Model description

xlm-roberta-base-finetuned-amharic is a Amharic RoBERTa model obtained by fine-tuning xlm-roberta-base model on Amharic language texts. It provides better performance than the XLM-RoBERTa on named entity recognition datasets.

Specifically, this model is a xlm-roberta-base model that was fine-tuned on Amharic corpus.

Intended uses & limitations

How to use

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።")

Limitations and bias

This model is limited by its training dataset of entity-annotated news articles from a specific span of time. This may not generalize well for all use cases in different domains.

Training data

This model was fine-tuned on Amharic 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)

Dataset XLM-R F1 am_roberta F1
MasakhaNER 70.96 77.97

BibTeX entry and citation info

By David Adelani

New: fine-tune this model in a few clicks by selecting AutoNLP in the "Train" menu!
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Hosted inference API
Mask token: <mask>
This model can be loaded on the Inference API on-demand.