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--- |
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license: apache-2.0 |
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base_model: |
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- google/canine-s |
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--- |
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This is an artifact of the SNAILS project. We finetuned google/canine-s to perform the task of word naturalness classification. |
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Full (unabbreviated) words are "Regular" naturalness (labeled as N1). Somewhat abbreviated words are "Low" naturalness (labeled as N2). |
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Very abbreviated or indecipherable words are "Least" naturalness (labeled as N3). |
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Inference using this model requires a token tagging pre-processing step. This is provided in tokenprocessing.py. |
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To most easily use this model, download the snails_naturalness_classifier.py and tokenprocessing.py files in this |
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repository and run snails_naturalness_classifier.py. |
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For more information about the SNAILS project and to access the training data: |
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GitHub repository: https://www.github.com/KyleLuoma/SNAILS |
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Read the paper: |
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https://dl.acm.org/doi/10.1145/3709727 |
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Citing this model: |
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|
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``` |
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@article{10.1145/3709727, |
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author = {Luoma, Kyle and Kumar, Arun}, |
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title = {SNAILS: Schema Naming Assessments for Improved LLM-Based SQL Inference}, |
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year = {2025}, |
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issue_date = {February 2025}, |
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publisher = {Association for Computing Machinery}, |
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address = {New York, NY, USA}, |
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volume = {3}, |
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number = {1}, |
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url = {https://doi.org/10.1145/3709727}, |
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doi = {10.1145/3709727}, |
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journal = {Proc. ACM Manag. Data}, |
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month = feb, |
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articleno = {77}, |
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numpages = {26}, |
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} |
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``` |
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