Amharic BERT
Collection
BERT transformer encoder models pretrained on 290 million tokens of Amharic text
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6 items
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Updated
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1
This model has the same architecture as bert-medium and was pretrained from scratch using the Amharic subsets of the oscar, mc4, and amharic-sentences-corpus datasets, on a total of 290 Million tokens. The tokenizer was trained from scratch on the same text corpus, and had a vocabulary size of 28k. It achieves the following results on the evaluation set:
Loss: 2.62
Perplexity: 13.74
Even though this model only has 40.5 Million parameters, its performance is comparable to the 7x larger 279 Million
parameter xlm-roberta-base multilingual model on the same Amharic evaluation set.
You can use this model directly with a pipeline for masked language modeling:
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='rasyosef/bert-medium-amharic')
>>> unmasker("ከሀገራቸው ከኢትዮጵያ ከወጡ ግማሽ ምዕተ [MASK] ተቆጥሯል።")
[{'score': 0.5135582089424133,
'token': 9345,
'token_str': 'ዓመት',
'sequence': 'ከሀገራቸው ከኢትዮጵያ ከወጡ ግማሽ ምዕተ ዓመት ተቆጥሯል ።'},
{'score': 0.2923661470413208,
'token': 9617,
'token_str': 'ዓመታት',
'sequence': 'ከሀገራቸው ከኢትዮጵያ ከወጡ ግማሽ ምዕተ ዓመታት ተቆጥሯል ።'},
{'score': 0.09527599066495895,
'token': 9913,
'token_str': 'አመት',
'sequence': 'ከሀገራቸው ከኢትዮጵያ ከወጡ ግማሽ ምዕተ አመት ተቆጥሯል ።'},
{'score': 0.06960058212280273,
'token': 10898,
'token_str': 'አመታት',
'sequence': 'ከሀገራቸው ከኢትዮጵያ ከወጡ ግማሽ ምዕተ አመታት ተቆጥሯል ።'},
{'score': 0.019061630591750145,
'token': 28157,
'token_str': '##ዓመት',
'sequence': 'ከሀገራቸው ከኢትዮጵያ ከወጡ ግማሽ ምዕተዓመት ተቆጥሯል ።'}]
This model was finetuned and evaluated on the following Amharic NLP tasks
The reported F1 scores are macro averages.
Model | Size (# params) | Perplexity | Sentiment (F1) | Named Entity Recognition (F1) |
---|---|---|---|---|
bert-medium-amharic | 40.5M | 13.74 | 0.83 | 0.68 |
bert-small-amharic | 27.8M | 15.96 | 0.83 | 0.68 |
bert-mini-amharic | 10.7M | 22.42 | 0.81 | 0.64 |
bert-tiny-amharic | 4.18M | 71.52 | 0.79 | 0.54 |
xlm-roberta-base | 279M | 0.83 | 0.73 | |
am-roberta | 443M | 0.82 | 0.69 |