TEXT = torchtext.legacy.data.Field(tokenize = 'spacy', tokenizer_language = 'en_core_web_sm', fix_length = max_review_len, lower = True, )
时间: 2023-03-29 21:02:29 浏览: 155
这是一个关于 torchtext.legacy.data.Field 的参数设置的问题,我可以回答。这个参数设置是用于对文本进行处理的,其中 tokenize 参数指定使用 spacy 进行分词,tokenizer_language 参数指定使用英文的 en_core_web_sm 模型进行分词,fix_length 参数指定文本的最大长度,lower 参数指定将文本转换为小写。
相关问题
pytorch==1.12.0,torchtext==0.13.0,升级这段代码from torchtext.legacy.data import Field, TabularDataset, BucketIterator # 最新版的torchtext from torchtext.data.metrics import bleu_score
如果您想将代码升级为使用torchtext的最新版本(0.9.0),您需要进行以下更改:
1. 导入包的方式改变了,不再使用`from torchtext.legacy.data`。修改为:
```python
from torchtext.data import Field, TabularDataset, BucketIterator
```
2. bleu_score度量现在位于`torchtext.data.metrics`模块中。修改为:
```python
from torchtext.data.metrics import bleu_score
```
请注意,新版本的torchtext可能有一些不同的API和用法,因此您可能需要进一步调整代码以适应新版本。建议查阅torchtext的官方文档以获取更多详细信息。
losses = tf.contrib.legacy_seq2seq.sequence_loss_by_example
(loss_weights=weights, logits=logits, targets=target_sequence)
This function calculates the sequence loss for a sequence-to-sequence model. It calculates the weighted cross-entropy loss for each element of the output sequence compared to the corresponding element of the target sequence. The loss_weights parameter is a list of weights for each element of the output sequence. The logits parameter is a tensor of shape [batch_size, sequence_length, vocabulary_size], representing the output sequence of the model. The targets parameter is a tensor of shape [batch_size, sequence_length], representing the target sequence. The function returns a tensor of shape [batch_size], representing the loss for each sequence in the batch.