Add fastText model #38
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* Fix package imports * Update README.md * Fix bug due to TAR/AR attribute check * Add BERT models * Add BERT tokenizer * Return logits from the model.py * Remove unused classes in models/bert * Return logits from the model.py (#12) * Remove unused classes in models/bert (#13) * Add initial main file * Add args for BERT * Add partial support for BERT * Initialize training and optimization * Draft the structure of Trainers for BERT * Remove duplicate tokenizer * Add utils * Move optimization to utils * Add more structure for trainer * Refactor the trainer (#15) * Refactor the trainer * Add more edits * Add support for our datasets * Add evaluator * Split data4bert module into multiple processors * Refactor BERT tokenizer * Integrate BERT into Castor framework (#17) * Remove unused classes in models/bert * Split data4bert module into multiple processors * Refactor BERT tokenizer * Add multilabel support in BertTrainer * Add multilabel support in BertEvaluator * Add get_test_samples method in dataset processors * Fix args.py for BERT * Add support for Reuters, IMDB datasets for BERT * Revert "Integrate BERT into Castor framework (#17)" This reverts commit e4244ec. * Fix paths to datasets in dataset classes and args * Add SST dataset * Add hedwig-data instructions to README.md * Fix KimCNN README * Fix RegLSTM README * Fix typos in README * Remove trec_eval from README * Add tensorboardX to requirements.txt * Rename processors module to bert_processors * Add method to print metrics after training * Add model check-pointing and early stopping for BERT * Add logos * Update README.md * Fix code comments in classification trainer * Add support for AAPD, Sogou, AGNews and Yelp2014 * Fix bug that deleted saved models * Update README for HAN * Update README for XML-CNN * Remove redundant TODOs from the READMEs * Fix logo in README.md * Update README for Char-CNN * Fix all the READMEs * Resolve conflict * Fix Typos * Re-Add SST2 Processor * Add support for evaluating trained model * Update args.py * Resolve issues due to DataParallel wrapper on saved model * Remove redundant Yelp processor * Fix bug for safely creating the saving directory * Change checkpoint paths to timestamps * Remove unwanted string.strip() from tokenizer * Create save path if it doesn't exist * Decouple model checkpoints from code * Remove model choice restrictions for BERT * Remove model/distill driver * Simplify checkpoint directory creation
# Conflicts: # datasets/reuters.py # models/mlp/args.py # models/mlp/model.py
daemon
approved these changes
Sep 8, 2019
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@daemon yeah we should look into that. Another part we should focus on is merging bert_trainer and classification_trainer, as they both do pretty much the same thing but for a different set of models. |
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A simple linear model where the word representations are then averaged into a text representation, which is in turn fed to a linear classifier [1].
[1] Joulin, Armand, et al. "Bag of Tricks for Efficient Text Classification." Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers. 2017.