metadata
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