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Achyudh Ram
951df4ada5
* 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 * Add ignore_lengths option to ReutersTrainer and ReutersEvaluator * Add AAPDCharQuantized and ReutersCharQuantized * Rename Reuters_hierarchical to ReutersHierarchical * Add CharacterCNN for document classification * Update README.md for CharacterCNN * Fix table in README.md for CharacterCNN * Add AAPDHierarchical for HAN * Update HAN for changes in Reuters dataset endpoints * Fix bug in CharCNN when running on CPU * Add AAPD dataset support for KimCNN * Fix dataset paths for SST-1 * Fix dimensions of FC1 in CharCNN * Add model checkpointing for Reuters based on F1 * Refactor LSTM baseline __main__ * Add precision, recall and F1 to Reuters evaluator * Checkpoint only at the end of an epoch for ReutersTrainer Add detailed log printing for dev evaluations * Fix log_template and dev_log_template in ReutersTrainer * Add IMDB dataset * Add support for single_label datasets in ReutersTrainer * Add support for IMDB dataset in lstm_baseline and lstm_reg
54 lines
1.9 KiB
Python
54 lines
1.9 KiB
Python
from .trainers.sick_trainer import SICKTrainer
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from .trainers.msrvid_trainer import MSRVIDTrainer
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from .trainers.trecqa_trainer import TRECQATrainer
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from .trainers.wikiqa_trainer import WikiQATrainer
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from .trainers.pit2015_trainer import PIT2015Trainer
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from .trainers.sst_trainer import SSTTrainer
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from .trainers.reuters_trainer import ReutersTrainer
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from .trainers.snli_trainer import SNLITrainer
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from .trainers.sts2014_trainer import STS2014Trainer
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from .trainers.quora_trainer import QuoraTrainer
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from nce.nce_pairwise_mp.trainers.trecqa_trainer import TRECQATrainerNCE
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from nce.nce_pairwise_mp.trainers.wikiqa_trainer import WikiQATrainerNCE
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class TrainerFactory(object):
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"""
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Get the corresponding Trainer class for a particular dataset.
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"""
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trainer_map = {
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'sick': SICKTrainer,
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'msrvid': MSRVIDTrainer,
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'SST-1': SSTTrainer,
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'SST-2': SSTTrainer,
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'trecqa': TRECQATrainer,
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'wikiqa': WikiQATrainer,
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'pit2015': PIT2015Trainer,
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'twitterurl': PIT2015Trainer,
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'Reuters': ReutersTrainer,
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'AAPD': ReutersTrainer,
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'IMDB': ReutersTrainer,
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'snli': SNLITrainer,
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'sts2014': STS2014Trainer,
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'quora': QuoraTrainer
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}
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trainer_map_nce = {
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'trecqa': TRECQATrainerNCE,
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'wikiqa': WikiQATrainerNCE
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}
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@staticmethod
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def get_trainer(dataset_name, model, embedding, train_loader, trainer_config, train_evaluator, test_evaluator, dev_evaluator=None, nce=False):
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if nce:
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trainer_map = TrainerFactory.trainer_map_nce
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else:
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trainer_map = TrainerFactory.trainer_map
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if dataset_name not in trainer_map:
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raise ValueError('{} is not implemented.'.format(dataset_name))
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return trainer_map[dataset_name](
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model, embedding, train_loader, trainer_config, train_evaluator, test_evaluator, dev_evaluator
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)
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