Create README.md

README.md

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+---
+language: ti
+datasets:
+  - NLLB
+library_name: transformers
+tags:
+  - tigrinya
+  - masked-language-modeling
+  - xlmr
+  - low-resource
+  - multilingual
+model_name: XLM-Roberta fine-tuned on Tigrinya (MLM)
+license: apache-2.0
+---
+
+# XLM-Roberta Fine-Tuned on Tigrinya (MLM)
+
+This model is a fine-tuned version of [`xlm-roberta-base`](https://huggingface.co/xlm-roberta-base) for the **Tigrinya language** (ትግርኛ), trained with the **Masked Language Modeling (MLM)** objective. It uses a custom BPE tokenizer adapted to Tigrinya using FastText-informed embedding initialization.
+
+## 🔧 Details
+
+- **Base model**: `xlm-roberta-base`
+- **Language**: Tigrinya
+- **Tokenizer**: Custom BPE tokenizer (non-morpheme-aware)
+- **Adaptation**: Embedding initialization using weighted averages of pretrained XLM-R embeddings, guided by Tigrinya FastText word vectors
+- **Training dataset**: Tigrinya side of the [NLLB (No Language Left Behind)](https://github.com/facebookresearch/flores) parallel corpus
+- **Objective**: Masked Language Modeling (MLM)
+
+## 🧪 Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForMaskedLM
+
+tokenizer = AutoTokenizer.from_pretrained("Hailay/xlmr-tigriyna-mlm")
+model = AutoModelForMaskedLM.from_pretrained("Hailay/xlmr-tigriyna-mlm")
+
+text = "ትግራይ ብምትሕብባ ንህዝቢ ግብሪ ቀጺሉ።"
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model(**inputs)
+📌 Intended Use
+Pretraining for Tigrinya NLP tasks
+
+Fine-tuning on classification, NER, QA, and other downstream tasks in Tigrinya
+
+Research in low-resource Semitic and morphologically rich languages
+
+📖 Citation
+@misc{hailay2025tigrinya,
+  title={Tigrinya MLM with XLM-R and FastText-Informed Embedding Initialization},
+  author={Hailay Kidu},
+  year={2025},
+  url={https://huggingface.co/Hailay/xlmr-tigriyna-mlm}
+}
+🏷️ License
+Apache License 2.0