r0.4.1.dev0

Summary

• Dev release to test out the upcoming 0.4.1.

What's Changed

• Update python version in readme to 3.8 by @haifeng-jin in #618
• Modify our pip install line so we upgrade tf by @mattdangerw in #616
• Use Adam optimizer for quick start by @mattdangerw in #620
• Clean up class name and self in calls to super() by @mbrukman in #628
• Update word_piece_tokenizer.py by @ADITYADAS1999 in #617
• Add DeBERTaV3 Conversion Script by @abheesht17 in #633
• Add AlbertTokenizer and AlbertPreprocessor by @abheesht17 in #627
• Create Backbone base class by @jbischof in #621
• Add TPU testing by @chenmoneygithub in #591
• Add Base Preprocessor Class by @abheesht17 in #638
• Add keras_nlp.samplers by @chenmoneygithub in #563
• Add ALBERT Backbone by @abheesht17 in #622
• Add a small script to count parameters in our presets by @mattdangerw in #610
• Clean up examples/ directory by @ADITYADAS1999 in #637
• Fix Small BERT Typo by @abheesht17 in #651
• Rename examples/bert -> examples/bert_pretraining by @mattdangerw in #647
• Add FNet Preprocessor by @abheesht17 in #646
• Add FNet Backbone by @abheesht17 in #643
• Small DeBERTa Docstring Fixes by @abheesht17 in #666
• Add Fenced Docstring Testing by @abheesht17 in #640
• Corrected the epsilon value by @soma2000-lang in #665
• Consolidate docstring formatting weirdness in Backbone and Preprocessor base classes by @mattdangerw in #654
• Fix value_dim in TransformerDecoder's cross-attn layer by @abheesht17 in #667
• Add ALBERT Presets by @abheesht17 in #655
• Add Base Task Class by @abheesht17 in #671
• Implement TopP, TopK and Beam samplers by @chenmoneygithub in #652
• Add FNet Presets by @abheesht17 in #659
• Bump the year to 2023 by @mattdangerw in #679
• Add BART Backbone by @abheesht17 in #661
• Handle trainable and name in the backbone base class by @mattdangerw in #680
• Ignore Task Docstring for Testing by @abheesht17 in #683
• Light-weight benchmarking script by @NusretOzates in #664
• Conditionally import tf_text everywhere by @mattdangerw in #684
• Expose token_embedding as a Backbone Property by @abheesht17 in #676
• Move from_preset to base tokenizer classes by @shivance in #673
• add f_net_classifier and f_net_classifier_test by @ADITYADAS1999 in #670
• import rouge_scorer directly from rouge_score package by @sampathweb in #691
• Fix typo in requirements file juypter -> jupyter by @mattdangerw in #693
• Temporary fix to get nightly green again by @mattdangerw in #696
• GPT2 Text Generation APIs by @chenmoneygithub in #592
• Run keras saving tests on nightly and fix RobertaClassifier test by @mattdangerw in #692
• Speed up pip install keras-nlp; simplify deps by @mattdangerw in #697
• Add AlbertClassifier by @shivance in #668
• Make tokenizer, backbone, preprocessor properties settable on base class by @mattdangerw in #700
• Update to latest black by @mattdangerw in #708
• RobertaMaskedLM task and preprocessor by @mattdangerw in #653
• Default compilation for BERT/RoBERTa classifiers by @jbischof in #695
• Add start/end token padding to GPT2Preprocessor by @chenmoneygithub in #704
• Don't install tf stable when building our nightly image by @mattdangerw in #711
• Add OPT Backbone and Tokenizer by @mattdangerw in #699
• Small OPT Doc-string Edits by @abheesht17 in #716
• Default compilation other classifiers by @Plutone11011 in #714
• Add BartTokenizer and BART Presets by @abheesht17 in #685
• Add an add_prefix_space Arg in BytePairTokenizer by @shivance in #715
• Opt presets by @mattdangerw in #707
• fix import of tensorflow_text in tf_utils by @sampathweb in #723
• Check for masked token in roberta tokenizer by @mattdangerw in #742
• Improve test coverage for special tokens in model tokenizers by @mattdangerw in #743
• Fix the sampler truncation strategy by @chenmoneygithub in #713
• Add ALBERT Conversion Script by @abheesht17 in #736
• Add FNet Conversion Script by @abheesht17 in #737
• Add BART Conversion Script by @abheesht17 in #739
• Pass Correct LayerNorm Epsilon value to TransformerEncoder in Backbones by @TheAthleticCoder in #731
• Improving the layer Description. by @Neeshamraghav012 in #734
• Adding ragged support to SinePositionEncoding by @apupneja in #751
• Fix trailing space by @mattdangerw in #755
• Adding an AlbertMaskedLM task model and preprocessor by @shivance in #725
• New docstring example for TokenAndPosition Embedding layer. by @Neeshamraghav012 in #760
• Add a note for TPU issues for deberta_v3 by @mattdangerw in #758
• Add missing exports to models API by @mattdangerw in #763
• Autogenerate preset table by @Cyber-Machine in #690
• Version bump to 0.5.0 by @mattdangerw in #767

