CHANGELOG.md

Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog, and this project adheres to Semantic Versioning.

0.28.0 - 2020-05-11

Added

• Sequence-to-Sequence task support added. This includes the following models:
  • BART
  • Marian
  • Generic Encoder-Decoder
• The args dict of a task-specific Simple Transformers model is now saved along with the model. When loading the model, these values will be read and used. Any new args passed into the model initialization will override the loaded values.

0.27.3 - 2020-05-10

Added

• Support for AutoModel in NER, QA, and LanguageModeling. @flozi00

Fixed

• Now predict function from NER_Model returns a value model_outputs that contains: A Python list of lists with dicts containing each word mapped to its list with raw model output. @flaviussn

Changed

• Pillow import is now optional. It only needs to be installed if MultiModal models are used.

0.27.2 - 2020-05-08

Fixed

• Fixed T5 lm_labels not being masked properly

Changed

• Torchvision import is now optional. It only needs to be installed if MultiModal models are used.

0.27.1 - 2020-05-05

Fixed

• Fixed issue with custom evaluation metrics not being handled correctly in MultiLabelClassificationModel. @galtay

0.27.0 - 2020-05-05

Added

• Added support for T5 Model.
• Added do_sample arg to language generation.
• NERModel.predict() now accepts a split_on_space optional argument. If set to False, to_predict must be a a list of lists, with the inner list being a list of strings consisting of the split sequences. The outer list is the list of sequences to predict on.

Changed

• eval_df argument in NERModel.train_model() renamed to eval_data to better reflect the input format. Added Deprecation Warning.

0.26.1 - 2020-04-27

Fixed

• Specifying verbose=False in LanguageGenerationModel.generate() method now correctly silences logger output.

0.26.0 - 2020-04-25

Added

• Added Electra model support for sequence classification (binary, multiclass, multilabel)
• Added Electra model support for question answering
• Added Roberta model support for question answering

Changed

• Reduced logger messages during question answering evaluation

0.25.0 - 2020-04-24

Added

• Added support for Language Generation tasks.

0.24.9 - 2020-04-22

Added

• Added support for custom metrics with QuestionAnsweringModel.

Fixed

• Fixed issue with passing proxies to ConvAI models. @Pradhy729

0.24.8 - 2020-04-13

Fixed

• Fixed incorrect indexes when extracting hidden layer outputs and embedding outputs with ClassificationModel.predict() method.

0.24.7 - 2020-04-13

Added

• Added option to get hidden layer outputs and embedding outputs with ClassificationModel.predict() method.
  • Setting config: {"output_hidden_states": True} will automatically return all embedding outputs and hidden layer outputs.

Changed

• global_args now has a config dictionary which can be used to override default values in the confg class.
  • This can be used with ClassificationModel, MultiLabelClassificationModel, NERModel, QuestionAnsweringModel, and LanguageModelingModel

0.24.6 - 2020-04-12

Added

• Added support for ELECTRA based NER models.

0.24.5 - 2020-04-11

Fixed

• Fixed bug in LanguageModelingModel when loading from a training checkpoint.

[0.24.4] - 2020-04-10

Fixed

• Fixed bug in LanguageModelingModel initialization with a trained tokenizer.

Added

• Added support for passing proxy information with ConvAI model.

0.24.3 - 2020-04-10

Fixed

• Fixed potential bug in NERModel predict() method when using custom labels.
• Fixed typo in the NERModel description in the readme.

0.24.2 - 2020-04-09

Fixed

• Fixed issues with vocab_size not being set properly in ELECTRA models.

0.24.1 - 2020-04-09

Fixed

• Fixed bugs in minimal examples for language modeling.

Changed

• Added vocab_size back to default args dict for clarity. (vocab_size is None by default)
• Changed error message when training a new tokenizer with incorrect parameters for clarity.

0.24.0 - 2020-04-09

Added

• Added ELECTRA pretraining support.
• Added better support for configuring model architectures when training language models from scratch.
  • Any options which should be overriden from the default config can now be specified in the args dict. (config key)

Changed

• Default entry for vocab_size removed from args for LanguageModelingModel as it differs for different model types.
  • vocab_size must now be specified whenever a new tokenizer is to be trained.

Fixed

• Fixed bugs when training BERT (with word piece tokenization) language models from scratch.
• Fixed incorrect special tokens being used with BERT models when training a new tokenizer.
• Fixed potential bugs with BERT tokenizer training.

0.23.3 - 2020-04-05

Fixed

• Fixed bug in QuestionAnsweringModel where the save_model() method wasn't being called properly.
• Fixed bug in calculating global step when resuming training.

0.23.2 - 2020-04-02

Fixed

• Prevent padding tokens being added when using openai-gpt and gpt2 models for language modeling.

0.23.1 - 2020-03-30

Fixed

• Fixed bug in binary classification evaluation when data only contains one label.
• Fixed typo in readme.

Changed

• Cache dir is no longer created when no_cache is used.

0.23.0 - 2020-03-30

Added

• Added support for training custom tokenizers.
• Added improved support for training language models from scratch.
• Added improved support for resuming training in classification, NER, and QnA tasks.

