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Linqing Liu
7dec34a106
* add SNLI dataset * add STS-2014 * add STS-2014 * add trainers and evaluators for Quora * add quora in datasets/ * process the merge confict in common/dataset.py * add sts2014_evaluator.py and quora_evaluator.py
32 lines
989 B
Python
32 lines
989 B
Python
import torch
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import torch.nn.functional as F
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from .evaluator import Evaluator
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class QuoraEvaluator(Evaluator):
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def get_scores(self):
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self.model.eval()
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test_kl_div_loss = 0
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acc_total = 0
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for batch in self.data_loader:
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# Select embedding
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sent1, sent2 = self.get_sentence_embeddings(batch)
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output = self.model(sent1, sent2, batch.ext_feats, batch.dataset.word_to_doc_cnt, batch.sentence_1_raw, batch.sentence_2_raw)
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test_kl_div_loss += F.kl_div(output, batch.label, size_average=False).item()
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true_label = torch.max(batch.label.data, 1)[1]
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prediction = torch.max(output, 1)[1]
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acc_total += ((true_label == prediction)).sum().item()
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del output
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test_kl_div_loss /= len(batch.dataset.examples)
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accuracy = acc_total / len(self.data_loader.dataset.examples)
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return [accuracy, test_kl_div_loss], ['accuracy', 'KL-divergence loss']
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