---
license: apache-2.0
language:
- am
- ti
- ha
- aa
base_model:
- Hailay/EXLMR
- FacebookAI/xlm-roberta-base
pipeline_tag: text-classification
---
---
## 1. Model Description
**Hailay/FT_EXLMR** is a fine-tuned version of the **EXLMR** model, designed specifically for sentiment analysis and text classification tasks in low-resource African languages such as Tigrinya, Amharic, and Oromo. This model leverages the architecture of EXLMR but has been further fine-tuned to improve its performance on multilingual tasks, especially for languages not widely represented in existing NLP models.
The model was trained using the AfriSent-Semeval-2023 dataset, a benchmark dataset for African languages, which is publicly available on GitHub:[AfriSent-Semeval-2023 GitHub Repository](https://github.com/afrisenti-semeval/afrisent-semeval-2023)

## 2.Intended Use
This model is ideal for: 
Researchers and developers who are working on multilingual sentiment analysis in African languages.
Applications that require text classification in low-resource languages.
It is designed specifically for tasks such as:
Sentiment analysis
Text classification

**Note:** Without further fine-tuning, the model is unsuitable for tasks like machine translation or named entity recognition.

## 3.Training Data
The **Hailay/FT_EXLMR** model was trained using the dataset from the
**SemEval 2023 Shared Task 12: Sentiment Analysis in African Languages (AfriSenti-SemEval)**.
This dataset comprises sentiment-labeled text from 14 African languages:

1. Algerian Arabic (arq) - Algeria
2. Amharic (ama) - Ethiopia
3. Hausa (hau) - Nigeria
4. Igbo (ibo) - Nigeria
5. Kinyarwanda (kin) - Rwanda
6. Moroccan Arabic/Darija (ary) - Morocco
7. Mozambique Portuguese (pt-MZ) - Mozambique
8. Nigerian Pidgin (pcm) - Nigeria
9. Oromo (orm) - Ethiopia
10. Swahili (swa) - Kenya/Tanzania
11. Tigrinya (tir) - Ethiopia 
12. Twi (twi) -       Ghana
13. Xithonga (tso) - Mozambique
14. Yoruba (yor) - Nigeria

The dataset covers diverse data for training multilingual models like **Hailay/FT_EXLMR**
We access the dataset from  [AfriSent-Semeval-2023 GitHub Repository](https://github.com/afrisenti-semeval/afrisent-semeval-2023).
The **Hailay/FT_EXLMR** model was trained using the following configuration:
Epochs: 3
Learning Rate: 1e-5
Optimizer: AdamW
Batch Size: 16

## 4. Evaluation

The model was evaluated using accuracy and loss as the primary metrics. The results are as follows:

Accuracy: Achieved strong performance on Tigrinya, Amharic, Afar, and Oromo text classification and sentiment analysis tasks.

Loss: Loss values showed steady convergence during the 3 epochs of training, reflecting a well-calibrated model.
The evaluation was carried out on the test set provided in the [AfriSent-Semeval-2023 GitHub Repository](https://github.com/afrisenti-semeval/afrisent-semeval-2023) dataset.