{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/bert-sort-a-zero-shot-mlm-semantic-encoder-on","title":"BERT-Sort: A Zero-shot MLM Semantic Encoder on Ordinal Features for AutoML","arxiv_id":null,"date":"2022-06-01","proceeding":"AutoML'2022 2022 6","authors":["Mehdi Bahrami","Wei-Peng Chen","Lei Liu","Mukul Prasad"],"abstract":"Data pre-processing is one of the key steps in creating machine learning pipelines for tabular data. One of the common data pre-processing operations implemented in AutoML systems is to encode categorical features as numerical features. Typically, this is implemented using a simple alphabetical sort on the categorical values, using functions such as OrdinalEncoder, LabelEncoder in Scikit-Learn and H2O. However, often there exist semantic ordinal relationships among the categorical values, such as: quality level (i.e., [’very good’ > ’good’ > ’normal’> ’poor’]), or month (i.e., [’Jan’< ’Feb’ < ’Mar’]). Such semantic relationships are not exploited by previous AutoML approaches. In this paper, we introduce BERT-Sort, a novel approach to semantically encode ordinal categorical values via zero-shot Masked Language Models (MLM) and apply it to AutoML for tabular data. We created a new benchmark of 42 features from 10 public data sets for sorting categorical ordinal values for the first time, where BERT-Sort significantly improves semantic encoding of ordinal values in comparison to the existing approaches with 27% improvement. We perform a comprehensive evaluation of BERT-Sort on different public MLMs, such as RoBERTa, XLM and DistilBERT. We also\r\ncompare the performance of raw data sets against encoded data sets through BERT-Sort in different AutoML platforms including AutoGluon, FLAML, H2O, and MLJAR to evaluate the proposed approach in an end-to-end scenario.","url_abs":"https://openreview.net/pdf?id=BCM8G-pSLe9","url_pdf":"https://openreview.net/pdf?id=BCM8G-pSLe9","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"bert-sort-a-zero-shot-mlm-semantic-encoder-on","repo_url":"https://github.com/marscod/BERT-Sort","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"automl","task_name":"AutoML"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bert","method_name":"BERT"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"distilbert","method_name":"DistilBERT"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-linear-decay","method_name":"Linear Warmup With Linear Decay"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"roberta","method_name":"RoBERTa"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"},{"method_slug":"wordpiece","method_name":"WordPiece"},{"method_slug":"xlm","method_name":"XLM"}],"datasets_introduced":[{"slug":"ordinaldataset","name":"OrdinalDataset","full_name":"Ordinal Encoding Data set"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/automl-on-ordinaldataset","task":"AutoML","dataset":"OrdinalDataset","model":"Zero-shot-BERT-SORT","rank_in_archive_order":1,"of":1,"metrics":{"1:1 Accuracy":"+55%"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}