PyText
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Getting Started
Installation
Train your first model
Execute your first model
Visualize Model Training with TensorBoard
Use PyText models in your app
Serve Models in Production
Config Files Explained
Config Commands
Training More Advanced Models
Train Intent-Slot model on ATIS Dataset
Hierarchical intent and slot filling
Multitask training with disjoint datasets
Data Parallel Distributed Training
XLM-RoBERTa
Semantic parsing with sequence-to-sequence models
Extending PyText
Architecture Overview
Custom Data Format
Custom Tensorizer
Using External Dense Features
Creating A New Model
Hacking PyText
References
pytext
config
data
batch_sampler
bert_tensorizer
data
data_handler
dense_retrieval_tensorizer
disjoint_multitask_data
disjoint_multitask_data_handler
dynamic_pooling_batcher
featurizer
packed_lm_data
PackedLMData.Config
roberta_tensorizer
sources
squad_for_bert_tensorizer
squad_tensorizer
tensorizers
tokenizers
exporters
loss
metric_reporters
models
optimizer
task
torchscript
trainers
pytext package
PyText
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packed_lm_data
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PackedLMData.Config
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