Releases: nyu-mll/jiant
v2.2.0: Easy to add any HF Transformers style model! DeBERTa added.
Making it easy to add a Hugging Face-style Transformers model
We refactored jiant
to make it easier to add a Transformers-style model to the library. Please see the guide to add a model for more details. We added DeBERTa V2 as part of these changes.
Breaking Changes
The simple API now uses hf_pretrained_model_name_or_path
instead of model_type
as an argument. hf_pretrained_model_name_or_path
is used as an input to Hugging Face's Auto Classes.
Features
de5437a Merge easy_add_model feature branch (#1309)
56ceae5 Updating notebooks, removing model_type (#1270)
723786a Switch export_model to use AutoModel and AutoTokenizer (#1260)
84f2f5a Adding Acceptability judgment and SentEval tasks (#1271)
f796e5a improve robustness of the simple runscript (#1307)
Tests
4d0f6a9 Add test matrix (#1308)
Bugfixes
ee65662 Update README.md
65888b4 Benchmark script fixes (#1301)
b4b5de0 Assert spans <= max_seq_len (#1302)
5ba72f7 axg->axb fix (#1300)
235f646 MNLI diagnostic example bug (#1294)
Maintenance
4ab0c08 Bump lxml from 4.6.2 to 4.6.3 (#1304)
741ab09 Documentation + cleanup (#1282)
dbbb4e6 export_model tweak (#1277)
Add downloaders for ARCT, MCTest, MCTACO, MuTual, and QuAIL
Tasks
e9d6c68 Adding download code for ARCT, MCTest, MCTACO, MuTual, and QuAIL (#1258)
Examples
a88956a Add edge probing notebook (#1261)
Bugfixes
d5e3b2e Prevents computing loss in multigpu when not available (#1257)
9cfe644 Truncate MCQ inputs from the start and not end (#1256)
33c4a26 Bump lxml from 4.5.1 to 4.6.2 (#1263)
Tests
Add ROPES and RACE tasks
SQuAD Tokenization Fix, Load (only) Encoder Weights
Bugfixes
838cdd2 SQuAD tokenization update (#1232)
5329c7e Winogrande Task Property (#1229)
50b0116 Further fix for encoder_only (#1242)
1f66050 Allow force_overwrite
to override "done" condition (#1241)
59438ed change checkpoint save default (#1233)
e2e85c9 guids_fix (#1225)
74c6ba0 fix notebooks path (#1231)
Features
18c41fc Load only encoder weights (#1240)
Documentation
711c6c8 Introduction filename correction (#1239)
f340d04 minor typo fix (#1238)
WinoGrande, FEVER, QuAIL, MCTest, MCTACO
Added Tasks
e4f1c4b Winogrande (#1203)
e7eefc6 Fever NLI task and data downloader (#1215)
cb601cf Quail (#1208)
c00360f MCTest and MCTACO (#1197)
76e2826 Mcscript Task Property (#1219)
Documentation
9892766 Add docs for adding tasks to data downloader (#1221)
Bugfixes
cb7ee4a Fix save-last behavior (#1220)
Cleanup
PIQA, MRQA, NewsQA, Quoref, MCScript, ARCT
Added Tasks
442a2b0 - piqa (#1216) (William Huang)
c535e78 - Natural Questions (MRQA), NewsQA, Quoref (#1207) (Jason Phang)
d1b14c1 - mcscript (#1152) (William Huang)
da7550d - Adding arc_easy, arc_challenge, mutual, mutual_plus (#1206) (yzpang)
f4bca4e - add arct task doc documentation (#1154) (jeswan)
b23c0f7 - arct (#1151) (William Huang)
58beb8f - anli download (#1147) (Jason Phang)
Features
0b3dff5 - Adding ability to resume run in Simple API (#1205) (Jason Phang)
Notebooks
b81254b - Fix git clone in example notebooks (#1155) (jeswan)
Bugfixes
