Codesota · Natural Language Processing · Fill-Mask · GLUETasks/Natural Language Processing/Fill-Mask
Fill-Mask · benchmark dataset · 2018 · EN

GLUE.

General Language Understanding Evaluation for masked language models

Submit a result ↵
§ 01 · Leaderboard

Best published scores.

3 results indexed across 1 metric. Shaded row marks current SOTA; ties broken by submission date.

Primary
accuracy · higher is better
avg-score
3 rows
#ModelOrgSubmittedPaper / codeavg-score
01DeBERTa-v3-largeOSSMicrosoftJan 2023DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Tra…91.37
02ALBERT-xxlarge-v2OSSGoogleFeb 2020ALBERT: A Lite BERT for Self-supervised Learning of Lang…89.40
03RoBERTa-largeOSSFacebook AIJul 2019RoBERTa: A Robustly Optimized BERT Pretraining Approach88.50
Fig 2 · Rows sorted by score within each metric. Shaded row marks SOTA. Dates reflect model or paper release where available, otherwise the date Codesota accessed the source.
§ 04 · Literature

3 papers
tied to this benchmark.

Every paper below corresponds to at least one row in the leaderboard above. Click through for the arXiv preprint and, when available, the reference implementation.

§ 06 · Contribute

Have a score that beats
this table?

Submit a checkpoint and a reproduction script. We will run it, publish the score, and — if it takes the top — annotate the step on the progress chart with your name.

Submit a result ↵Read submission guide
What a submission needs
  • 01A public checkpoint or API endpoint
  • 02A reproduction script with frozen commit + seed
  • 03Declared evaluation environment (Python, deps)
  • 04One row per metric declared by this dataset
  • 05A contact so we can follow up on discrepancies