Update README.md

README.md

@@ -21,3 +21,48 @@ It achieves the following results on the evaluation set:
 - `Perplexity: 35.52`
 
 Even though this model only has `9.7 Million` parameters, its performance is only slightly behind the 28x larger `279 Million` parameter [xlm-roberta-base](https://huggingface.co/FacebookAI/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:
+
+```python
+>>> from transformers import pipeline
+>>> unmasker = pipeline('fill-mask', model='rasyosef/bert-mini-amharic')
+>>> unmasker("ከሀገራቸው ከኢትዮጵያ ከወጡ ግማሽ ምዕተ [MASK] ተቆጥሯል።")
+
+[{'score': 0.4713546335697174,
+  'token': 9308,
+  'token_str': 'ዓመት',
+  'sequence': 'ከሀገራቸው ከኢትዮጵያ ከወጡ ግማሽ ምዕተ ዓመት ተቆጥሯል ።'},
+ {'score': 0.25726795196533203,
+  'token': 9540,
+  'token_str': 'ዓመታት',
+  'sequence': 'ከሀገራቸው ከኢትዮጵያ ከወጡ ግማሽ ምዕተ ዓመታት ተቆጥሯል ።'},
+ {'score': 0.07067586481571198,
+  'token': 10354,
+  'token_str': 'አመት',
+  'sequence': 'ከሀገራቸው ከኢትዮጵያ ከወጡ ግማሽ ምዕተ አመት ተቆጥሯል ።'},
+ {'score': 0.07064681500196457,
+  'token': 11212,
+  'token_str': 'አመታት',
+  'sequence': 'ከሀገራቸው ከኢትዮጵያ ከወጡ ግማሽ ምዕተ አመታት ተቆጥሯል ።'},
+ {'score': 0.012558948248624802,
+  'token': 10588,
+  'token_str': 'ወራት',
+  'sequence': 'ከሀገራቸው ከኢትዮጵያ ከወጡ ግማሽ ምዕተ ወራት ተቆጥሯል ።'}]
+```
+
+# Fine-tuning
+
+The following github repository contains a notebook that fine-tunes this model for an Amharic text classification task. 
+
+https://github.com/rasyosef/amharic-news-category-classification
+
+#### Fine-tuned Model Performance
+Since this is a multi-class classification task, the reported precision, recall, and f1 metrics are macro averages.
+
+|Model|Size(# params)|Accuracy|Precision|Recall|F1|
+|-----|--------------|--------|---------|------|--|
+|bert-mini-amharic|9.67M|0.87|0.83|0.83|0.83|
+|bert-small-amharic|25.7M|0.89|0.86|0.87|0.86|
+|xlm-roberta-base|279M|0.9|0.88|0.88|0.88|