aa4d111 - Bugfix for single-task configurator (#1143) (Jason Phang)
14fac1c - Fix colab link in README (#1142) (Jonathan Chang)
02bb070 - setup.py fix (#1141) (Jason Phang)
7d1cc29 - Adding SingleTaskConfigurator, some cleanup (#1135) (Jason Phang)
Maintenance
bump torch>=1.5.0. bump transformers==3.1.0. notebook installation switched to local pip install. (#1218) (jeswan)
b20f30a - resolve_is_lower_case fix (#1204) (Jason Phang)
5724fee - Adjust case for span prediction (#1201) (Jason Phang)
c3387a3 - nlp to datasets (#1137) (Jason Phang)
04bbb39 - update issue numbers from jiant-dev to jiant transfer (#1196) (jeswan)
392976c - Task tweaks (#1149) (Jason Phang)
82ed396 - use hidden_size (#1148) (Jason Phang)
v2.0.0
Highlighted changes:
jiant
2.0 is a complete rewrite ofjiant
, built natively ontransformers
andnlp
/datasets
- Support for 50+ natural language understanding task, including the GLUE, SuperGLUE and XTREME benchmarks
- Support for BERT, RoBERTa, ALBERT, XLM-R, etc
- Includes data-downloading, tokenize-and-caching, training-and-evaluation code, and benchmark submission code for relevant tasks
v1.3.2
Highlighted changes:
New Tasks
- Masked Language Modeling (for RoBERTa and ALBERT) (#1030) (@pruksmhc and @phu-pmh)
- Sentence Order Prediction (for ALBERT) (#1061) (@pruksmhc and @phu-pmh)
Minor changes and fixes
- Fixed target training data fraction bug where target training data fraction was not reflected in logging and scheduler (#1071) (@HaokunLiu)
- Fixed target train data fraction overwriting pretrain data fraction bug (#1070) (@pyeres)
- Added CONTRIBUTING.md (#1036, #1038) (@pyeres)
Dependency changes
v1.3.1
Minor changes and fixes
- Fixed QAMR and QASRL tasks (#1019) (@pyeres)
- Changed tasks names using underscores to use hyphens (#1016) (@HaokunLiu)
- Fixed cola inference script (#1023) (@zphang)
- Re-ordered GPT-style inputs for consistency with GPT paper (#1031) (@HaokunLiu)
- Fixed edge probing and Senteval tasks (#1025) (@pruksmhc)
v1.3.0
Highlighted changes:
New Tasks
- QA-SRL (#716) (@zphang)
- QAMR (#932) (@zphang)
- Abductive NLI (aNLI) (#922) (@zphang)
- SocialIQA (#924) (@pruksmhc)
- SentEval Probing (#926) (@pruksmhc)
- SciTail (#943) (@phu-pmh)
- CommonsenseQA (#942) (@HaokunLiu)
- HellaSwag (#942) (@HaokunLiu)
- Acceptability probing (#949) (@HaokunLiu)
- Adversarial NLI (#966) (@pyeres)
- Two-class MNLI variant (#976) (@sleepinyourhat)
- WinoGrande (#996) (@HaokunLiu)
New Models
- ALBERT (#990) (@HaokunLiu)
New Features
- Faster retokenization (#935) (@pruksmhc)
- Gradient accumulation option (#980) (@pyeres)
- Full/stable data-parallel multi-GPU support (#992) (@pruksmhc)
Minor changes and fixes
- Fixed bug in restoring checkpoints in multi-GPU mode (#928) (@pruksmhc)
- Fixed bugs in RoBERTa retokenization (#982) (@HaokunLiu) and ids (#959) (@njjiang)
- Fixed load_target_train_checkpoint with mixing setting (#960) (@pruksmhc)
- Fixed bug in CCG loss function that artificially reduced accuracy (#948) (@HaokunLiu)
- Fixed label parsing for QQP (#956) (@zphang)
- Updated CoLA inference script (#931) (@zphang)