---
library_name: transformers
datasets:
- rasyosef/amharic-sentiment
language:
- am
metrics:
- accuracy
- precision
- recall
- f1
pipeline_tag: text-classification
widget:
- text: "አሪፍ ፊልም ነው።"
  example_title: "Example 1"
- text: "ዩክሬን እና ሩስያ ከባድ ውግያ ላይ ናቸው።"
  example_title: "Example 2"
- text: "ቆንጆ ልብስ"
  example_title: "Example 3"
- text: "ጅላንፎ"
  example_title: "Example 4"
---

This model is a fine-tuned version of [bert-medium-amharic](https://huggingface.co/rasyosef/bert-medium-amharic) on the [amharic-sentiment](https://huggingface.co/datasets/rasyosef/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:

```python
>>> 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