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This model is a fine-tuned version of bert-medium-amharic on the amharic-sentiment dataset for sentiment classification. It achieves the following results on the evaluation set:

  • Accuracy: 0.83
  • Precision: 0.84
  • Recall: 0.83
  • F1: 0.83

How to use

You can use this model directly with a pipeline for text classification:

>>> from transformers import pipeline
>>> bert_sentiment = pipeline("text-classification", model="rasyosef/bert-medium-amharic-finetuned-sentiment")
>>> bert_sentiment(["አሪፍ ፊልም ነው።", "ዩክሬን እና ሩስያ ከባድ ውግያ ላይ ናቸው።"])

[{'label': 'positive', 'score': 0.9863048791885376},
 {'label': 'negative', 'score': 0.9570127129554749}]

Code

https://github.com/rasyosef/amharic-sentiment-classification

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Dataset used to train rasyosef/bert-medium-amharic-finetuned-sentiment

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