mirror of
https://github.com/wassname/Castor.git
synced 2026-10-05 12:20:28 +08:00
Achyudh Ram
6daa5a128f
* 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
59 lines
2.1 KiB
Python
59 lines
2.1 KiB
Python
from .evaluators.sick_evaluator import SICKEvaluator
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from .evaluators.msrvid_evaluator import MSRVIDEvaluator
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from .evaluators.sst_evaluator import SSTEvaluator
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from .evaluators.trecqa_evaluator import TRECQAEvaluator
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from .evaluators.wikiqa_evaluator import WikiQAEvaluator
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from .evaluators.pit2015_evaluator import PIT2015Evaluator
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from .evaluators.reuters_evaluator import ReutersEvaluator
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from .evaluators.snli_evaluator import SNLIEvaluator
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from .evaluators.sts2014_evaluator import STS2014Evaluator
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from .evaluators.quora_evaluator import QuoraEvaluator
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from nce.nce_pairwise_mp.evaluators.trecqa_evaluator import TRECQAEvaluatorNCE
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from nce.nce_pairwise_mp.evaluators.wikiqa_evaluator import WikiQAEvaluatorNCE
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class EvaluatorFactory(object):
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"""
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Get the corresponding Evaluator class for a particular dataset.
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"""
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evaluator_map = {
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'sick': SICKEvaluator,
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'msrvid': MSRVIDEvaluator,
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'SST-1': SSTEvaluator,
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'SST-2': SSTEvaluator,
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'trecqa': TRECQAEvaluator,
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'wikiqa': WikiQAEvaluator,
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'pit2015': PIT2015Evaluator,
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'twitterurl': PIT2015Evaluator,
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'Reuters': ReutersEvaluator,
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'AAPD': ReutersEvaluator,
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'SNLI': SNLIEvaluator,
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'sts2014': STS2014Evaluator,
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'Quora': QuoraEvaluator
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}
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evaluator_map_nce = {
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'trecqa': TRECQAEvaluatorNCE,
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'wikiqa': WikiQAEvaluatorNCE
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}
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@staticmethod
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def get_evaluator(dataset_cls, model, embedding, data_loader, batch_size, device, nce=False, keep_results=False):
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if data_loader is None:
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return None
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if nce:
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evaluator_map = EvaluatorFactory.evaluator_map_nce
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else:
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evaluator_map = EvaluatorFactory.evaluator_map
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if not hasattr(dataset_cls, 'NAME'):
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raise ValueError('Invalid dataset. Dataset should have NAME attribute.')
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if dataset_cls.NAME not in evaluator_map:
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raise ValueError('{} is not implemented.'.format(dataset_cls))
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return evaluator_map[dataset_cls.NAME](
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dataset_cls, model, embedding, data_loader, batch_size, device, keep_results
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)
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