Model description

bert-base-multilingual-cased-finetuned-amharic is a Amharic BERT model obtained by replacing mBERT vocabulary by amharic vocabulary because the language was not supported, and fine-tuning bert-base-multilingual-cased model on Amharic language texts. It provides better performance than the multilingual Amharic on named entity recognition datasets.

Specifically, this model is a bert-base-multilingual-cased model that was fine-tuned on Amharic corpus using Amharic vocabulary.

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/bert-base-multilingual-cased-finetuned-amharic')
>>> 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 mBERT F1 am_bert F1
MasakhaNER 0.0 60.89

BibTeX entry and citation info

By David Adelani

New: fine-tune this model in a few clicks by selecting AutoNLP in the "Train" menu!
Downloads last month
Hosted inference API
Mask token: [MASK]
This model can be loaded on the Inference API on-demand.