[0.22.1] - 2020-03-19

Added

• Added support for XLMRoberta for multi-label tasks.

0.22.0 - 2020-03-14

Added

• Added support for language model training (from scratch or fine-tuning).
• Added option to choose which metric should be used for early stopping.

Changed

• Switched to using the logging module over print for everything except running loss. (QuestionAnsweringModel - @travis-harper)
• Replaced more concatenated string paths with os.path.join() when creating training_progress_scores.csv.

0.21.5 - 2020-03-12

Changed

• Replaced concatenated string paths with os.path.join() when creating training_progress_scores.csv. @sarthakTUM

0.21.4 - 2020-03-12

Fixed

• Fixed issue with cached eval features being used even when using predict() in ClassificationModel and NERModel.

0.21.3 - 2020-03-03

Added

• Added classification report for NER for per-tag scores. @seo-95

0.21.2 - 2020-03-01

Fixed

• Fixed bug with empty answers in QuestionAnsweringModel. @jacky18008

0.21.1 - 2020-02-29

Fixed

• Fixed bug in ConvAIModel where reprocess_input_data and use_cached_eval_features args were ignored.

0.21.0 - 2020-02-29

Added

• Added support for training Conversational AI models.
• Added cuda_device parameter to MultiLabelClassificationModel.

Fixed

• Fixed bug in MultiModalClassificationModel when num_labels is not given.

0.20.3 - 2020-02-22

Changed

• reprocess_input_data changed to True by default.
• use_cached_eval_features changed to False by default.

0.20.2 - 2020-02-22

Fixed

• Fixed issue with early stopping not working with Question Answering.

0.20.1 - 2020-02-22

Fixed

• Fixed issue with predict() function using cached features.

0.20.0 - 2020-02-21

Added

• Added support for Multi Modal Classification tasks.

Fixed

• Fixed missing variable wandb_available in Multilabel Classification.

0.19.9 - 2020-02-18

Fixed

• Fixed missing variable wandb_available in Multilabel Classification.

0.19.8 - 2020-02-14

Fixed

• Fixed missing variable wandb_available in Multilabel Classification.

0.19.7 - 2020-02-11

Changed

• Removed wandb as a dependency. Installing wandb in now optional.

0.19.6 - 2020-02-11

Added

• Added support for multilabel classification with FlauBERT.@adrienrenaud

0.19.5 - 2020-02-11

Added

• Added support for FlauBERT with classification tasks (except multi-label).@adrienrenaud

0.19.4 - 2020-02-04

Fixed

• Fixed error that occured when args is not given when creating a Model.

0.19.3 - 2020-02-03

Added

• Added manual_seed to global_args . Can be used when training needs to be reproducible.

0.19.2 - 2020-01-31

Added

• Added early stopping support for NER and Question Answering tasks.

Fixed

• Fixed issue with nested file paths not being created.
• wandb_kwargs not being used with NER and Question Answering.

0.19.1 - 2020-01-27

Fixed

• Fixed issue with evaluation at the end of epochs not being considered for best model.

0.19.0 - 2020-01-26

Added

• Added early stopping support for Classification tasks.
  • Set use_early_stopping to True to enable.
• The best model will now be saved to {output_dir}/best_model/ when evaluate_during_training is used.
• Added evaluate_during_training_verbose to args dict to control whether evaluation during training outputs are printed to console.
• Added all-contributors to README to recognize contributors.

Changed

• Evaluation during training no longer displays progress bars.
• Evaluation during training no longer prints results to console by default.
• Moved model/results saving logic to _save_model for readability and maintainability.
• Updated README.

0.18.12 - 2020-01-25

Fixed

• Added missing extra SEP token in RoBERTa, CamemBERT, and XLMRoBERTA in sentence pair tasks.

0.18.11 - 2020-01-21

Added

• Added no_cache option to global_args which disables caching (saving and loading) of features to/from disk.

0.18.10 - 2020-01-20

Added

• Added Makefile with tests dependency installation, test code, formatter and types.
• Added setup.cfg file with Make configuration
• Added some tests for the functionality

Changed

• Files linted using flake8
• Files formatted using black
• Test tested with pytest
• Unused variables deleted

0.18.9 - 2020-01-20

Fixed

• Fixed bug with importing certain pre-trained models in MultiLabelClassificationModel .

0.18.8 - 2020-01-20

Added

• Added **kwargs to the init methods of ClassificationModel , MultiLabelClassificationModel , QuestionAnsweringModel , and NERModel . These will be passed to the from_pretrained() method of the underlying model class.

0.18.6 - 2020-01-18

Changed

• Reverted change made in 0.18.4 (Model checkpoint is no longer saved at the end of the last epoch as this is the same model saved in ouput_dir at the end of training).

Model checkpoint is now saved for all epochs again.

0.18.5 - 2020-01-18

Fixed

• Fixed bug when using sliding_window .

0.18.4 - 2020-01-17

Fixed

• Typo in classification_utils.py .

Changed

• Model checkpoint is no longer saved at the end of the last epoch as this is the same model saved in ouput_dir at the end of training.

