---
language:
- am
size_categories:
- 1K<n<10K
task_categories:
- text-classification
dataset_info:
  features:
  - name: clean_tweet
    dtype: string
  - name: label
    dtype:
      class_label:
        names:
          '0': negative
          '1': positive
  splits:
  - name: train
    num_bytes: 468510
    num_examples: 2223
  - name: dev
    num_bytes: 56319
    num_examples: 279
  - name: test
    num_bytes: 58731
    num_examples: 279
  download_size: 338974
  dataset_size: 583560
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: dev
    path: data/dev-*
  - split: test
    path: data/test-*
---

# Amharic Sentiment Dataset
This dataset contains `2781` cleaned Amharic Tweets, labeled as having either `positive` or `negative` sentiment. This dataset can be used to train a sentiment classification model. 

### Dataset Source 

https://github.com/liyaSileshi/amharic-sentiment-analysis/blob/main/data_preprocess/train.csv

### Finetuned Models

The following models were finetuned using this dataset. The reported precision, recall, and f1 metrics are macro averages.

|Model|Size (# params)| Accuracy | Precision | Recall | F1 |
| --- | ------------- | -------- | --------- | ------ | -- |
|bert-medium-amharic|40.5M|0.83|0.83|0.82|0.83|
|bert-small-amharic|27.8M|0.83|0.83|0.82|0.83|
|bert-mini-amharic|10.7M|0.81|0.81|0.81|0.81|
|bert-tiny-amharic|4.18M|0.79|0.79|0.79|0.79|
|xlm-roberta-base|279M|0.83|0.83|0.83|0.83|
|am-roberta|443M|0.82|0.83|0.82|0.82|

#### Code
In this repository, you can find notebooks for finetuning each of the above models using this dataset

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