Update README.md

README.md

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----
-license: cc-by-sa-4.0
----
+---
+language:
+- lb
+license: cc-by-sa-4.0
+library_name: transformers
+pipeline_tag: fill-mask
+tags:
+- modernbert
+- encoder
+- luxembourgish
+- multilingual
+- masked-language-modeling
+---
+
+# LTZ Encoder (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`.
+
+```python
+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
+
+A paper describing this model will be published soon. In the meantime, please cite this repository if you use this model in your work.