transformers safetensors bert text-classification am dataset:rasyosef/amharic-sentiment autotrain_compatible endpoints_compatible region:us
---
datasets:
- rasyosef/amharic-sentiment
language:
- am
library_name: transformers
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