Perplexity(name='Perplexity', *args, **kwargs)[源代码]¶
Perplexity is a metric used to judge how good a language model is. We can define perplexity as the inverse probability of the test set, normalised by the number of the words in the test set. Perplexity is calculated using cross entropy. It supports both padding data and no padding data.
If data is not padded, users should provide
Metricinitialization. If data is padded, your label should contain
seq_mask, which indicates the actual length of samples.
This Perplexity requires that the output of your network is prediction, label and sequence length (optional). If the Perplexity here doesn't meet your needs, you could override the
updatemethod for calculating Perplexity.
seq_len (int) -- Sequence length of each sample, it must be provided while data is not padded. Defaults to 20.
name (str) -- Name of
Metricinstance. Defaults to 'Perplexity'.
import paddle from paddlenlp.transformers import BertTokenizer from paddlenlp.metrics import Perplexity paddle.seed(2021) tokenizer = BertTokenizer.from_pretrained('bert-base-uncased') batch_size, seq_len, vocab_size = 1, 4, tokenizer.vocab_size logits = paddle.rand([batch_size, seq_len, vocab_size]) labels= paddle.to_tensor([[1,0,1,1]]) perplexity = Perplexity() correct = perplexity.compute(logits,labels) perplexity.update(correct.numpy()) res = perplexity.accumulate() print(res) # 48263.528820122105
compute(pred, label, seq_mask=None)[源代码]¶
Computes cross entropy loss.
pred (Tensor) -- Predictor tensor, and its dtype is float32 or float64, and has a shape of [batch_size, sequence_length, vocab_size].
label (Tensor) -- Label tensor, and its dtype is int64, and has a shape of [batch_size, sequence_length, 1] or [batch_size, sequence_length].
seq_mask (Tensor, optional) -- Sequence mask tensor, and its type could be float32, float64, int32 or int64, and has a shape of [batch_size, sequence_length]. It's used to calculate loss. Defaults to None.
Returns tuple (
ce, word_num) if
seq_maskis not None. Otherwise, returns tensor
ceit the cross entropy loss, its shape is [batch_size, sequence_length] and its data type should be float32.
tuple or Tensor
Updates metric states.
Calculates and returns the value of perplexity.
perplexity, the calculation results.