Instructions to use WesScivetti/SNACS_Multilingual with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use WesScivetti/SNACS_Multilingual with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="WesScivetti/SNACS_Multilingual")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("WesScivetti/SNACS_Multilingual") model = AutoModelForTokenClassification.from_pretrained("WesScivetti/SNACS_Multilingual", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 4,074 Bytes
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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.
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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).
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