from typing import List, Tuple
import pickle
import numpy as np
import sentencepiece as spm
from ..tokenizer import TreebankWordTokenize
from ..utils import check_and_download, load_keras_tokenizer
from ._spu_context_utils import create_spucontext_pos_model, process_single_word_input
# Resolving parent dependencies
from inspect import getsourcefile
import os
import sys
current_path = os.path.abspath(getsourcefile(lambda:0))
current_dir = os.path.dirname(current_path)
parent_dir = current_dir[:current_dir.rfind(os.path.sep)]
sys.path.insert(0, parent_dir)
RESOURCES_PATH = os.path.join(os.path.dirname(__file__), "resources/")
PROD_WEIGHTS_LOC = RESOURCES_PATH + "PoS_SPUContext_prod.weights"
EVAL_WEIGHTS_LOC = RESOURCES_PATH + "PoS_SPUContext_eval.weights"
WORD_EMBEDDING_MATRIX_LOC = os.path.abspath(os.path.join(os.path.dirname( __file__ ), '..', 'resources/SPUTokenized_word_embedding_16k.matrix'))
PROD_WEIGHTS_LINK = "https://vnlp-model-weights.s3.eu-west-1.amazonaws.com/PoS_SPUContext_prod.weights"
EVAL_WEIGHTS_LINK = "https://vnlp-model-weights.s3.eu-west-1.amazonaws.com/PoS_SPUContext_eval.weights"
WORD_EMBEDDING_MATRIX_LINK = "https://vnlp-model-weights.s3.eu-west-1.amazonaws.com/SPUTokenized_word_embedding_16k.matrix"
SPU_TOKENIZER_WORD_LOC = os.path.abspath(os.path.join(os.path.dirname( __file__ ), '..', 'resources/SPU_word_tokenizer_16k.model'))
TOKENIZER_LABEL_LOC = RESOURCES_PATH + "PoS_label_tokenizer.json"
# Data Preprocessing Config
TOKEN_PIECE_MAX_LEN = 8
SENTENCE_MAX_LEN = 40
# Loading Tokenizers
spu_tokenizer_word = spm.SentencePieceProcessor(SPU_TOKENIZER_WORD_LOC)
tokenizer_label = load_keras_tokenizer(TOKENIZER_LABEL_LOC)
sp_key_to_index = {spu_tokenizer_word.id_to_piece(id): id for id in range(spu_tokenizer_word.get_piece_size())}
sp_index_to_key = {id: spu_tokenizer_word.id_to_piece(id) for id in range(spu_tokenizer_word.get_piece_size())}
LABEL_VOCAB_SIZE = len(tokenizer_label.word_index)
WORD_EMBEDDING_VOCAB_SIZE = len(sp_key_to_index)
WORD_EMBEDDING_VECTOR_SIZE = 128
WORD_EMBEDDING_MATRIX = np.zeros((WORD_EMBEDDING_VOCAB_SIZE, WORD_EMBEDDING_VECTOR_SIZE))
NUM_RNN_STACKS = 1
RNN_UNITS_MULTIPLIER = 1
NUM_RNN_UNITS = WORD_EMBEDDING_VECTOR_SIZE * RNN_UNITS_MULTIPLIER
FC_UNITS_MULTIPLIER = (2, 1)
DROPOUT = 0.2
[docs]class SPUContextPoS:
"""
SentencePiece Unigram Context Part of Speech Tagger class.
- This is a context aware Deep GRU based Part of Speech Tagger that uses `SentencePiece Unigram <https://arxiv.org/abs/1804.10959>`_ tokenizer and pre-trained Word2Vec embeddings.
- It achieves 0.9010 Accuracy and 0.7623 F1 macro score on all of test sets of Universal Dependencies 2.9.
- For more details about the training procedure, dataset and evaluation metrics, see `ReadMe <https://github.com/vngrs-ai/VNLP/blob/main/vnlp/part_of_speech_tagger/ReadMe.md>`_.
"""
def __init__(self, evaluate):
self.model = create_spucontext_pos_model(TOKEN_PIECE_MAX_LEN, SENTENCE_MAX_LEN, WORD_EMBEDDING_VOCAB_SIZE, LABEL_VOCAB_SIZE,
WORD_EMBEDDING_VECTOR_SIZE, WORD_EMBEDDING_MATRIX,
NUM_RNN_UNITS, NUM_RNN_STACKS, FC_UNITS_MULTIPLIER,
DROPOUT)
# Check and download word embedding matrix and model weights
check_and_download(WORD_EMBEDDING_MATRIX_LOC, WORD_EMBEDDING_MATRIX_LINK)
if evaluate:
MODEL_WEIGHTS_LOC = EVAL_WEIGHTS_LOC
MODEL_WEIGHTS_LINK = EVAL_WEIGHTS_LINK
else:
MODEL_WEIGHTS_LOC = PROD_WEIGHTS_LOC
MODEL_WEIGHTS_LINK = PROD_WEIGHTS_LINK
check_and_download(MODEL_WEIGHTS_LOC, MODEL_WEIGHTS_LINK)
# Load Word embedding matrix
word_embedding_matrix = np.load(WORD_EMBEDDING_MATRIX_LOC)
# Load Model weights
with open(MODEL_WEIGHTS_LOC, 'rb') as fp:
model_weights = pickle.load(fp)
# Insert word embedding weights to correct position (0 for SPUContextPoS model)
model_weights.insert(0, word_embedding_matrix)
# Set model weights
self.model.set_weights(model_weights)
self.spu_tokenizer_word = spu_tokenizer_word
self.tokenizer_label = tokenizer_label
[docs] def predict(self, sentence: str) -> List[Tuple[str, str]]:
"""
Args:
sentence:
Input text(sentence).
Returns:
List of (token, pos_label).
"""
tokenized_sentence = TreebankWordTokenize(sentence)
num_tokens_in_sentence = len(tokenized_sentence)
int_preds = []
for t in range(num_tokens_in_sentence):
# t is the index of token/word
X = process_single_word_input(t, tokenized_sentence, self.spu_tokenizer_word, self.tokenizer_label, int_preds)
# Predicting
raw_pred = self.model(X).numpy()[0]
int_pred = np.argmax(raw_pred, axis = -1)
int_preds.append(int_pred)
# Converting integer labels to text form
pos_labels = [self.tokenizer_label.sequences_to_texts([[pos_int_label]])[0] for pos_int_label in int_preds]
result = [(token, pos_label) for token, pos_label in zip(tokenized_sentence, pos_labels)]
return result