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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: The NERT Lab + Lauren Levine at Georgetown University.
- Primary Maintainer: Wesley Scivetti
- Model type: Fine-tuned XLM-R for SNACS token/span classification.
- Language(s): Trained on Chinese, English, Gujarati, Hindi, and Japanese. Potentially some zero-shot capabilities in other languages.
- License: [More Information Needed]
- Finetuned from model: XLM-R Large
Model Sources [optional]
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- Paper: Multilingual Supervision Improves Semantic Disambiguation of Adpositions (LREC-COLING 2024)
- Demo: Running on Huggingface Spaces!
Uses
SNACS Classification tasks, which assign semantic labels to adpositions and case markers across languages.
Bias, Risks, and Limitations
Training was limited to the five languages listed above. Additional multilingual zero-shot capabilities are not empirically verified.
How to Get Started with the Model
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Training Details
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Training Procedure
Fine-tuning for token classification with robust hyperparameter search. See paper for details.
Evaluation
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Summary
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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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Model Architecture and Objective
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