.
├── data
│ ├── processed
│ │ └── 266-comp-lex-master
│ │ ├── fe-test-labels
│ │ ├── fe-train
│ │ └── fe-trial-val
│ └── raw
│ ├── cwi18-complex-word-identification-master
│ │ ├── testset
│ │ │ ├── english
│ │ │ ├── french
│ │ │ ├── german
│ │ │ └── spanish
│ │ └── traindevset
│ │ ├── english
│ │ ├── german
│ │ └── spanish
│ └── se21-t1-comp-lex-master
│ ├── test
│ ├── test-labels
│ ├── train
│ └── trial
├── literature
│ ├── 2016-2018 CWI
│ │ └── 2018 results
│ │ ├── 1-cwirankingclass
│ │ └── CWI-results-regression-teams-SS-new
│ ├── 2021-task-1-lexical-complexity-prediction
│ └── model optimization
├── models
├── notebooks
│ ├── 1-2 Data Engineering
│ ├── 3_0 Baselines
│ ├── 3_0-3_5 Pipeline Development and Hyperparameter Refinement
│ ├── 3_6-3_8 Ablation Studies
│ ├── 4_0 Training Results Log Parser
│ ├── 5_0 Visualizations
│ ├── 6_0 Error Analysis
│ └── pdf_converter
├── paper_and_slides
└── results
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We introduce a binarized approach to Lexical Complexity Prediction (Binary LCP) and systematically compare two generations of encoder-only Transformer models: BERT and ModernBERT. Work completed as part of Natural Language Processing, DATASCI 266.
JH-UCB/266-fp
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We introduce a binarized approach to Lexical Complexity Prediction (Binary LCP) and systematically compare two generations of encoder-only Transformer models: BERT and ModernBERT. Work completed as part of Natural Language Processing, DATASCI 266.
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