Skip to content

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#

v1.1.0 - 2020-09-08#

Fixed#

  • 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.