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]

  • …

0.0.6 (2021-07-16)

Fixed

  • Fixed max supported version of requirements like torchtext (Issue #30).

0.0.5 (2021-05-20)

Fixed

  • Fix clamping on MaskedAttention and MultitokenAvgEmbed to small value less than 1. That’s the proper behavior to re-scale attentions that sum to less than 1 and ignore ones that sum to 0. This was only causing a minor decrease in F1 score, though.

Added

  • Add –use_gpu option to CLI (before it would always use a GPU if available)

  • Colab notebooks (see README)

  • Conda compatibility (see README)

Changed

  • Simplify fix_pool_weights code. Same behavior.

0.0.4 (2021-04-20)

Fixed

  • Fixed field_mask on FieldsEmbedNet by clamping values to 1. Before, this mask was multiplying field embeddings by the field length in tokens. Now, the correct behavior is implemented: multiply by 0 the empty fields, and by 1 the non-empty fields. This was only causing a minor decrease in F1 score, though.

0.0.3 (2021-04-20)

Added

  • Example on how to do pairwise matching of candidate pairs at notebooks/End-to-End-Matching-Example.ipynb.

  • Enable return of field_embedding_dict from BlockerNet for assisting pairwise matching. Use return_field_embeddings parameter.

  • Enable return of attention scores for interpretation from MultitokenAttentionEmbed. Use _forward method.

Changed

  • Use of LayerNorm in EntityAvgPoolNet instead of F.normalize, it’s less “esoteric”.

  • Zeroing of empty field embeddings in FieldsEmbedNet instead of BlockerNet.

0.0.2 (2021-04-06)

Added

  • Documentation.

  • example-data/ in repo.

Changed

  • Simpler API for validation and test.

  • Better naming of various API objects and methods.

  • Consider -1 in min_epochs since epochs start from 0.

  • Upgrade pytorch-metric-learning to 0.9.98.

0.0.1 (2021-03-30)

  • First release on PyPI.