diff --git a/docs/source/tutorials/tutorial_4_loss_optimizer.rst b/docs/source/tutorials/tutorial_4_loss_optimizer.rst index a6e1730a..f863a7a8 100644 --- a/docs/source/tutorials/tutorial_4_loss_optimizer.rst +++ b/docs/source/tutorials/tutorial_4_loss_optimizer.rst @@ -158,6 +158,7 @@ Vocabulary 的使用 损失函数 训练模型需要提供一个损失函数 ,fastNLP中提供了直接可以导入使用的四种loss，分别为： + * :class:`~fastNLP.CrossEntropyLoss`：包装了torch.nn.functional.cross_entropy()函数，返回交叉熵损失（可以运用于多分类场景） * :class:`~fastNLP.BCELoss`：包装了torch.nn.functional.binary_cross_entropy()函数，返回二分类的交叉熵 * :class:`~fastNLP.L1Loss`：包装了torch.nn.functional.l1_loss()函数，返回L1 损失 @@ -209,7 +210,7 @@ Vocabulary 的使用 #使用CNNText的时候第一个参数输入一个tuple,作为模型定义embedding的参数 #还可以传入 kernel_nums, kernel_sizes, padding, dropout的自定义值 - model_cnn = CNNText((len(vocab),EMBED_DIM), num_classes=3, padding=2, dropout=0.1) + model_cnn = CNNText((len(vocab),EMBED_DIM), num_classes=3, dropout=0.1) #如果在定义trainer的时候没有传入optimizer参数，模型默认的优化器为torch.optim.Adam且learning rate为lr=4e-3 #这里只使用了optimizer_1作为优化器输入，感兴趣可以尝试optimizer_2或者其他优化器作为输入 diff --git a/docs/source/tutorials/tutorial_5_datasetiter.rst b/docs/source/tutorials/tutorial_5_datasetiter.rst index 23d26deb..e81b18dd 100644 --- a/docs/source/tutorials/tutorial_5_datasetiter.rst +++ b/docs/source/tutorials/tutorial_5_datasetiter.rst @@ -192,7 +192,7 @@ sampler import time embed_dim = 100 - model = CNNText((len(vocab),embed_dim), num_classes=3, padding=2, dropout=0.1) + model = CNNText((len(vocab),embed_dim), num_classes=3, dropout=0.1) def train(epoch, data, devdata): optimizer = torch.optim.Adam(model.parameters(), lr=0.001) diff --git a/fastNLP/embeddings/bert_embedding.py b/fastNLP/embeddings/bert_embedding.py index 8ec5fd50..7a9738fe 100644 --- a/fastNLP/embeddings/bert_embedding.py +++ b/fastNLP/embeddings/bert_embedding.py @@ -27,6 +27,7 @@ class BertEmbedding(ContextualEmbedding): >>> import torch >>> from fastNLP import Vocabulary + >>> from fastNLP.embeddings import BertEmbedding >>> vocab = Vocabulary().add_word_lst("The whether is good .".split()) >>> embed = BertEmbedding(vocab, model_dir_or_name='en-base-uncased', requires_grad=False, layers='4,-2,-1') >>> words = torch.LongTensor([[vocab.to_index(word) for word in "The whether is good .".split()]]) diff --git a/fastNLP/embeddings/char_embedding.py b/fastNLP/embeddings/char_embedding.py index b670313e..783dec41 100644 --- a/fastNLP/embeddings/char_embedding.py +++ b/fastNLP/embeddings/char_embedding.py @@ -24,6 +24,9 @@ class CNNCharEmbedding(TokenEmbedding): Example:: + >>> import torch + >>> from fastNLP import Vocabulary + >>> from fastNLP.embeddings import CNNCharEmbedding >>> vocab = Vocabulary().add_word_lst("The whether is good .".split()) >>> embed = CNNCharEmbedding(vocab, embed_size=50) >>> words = torch.LongTensor([[vocab.to_index(word) for word in "The whether is good .".split()]]) @@ -167,6 +170,9 @@ class LSTMCharEmbedding(TokenEmbedding): Example:: + >>> import torch + >>> from fastNLP import Vocabulary + >>> from fastNLP.embeddings import LSTMCharEmbedding >>> vocab = Vocabulary().add_word_lst("The whether is good .".split()) >>> embed = LSTMCharEmbedding(vocab, embed_size=50) >>> words = torch.LongTensor([[vocab.to_index(word) for word in "The whether is good .".split()]]) diff --git a/fastNLP/embeddings/elmo_embedding.py b/fastNLP/embeddings/elmo_embedding.py index 590aba74..435e0b98 100644 --- a/fastNLP/embeddings/elmo_embedding.py +++ b/fastNLP/embeddings/elmo_embedding.py @@ -21,6 +21,9 @@ class ElmoEmbedding(ContextualEmbedding): Example:: + >>> import torch + >>> from fastNLP import Vocabulary + >>> from fastNLP.embeddings import ElmoEmbedding >>> vocab = Vocabulary().add_word_lst("The whether is good .".split()) >>> # 使用不同层的concat的结果 >>> embed = ElmoEmbedding(vocab, model_dir_or_name='en', layers='1,2', requires_grad=False) diff --git a/fastNLP/embeddings/embedding.py b/fastNLP/embeddings/embedding.py index 9447c6ad..8c5396b7 100644 --- a/fastNLP/embeddings/embedding.py +++ b/fastNLP/embeddings/embedding.py @@ -20,6 +20,7 @@ class Embedding(nn.Module): Example:: >>> import numpy as np + >>> from fastNLP.embeddings import Embedding >>> init_embed = (2000, 100) >>> embed = Embedding(init_embed) # 随机初始化一个具有2000个词，每个词表示为100维的词向量 >>> init_embed = np.zeros((2000, 100)) diff --git a/fastNLP/embeddings/stack_embedding.py b/fastNLP/embeddings/stack_embedding.py index 8091d598..d3ce462b 100644 --- a/fastNLP/embeddings/stack_embedding.py +++ b/fastNLP/embeddings/stack_embedding.py @@ -17,7 +17,7 @@ class StackEmbedding(TokenEmbedding): >>> from fastNLP import Vocabulary >>> from fastNLP.embeddings import StaticEmbedding >>> vocab = Vocabulary().add_word_lst("The whether is good .".split()) - >>> embed_1 = StaticEmbedding(vocab, model_dir_or_name='en-glove-6b-50', requires_grad=True) + >>> embed_1 = StaticEmbedding(vocab, model_dir_or_name='en-glove-6b-50d', requires_grad=True) >>> embed_2 = StaticEmbedding(vocab, model_dir_or_name='en-word2vec-300', requires_grad=True) :param embeds: 一个由若干个TokenEmbedding组成的list，要求每一个TokenEmbedding的词表都保持一致 @@ -91,4 +91,4 @@ class StackEmbedding(TokenEmbedding): for embed in self.embeds: outputs.append(embed(words)) outputs = self.dropout(torch.cat(outputs, dim=-1)) - return outputs \ No newline at end of file + return outputs diff --git a/fastNLP/embeddings/static_embedding.py b/fastNLP/embeddings/static_embedding.py index 4b25ea8d..050a7fe1 100644 --- a/fastNLP/embeddings/static_embedding.py +++ b/fastNLP/embeddings/static_embedding.py @@ -22,12 +22,14 @@ class StaticEmbedding(TokenEmbedding): 当前支持自动下载的预训练vector有以下的几种(待补充); Example:: - + + >>> from fastNLP import Vocabulary + >>> from fastNLP.embeddings import StaticEmbedding >>> vocab = Vocabulary().add_word_lst("The whether is good .".split()) - >>> embed = StaticEmbedding(vocab, model_dir_or_name='en-glove-50') + >>> embed = StaticEmbedding(vocab, model_dir_or_name='en-glove-50d') >>> vocab = Vocabulary().add_word_lst(["The", 'the', "THE"]) - >>> embed = StaticEmbedding(vocab, model_dir_or_name="en-glove-50", lower=True) + >>> embed = StaticEmbedding(vocab, model_dir_or_name="en-glove-50d", lower=True) >>> # "the", "The", "THE"它们共用一个vector，且将使用"the"在预训练词表中寻找它们的初始化表示。 >>> vocab = Vocabulary().add_word_lst(["The", "the", "THE"])