Update README.md

Edited Model Card

README.md

@@ -8,6 +8,10 @@ metrics:
 model-index:
 - name: xlm-yoruba-tweets-classifications
   results: []
+datasets:
+- shmuhammad/AfriSenti-twitter-sentiment
+language:
+- yo
 ---
 
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -15,18 +19,30 @@ should probably proofread and complete it, then remove this comment. -->
 
 # xlm-yoruba-tweets-classifications
 
-This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an unknown dataset.
+This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on an [shmuhammad/AfriSenti-twitter-sentiment](https://huggingface.co/datasets/shmuhammad/AfriSenti-twitter-sentiment)
 It achieves the following results on the evaluation set:
 - Loss: 0.7641
 - Accuracy: 0.6871
 
 ## Model description
 
-More information needed
+This model is a fine-tuned version of the [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) pre-trained model, specifically trained on the [shmuhammad/AfriSenti-twitter-sentiment](https://huggingface.co/datasets/shmuhammad/AfriSenti-twitter-sentiment) dataset focusing on Yoruba tweets. It aims to perform sentiment classification on Yoruba tweets.
+## Key details:
+•	Type: Fine-tuned language model
+•	Base model: xlm-roberta-base
+•	Task: Yoruba tweet sentiment classification
+•	Dataset: shmuhammad/AfriSenti-twitter-sentiment (Yoruba subset)
 
-## Intended uses & limitations
 
-More information needed
+## Intended uses:
+•	Classifying sentiment (positive, negative, neutral) on Yoruba tweets.
+•	Can be used as a starting point for further fine-tuning on specific Yoruba tweet classification tasks.
+
+## Limitations:
+•	Trained on a limited dataset, potentially impacting performance on unseen data.
+•	Fine-tuned only for sentiment classification, not suitable for other tasks.
+•	Accuracy might not be optimal for all applications.
+
 
 ## Training and evaluation data
 
@@ -34,6 +50,11 @@ More information needed
 
 ## Training procedure
 
+•	Dataset: shmuhammad/AfriSenti-twitter-sentiment (Yoruba subset)
+•	Data size: Specify the number of Yoruba tweets used for training and evaluation.
+•	Data description: Briefly describe the content and distribution of sentiment labels in the dataset.
+•	Data source: https://huggingface.co/datasets/shmuhammad/AfriSenti-twitter-sentiment 
+
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
@@ -59,4 +80,4 @@ The following hyperparameters were used during training:
 - Transformers 4.35.2
 - Pytorch 2.1.0+cu121
 - Datasets 2.16.1
-- Tokenizers 0.15.1
+- Tokenizers 0.15.1