Fill-Mask
Transformers
Safetensors
roberta
low-resource
sigtyp
ancient-languages
historical-languages
shared-task
Instructions to use ljvmiranda921/LiBERTus-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ljvmiranda921/LiBERTus-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="ljvmiranda921/LiBERTus-base")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("ljvmiranda921/LiBERTus-base") model = AutoModelForMaskedLM.from_pretrained("ljvmiranda921/LiBERTus-base") - Notebooks
- Google Colab
- Kaggle
LiBERTus-base
Submission to Task 1 (Constrained) of the SIGTYP 2024 Shared Task on Word
Embedding Evaluation for Ancient and Historical
Languages. The system is built by
first pretraining a multilingual language model and then finetuning it for a
downstream task. The submission for Phase 1 and 2 of the Shared Task can be
found in the submission_p1 and submission_p2 directories.
If you're using this model, please cite:
@inproceedings{miranda-2024-allen,
title = "{A}llen Institute for {AI} @ {SIGTYP} 2024 Shared Task on Word Embedding Evaluation for Ancient and Historical Languages",
author = "Miranda, Lester James",
booktitle = "Proceedings of the 6th Workshop on Research in Computational Linguistic Typology and Multilingual NLP",
month = mar,
year = "2024",
address = "St. Julian's, Malta",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.sigtyp-1.18",
pages = "151--159",
}
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