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CHANGELOG.md

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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.

Unreleased

v1.2.2 - 2020-11-17

Changed

  • Changed AllenNLP dependency for releases to allow for a range of versions, instead of being pinned to an exact version.
  • There will now be multiple Docker images pushed to Docker Hub for releases, each corresponding to a different supported CUDA version (currently just 10.2 and 11.0).

Fixed

  • Fixed pair-classification-esim pretrained model.
  • Fixed ValueError error message in Seq2SeqDatasetReader.
  • Better check for start and end symbols in Seq2SeqDatasetReader that doesn't fail for BPE-based tokenizers.

Added

  • Added short_description field to ModelCard.
  • Information updates for all model cards.

v1.2.1 - 2020-11-10

Added

  • Added the TaskCard class and task cards for common tasks.
  • Added a test for the interpret functionality

Changed

  • Added more information to model cards for pair classification models (pair-classification-decomposable-attention-elmo, pair-classification-roberta-snli, pair-classification-roberta-mnli, pair-classification-esim).

Fixed

  • Fixed TransformerElmo config to work with the new AllenNLP
  • Pinned the version of torch more tightly to make AMP work
  • Fixed the somewhat fragile Bidaf test

v1.2.0 - 2020-10-29

Changed

  • Updated docstring for Transformer MC.
  • Added more information to model cards for multiple choice models (mc-roberta-commonsenseqa, mc-roberta-piqa, and mc-roberta-swag).

Fixed

  • Fixed many training configs to work out-of-the box. These include the configs for bart_cnn_dm, swag, bidaf, bidaf_elmo, naqanet, and qanet.
  • Fixed minor bug in MaskedLanguageModel, where getting token ids used hard-coded assumptions (that could be wrong) instead of our standard utility function.

v1.2.0rc1 - 2020-10-22

Added

  • Added dataset reader support for SQuAD 2.0 with both the SquadReader and TransformerSquadReader.
  • Updated the SQuAD v1.1 metric to work with SQuAD 2.0 as well.
  • Updated the TransformerQA model to work for SQuAD 2.0.
  • Added official support for Python 3.8.
  • Added a json template for model cards.
  • Added training_config as a field in model cards.
  • Added a BeamSearchGenerator registrable class which can be provided to a NextTokenLM model to utilize beam search for predicting a sequence of tokens, instead of a single next token. BeamSearchGenerator is an abstract class, so a concrete registered implementation needs to be used. One implementation is provided so far: TransformerBeamSearchGenerator, registered as transformer, which will work with any NextTokenLM that uses a PretrainedTransformerEmbedder.
  • Added an overrides parameter to pretrained.load_predictor().

Changed

  • rc-transformer-qa pretrained model is now an updated version trained on SQuAD v2.0.
  • skip_invalid_examples parameter in SQuAD dataset readers has been deprecated. Please use skip_impossible_questions instead.

Fixed

  • Fixed lm-masked-language-model pretrained model.
  • Fixed BART for latest transformers version.
  • Fixed BiDAF predictor and BiDAF predictor tests.
  • Fixed a bug with Seq2SeqDatasetReader that would cause an exception when the desired behavior is to not add start or end symbols to either the source or the target and the default start_symbol or end_symbol are not part of the tokenizer's vocabulary.

v1.1.0 - 2020-09-08

Fixed

  • Updated LanguageModelTokenEmbedder to allow allow multiple token embedders, but only use first with non-empty type
  • Fixed evaluation of metrics when using distributed setting.
  • Fixed a bug introduced in 1.0 where the SRL model did not reproduce the original result.

v1.1.0rc4 - 2020-08-21

Added

  • Added regression tests for training configs that run on a scheduled workflow.
  • Added a test for the pretrained sentiment analysis model.
  • Added way for questions from quora dataset to be concatenated like the sequences in the SNLI dataset.

v1.1.0rc3 - 2020-08-12

Fixed

  • Fixed GraphParser.get_metrics so that it expects a dict from F1Measure.get_metric.
  • CopyNet and SimpleSeq2Seq models now work with AMP.
  • Made the SST reader a little more strict in the kinds of input it accepts.

v1.1.0rc2 - 2020-07-31

Changed

  • Updated to PyTorch 1.6.

Fixed

  • Updated the RoBERTa SST config to make proper use of the CLS token
  • Updated RoBERTa SNLI and MNLI pretrained models for latest transformers version

Added

  • Added BART model
  • Added ModelCard and related classes. Added model cards for all the pretrained models.
  • Added a field registered_predictor_name to ModelCard.
  • Added a method load_predictor to allennlp_models.pretrained.
  • Added support to multi-layer decoder in simple seq2seq model.

v1.1.0rc1 - 2020-07-14

Fixed

  • Updated the BERT SRL model to be compatible with the new huggingface tokenizers.
  • CopyNetSeq2Seq model now works with pretrained transformers.
  • A bug with NextTokenLM that caused simple gradient interpreters to fail.
  • A bug in training_config of qanet and bimpm that used the old version of regularizer and initializer.
  • The fine-grained NER transformer model did not survive an upgrade of the transformers library, but it is now fixed.
  • Fixed many minor formatting issues in docstrings. Docs are now published at https://docs.allennlp.org/models/.

Changed

  • CopyNetDatasetReader no longer automatically adds START_TOKEN and END_TOKEN to the tokenized source. If you want these in the tokenized source, it's up to the source tokenizer.

Added

  • Added two models for fine-grained NER
  • Added a category for multiple choice models, including a few reference implementations
  • Implemented manual distributed sharding for SNLI dataset reader.

v1.0.0 - 2020-06-16

No additional note-worthy changes since rc6.

v1.0.0rc6 - 2020-06-11

Changed

  • Removed deprecated "simple_seq2seq" predictor

Fixed

  • Replaced deepcopy of Instances with new Instance.duplicate() method.
  • A bug where pretrained sentence taggers would fail to be initialized because some of the models were not imported.
  • A bug in some RC models that would cause mixed precision training to crash when using NVIDIA apex.
  • Predictor names were inconsistently switching between dashes and underscores. Now they all use underscores.

Added

  • Added option to SemanticDependenciesDatasetReader to not skip instances that have no arcs, for validation data
  • Added a default predictors to several models
  • Added sentiment analysis models to pretrained.py
  • Added NLI models to pretrained.py

v1.0.0rc5 - 2020-05-14

Changed

  • Moved the models into categories based on their format

Fixed

  • Made transformer_qa predictor accept JSON input with the keys "question" and "passage" to be consistent with the reading_comprehension predictor.

Added

  • conllu dependency (previously part of allennlp's dependencies)

v1.0.0rc4 - 2020-05-14

We first introduced this CHANGELOG after release v1.0.0rc4, so please refer to the GitHub release notes for this and earlier releases.