bert-mini-amharic

bert-mini-amharic

This model has the same architecture as bert-mini and was pretrained from scratch using the Amharic subsets of the oscar and mc4 datasets, on a total of 137 Million tokens. The tokenizer was trained from scratch on the same text corpus, and had a vocabulary size of 24k. It achieves the following results on the evaluation set:

• Loss: 3.11
• Perplexity: 22.42

Even though this model only has 10.7 Million parameters, its performance is only slightly behind the 26x larger 279 Million parameter xlm-roberta-base model on the same Amharic evaluation set.

How to use

You can use this model directly with a pipeline for masked language modeling:

>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='rasyosef/bert-mini-amharic')
>>> unmasker("ከሀገራቸው ከኢትዮጵያ ከወጡ ግማሽ ምዕተ [MASK] ተቆጥሯል።")

[{'score': 0.6525624394416809,
  'token': 9617,
  'token_str': 'ዓመታት',
  'sequence': 'ከሀገራቸው ከኢትዮጵያ ከወጡ ግማሽ ምዕተ ዓመታት ተቆጥሯል ።'},
 {'score': 0.22671808302402496,
  'token': 9345,
  'token_str': 'ዓመት',
  'sequence': 'ከሀገራቸው ከኢትዮጵያ ከወጡ ግማሽ ምዕተ ዓመት ተቆጥሯል ።'},
 {'score': 0.07071439921855927,
  'token': 10898,
  'token_str': 'አመታት',
  'sequence': 'ከሀገራቸው ከኢትዮጵያ ከወጡ ግማሽ ምዕተ አመታት ተቆጥሯል ።'},
 {'score': 0.02838180586695671,
  'token': 9913,
  'token_str': 'አመት',
  'sequence': 'ከሀገራቸው ከኢትዮጵያ ከወጡ ግማሽ ምዕተ አመት ተቆጥሯል ።'},
 {'score': 0.006343209184706211,
  'token': 22459,
  'token_str': 'ዓመታትን',
  'sequence': 'ከሀገራቸው ከኢትዮጵያ ከወጡ ግማሽ ምዕተ ዓመታትን ተቆጥሯል ።'}]

Finetuning

This model was finetuned and evaluated on the following Amharic NLP tasks

• Sentiment Classification
  • Dataset: amharic-sentiment
  • Code: https://github.com/rasyosef/amharic-sentiment-classification
• Named Entity Recognition
  • Dataset: amharic-named-entity-recognition
  • Code: https://github.com/rasyosef/amharic-named-entity-recognition
• News Category Classification
  • Dataset: amharic-news-category-classification
  • Code: https://github.com/rasyosef/amharic-news-category-classification

Finetuned Model Performance

The reported F1 scores are macro averages.

Amharic News Category Classification