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--- |
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dataset_info: |
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features: |
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- name: headline |
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dtype: string |
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- name: category |
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dtype: string |
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- name: date |
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dtype: string |
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- name: views |
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dtype: string |
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- name: article |
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dtype: string |
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- name: link |
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dtype: string |
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- name: word_len |
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dtype: int64 |
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- name: label |
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dtype: |
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class_label: |
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names: |
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'0': ሀገር አቀፍ ዜና |
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'1': መዝናኛ |
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'2': ስፖርት |
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'3': ቢዝነስ |
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'4': ዓለም አቀፍ ዜና |
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'5': ፖለቲካ |
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splits: |
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- name: train |
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num_bytes: 191486316 |
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num_examples: 49971 |
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download_size: 86414046 |
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dataset_size: 191486316 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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license: cc-by-4.0 |
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task_categories: |
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- text-classification |
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- summarization |
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language: |
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- am |
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size_categories: |
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- 10K<n<100K |
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--- |
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# Amharic News Category Classification |
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This amharic text dataset can be used to train/finetune models for the following tasks |
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* **classification** : using the categories |
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* **summarization** : using the headlines |
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## Finetuning |
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Here is a github repo that contains three notebooks that use this dataset to finetune the following models. |
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* [xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) : a multilingual transformer model with 280M parameters |
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* [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 |
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* [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 |
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https://github.com/rasyosef/amharic-news-category-classification |
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The finetuned model classifies a given Amharic news article into one of the following 6 categories. |
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- ሀገር አቀፍ ዜና (Local News) |
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- መዝናኛ (Entertainment) |
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- ስፖርት (Sports) |
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- ቢዝነስ (Business) |
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- ዓለም አቀፍ ዜና (International News) |
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- ፖለቲካ (Politics) |
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#### Fine-tuned Model Performance |
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Since this is a multi-class classification task, the reported precision, recall, and f1 metrics are macro averages. |
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|Model|Size (# params)|Accuracy|Precision|Recall|F1| |
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|-----|----|--------|---------|------|--| |
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|xlm-roberta-base|279M|0.9|0.88|0.88|0.88| |
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|bert-small-amharic|25.7M|0.89|0.86|0.87|0.86| |
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|bert-mini-amharic|9.67M|0.87|0.83|0.83|0.83| |
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## Original CSV and Paper |
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The original csv file can be found in this git repository https://github.com/IsraelAbebe/An-Amharic-News-Text-classification-Dataset |
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Paper: https://arxiv.org/abs/2103.05639 |
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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. |