New Contributors

• @haifeng-jin made their first contribution in #618
• @mbrukman made their first contribution in #628
• @soma2000-lang made their first contribution in #665
• @NusretOzates made their first contribution in #664
• @shivance made their first contribution in #673
• @Plutone11011 made their first contribution in #714
• @TheAthleticCoder made their first contribution in #731
• @Neeshamraghav012 made their first contribution in #734
• @apupneja made their first contribution in #751
• @Cyber-Machine made their first contribution in #690

Full Changelog: v0.4.0...v0.4.1.dev0

v0.4.0

The 0.4 release adds support for pretrained models to the library via keras_nlp.models. You can read an
introduction to the new API in our Getting Started Guide.

If you encounter any problems or have questions, please open an issue!

Breaking Changes

• Renamed keras_nlp.layers.MLMHead -> keras_nlp.layers.MaskedLMHead.
• Renamed keras_nlp.layers.MLMMaskGenerator -> keras_nlp.layers.MaskedLMMaskGenerator.
• Renamed keras_nlp.layers.UnicodeCharacterTokenizer -> keras_nlp.layers.UnicodeCodepointTokenizer.
• Switched the default of lowercase in keras_nlp.tokenizers.WordPieceTokenizer from True to False.
• Renamed the token id output of MaskedLMMaskGenerator from "tokens" to "tokens_ids".

Summary

• Added the keras_nlp.models API.
  • Added support for BERT, DistilBERT, RoBERTa, and XLM-RoBERTa models and pretrained checkpoints.
  • See our Getting Started Guide for more details.
• Added new metrics.
  • keras_nlp.metrics.Bleu and keras_nlp.metrics.EditDistance.
• Added new vocabulary training utilities.
  • keras_nlp.tokenizers.compute_word_piece_vocabulary and keras_nlp.tokenizers.compute_sentence_piece_proto.
• Added new preprocessing layers.
  • keras_nlp.layers.RandomSwap and keras_nlp.layers.RandomDeletion.

