from typing import List
import numpy as np
from ..base import BaseTextEncoder
from ...helper import batching, pooling_simple, as_numpy_array
[docs]class Word2VecEncoder(BaseTextEncoder):
is_trained = True
def __init__(self, model_dir: str,
skiprows: int = 1,
dimension: int = 300,
pooling_strategy: str = 'REDUCE_MEAN', *args, **kwargs):
super().__init__(*args, **kwargs)
self.model_dir = model_dir
self.skiprows = skiprows
self.pooling_strategy = pooling_strategy
self.dimension = dimension
[docs] def post_init(self):
from ...helper import Tokenizer
count = 0
self.word2vec_df = {}
with open(self.model_dir, 'r') as f:
for line in f.readlines():
line = line.strip().split(' ')
if count < self.skiprows:
count += 1
continue
if len(line) > self.dimension:
self.word2vec_df[line[0]] = np.array([float(i) for i in line[1:]], dtype=np.float32)
self.empty = np.zeros([self.dimension], dtype=np.float32)
self.cn_tokenizer = Tokenizer()
[docs] @batching
@as_numpy_array
def encode(self, text: List[str], *args, **kwargs) -> np.ndarray:
# tokenize text
batch_tokens = [self.cn_tokenizer.tokenize(sent) for sent in text]
pooled_data = []
for tokens in batch_tokens:
_layer_data = [self.word2vec_df.get(token, self.empty) for token in tokens]
pooled_data.append(pooling_simple(_layer_data, self.pooling_strategy))
return pooled_data