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
exporters
loss
metric_reporters
models
bert_classification_models
bert_regression_model
decoders
disjoint_multitask_model
doc_model
embeddings
ensembles
joint_model
language_models
masked_lm
model
module
output_layers
pair_classification_model
qna
query_document_pairwise_ranking_model
r3f_models
representations
roberta
semantic_parsers
seq_models
attention
base
contextual_intent_slot
rnn_decoder
rnn_encoder
rnn_encoder_decoder
seq2seq_model
seq2seq_output_layer
seqnn
two_tower_classification_model
word_model
optimizer
task
torchscript
trainers
pytext package
PyText
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seq2seq_output_layer
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seq2seq_output_layer
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Seq2SeqOutputLayer.Config
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