What's Changed

• Add Edit Distance Metric by @abheesht17 in #231
• Minor fix to simplify and test handling of max_length prompts by @jbischof in #258
• Remove split regex args for WordPieceTokenizer by @mattdangerw in #255
• Add instructions on installing the latest changes by @mattdangerw in #261
• Add warning when k > vocab_size in top_k_search by @jbischof in #260
• Fix keras library imports and usage by @jbischof in #262
• Add BLEU Score by @abheesht17 in #222
• Configure GKE-based accelerator testing by @chenmoneygithub in #265
• Added WordPieceTokenizer training function by @jessechancy in #256
• Add requirements.txt for cloud build by @chenmoneygithub in #267
• Global Seed Bug Fix by @jessechancy in #269
• Update accelerator testing to use the new GCP project by @chenmoneygithub in #272
• Fixed typo: "recieved" by @ehrencrona in #273
• Reuse dense pooled output for fine tuning by @mattdangerw in #251
• Simplify BERT modeling, use keras embeddings by @mattdangerw in #253
• Rename UnicodeCharacterTokenizer>UnicodeCodepointTokenizer by @mattdangerw in #254
• Add README for accelerator testing config folder by @chenmoneygithub in #276
• Random Deletion Layer by @aflah02 in #214
• Made trainer more efficient. Loading full files instead of using TextLineDataset. by @jessechancy in #280
• Use KerasNLP for BERT preprocessing for GLUE by @mattdangerw in #252
• Minor fixes to the Random Deletion Layer by @aflah02 in #286
• Fixes for WordPieceTrainer by @aflah02 in #293
• Update default to strip_accents=False by @jessechancy in #289
• Move Bert to models folder by @jbischof in #288
• Make Decoding Functions Graph-compatible (with XLA Support!) by @abheesht17 in #271
• SentencePieceTrainer by @aflah02 in #281
• Rename models.Bert() to models.BertCustom() by @jbischof in #310
• Add a test for variable sequence length inputs by @mattdangerw in #313
• Support checkpoint loading for BertBase by @jbischof in #299
• RoBERTa pretrained model forward pass by @jessechancy in #304
• Register objects as serializable by @mattdangerw in #292
• Style merging for Bert and Roberta by @jbischof in #315
• Streamline and speed up tests by @jbischof in #324
• Add Support for CJK Char Splitting for WordPiece Tokenizer by @abheesht17 in #318
• Clean up model input names for consistency by @mattdangerw in #327
• Return a single tensor from roberta by @mattdangerw in #328
• BERT, RoBERTa: Add model.compile UTs by @abheesht17 in #330
• Continue rename of bert model inputs by @mattdangerw in #329
• Text Generation Utilities: Add Support for Ragged Inputs by @abheesht17 in #300
• bert_base_zh, bert_base_multi_cased: Add BERT Base Variants by @abheesht17 in #319
• WordPiece vocabularies trainer on Wikipedia dataset by @jessechancy in #316
• Use the exported ragged ops for RandomDeletion by @mattdangerw in #332
• Random Swap Layer by @aflah02 in #224
• Fixes for Random Deletion Layer by @aflah02 in #339
• Move cloudbuild to a hidden directory by @mattdangerw in #345
• Fix the build by @mattdangerw in #349
• Migrating from Datasets to TFDS for GLUE Example by @aflah02 in #340
• Move network_tests into keras_nlp/ by @mattdangerw in #344
• Stop hardcoding 2.9 by @mattdangerw in #351
• Add BERT Large by @abheesht17 in #331
• Add normalize_first arg to Transformer Layers by @abheesht17 in #350
• Add Small BERT Variants by @abheesht17 in #338
• Beam Search: Add Ragged and XLA Support by @abheesht17 in #341
• Fix download paths for bert weights by @mattdangerw in #356
• Add a BertPreprocessor class by @mattdangerw in #343
• Text Generation Functions: Add Benchmark Script by @abheesht17 in #342
• Improve readability for encoder/decoder blocks by @mattdangerw in #353
• Add GPT-2 Model and its Variants by @abheesht17 in #354
• Clean up BERT, RoBERTa doc-strings by @abheesht17 in #359
• Create unique string id for each BERT backbone by @jbischof in #361
• Use model.fit() for BERT Example by @abheesht17 in #360
• Minor Fixes in BertPreprocessor Layer by @abheesht17 in #373
• Clone user passed initializers called multiple times by @mattdangerw in #371
• Update BERT model file structure by @mattdangerw in #376
• Move gpt model code into a directory by @mattdangerw in #379
• Move roberta model code into a directory by @mattdangerw in #380
• Reorg test directories by @mattdangerw in #384
• Add XLM-RoBERTa by @abheesht17 in #372
• Add DistilBERT by @abheesht17 in #382
• Stop running CI on Windows by @mattdangerw in #386
• Fix Bert serialization by @mattdangerw in #385
• Improve MacOS support and pin tensorflow version during testing by @mattdangerw in #383
• Unify BERT model API in one class by @jbischof in #387
• Add from_preset constructor to BertPreprocessor by @jbischof in #390
• More robustly test BERT preprocessing by @mattdangerw in #394
• Move name and trainable to kwargs by @jbischof in #399
• Add backbone as property for task models by @jbischof in #398
• Set default name of Bert instance to "backbone" by @jbischof in #397
• Fix gpt2 serialization by @mattdangerw in https://github.com/kera...

v0.4.0.dev0

⚠️⚠️⚠️ This is a pre-release for testing purposes, documentation for this release has not yet shipped.

The KerasNLP 0.4 adds support for pretrained models to the API via keras_nlp.models. If you encounter any problems or have questions, please open an issue or discussion of the discussion tab!

