Update README.md

README.md

@@ -3,25 +3,32 @@ 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.
+**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:
-
+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:** The model is not suitable for other tasks like machine translation or named entity recognition without further fine-tuning.
+**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:
+## 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
@@ -38,8 +45,8 @@ The **Hailay/FT_EXLMR** model was trained using the dataset from the **SemEval 2
 13. Xithonga (tso) - Mozambique
 14. Yoruba (yor) - Nigeria
 
-The dataset covers diverse data for training multilingual models like `Hailay/FT_EXLMR`.
-You can access the dataset via the [AfriSent-Semeval-2023 GitHub Repository](https://github.com/afrisenti-semeval/afrisent-semeval-2023).
+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
@@ -50,6 +57,7 @@ Batch Size: 16
 
 The model was evaluated using accuracy and loss as the primary metrics. The results are as follows:
 
-Accuracy: Achieved strong performance on Tigrinya, Amharic, and Oromo text classification tasks, with accuracy scores ranging between 78% and 88%.
+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.