Skip to content
This repository was archived by the owner on Jul 7, 2023. It is now read-only.

v.1.12.0
Compare
Choose a tag to compare
@afrozenator afrozenator released this 11 Jan 23:46
v.1.12.0
0b099cf

Summary of changes:

PRs:

  • A lot of code cleanup thanks a ton to @lgeiger ! This goes a long way with regards to code maintainability and is much appreciated. Ex: PR #1361 , #1350 , #1344 , #1346 , #1345 , #1324
  • Fixing LM decode, thanks @mikeymezher - PR #1282
  • More fast decoding by @gcampax, thanks! - PR #999
  • Avoid error on beam search - PR #1302 by @aeloyq , thanks!
  • Fix invalid list comprehension, unicode simplifications, py3 fixes #1343, #1318 , #1321, #1258 thanks @cclauss !
  • Fix is_generate_per_split hard to spot bug, thanks a lot to @kngxscn in PR #1322
  • Fix py3 compatibility issues in PR #1300 by @ywkim , thanks a lot again!
  • Separate train and test data in MRPC and fix broken link in PR #1281 and #1247 by @ywkim - thanks for the hawk eyed change!
  • Fix universal transformer decoding by @artitw in PR #1257
  • Fix babi generator by @artitw in PR #1235
  • Fix transformer moe in #1233 by @twilightdema - thanks!
  • Universal Transformer bugs corrected in #1213 by @cfiken - thanks!
  • Change beam decoder stopping condition, makes decode faster in #965 by @mirkobronzi - many thanks!
  • Bug fix, problem_0_steps variable by @senarvi in #1273
  • Fixing a typo, by @hsm207 in PR #1329 , thanks a lot!

New Model and Problems:

  • New problem and model by @artitw in PR #1290 - thanks!
  • New model for scalar regression in PR #1332 thanks to @Kotober
  • Text CNN for classification in PR #1271 by @ybbaigo - thanks a lot!
  • en-ro translation by @lukaszkaiser !
  • CoNLL2002 Named Entity Recognition problem added in #1253 by @ybbaigo - thanks!

New Metrics:

  • Pearson Correlation metrics in #1274 by @luffy06 - thanks a lot!
  • Custom evaluation metrics, this was one of the most asked features, thanks a lot @ywkim in PR #1336
  • Word Error Rate metric by @stefan-falk in PR #1242 , many thanks!
  • SARI score for paraphrasing added.

Enhancements:

  • Fast decoding !! Huge thanks to @aeloyq in #1295
  • Fast GELU unit
  • Relative dot product visualization PR #1303 thanks @aeloyq !
  • New MTF models and enhacements, thanks to Noam, Niki and the MTF team
  • Custom eval hooks in PR #1284 by @theorm - thanks a lot !

RL:
Lots of commits to Model Based Reinforcement Learning code by @konradczechowski @koz4k @blazejosinski @piotrmilos - thanks all !

Assets 2
Loading