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增加char_embedding可使用预训练的character embedding的功能

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YanqunJiang 2019-08-16 17:51:07 +08:00
parent f6bb8c83f3
commit fb82c66b4c
1 changed files with 19 additions and 4 deletions
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fastNLP/embeddings/char_embedding.py
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@ -9,6 +9,7 @@ import torch.nn as nn
import torch.nn.functional as F import torch.nn.functional as F
from typing import List from typing import List
from .static_embedding import StaticEmbedding
from ..modules.encoder.lstm import LSTM from ..modules.encoder.lstm import LSTM
from ..core.vocabulary import Vocabulary from ..core.vocabulary import Vocabulary
from .embedding import TokenEmbedding from .embedding import TokenEmbedding
@ -41,10 +42,13 @@ class CNNCharEmbedding(TokenEmbedding):
:param pool_method: character的表示在合成一个表示时所使用的pool方法，支持'avg', 'max'. :param pool_method: character的表示在合成一个表示时所使用的pool方法，支持'avg', 'max'.
:param activation: CNN之后使用的激活方法，支持'relu', 'sigmoid', 'tanh' 或者自定义函数. :param activation: CNN之后使用的激活方法，支持'relu', 'sigmoid', 'tanh' 或者自定义函数.
:param min_char_freq: character的最少出现次数。默认值为2. :param min_char_freq: character的最少出现次数。默认值为2.
:param pre_train_char_embed:可以有两种方式调用预训练好的static embedding：第一种是传入embedding文件夹(文件夹下应该只有一个
以.txt作为后缀的文件)或文件路径；第二种是传入embedding的名称，第二种情况将自动查看缓存中是否存在该模型，没有的话将自动下载。
如果输入为None则使用embedding_dim的维度随机初始化一个embedding.
""" """
def __init__(self, vocab: Vocabulary, embed_size: int=50, char_emb_size: int=50, word_dropout:float=0, def __init__(self, vocab: Vocabulary, embed_size: int=50, char_emb_size: int=50, word_dropout:float=0,
dropout:float=0.5, filter_nums: List[int]=(40, 30, 20), kernel_sizes: List[int]=(5, 3, 1), dropout:float=0.5, filter_nums: List[int]=(40, 30, 20), kernel_sizes: List[int]=(5, 3, 1),
pool_method: str='max', activation='relu', min_char_freq: int=2): pool_method: str='max', activation='relu', min_char_freq: int=2, pre_train_char_embed: str=''):
super(CNNCharEmbedding, self).__init__(vocab, word_dropout=word_dropout, dropout=dropout) super(CNNCharEmbedding, self).__init__(vocab, word_dropout=word_dropout, dropout=dropout)
for kernel in kernel_sizes: for kernel in kernel_sizes:
@ -85,7 +89,11 @@ class CNNCharEmbedding(TokenEmbedding):
self.words_to_chars_embedding[index, :len(word)] = \ self.words_to_chars_embedding[index, :len(word)] = \
torch.LongTensor([self.char_vocab.to_index(c) for c in word]) torch.LongTensor([self.char_vocab.to_index(c) for c in word])
self.word_lengths[index] = len(word) self.word_lengths[index] = len(word)
self.char_embedding = nn.Embedding(len(self.char_vocab), char_emb_size) # self.char_embedding = nn.Embedding(len(self.char_vocab), char_emb_size)
if len(pre_train_char_embed):
self.char_embedding = StaticEmbedding(self.char_vocab, pre_train_char_embed)
else:
self.char_embedding = nn.Embedding(len(self.char_vocab), char_emb_size)
self.convs = nn.ModuleList([nn.Conv1d( self.convs = nn.ModuleList([nn.Conv1d(
char_emb_size, filter_nums[i], kernel_size=kernel_sizes[i], bias=True, padding=kernel_sizes[i] // 2) char_emb_size, filter_nums[i], kernel_size=kernel_sizes[i], bias=True, padding=kernel_sizes[i] // 2)
@ -184,10 +192,13 @@ class LSTMCharEmbedding(TokenEmbedding):
:param activation: 激活函数，支持'relu', 'sigmoid', 'tanh', 或者自定义函数. :param activation: 激活函数，支持'relu', 'sigmoid', 'tanh', 或者自定义函数.
:param min_char_freq: character的最小出现次数。默认值为2. :param min_char_freq: character的最小出现次数。默认值为2.
:param bidirectional: 是否使用双向的LSTM进行encode。默认值为True。 :param bidirectional: 是否使用双向的LSTM进行encode。默认值为True。
:param pre_train_char_embed:可以有两种方式调用预训练好的static embedding：第一种是传入embedding文件夹(文件夹下应该只有一个
以.txt作为后缀的文件)或文件路径；第二种是传入embedding的名称，第二种情况将自动查看缓存中是否存在该模型，没有的话将自动下载。
如果输入为None则使用embedding_dim的维度随机初始化一个embedding.
""" """
def __init__(self, vocab: Vocabulary, embed_size: int=50, char_emb_size: int=50, word_dropout:float=0, def __init__(self, vocab: Vocabulary, embed_size: int=50, char_emb_size: int=50, word_dropout:float=0,
dropout:float=0.5, hidden_size=50,pool_method: str='max', activation='relu', min_char_freq: int=2, dropout:float=0.5, hidden_size=50,pool_method: str='max', activation='relu', min_char_freq: int=2,
bidirectional=True): bidirectional=True, pre_train_char_embed: str=''):
super(LSTMCharEmbedding, self).__init__(vocab) super(LSTMCharEmbedding, self).__init__(vocab)
assert hidden_size % 2 == 0, "Only even kernel is allowed." assert hidden_size % 2 == 0, "Only even kernel is allowed."
@ -227,7 +238,11 @@ class LSTMCharEmbedding(TokenEmbedding):
self.words_to_chars_embedding[index, :len(word)] = \ self.words_to_chars_embedding[index, :len(word)] = \
torch.LongTensor([self.char_vocab.to_index(c) for c in word]) torch.LongTensor([self.char_vocab.to_index(c) for c in word])
self.word_lengths[index] = len(word) self.word_lengths[index] = len(word)
self.char_embedding = nn.Embedding(len(self.char_vocab), char_emb_size) # self.char_embedding = nn.Embedding(len(self.char_vocab), char_emb_size)
if len(pre_train_char_embed):
self.char_embedding = StaticEmbedding(self.char_vocab, pre_train_char_embed)
else:
self.char_embedding = nn.Embedding(len(self.char_vocab), char_emb_size)
self.fc = nn.Linear(hidden_size, embed_size) self.fc = nn.Linear(hidden_size, embed_size)
hidden_size = hidden_size // 2 if bidirectional else hidden_size hidden_size = hidden_size // 2 if bidirectional else hidden_size