Breaking Changes

• Renamed keras_nlp.layers.MLMHead -> keras_nlp.layers.MaskedLMHead.
• Renamed keras_nlp.layers.MLMMaskGenerator -> keras_nlp.layers.MaskedLMMaskGenerator.
• Renamed keras_nlp.layers.UnicodeCharacterTokenizer -> keras_nlp.layers.UnicodeCodepointTokenizer.
• Switched the default of lowercase in keras_nlp.tokenizers.WordPieceTokenizer from True to False.
• Renamed the token id output of MaskedLMMaskGenerator from "tokens" to "tokens_ids".

Summary

• Added the keras_nlp.models API.
  • Adds support for BERT, DistilBERT, RoBERTa, and XLM-RoBERTa models and pretrained checkpoints.
• Added new metrics.
  • keras_nlp.metrics.Bleu and keras_nlp.metrics.EditDistance.
• Added new vocabulary training utilities.
  • keras_nlp.tokenizers.compute_word_piece_vocabulary and keras_nlp.tokenizers.compute_sentence_piece_proto.

What's Changed

• Add Edit Distance Metric by @abheesht17 in #231
• Minor fix to simplify and test handling of max_length prompts by @jbischof in #258
• Remove split regex args for WordPieceTokenizer by @mattdangerw in #255
• Add instructions on installing the latest changes by @mattdangerw in #261
• Add warning when k > vocab_size in top_k_search by @jbischof in #260
• Fix keras library imports and usage by @jbischof in #262
• Add BLEU Score by @abheesht17 in #222
• Configure GKE-based accelerator testing by @chenmoneygithub in #265
• Added WordPieceTokenizer training function by @jessechancy in #256
• Add requirements.txt for cloud build by @chenmoneygithub in #267
• Global Seed Bug Fix by @jessechancy in #269
• Update accelerator testing to use the new GCP project by @chenmoneygithub in #272
• Fixed typo: "recieved" by @ehrencrona in #273
• Reuse dense pooled output for fine tuning by @mattdangerw in #251
• Simplify BERT modeling, use keras embeddings by @mattdangerw in #253
• Rename UnicodeCharacterTokenizer>UnicodeCodepointTokenizer by @mattdangerw in #254
• Add README for accelerator testing config folder by @chenmoneygithub in #276
• Random Deletion Layer by @aflah02 in #214
• Made trainer more efficient. Loading full files instead of using TextLineDataset. by @jessechancy in #280
• Use KerasNLP for BERT preprocessing for GLUE by @mattdangerw in #252
• Minor fixes to the Random Deletion Layer by @aflah02 in #286
• Fixes for WordPieceTrainer by @aflah02 in #293
• Update default to strip_accents=False by @jessechancy in #289
• Move Bert to models folder by @jbischof in #288
• Make Decoding Functions Graph-compatible (with XLA Support!) by @abheesht17 in #271
• SentencePieceTrainer by @aflah02 in #281
• Rename models.Bert() to models.BertCustom() by @jbischof in #310
• Add a test for variable sequence length inputs by @mattdangerw in #313
• Support checkpoint loading for BertBase by @jbischof in #299
• RoBERTa pretrained model forward pass by @jessechancy in #304
• Register objects as serializable by @mattdangerw in #292
• Style merging for Bert and Roberta by @jbischof in #315
• Streamline and speed up tests by @jbischof in #324
• Add Support for CJK Char Splitting for WordPiece Tokenizer by @abheesht17 in #318
• Clean up model input names for consistency by @mattdangerw in #327
• Return a single tensor from roberta by @mattdangerw in #328
• BERT, RoBERTa: Add model.compile UTs by @abheesht17 in #330
• Continue rename of bert model inputs by @mattdangerw in #329
• Text Generation Utilities: Add Support for Ragged Inputs by @abheesht17 in #300
• bert_base_zh, bert_base_multi_cased: Add BERT Base Variants by @abheesht17 in #319
• WordPiece vocabularies trainer on Wikipedia dataset by @jessechancy in #316
• Use the exported ragged ops for RandomDeletion by @mattdangerw in #332
• Random Swap Layer by @aflah02 in #224
• Fixes for Random Deletion Layer by @aflah02 in #339
• Move cloudbuild to a hidden directory by @mattdangerw in #345
• Fix the build by @mattdangerw in #349
• Migrating from Datasets to TFDS for GLUE Example by @aflah02 in #340
• Move network_tests into keras_nlp/ by @mattdangerw in #344
• Stop hardcoding 2.9 by @mattdangerw in #351
• Add BERT Large by @abheesht17 in #331
• Add normalize_first arg to Transformer Layers by @abheesht17 in #350
• Add Small BERT Variants by @abheesht17 in #338
• Beam Search: Add Ragged and XLA Support by @abheesht17 in #341
• Fix download paths for bert weights by @mattdangerw in #356
• Add a BertPreprocessor class by @mattdangerw in #343
• Text Generation Functions: Add Benchmark Script by @abheesht17 in #342
• Improve readability for encoder/decoder blocks by @mattdangerw in #353
• Add GPT-2 Model and its Variants by @abheesht17 in #354
• Clean up BERT, RoBERTa doc-strings by @abheesht17 in #359
• Create unique string id for each BERT backbone by @jbischof in #361
• Use model.fit() for BERT Example by @abheesht17 in #360
• Minor Fixes in BertPreprocessor Layer by @abheesht17 in #373
• Clone user passed initializers called multiple times by @mattdangerw in #371
• Update BERT model file structure by @mattdangerw in #376
• Move gpt model code into a directory by @mattdangerw in #379
• Move roberta model code into a directory by @mattdangerw in #380
• Reorg test directories by @mattdangerw in #384
• Add XLM-RoBERTa by @abheesht17 in #372
• Add DistilBERT by @abheesht17 in #382
• Stop running CI on Windows by @mattdangerw in #386
• Fix Bert serialization by @mattdangerw in #385
• Improve MacOS support and pin tensorflow version during testing by @mattdangerw in #383
• Unify BERT model API in one class by @jbischof in #387
• Add from_preset constructor to BertPreprocessor by @jbischof in #390
• More robustly test BERT preprocessing by @mattdangerw in #394
• Move name and trainable to kwargs by @jbischof in #399
• Add backbone as property for task models by @jbischof in #398
• Set default name of Bert instance to "backbone" by @jbischof in #397
• Fix gpt2 serialization by @mattdangerw in #391
• Fix distilbert serialization by @mattdangerw in #392
• Fix roberta and xlm-roberta serialization by @mattdangerw in https:...

