WesScivetti/SNACS_English

Model Card for Model ID

This is the English SNACS Classification Model for Semantic Supersense Classification of English Prepositions and Possessive Markers. See Schneider et al., (2018) for a description of SNACS. More info on this model and others can be found at: https://github.com/WesScivetti/snacs

Model Details

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• Developed by: Wesley Scivetti, Lauren Levine, Nathan Schneider
• Shared by [optional]: Wesley Scivetti
• Model type: Fine-tuned Roberta-large for token classification
• Language(s) (NLP): English
• License: [More Information Needed]
• Finetuned from model [optional]: Roberta-large

Model Sources [optional]

• Repository: https://github.com/WesScivetti/snacs/
• Paper [optional]: https://aclanthology.org/2025.coling-main.247/

Uses

Used for generating SNACS predictions for English preposition ssense disambiguation.

Direct Use

This model has already been fine-tuned and does not need any further training.

Out-of-Scope Use

Only appropriate for English. For other languages use the appropriate model.

Bias, Risks, and Limitations

Biases, limitations associated with original Roberta-large model.

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

English Streusle Dataset: https://github.com/nert-nlp/streusle

Training Procedure

Fine-tuning for token classification, predicting LexTag field from Streusle.

Preprocessing [optional]

Takes Conllulex file format as input, see streusle repo for more details.

Training Hyperparameters

• Training regime: See paper for hyperparameter details.

Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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• Hours used: [More Information Needed]
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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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