GPT as a Language Model

I am interested to use GPT as Language Model to assign Language modeling score (Perplexity score) of a sentence. Here is what I am using

import math
from pytorch_pretrained_bert import OpenAIGPTTokenizer, OpenAIGPTModel, OpenAIGPTLMHeadModel
# Load pre-trained model (weights)
model = OpenAIGPTLMHeadModel.from_pretrained('openai-gpt')
model.eval()
# Load pre-trained model tokenizer (vocabulary)
tokenizer = OpenAIGPTTokenizer.from_pretrained('openai-gpt')

def score(sentence):
    tokenize_input = tokenizer.tokenize(sentence)
    tensor_input = torch.tensor([tokenizer.convert_tokens_to_ids(tokenize_input)])
    loss=model(tensor_input, lm_labels=tensor_input)
    return math.exp(loss)


a=['there is a book on the desk',
                'there is a plane on the desk',
                        'there is a book in the desk']
print([score(i) for i in a])
21.31652459381952, 61.45907380241148, 26.24923942649312

Is it the right way to score a sentence ?

[Namrata96]

I’m confused whether the right way to calculate the perplexity for GPT2 is what the OP has done or as per the documentation https://huggingface.co/transformers/perplexity.html? Or both are equivalent for some value of the stride?

[Lilo2305]

Is this score normalized on sentence lenght? And if not, what do I need to change to normalize it?