---
dataset_info:
  features:
  - name: headline
    dtype: string
  - name: category
    dtype: string
  - name: date
    dtype: string
  - name: views
    dtype: string
  - name: article
    dtype: string
  - name: link
    dtype: string
  - name: word_len
    dtype: int64
  - name: label
    dtype:
      class_label:
        names:
          '0': ሀገር አቀፍ ዜና
          '1': መዝናኛ
          '2': ስፖርት
          '3': ቢዝነስ
          '4': ዓለም አቀፍ ዜና
          '5': ፖለቲካ
  splits:
  - name: train
    num_bytes: 191486316
    num_examples: 49971
  download_size: 86414046
  dataset_size: 191486316
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-4.0
task_categories:
- text-classification
- summarization
language:
- am
size_categories:
- 10K<n<100K
---
# Amharic News Category Classification

This amharic text dataset can be used to train/finetune models for the following tasks
* **classification** : using the categories 
* **summarization** : using the headlines

## Finetuning
Here is a github repo that contains three notebooks that use this dataset to finetune the following models.

* [xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) : a multilingual transformer model with 280M parameters
* [bert-small-amharic](https://huggingface.co/rasyosef/bert-small-amharic) : a new amharic version of the bert-small transformer model with 25.7M parameters, pretrained from scratch using unlabelled amharic text data
* [bert-mini-amharic](https://huggingface.co/rasyosef/bert-mini-amharic) : a new amharic version of the bert-mini transformer model with 9.67M parameters, pretrained from scratch using unlabelled amharic text data

https://github.com/rasyosef/amharic-news-category-classification

The finetuned model classifies a given Amharic news article into one of the following 6 categories.
- ሀገር አቀፍ ዜና (Local News)
- መዝናኛ (Entertainment)
- ስፖርት (Sports)
- ቢዝነስ (Business)
- ዓለም አቀፍ ዜና (International News)
- ፖለቲካ (Politics)

#### 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|
|-----|----|--------|---------|------|--|
|xlm-roberta-base|279M|0.9|0.88|0.88|0.88|
|bert-small-amharic|25.7M|0.89|0.86|0.87|0.86|
|bert-mini-amharic|9.67M|0.87|0.83|0.83|0.83|

## Original CSV and Paper
The original csv file can be found in this git repository https://github.com/IsraelAbebe/An-Amharic-News-Text-classification-Dataset

Paper: https://arxiv.org/abs/2103.05639

While there is a version of this dataset that's already available on huggingface hub ([israel/Amharic-News-Text-classification-Dataset](https://huggingface.co/datasets/israel/Amharic-News-Text-classification-Dataset)), that version had been preprocessed to remove punctuation from the articles, while this version contains the entire text along with punctuations. As a result, this version is more preferable for finetuning transformer models.