0.3.0
Hello bot!
Installation
Conceptual overview
Key Concepts
Features
Components
NER component
Slot filling components
Classification component
Goal-oriented bot
Seq2seq goal-oriented bot
Automatic spelling correction component
Ranking component
TF-IDF Ranker component
Question Answering component
Morphological tagging component
Frequently Asked Questions (FAQ) component
Skills
eCommerce bot
ODQA
AutoML
Hyperparameters optimization
Embeddings
Pre-trained embeddings for the Russian language
Examples of some components
Configuration files
Variables
Training
Train config
Train Parameters
Metrics
DatasetReader
DataLearningIterator and DataFittingIterator
Inference
Pre-trained embeddings
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
Components
Data Processors
Preprocessors
Tokenizers
Embedders
Vectorizers
BERT-based models
BERT for Classification
BERT for Named Entity Recognition (Sequence Tagging)
BERT for Context Question Answering (SQuAD)
BERT for Ranking
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
Classification
Quick start
Command line
Python code
BERT models
Neural Networks on Keras
Sklearn models
Pre-trained models
How to train on other datasets
Comparison
How to improve the performance
References
Morphological Tagger
Usage examples.
Python:
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
Literature
Neural Ranking
Training and inference models on predifined datasets
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
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
Description
Use the model
Skills
Goal-Oriented Dialogue Bot
Intro
Usage
Requirements
Configs:
Usage example
Config parameters
Datasets
DSTC2
Your data
Dialogs
Templates
Database (optional)
Comparison
References
Open-Domain Question Answering
Task definition
Quick Start
Languages
Models
Running ODQA
Training
Interacting
Configuration
Comparison
References
Pattern Matching
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
eCommerce Bot
Quick Start
Building
Inference
Usage
Config file
Usage example
Configuration settings
eCommerce bot with BLEU-based ranker
eCommerce bot with TfIdf-based ranker
References
AIML
Quick Start
Usage
Package Reference
agents
deeppavlov.agents.default_agent
deeppavlov.agents.filters
deeppavlov.agents.hello_bot_agent
deeppavlov.agents.processors
deeppavlov.agents.rich_content
core
deeppavlov.core.agent
deeppavlov.core.commands
deeppavlov.core.common
deeppavlov.core.data
deeppavlov.core.models
deeppavlov.core.skill
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.go_bot
deeppavlov.models.kbqa
deeppavlov.models.morpho_tagger
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.tokenizers
deeppavlov.models.vectorizers
skills
deeppavlov.skills.aiml_skill
deeppavlov.skills.default_skill
deeppavlov.skills.ecommerce_skill
deeppavlov.skills.pattern_matching_skill
vocabs
Developer Guides
Amazon Alexa integration
1. Skill setup
2. DeepPavlov skill/component REST service mounting
Amazon AWS deployment
1. AWS EC2 machine launch
2. DeepPavlov ODQA deployment
3. Accessing your ODQA API
Extending the library
Microsoft Bot Framework integration
1. Web App Bot setup
2. DeepPavlov skill/component REST service mounting
REST API
DeepPavlov settings
1. Settings files access and management
2. Dialog logging
3. Environment variables
Yandex Alice integration
Pipelines
Agents
DeepPavlov
Docs
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models
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deeppavlov.models.bert
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deeppavlov.models.bert
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v: 0.3.0
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