LTZ E1 (mini)
A ModernBERT-based masked language model pretrained on Luxembourgish, following the Ettin recipe (see here: https://huggingface.co/jhu-clsp/ettin-encoder-68m)
Model Details
- Architecture: ModernBERT (encoder)
- Size: mini
- Vocabulary: 50,368 tokens (BPE, GPTNeoXTokenizerFast)
- Context length: 1,024 tokens
- Language: Luxembourgish (
lb/ltz) - License: CC BY-SA 4.0
Usage
Requires transformers>=4.48.0.
from transformers import AutoModelForMaskedLM, AutoTokenizer
import torch
tokenizer = AutoTokenizer.from_pretrained("instilux/ltz-e1-mini")
model = AutoModelForMaskedLM.from_pretrained("instilux/ltz-e1-mini")
inputs = tokenizer("Wéi spéit [MASK] et?", return_tensors="pt")
mask_pos = (inputs["input_ids"] == tokenizer.mask_token_id).nonzero(as_tuple=True)[1]
with torch.no_grad():
outputs = model(**inputs)
top_tokens = outputs.logits[0, mask_pos].topk(5)
for token_id, score in zip(top_tokens.indices[0], top_tokens.values[0]):
token = tokenizer.decode(token_id)
print(f"{token:15s} {score:.3f}")
Tokenizer Notes
The tokenizer is BPE-based (GPTNeoXTokenizerFast) with BERT-style special tokens ([CLS], [SEP], [MASK], [PAD]). A [CLS] token is prepended automatically (add_bos_token: true).
Citation
Please cite this paper (preprint, accepted to ACL 2026 Findings) if you use this model in your work.
@misc{plum2026ltzglueluxembourgishgenerallanguage, title={ltzGLUE: Luxembourgish General Language Understanding Evaluation}, author={Alistair Plum and Felicia Körner and Anne-Marie Lutgen and Laura Bernardy and Fred Philippy and Emilia Milano and Nils Rehlinger and Cédric Lothritz and Tharindu Ranasinghe and Barbara Plank and Christoph Purschke}, year={2026}, eprint={2604.17976}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2604.17976}, }