v0.3.1

Summary

• Add keras_nlp.tokenizers.BytePairTokenizer with tf.data friendly support for the tokenization used by GPT-2, RoBERTa and other models.
• Remove the hard dependency on tensorflow and tensorflow-text when pip installing on MacOS, to accommodate M1 chips. See this section of our contributor guide for more information on MacOS development.

What's Changed

• Cherry picks 0.3 by @mattdangerw in #454
• Bump version for 0.3.1 pre release by @mattdangerw in #456
• Remove dev prefix for 0.3.1 release by @mattdangerw in #457

Full Changelog: v0.3.0...v0.3.1

v0.3.0

Summary

• Added keras_nlp.tokenizers.SentencePieceTokenizer.
• Added two token packing layers keras_nlp.layers.StartEndPacker and keras_nlp.layers.MultiSegmentPacker.
• Added two metrics, keras_nlp.metrics.RougeL and keras_nlp.metrics.RougeN based on the rouge-score package.
• Added five utilities for generating sequences, keras_nlp.utils.greedy_search, keras_nlp.utils.random_search, keras_nlp.utils.top_k_search, keras_nlp.utils.top_p_search, keras_nlp.utils.beam_search.

What's Changed

• Greedy text generation util by @chenmoneygithub in #154
• Remove incorrect embedding size limit by @mattdangerw in #195
• Fix inits for bert heads by @mattdangerw in #192
• Add keras.io links to README by @mattdangerw in #196
• Minor Corrections In ROADMAP.md by @saiteja13427 in #200
• Fix Loose Dependency Imports by @abheesht17 in #199
• Reorganize examples by @mattdangerw in #179
• Remove bert config arguments from README by @mattdangerw in #205
• Add checkpoints to BERT training by @chenmoneygithub in #184
• Run keras tuner from a temp directory by @mattdangerw in #202
• Token and position embedding minor fixes by @mattdangerw in #203
• Correct typo in WordPieceTokenizer by @abheesht17 in #208
• Add TPU support to BERT example by @chenmoneygithub in #207
• Remove type annotations for complex types by @mattdangerw in #194
• Issue 182: Modified TransformerDecoder with optional parameter by @jessechancy in #217
• Add StartEndPacker layer by @abheesht17 in #221
• Add a layer for packing inputs for BERT-likes by @mattdangerw in #88
• Ignore UserWarning to fix nightly testing breakage by @chenmoneygithub in #227
• Add ROUGE Metric by @abheesht17 in #122
• Allow long lines for links in docstrings by @mattdangerw in #229
• Random Sampling Util for Text Generation by @jessechancy in #228
• added top k search util by @jessechancy in #232
• top p search and testing by @jessechancy in #233
• Add a SentencePiece tokenizer by @mattdangerw in #218
• Add cloud training support for BERT example by @chenmoneygithub in #226
• Bump version to 0.3.0 for upcoming release by @mattdangerw in #239
• Add support for StartEndPacker packing 2D tensor by @jessechancy in #240
• Fixed Bug with Unicode Tokenizer Vocab Size by @aflah02 in #243
• Fixed Import for top_p_search util by @aflah02 in #245
• MultiSegmentPacker support for 2D dense tensor by @jessechancy in #244
• Minor fixes for multi-segment packer by @mattdangerw in #246
• Add beam search decoding util by @jessechancy in #237

New Contributors

• @saiteja13427 made their first contribution in #200
• @jessechancy made their first contribution in #217

Full Changelog: v0.2.0...v0.3.0

v0.2.0

Summary

• Documentation live on keras.io.
