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Replication of STOA for Reuters Dataset (#152)

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* Add ReutersTrainer, ReutersEvaluator options in Factory classes

* Add Reuters to Kim-CNN command line arguments

* Fix SST dataset path according to changes in Kim-CNN args

The dataset path in args.py was made to point at the dataset folder rather than dataset/SST folder. Hence SST folder was added to paths in the SST dataset class

* Add Reuters dataset class, and support in __main__

* Add Reuters dataset trainers and evaluators

* Remove debug print statement in reuters_evaluator

* Fix rounding bug in reuters_trainer and reuters_evaluator

* Add LSTM for baseline text classification measurements

* Add eval metrics for lstm_baseline

* Set batch_first param in lstm_baseline

* Remove onnx args from lstm_baseline

* Pack padded sequences in LSTM_baseline

* Add TensorBoardX support for Reuters trainer

* Add Arxiv Academic Paper Dataset (AAPD)

* Add Hidden Bottleneck Layer to BiLSTM

* Fix packing of padded tensors in Reuters

* Add cmdline args for Hidden Bottleneck Layer for BiLSTM

* Include pre-padding lengths in AAPD dataset

* Remove duplication of preprocessing code in AAPD

* Remove batch_size condition in ReutersTrainer
This commit is contained in:
Achyudh Ram
2018-10-26 19:10:19 -04:00
committed by Peng Shi
parent 650882fb6e
commit 6daa5a128f
10 changed files with 121 additions and 17 deletions
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common/evaluators/reuters_evaluator.py
+5 -2
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@@ -14,11 +14,14 @@ class ReutersEvaluator(Evaluator):
total_loss = 0
for batch_idx, batch in enumerate(self.data_loader):
scores = self.model(batch.text)
scores = self.model(batch.text[0], lengths=batch.text[1])
scores_rounded = F.sigmoid(scores).round().long()
# Using binary accuracy
for tensor1, tensor2 in zip(F.sigmoid(scores).round().long(), batch.label):
for tensor1, tensor2 in zip(scores_rounded, batch.label):
if np.array_equal(tensor1, tensor2):
n_dev_correct += 1
total_loss += F.binary_cross_entropy_with_logits(scores, batch.label.float(), size_average=False).item()
accuracy = 100. * n_dev_correct / len(self.data_loader.dataset.examples)