docs.deeppavlov.ai

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• http://docs.deeppavlov.ai/#
• Next
• Install with pip
• Install from source
• Editable install
• Docker Images
• QuickStart
• Command line interface (CLI)
• Python
• Using GPU
• Pretrained models
• Docker images
• http://docs.deeppavlov.ai/intro/quick_start.html#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
• http://docs.deeppavlov.ai/intro/configuration.html#training
• Train config
• http://docs.deeppavlov.ai/intro/configuration.html#train-parameters
• Metrics
• DatasetReader
• DataLearningIterator and DataFittingIterator
• Inference
• http://docs.deeppavlov.ai/intro/configuration.html#model-configuration
• Preprocessors
• Tokenizers
• Embedders
• Vectorizers
• Python pipelines
• Models overview
• http://docs.deeppavlov.ai/features/overview.html#models
• NER model
• Classification model
• Automatic spelling correction model
• Ranking model
• TF-IDF Ranker model
• Question Answering model
• ODQA
• http://docs.deeppavlov.ai/features/overview.html#automl
• Hyperparameters optimization
• http://docs.deeppavlov.ai/features/overview.html#embeddings
• Pre-trained embeddings
• Examples of some models
• Pre-trained embeddings
• BERT
• License
• Downloads
• ELMo
• Downloads
• fastText
• License
• Downloads
• Word vectors training parameters
• AutoML
• Cross-validation
• Parameters
• Special parameters in config
• Results
• Multitask BERT
• Train config
• Context Question Answering
• Table of contents
• 1. Introduction to the task
• 2. Get started with the model
• 3. Models list
• http://docs.deeppavlov.ai/features/models/SQuAD.html#4.-Use-the-model-for-prediction
• 4.1 Predict using Python
• 4.2 Predict using CLI
• http://docs.deeppavlov.ai/features/models/SQuAD.html#5.-Train-the-model-on-your-data
• http://docs.deeppavlov.ai/features/models/SQuAD.html#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
• http://docs.deeppavlov.ai/features/models/SQuAD.html#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
• http://docs.deeppavlov.ai/features/models/classification.html#4.-Use-the-model-for-prediction
• 4.1 Predict using Python
• 4.2 Predict using CLI
• http://docs.deeppavlov.ai/features/models/classification.html#5.-Evaluation
• 5.1 Evaluate from Python
• 5.2 Evaluate from CLI
• http://docs.deeppavlov.ai/features/models/classification.html#6.-Train-the-model-on-your-data
• http://docs.deeppavlov.ai/features/models/classification.html#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
• http://docs.deeppavlov.ai/features/models/classification.html#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
• http://docs.deeppavlov.ai/features/models/few_shot_classification.html#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
• http://docs.deeppavlov.ai/features/models/NER.html#4.-Use-the-model-for-prediction
• 4.1 Predict using Python
• 4.2 Predict using CLI
• http://docs.deeppavlov.ai/features/models/NER.html#5.-Evaluate
• 5.1 Evaluate from Python
• 5.2 Evaluate from CLI
• http://docs.deeppavlov.ai/features/models/NER.html#6.-Customize-the-model
• http://docs.deeppavlov.ai/features/models/NER.html#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
• http://docs.deeppavlov.ai/features/models/entity_extraction.html#4.-Use-the-model-for-prediction
• http://docs.deeppavlov.ai/features/models/entity_extraction.html#4.1-Predict-using-Python
• Entity Detection
• Entity Linking
• Entity Extraction
• 4.2 Predict using CLI
• http://docs.deeppavlov.ai/features/models/entity_extraction.html#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
• http://docs.deeppavlov.ai/features/models/morpho_tagger.html#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
• http://docs.deeppavlov.ai/features/models/neural_ranking.html#4.-Use-the-model-for-prediction
• http://docs.deeppavlov.ai/features/models/neural_ranking.html#4.1-Predict-using-Python
• English
• Russian
• http://docs.deeppavlov.ai/features/models/neural_ranking.html#4.2-Predict-using-CLI
• English
• Russian
• http://docs.deeppavlov.ai/features/models/neural_ranking.html#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
• http://docs.deeppavlov.ai/features/models/spelling_correction.html#4.-Use-the-model-for-prediction
• http://docs.deeppavlov.ai/features/models/spelling_correction.html#4.1-Predict-using-Python
• 4.1.1 Levenshtein corrector
• 4.1.2 Brillmoore
• 4.2 Predict using CLI
• http://docs.deeppavlov.ai/features/models/spelling_correction.html#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
• http://docs.deeppavlov.ai/features/models/syntax_parser.html#4.-Use-the-model-for-prediction
• http://docs.deeppavlov.ai/features/models/syntax_parser.html#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
• http://docs.deeppavlov.ai/features/models/tfidf_ranking.html#4.-Use-the-model-for-prediction
• http://docs.deeppavlov.ai/features/models/tfidf_ranking.html#4.1-Predict-using-Python
• English
• Russian
• 4.2 Predict using CLI
• http://docs.deeppavlov.ai/features/models/tfidf_ranking.html#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
• http://docs.deeppavlov.ai/features/models/popularity_ranking.html#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
• http://docs.deeppavlov.ai/features/models/KBQA.html#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
• http://docs.deeppavlov.ai/features/models/KBQA.html#5.-Customize-the-model
• 5.1 Description of config parameters
• http://docs.deeppavlov.ai/features/models/KBQA.html#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
• http://docs.deeppavlov.ai/features/models/relation_extraction.html#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
• http://docs.deeppavlov.ai/features/models/relation_extraction.html#4.-Use-the-model-for-prediction
• http://docs.deeppavlov.ai/features/models/relation_extraction.html#4.1-Predict-using-Python
• English
• Russian
• 4.2 Predict using CLI
• http://docs.deeppavlov.ai/features/models/relation_extraction.html#5.-Customize-the-model
• 5.1 Description of config parameters
• http://docs.deeppavlov.ai/features/models/relation_extraction.html#5.2-Train-Relation-Extraction-on-custom-data
• Train with
• Train with
• Train the model using Python:
• http://docs.deeppavlov.ai/features/models/relation_extraction.html#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
• http://docs.deeppavlov.ai/features/models/ODQA.html#4.-Use-the-model-for-prediction
• http://docs.deeppavlov.ai/features/models/ODQA.html#4.1-Predict-using-Python
• English
• Russian
• 4.2 Predict using CLI
• http://docs.deeppavlov.ai/features/models/ODQA.html#5.-Customize-the-model
• 5.1 Description of config parameters
• 5.2 Building the index and training the reader model
• 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
• Contribution guide
• Register your model
• Internships
• 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
• View page source
• ¶
• ¶
• http://docs.deeppavlov.ai/genindex.html
• http://docs.deeppavlov.ai/py-modindex.html

• Ask a Question
• try our Demo
• https://medium.com/deeppavlov
• https://twitter.com/deeppavlov
• https://www.youtube.com/channel/UCJ-6K2HGA0hpQytlSM7FBVQ
• https://t.me/deeppavlov
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