• Added two tokenizers: ByteTokenizer and UnicodeCharacterTokenizer.
• Added a Perplexity metric.
• Added three layers TokenAndPositionEmbedding, MLMMaskGenerator and MLMHead.
• Contributing guides and roadmap.

What's Changed

• Add Byte Tokenizer by @abheesht17 in #80
• Fixing rank 1 outputs for WordPieceTokenizer by @aflah02 in #92
• Add tokenizer accessors to the base class by @mattdangerw in #89
• Fix word piece attributes by @mattdangerw in #97
• Small fix: change assertEquals to assertEqual by @chenmoneygithub in #103
• Added a Learning Rate Schedule for the BERT Example by @Stealth-py in #96
• Add Perplexity Metric by @abheesht17 in #68
• Use the black profile for isort by @mattdangerw in #117
• Update README with release information by @mattdangerw in #118
• Add a class to generate LM masks by @chenmoneygithub in #61
• Add docstring testing by @mattdangerw in #116
• Fix broken docstring in MLMMaskGenerator by @chenmoneygithub in #121
• Adding a UnicodeCharacterTokenizer by @aflah02 in #100
• Added Class by @adhadse in #91
• Fix bert example so it is runnable by @mattdangerw in #123
• Fix the issue that MLMMaskGenerator does not work in graph mode by @chenmoneygithub in #131
• Actually use layer norm epsilon in encoder/decoder by @mattdangerw in #133
• Whitelisted formatting and lint check targets by @adhadse in #126
• Updated CONTRIBUTING.md for setup of venv and standard pip install by @adhadse in #127
• Fix mask propagation of transformer layers by @chenmoneygithub in #139
• Fix masking for TokenAndPositionEmbedding by @mattdangerw in #140
• Fixed no oov token error in vocab for WordPieceTokenizer by @adhadse in #136
• Add a MLMHead layer by @mattdangerw in #132
• Bump version for 0.2.0 dev release by @mattdangerw in #142
• Added WSL setup text to CONTRIBUTING.md by @adhadse in #144
• Add attribution for the BERT modeling code by @mattdangerw in #151
• Remove preprocessing subdir by @mattdangerw in #150
• Word piece arg change by @mattdangerw in #148
• Rename max_length to sequence_length by @mattdangerw in #149
• Don't accept a string dtype for unicode tokenizer by @mattdangerw in #147
• Adding Utility to Detokenize as list of Strings to Tokenizer Base Class by @aflah02 in #124
• Fixed Import Error by @aflah02 in #161
• Added KerasTuner Hyper-Parameter Search for the BERT fine-tuning script. by @Stealth-py in #143
• Docstring updates for upcoming doc publish by @mattdangerw in #146
• version bump for 0.2.0.dev2 pre-release by @mattdangerw in #165
• Added a vocabulary_size argument to UnicodeCharacterTokenizer by @aflah02 in #163
• Simplified utility to preview a tfrecord by @mattdangerw in #168
• Update BERT example's README with data downloading instructions by @chenmoneygithub in #169
• Add a call to repeat during pretraining by @mattdangerw in #172
• Add an integration test matching our quick start by @mattdangerw in #162
• Modify README of bert example by @chenmoneygithub in #174
• Fix the finetuning script's loss and metric config by @chenmoneygithub in #176
• Minor improvements to the position embedding docs by @mattdangerw in #180
• Update docs for upcoming 0.2.0 release by @mattdangerw in #158
• Restore accidentally deleted line from README by @mattdangerw in #185
• Bump version for 0.2.0 release by @mattdangerw in #186
• Pre release fix by @mattdangerw in #187

