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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
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
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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core
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core
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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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