Update README.md

README.md

@@ -5,23 +5,23 @@ language:
 - ti
 ---
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-## ***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.
+## 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
+## 2.Intended Use
 This model is ideal for:
 
-Researchers and developers working on multilingual sentiment analysis in African languages.
+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.
 
-## ***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:
+**Note:** The model is not suitable for other tasks like machine translation or named entity recognition without further fine-tuning.
+
+## 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
@@ -33,19 +33,20 @@ The `Hailay/FT_EXLMR` model was trained using the dataset from the **SemEval 202
 8. Nigerian Pidgin (pcm) - Nigeria
 9. Oromo (orm) - Ethiopia
 10. Swahili (swa) - Kenya/Tanzania
-11. Tigrinya (tir) - Ethiopia
-12. Twi (twi) - Ghana
+11. Tigrinya (tir) - Ethiopia 
+12. Twi (twi) -       Ghana
 13. Xithonga (tso) - Mozambique
 14. Yoruba (yor) - Nigeria
 
-The dataset covers multiple countries and linguistic groups, providing 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 Hailay/FT_EXLMR model was trained using the following configuration:
+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 **Hailay/FT_EXLMR** model was trained using the following configuration:
 Epochs: 3
 Learning Rate: 1e-5
 Optimizer: AdamW
 Batch Size: 16
 
-## *** 4. Evaluation
+## 4. Evaluation
 
 The model was evaluated using accuracy and loss as the primary metrics. The results are as follows: