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library_name: transformers
tags: []
---

# Model Card for Model ID

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## Model Details

### Model Description

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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](http://nert.georgetown.edu/) + 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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- **Repository:** [More Information Needed]
- **Paper:** [Multilingual Supervision Improves Semantic Disambiguation of Adpositions (LREC-COLING 2024)](https://aclanthology.org/2025.coling-main.247/)
- **Demo:** [Running on Huggingface Spaces!](https://huggingface.co/spaces/WesScivetti/SNACS_English_Demo)

## 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

### Training Data

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### Training Procedure

Fine-tuning for token classification with robust hyperparameter search. See paper for details. 

## Evaluation

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### 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

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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]

### Model Architecture and Objective

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#### Software

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## Citation [optional]

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## Glossary [optional]

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