0.18.3 - 2020-01-15

Fixed

• Potential bugfix for CamemBERT models which were giving identical outputs to all inputs.

0.18.2 - 2020-01-15

Added

• Added option to turn off model saving at the end of every epoch with save_model_every_epoch .

Fixed

• Fixed bug with missing tensorboard_folder key in certain situations.

Changed

• Moved args items common to all classes to one place ( config/global_args.py ) for maintainability. Does not make any usage changes.

0.18.1 - 2020-01-15

Fixed

• Fixed bug with missing regression key when using MultiLabelClassification.

0.18.0 - 2020-01-15

Added

• Sentence pair tasks are now supported.
• Regression tasks are now supported.
• use_cached_eval_features to args . Evaluation during training will now use cached features by default. Set to False if features should be reprocessed.

Changed

• Checkpoints saved at the end of an epoch now follow the `checkpoint-{global_step}-epoch-{epoch_number} format.

0.17.1 - 2020-01-14

Fixed

• Fixed wandb_kwargs key missing in args bug.

0.17.0 - 2020-01-14

Added

• Added new model XLM-RoBERTa. Can now be used with ClassificationModel and NERModel .

0.16.6 - 2020-01-13

Added

• Added evaluation scores from end-of-epoch evaluation to training_progress_scores.csv .

Fixed

• Typos in README.md .

0.16.5 - 2020-01-09

Fixed

• Reverted missed logging commands to print statements.

0.16.4 - 2020-01-09

Changed

• Removed logging import.

0.16.3 - 2020-01-09

Fixed

• Reverted to using print instead of logging as logging seems to be causing issues.

0.16.2 - 2020-01-08

Changed

• Changed print statements to logging.

0.16.1 - 2020-01-07

Added

• Added wandb_kwargs to args which can be used to specify keyword arguments to wandb.init() method.

0.16.0 - 2020-01-07

Added

• Added support for training visualization using the W&B framework.
• Added save_eval_checkpoints attribute to args which controls whether or not a model checkpoint will be saved with every evaluation.

0.15.7 - 2020-01-05

Added

• Added **kwargs for different accuracy measures during multilabel training.

0.15.6 - 2020-01-05

Added

• Added train_loss to training_progress_scores.csv (which contains the evaluation results of all checkpoints) in the output directory.

0.15.5 - 2020-01-05

Added

• Using evaluate_during_training now generates training_progress_scores.csv (which contains the evaluation results of all checkpoints) in the output directory.

0.15.4 - 2019-12-31

Fixed

• Fixed bug in QuestonAnsweringModel when using evaluate_during_training .

0.15.3 - 2019-12-31

Fixed

• Fixed bug in MultiLabelClassificationModel due to tensorboard_dir being missing in parameter dictionary.

Changed

• Renamed tensorboard_folder to tensorboard_dir for consistency.

0.19.8 - 2020-02-14

Fixed

• Fixed missing variable wandb_available in Multilabel Classification.

Added

• Added tensorboard_folder to parameter dictionary which can be used to specify the directory in which the tensorboard files will be stored.

0.15.1 - 2019-12-27

Added

• Added **kwargs to support different accuracy measures at training time.

0.15.0 - 2019-12-24

Added

• Added evaluate_during_training_steps parameter that specifies when evaluation should be performed during training.

Changed

• A model checkpoint will be created for each evaluation during training and the evaluation results will be saved along with the model.

0.14.0 - 2019-12-24

Added

• Added option to specify a GPU to be used when multiple GPUs are available. E.g.: cuda_device=1
• Added do_lower_case argument for uncased models.

Fixed

• Fixed possible bug with output directory not being created before evaluation is run when using evaluate_during_training .

0.13.4 - 2019-12-21

Fixed

• Fixed bug with when using eval_during_training with QuestionAnswering model.

0.13.3 - 2019-12-21

Fixed

• Fixed bug with loading Multilabel classification models.
• Fixed formatting in README.md.

0.13.2 - 2019-12-20

Fixed

• Fixed formatting in README.md.

0.13.1 - 2019-12-20

Fixed

• Bug in Multilabel Classification due to missing entries in default args dict.

0.13.0 - 2019-12-19

Added

• Sliding window feature for Binary and Multiclass Classification tasks.

0.12.0 - 2019-12-19

Added

• Minimal examples have been added to the examples directory as Python scripts.

Changed

• Readme updated to include the addition of examples.

0.11.2 - 2019-12-18

Fixed

• Evaluation during training fixed for multilabel classification.

0.11.1 - 2019-12-18

Fixed

• Broken multiprocessing support for NER tasks fixed.

0.11.0 - 2019-12-15

Added

• CamemBERT can now be used with NERModel.

Changed

• Readme changed to include CamemBERT for NER.

0.10.8 - 2019-12-15

Added

• DistilBERT can now be used with NERModel.

Changed

• Readme changed to include DistilBERT for NER.

0.10.7 - 2019-12-15

Added

• This CHANGELOG file to hopefully serve as an evolving example of a standardized open source project CHANGELOG.