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xlmr-sentence-splitter
Famezz/xlmr-sentence-splitter
xlmr-sentence-splitter is a token classification model from Famezz. Use it when you need labels on individual words, such as names. The card lists the license as mit.
Fine-tuned XLM-RoBERTa-large (560M params) for sentence boundary detection on 8 Universal Dependencies treebanks (4 English, 4 Italian).
Access
Public
Updated Apr 7, 2026
Repo size
2.2 GB
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1
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Repo makeup
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.pt2.2 GB · 100%
At a glance
- Task
- Token Classification
- License
- mit
- Access
- Public
- Created
- Apr 7, 2026
- Updated
- Apr 7, 2026
- SHA
- d315ab8c
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Base models
Datasets
Model card
From the Hugging Face model README
XLM-RoBERTa Sentence Splitter
Fine-tuned XLM-RoBERTa-large (560M params) for sentence boundary detection on 8 Universal Dependencies treebanks (4 English, 4 Italian).
Results
| NLTK | spaCy | This model | |
|---|---|---|---|
| Macro F1 | 0.9411 | 0.9519 | 0.9863 |
Usage
git clone https://github.com/LucaTamSapienza/sentence_splitter.git
cd sentence_splitter
pip install -r requirements.txt
python download_model.py
python src/predict.py --input input/ --output output/ --model_path checkpoints/best_xlmr_model.pt
Architecture
XLM-RoBERTa-large -> Dropout(0.1) -> Linear(1024, 1) -> per-token sigmoid
Trained with focal loss (alpha=0.75, gamma=2.0), sliding windows (510 tokens, stride 256), FP16, AdamW (lr=2e-5).
Citation
@misc{tam2026sentencesplitter,
author = {Luca Tam},
title = {Sentence Splitter: Fine-tuning XLM-RoBERTa for Sentence Boundary Detection},
year = {2026},
url = {https://github.com/LucaTamSapienza/sentence_splitter}
}
- Task
- Token Classification
- License
- mit
- Languages
- en, it
- Created
- Apr 7, 2026
- Updated
- Apr 7, 2026