PyText
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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
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pair_classification_model
qna
query_document_pairwise_ranking_model
r3f_models
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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
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bilstm_doc_slot_attention
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bilstm_doc_slot_attention
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BiLSTMDocSlotAttention.Config
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