问题
I have the sentence below :
I want to ____ the car because it is cheap.
I want to predict the missing word ,using an NLP model. What NLP model shall I use? Thanks.
回答1:
TL;DR
Try this out: https://github.com/huggingface/pytorch-pretrained-BERT
First you have to set it up, properly with
pip install -U pytorch-pretrained-bert
Then you can use the "masked language model" from the BERT algorithm, e.g.
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)
[out]:
buy
In Long
To truly understand why you need the [CLS], [MASK] and segment tensors, please do read the paper carefully, https://arxiv.org/abs/1810.04805
And if you're lazy, you can read this nice blogpost from Lilian Weng, https://lilianweng.github.io/lil-log/2019/01/31/generalized-language-models.html
Other than BERT, there are a lot of other models that can perform the task of filling in the blank. Do look at the other models in the pytorch-pretrained-BERT repository, but more importantly dive deeper into the task of "Language Modeling", i.e. the task of predicting the next word given a history.
回答2:
There are numerous models you might be able to use. But I think the most recently being used model for such sequence learning problems, are bidirectional RNNs( like Bidirectional LSTM), you can get a hint from here
But be advised, Bidirectional RNNs are very expensive to train. Depending on your problem to solve, I highly advice to use some pre-trained model. Good luck!
来源：https://stackoverflow.com/questions/54978443/predicting-missing-words-in-a-sentence-natural-language-processing-model