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
attention
augmented_lstm
bilstm
bilstm_doc_attention
bilstm_doc_slot_attention
bilstm_slot_attn
biseqcnn
contextual_intent_slot_rep
deepcnn
docnn
huggingface_bert_sentence_encoder
huggingface_electra_sentence_encoder
jointcnn_rep
ordered_neuron_lstm
pass_through
pooling
pure_doc_attention
representation_base
seq_rep
slot_attention
sparse_transformer_sentence_encoder
stacked_bidirectional_rnn
transformer
transformer_sentence_encoder
transformer_sentence_encoder_base
roberta
semantic_parsers
seq_models
two_tower_classification_model
word_model
optimizer
task
torchscript
trainers
pytext package
PyText
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pure_doc_attention
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pure_doc_attention
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PureDocAttention.Config
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