New Contributors

• @Stealth-py made their first contribution in #96
• @adhadse made their first contribution in #91

Full Changelog: v0.1.1...v0.2.0

v0.2.0.dev2

What's Changed

• Added WSL setup text to CONTRIBUTING.md by @adhadse in #144
• Add attribution for the BERT modeling code by @mattdangerw in #151
• Remove preprocessing subdir by @mattdangerw in #150
• Word piece arg change by @mattdangerw in #148
• Rename max_length to sequence_length by @mattdangerw in #149
• Don't accept a string dtype for unicode tokenizer by @mattdangerw in #147
• Adding Utility to Detokenize as list of Strings to Tokenizer Base Class by @aflah02 in #124
• Fixed Import Error by @aflah02 in #161
• Added KerasTuner Hyper-Parameter Search for the BERT fine-tuning script. by @Stealth-py in #143
• Docstring updates for upcoming doc publish by @mattdangerw in #146
• version bump for 0.2.0.dev2 pre-release by @mattdangerw in #165

Full Changelog: v0.2.0-dev.1...v0.2.0.dev2

v0.2.0-dev.1

What's Changed

• Add Byte Tokenizer by @abheesht17 in #80
• Fixing rank 1 outputs for WordPieceTokenizer by @aflah02 in #92
• Add tokenizer accessors to the base class by @mattdangerw in #89
• Fix word piece attributes by @mattdangerw in #97
• Small fix: change assertEquals to assertEqual by @chenmoneygithub in #103
• Added a Learning Rate Schedule for the BERT Example by @Stealth-py in #96
• Add Perplexity Metric by @abheesht17 in #68
• Use the black profile for isort by @mattdangerw in #117
• Update README with release information by @mattdangerw in #118
• Add a class to generate LM masks by @chenmoneygithub in #61
• Add docstring testing by @mattdangerw in #116
• Fix broken docstring in MLMMaskGenerator by @chenmoneygithub in #121
• Adding a UnicodeCharacterTokenizer by @aflah02 in #100
• Added TokenAndPositionEmbedding Class by @adhadse in #91
• Fix bert example so it is runnable by @mattdangerw in #123
• Fix the issue that MLMMaskGenerator does not work in graph mode by @chenmoneygithub in #131
• Actually use layer norm epsilon in encoder/decoder by @mattdangerw in #133
• Whitelisted formatting and lint check targets by @adhadse in #126
• Updated CONTRIBUTING.md for setup of venv and standard pip install by @adhadse in #127
• Fix mask propagation of transformer layers by @chenmoneygithub in #139
• Fix masking for TokenAndPositionEmbedding by @mattdangerw in #140
• Fixed no oov token error in vocab for WordPieceTokenizer by @adhadse in #136
• Add a MLMHead layer by @mattdangerw in #132
• Bump version for 0.2.0 dev release by @mattdangerw in #142

New Contributors

• @Stealth-py made their first contribution in #96
• @adhadse made their first contribution in #91

Full Changelog: v0.1.1...v0.2.0-dev.1

v0.1.1

What's Changed

• Add tokenizer helper to convert tokens to ids by @mattdangerw in #75
• Add a sinusoidal embedding layer by @amantayal44 in #59
• Add a learned positional embedding layer by @hertschuh in #47
• Fix typo in position embedding docstring by @mattdangerw in #86
• Bump version number to 0.1.1 by @mattdangerw in #90

New Contributors

• @amantayal44 made their first contribution in #59
• @hertschuh made their first contribution in #47

Full Changelog: v0.1.0...v0.1.1

v0.1.0

Initial release of keras-nlp with word piece tokenizer and transformer encoder/decoder blocks.

This is a v0 release, with no API compatibility guarantees.