预测句子中的遗漏单词-自然语言处理模型

我的句子如下：

I want to ____ the car because it is cheap.

我想使用NLP模型来预测丢失的单词。我应该使用哪种NLP模型？谢谢。

试试看：https : //github.com/huggingface/pytorch-pretrained-BERT

首先，您必须正确设置

pip install -U pytorch-pretrained-bert

然后，您可以使用BERT算法中的“屏蔽语言模型”，例如

import torch
from pytorch_pretrained_bert import BertTokenizer, BertModel, BertForMaskedLM

# OPTIONAL: if you want to have more information on what's happening, activate the logger as follows
import logging
logging.basicConfig(level=logging.INFO)

# Load pre-trained model tokenizer (vocabulary)
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

text = '[CLS] I want to [MASK] the car because it is cheap . [SEP]'
tokenized_text = tokenizer.tokenize(text)
indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)

# Create the segments tensors.
segments_ids = [0] * len(tokenized_text)

# Convert inputs to PyTorch tensors
tokens_tensor = torch.tensor([indexed_tokens])
segments_tensors = torch.tensor([segments_ids])

# Load pre-trained model (weights)
model = BertForMaskedLM.from_pretrained('bert-base-uncased')
model.eval()

# Predict all tokens
with torch.no_grad():
    predictions = model(tokens_tensor, segments_tensors)

predicted_index = torch.argmax(predictions[0, masked_index]).item()
predicted_token = tokenizer.convert_ids_to_tokens([predicted_index])[0]

print(predicted_token)

[出]：

buy

要真正理解为什么你需要的[CLS]，[MASK]和段张量，请仔细阅读本文，https://arxiv.org/abs/1810.04805

如果您很懒惰，可以阅读来自Lilian Weng的这篇不错的博文，https： //lilianweng.github.io/lil-log/2019/01/31/generalized-language-models.html

除BERT以外，还有许多其他模型可以执行填补空白的任务。请查看pytorch-pretrained-BERT存储库中的其他模型，但更重要的是，应更深入地研究“语言建模”的任务，即根据历史预测下一个单词的任务。