Xlm Roberta Base Finetuned Amharic

🚀 xlm-roberta-base-finetuned-amharic

A fine - tuned Amharic RoBERTa model based on xlm - roberta - base, offering better performance in named entity recognition.

🚀 Quick Start

This model, xlm - roberta - base - finetuned - amharic, is an Amharic RoBERTa model. It's derived from fine - tuning the xlm - roberta - base model on Amharic language texts. It outperforms the XLM - RoBERTa on named entity recognition datasets.

✨ Features

• Enhanced Performance: Delivers better results than XLM - RoBERTa on named entity recognition datasets.
• Fine - Tuned on Amharic: Specifically fine - tuned on Amharic corpus for better language adaptation.

📦 Installation

No installation steps are provided in the original document.

💻 Usage Examples

Basic Usage

You can use this model with Transformers pipeline for masked token prediction.

>>> from transformers import pipeline
>>> unmasker = pipeline('fill - mask', model='Davlan/xlm - roberta - base - finetuned - hausa')
>>> unmasker("የአሜሪካ የአፍሪካ ቀንድ ልዩ መልዕክተኛ ጄፈሪ ፌልትማን በአራት አገራት የሚያደጉትን <mask> መጀመራቸውን የአሜሪካ የውጪ ጉዳይ ሚንስቴር አስታወቀ።")

📚 Documentation

Intended uses & limitations

How to use

As shown in the code example above, you can use the model with the Transformers pipeline for masked token prediction.

Limitations and bias

This model is limited by its training dataset, which consists of entity - annotated news articles from a specific time period. Thus, it may not perform well across all use cases in different domains.

🔧 Technical Details

Training data

This model was fine - tuned on [Amharic CC - 100](http://data.statmt.org/cc - 100/).

Training procedure

This model was trained on a single NVIDIA V100 GPU.

Eval results on Test set (F - score, average over 5 runs)

BibTeX entry and citation info

By David Adelani