model from Python
        • Provide your data path
        • SQuAD dataset info
        • Train the model using new config
      • 5.2 Train your model from CLI
    • 6. Evaluate
      • 6.1 Evaluate from Python
      • 6.2 Evaluate from CLI
  • Classification
    • Table of contents
    • 1. Introduction to the task
    • 2. Get started with the model
    • 3. Models list
    • 4. Use the model for prediction
      • 4.1 Predict using Python
      • 4.2 Predict using CLI
    • 5. Evaluation
      • 5.1 Evaluate from Python
      • 5.2 Evaluate from CLI
    • 6. Train the model on your data
      • 6.1 Train your model from Python
        • Provide your data path
        • Train dataset format
        • Train the model using new config
      • 6.2 Train your model from CLI
    • 7. Simple few-shot classifiers
      • 7.1 Few-shot setting
      • 7.2 Multiple languages support
      • 7.3 Dataset and Scores
  • Few-shot Classification
    • Table of contents
    • 1. Introduction to the task
    • 2. Get started with the model
    • 3. Models list
    • 4. Use the model for prediction
      • 4.1 Dataset format
      • 4.2 Predict using Python
      • 4.3 Predict using CLI
    • 5. Customize the model
  • Named Entity Recognition
    • Table of contents
    • 1. Introduction to the task
    • 2. Get started with the model
    • 3. Models list
    • 4. Use the model for prediction
      • 4.1 Predict using Python
      • 4.2 Predict using CLI
    • 5. Evaluate
      • 5.1 Evaluate from Python
      • 5.2 Evaluate from CLI
    • 6. Customize the model
      • 6.1 Train your model from Python
        • Provide your data path
        • Train dataset format
        • Train the model using new config
      • 6.2 Train your model from CLI
    • 7. NER-tags list
  • Entity Extraction
    • Table of contents
    • 1. Introduction to the task
    • 2. Get started with the model
    • 3. Models list
    • 4. Use the model for prediction
      • 4.1 Predict using Python
        • Entity Detection
        • Entity Linking
        • Entity Extraction
      • 4.2 Predict using CLI
    • 5. Customize the model
      • 5.1 Description of config parameters
      • 5.2 Training entity detection model
      • 5.3 Using custom knowledge base
  • BERT-based models
    • BERT as Embedder
    • BERT for Classification
    • BERT for Named Entity Recognition (Sequence Tagging)
    • BERT for Context Question Answering (SQuAD)
    • Using custom BERT in DeepPavlov
  • Morphological Tagging
    • Table of contents
    • 1. Introduction to the task
    • 2. Get started with the model
    • 3. Models list
    • 4. Use the model for prediction
      • 4.1 Predict using Python
      • 4.2 Predict using CLI
    • 5. Customize the model
  • Neural Ranking
    • Table of contents
    • 1. Introduction to the task
    • 2. Get started with the model
    • 3. Models list
    • 4. Use the model for prediction
      • 4.1 Predict using Python
        • English
        • Russian
      • 4.2 Predict using CLI
        • English
        • Russian
    • 5. Customize the model
      • English
      • Russian
  • Spelling Correction
    • Table of contents
    • 1. Introduction to the task
    • 2. Get started with the model
    • 3. Models list
    • 4. Use the model for prediction
      • 4.1 Predict using Python
        • 4.1.1 Levenshtein corrector
        • 4.1.2 Brillmoore
      • 4.2 Predict using CLI
    • 5. Customize the model
      • 5.1 Training configuration
      • 5.2 Language model
    • 6. Comparison
  • Syntactic Parsing
    • Table of contents
    • 1. Introduction to the task
    • 2. Get started with the model
    • 3. Models list
    • 4. Use the model for prediction
      • 4.1 Predict using Python
        • Syntax Parser
        • Joint Syntax Parser and Morphological tagger
      • 4.2 Predict using CLI
    • 5. Customize the model
  • TF-IDF Ranking
    • Table of contents
    • 1. Introduction to the task
    • 2. Get started with the model
    • 3. Models list
    • 4. Use the model for prediction
      • 4.1 Predict using Python
        • English
        • Russian
      • 4.2 Predict using CLI
    • 5. Customize the model
      • 5.1 Fit on Wikipedia
      • 5.2 Download, parse new Wikipedia dump, build database and index
  • Popularity Ranking
    • Quick Start
    • Configuration
    • Running the Ranker
      • Interacting
    • Available Data and Pretrained Models
    • References
  • Knowledge Base Question answering
    • Table of contents
    • 1. Introduction to the task
    • 2. Get started with the model
    • 3. Models list
    • 4. Use the model for prediction
      • 4.1 Predict using Python
      • 4.2 Predict using CLI
      • 4.3 Using entity linking and Wiki parser as standalone tools for KBQA
    • 5. Customize the model
      • 5.1 Description of config parameters
      • 5.2 Train KBQA components
        • Train Query Prediction Model
        • Train Entity Detection Model
        • Train Path Ranking Model
        • Adding Templates For New SPARQL Queries
  • Relation Extraction
    • Table of contents
    • 1. Introduction to the task
    • 2. Get started with the model
    • 3. Models list
      • Some details on DocRED corpus English RE model was trained on
      • Some details on RuRED corpus Russian RE model was trained on
    • 4. Use the model for prediction
      • 4.1 Predict using Python
        • English
        • Russian
      • 4.2 Predict using CLI
    • 5. Customize the model
      • 5.1 Description of config parameters
      • 5.2 Train Relation Extraction on custom data
        • Train with docred_reader
        • Train with rured_reader
  • Train the model using Python:
    • 6. Relations list
      • 6.1 Relations used in English model
      • 6.2 Relations used in Russian model
  • SuperGLUE Submission
    • Task definition
    • Train your model
    • Create your submission files
    • Scores
  • Open-Domain Question Answering
    • Table of contents
    • 1. Introduction to the task
    • 2. Get started with the model
    • 3. Models list
    • 4. Use the model for prediction
      • 4.1 Predict using Python
        • English
        • Russian
      • 4.2 Predict using CLI
    • 5. Customize the model
      • 5.1 Description of config parameters
      • 5.2 Building the index and training the reader model

Integrations

  • REST API
    • API routes
      • /model
      • /probe
      • /api
      • /docs
      • /metrics
    • Advanced configuration
  • REST API Usage Example
  • Socket API
    • Advanced configuration
    • Socket client example (Python)
  • 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

Internships

  • Internships

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.classifiers
    • deeppavlov.models.doc_retrieval
    • deeppavlov.models.embedders
    • deeppavlov.models.entity_extraction
    • deeppavlov.models.kbqa
    • deeppavlov.models.preprocessors
    • deeppavlov.models.relation_extraction
    • deeppavlov.models.sklearn
    • deeppavlov.models.spelling_correction
    • deeppavlov.models.tokenizers
    • deeppavlov.models.torch_bert
    • deeppavlov.models.vectorizers
  • vocabs
DeepPavlov
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  • models
  • View page source

modelsΒΆ

Concrete Model classes.

Models

  • deeppavlov.models.api_requester
  • deeppavlov.models.classifiers
  • deeppavlov.models.doc_retrieval
  • deeppavlov.models.embedders
  • deeppavlov.models.entity_extraction
  • deeppavlov.models.kbqa
  • deeppavlov.models.preprocessors
  • deeppavlov.models.relation_extraction
  • deeppavlov.models.sklearn
  • deeppavlov.models.spelling_correction
  • deeppavlov.models.tokenizers
  • deeppavlov.models.torch_bert
  • deeppavlov.models.vectorizers
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