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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
Paraphrase Detection
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. Use the model for prediction
3.1 Predict using Python
3.2 Predict using CLI
4. Train the model on your data
4.1 Train your model from Python
Provide your data path
SQuAD dataset info
Train the model using new config
4.2 Train your model from CLI
5. Evaluate
5.1 Evaluate from Python
5.1 Evaluate from CLI
6. Models list
Classification
Table of contents
1. Introduction to the task
2. Get started with the model
3. Use the model for prediction
3.1 Predict using Python
3.2 Predict using CLI
4. Evaluation
4.1 Evaluate from Python
4.2 Evaluate from CLI
5. Train the model on your data
5.1 Train your model from Python
Provide your data path
Train dataset format
Train the model using new config
5.2 Train your model from CLI
6. Models list
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
4.2 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. Use the model for prediction
3.1 Predict using Python
3.2 Predict using CLI
4. Evaluate
4.1 Evaluate from Python
4.1 Evaluate from CLI
5. Train the model on your data
5.1 Train your model from Python
Provide your data path
Train dataset format
Train the model using new config
5.2 Train your model from CLI
6. Models list
7. NER-tags list
Entity Extraction
Use the model
Use the model
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
Neural Ranking
Training and inference models on predifined datasets
BERT Ranking
Paraphrase identification
Paraphraser.ru dataset
Paraphrase identification
Spelling Correction
Quick start
levenshtein_corrector
Component config parameters:
brillmoore
Component config parameters:
Training configuration
Language model
Comparison
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
Overview
Built-In Models
How Do I: Using KBQA In CLI & Python
How Do I: Train KBQA Model
How Do I: Train Query Prediction Model
How Do I: Train Entity Detection Model
How Do I: Train Relation and Path Ranking Models
How Do I: Adding Templates For New SPARQL Queries
Advanced: Using Wiki Parser As Standalone Service For KBQA
Relation Extraction
English RE model
Russian RE model
RE Model Architecture
SuperGLUE Submission
Task definition
Train your model
Create your submission files
Scores
Open-Domain Question Answering
Task definition
Quick Start
Languages
Models
Running ODQA
Training
Interacting
Configuration
Comparison
References
Integrations
REST API
API routes
/model
/probe
/api
/docs
/metrics
Advanced configuration
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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DeepPavlov Core
Core
deeppavlov.core.commands
deeppavlov.core.common
deeppavlov.core.data
deeppavlov.core.models
deeppavlov.core.trainers
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