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Quick Start Guide
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NLP with Forte
Chapter 1. Handling Data
Building and Understanding Ontology
A first glimpse of ontology generation
Before we start
A simple ontology config
Breakdown of the simple ontology
Major ontology types, Annotations, Links, Groups and Generics
Importing another ontology
Package Naming Convention
Generating Python classes from ontology.
Ontology Generation Steps
Cleaning the generated ontology
Handling Structured Data in DataPack
Related Readings
Retrieve data
Annotation
AudioAnnotation
Build Coverage Index
Loading Data As Needed
Chapter 2. Building NLP Pipeline
Pipeline
Basics
An working example
Life Cycle
Pseudocode with PipelineComponent
Pipeline Component
Basics
Related Readings
Reader
Related Readings
Functions
Examples
Processor
Related Readings
Functions
Selector
Related Readings
Machine Translation Inference Pipeline
Packages
Background
Inference Workflow
Pipeline
Reader
Processor
Examples
Zero-Shot Text Classification
Introduction
Dataset
Data Visualization
Prediction
Evaluation
Aggregate
Accuracy
Experiment
HomeWork
Chapter 3. Information Extraction
Named Entity Recognition
Relation Extraction
Medical Notes Analysis System
Chapter 4. Information Retrieval
Indexing
Ranking
Chapter 5. Machine Translation
Machine Translation System
Building a Machine Translation System with Forte
Overview
Start with the Reader
Overview
DataPack
Add a pre-built Forte processor to the pipeline
Overview
Ontology
Create a Machine Translation Processor
Overview
A better way to store source and target text: MultiPack
Overview
New Requirement: Handle HTML data
Overview
How to preserve HTML tags/structures
Selector
Replace our MT model with online translation API
Overview
Save the whole pipeline with save()
Overview
Chapter 6. Text Generation
Text Generation
Chapter 7. Question Answering
Question Answering
Chapter 8. Chatbot
Chatbot
Eliza Example
Introduction
Quick Start
Install Dependencies
Start an Eliza pipeline service
Visualize the chatbot in
stave
Code Explained
Overview
Start a Pipeline Service
Call a Pipeline Service
Create Your Own Chatbot Service
Chapter 9. Tasks on Other modalities
Audio Processing
Basics
Audio DataPack
Audio Reader
Automatic Speech Recognition
Speaker Segmentation
Example script
Automatic Speech Recognition
Wav2Vec2
Speaker Segmentation
Speech To Text
Output
Image Processing
Optical character recognition
Introduction
Installation
Pipeline Components for OCR
OCR Reader
OCR Character Processor
OCR Token Processor
Conclusion
Discussion
APPENDICES
Appendices
Core Design Principles
Glossary
Examples
API
Common
Exceptions
Configuration
Configurable
Resources
Data
Ontology
base
core
Entry
top
Packs
BasePack
DataPack
MultiPack
BaseMeta
Meta
DataIndex
MultiPack
MultiPackMeta
MultiPack
MultiPackLink
MultiPackGroup
Readers
BaseReader
PackReader
MultiPackReader
CoNLL03Reader
ConllUDReader
BaseDeserializeReader
RawDataDeserializeReader
RecursiveDirectoryDeserializeReader
HTMLReader
MSMarcoPassageReader
MultiPackSentenceReader
MultiPackTerminalReader
OntonotesReader
PlainTextReader
ProdigyReader
RACEMultiChoiceQAReader
StringReader
SemEvalTask8Reader
OpenIEReader
SquadReader
ClassificationDatasetReader
Selector
Selector
DummySelector
SinglePackSelector
NameMatchSelector
RegexNameMatchSelector
FirstPackSelector
AllPackSelector
Index
BaseIndex
Store
BaseStore
Data Store
DataStore
DataPack Dataset
DataPackIterator
DataPackDataset
RawExample
FeatureCollection
Batchers
ProcessingBatcher
FixedSizeDataPackBatcherWithExtractor
FixedSizeRequestDataPackBatcher
FixedSizeMultiPackProcessingBatcher
FixedSizeDataPackBatcher
Caster
Caster
MultiPackBoxer
MultiPackUnboxer
Container
EntryContainer
Types
ReplaceOperationsType
DataRequest
MatrixLike
Data Utilities
maybe_download
batch_instances
merge_batches
slice_batch
dataset_path_iterator
Entry Utilities
create_utterance
get_last_utterance
Pipeline
Process Pipeline
Train Pipeline
Pipeline Component
Process
ProcessJobStatus
ProcessManager
Processors
Base Processors
BaseProcessor
BasePackProcessor
BaseBatchProcessor
PackingBatchProcessor
MultiPackBatchProcessor
RequestPackingProcessor
FixedSizeBatchProcessor
Predictor
Pack Processors
PackProcessor
MultiPackProcessor
Task Processors
ElizaProcessor
SubwordTokenizer
CoNLLNERPredictor
SRLPredictor
VocabularyProcessor
Alphabet
PeriodSentenceSplitter
WhiteSpaceTokenizer
RemoteProcessor
LowerCaserProcessor
DeleteOverlapEntry
AttributeMasker
AnnotationRemover
Models
Named Entity Recognizer
Semantic Role Labeling
Training System
Train Preprocessor
Converter
Feature
Extractor
BaseExtractor
AttributeExtractor
LinkExtractor
SubwordExtractor
CharExtractor
BioSeqTaggingExtractor
Predictor
Feature
Evaluation
Base Evaluator
Task Evaluators
Data Augmentation
Data Augmentation Processors
BaseDataAugmentProcessor
DataAugProcessor
ReplacementDataAugmentProcessor
BaseElasticSearchDataSelector
RandomDataSelector
QueryDataSelector
UDAIterator
Data Augmentation Ops
TextReplacementOp
SingleAnnotationAugmentOp
DistributionReplacementOp
Sampler
UniformSampler
UnigramSampler
MachineTranslator
MarianMachineTranslator
BackTranslationOp
DictionaryReplacementOp
Dictionary
WordnetDictionary
TypoReplacementOp
CharacterFlipOp
WordSplittingOp
BaseDataAugmentationOp
EmbeddingSimilarityReplacementOp
UniformTypoGenerator
RandomSwapDataAugmentOp
RandomInsertionDataAugmentOp
RandomDeletionDataAugmentOp
Data Augmentation Models
Reinforcement Learning
Vocabulary
Vocabulary
VocabFilter
FrequencyVocabFilter
Forte
Chapter 5. Machine Translation
Machine Translation System
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Machine Translation System
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