Tigre-xlm-roberta-base

This model is a multilingual masked language model (MLM) based on XLM-RoBERTa, fine-tuned on diverse linguistic data with a strong focus on Tigre (tig_Ethi). It supports multiple languages, including:

• [tig_Ethi] Tigre
• [tir_Ethi] Tigrinya
• [amh_Ethi] Amharic
• [gez_Ethi] Ge'ez
• [ara_Arab] Arabic
• [eng_Latn] English
• [deu_Latn] German
• [swe_Latn] Swedish
• [nno_Latn], [nob_Latn] Norwegian

Usage

The model is designed for masked language modeling — predicting missing words in a sentence. Use it to:

• Fill in blanks in multilingual text
• Study language understanding in low-resource languages
• Build downstream tools for Tigre and related languages

Example (Python)

from transformers import AutoTokenizer, XLMRobertaForMaskedLM

tokenizer = AutoTokenizer.from_pretrained("BeitTigreAI/tigre-xlm-roberta-base")
model = XLMRobertaForMaskedLM.from_pretrained("BeitTigreAI/tigre-xlm-roberta-base")

text = "[tig_Ethi] መርሐበ ብኩም እት <mask>"

inputs = tokenizer(text, return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits

mask_token_index = (inputs.input_ids[0] == tokenizer.mask_token_id).nonzero().item()
predicted_token_id = logits[0, mask_token_index].argmax(-1)
predicted_word = tokenizer.decode([predicted_token_id])

print(predicted_word)  # Output: likely a word like "ቤት" (home)