#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
from typing import Optional
from pytext.data.tensorizers import SeqTokenTensorizer
from pytext.models.embeddings import EmbeddingList, WordEmbedding
from pytext.models.joint_model import IntentSlotModel
from pytext.models.module import create_module
from pytext.models.representations.contextual_intent_slot_rep import (
ContextualIntentSlotRepresentation,
)
[docs]class ContextualIntentSlotModel(IntentSlotModel):
"""
Joint Model for Intent classification and slot tagging with inputs of contextual
information (sequence of utterances) and dictionary feature of the last utterance.
Training data should include:
doc_label (string): intent classification label of either the sequence of \
utterances or just the last sentence
word_label (string): slot tagging label of the last utterance in the format\
of start_idx:end_idx:slot_label, multiple slots are separated by a comma
text (list of string): sequence of utterances for training
dict_feat (dict): a dict of features that contains the feature of each word\
in the last utterance
Following is an example of raw columns from training data:
========== =======================================================================
doc_label reply-where
word_label 10:20:restaurant_name
text ["dinner at 6?", "wanna try Tomi Sushi?"]
dict_feat {"tokenFeatList": [{"tokenIdx": 2, "features": {"poi:eatery": 0.66}},
{"tokenIdx": 3, "features": {"poi:eatery": 0.66}}]}
========== =======================================================================
"""
[docs] class Config(IntentSlotModel.Config):
class ModelInput(IntentSlotModel.Config.ModelInput):
seq_tokens: Optional[
SeqTokenTensorizer.Config
] = SeqTokenTensorizer.Config()
inputs: ModelInput = ModelInput()
seq_embedding: Optional[WordEmbedding.Config] = WordEmbedding.Config()
representation: ContextualIntentSlotRepresentation.Config = (
ContextualIntentSlotRepresentation.Config()
)
[docs] @classmethod
def create_embedding(cls, config, tensorizers):
word_emb = create_module(
config.word_embedding,
tensorizer=tensorizers["tokens"],
init_from_saved_state=config.init_from_saved_state,
)
seq_emb_tensorizer = tensorizers["seq_tokens"]
seq_emb = create_module(config.seq_embedding, tensorizer=seq_emb_tensorizer)
return EmbeddingList(
[EmbeddingList([word_emb], concat=True), seq_emb], concat=False
)
[docs] def vocab_to_export(self, tensorizers):
return {
"tokens_vals": list(tensorizers["tokens"].vocab),
"seq_tokens_vals": list(tensorizers["seq_tokens"].vocab),
}
[docs] def get_export_input_names(self, tensorizers):
return ["tokens_vals", "seq_tokens_vals", "tokens_lens", "seq_tokens_lens"]
[docs] def arrange_model_inputs(self, tensor_dict):
tokens, seq_lens, _ = tensor_dict["tokens"]
arranged_inputs = [tokens]
seq_emb_inputs, _, seq_word_lens = tensor_dict.get("seq_tokens")
arranged_inputs.append(seq_emb_inputs)
arranged_inputs.append(seq_lens)
arranged_inputs.append(seq_word_lens)
return tuple(arranged_inputs)