xlm-yoruba-tweets-classifications

This model is a fine-tuned version of xlm-roberta-base on an shmuhammad/AfriSenti-twitter-sentiment It achieves the following results on the evaluation set:

• Loss: 0.7641
• Accuracy: 0.6871

Model description

This model is a fine-tuned version of the xlm-roberta-base pre-trained model, specifically trained on the 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:

• 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

• train: Dataset({ features: ['tweet', 'label'], num_rows: 8522 })
• validation: Dataset({ features: ['tweet', 'label'], num_rows: 2090 })

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 3

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
0.9621	1.0	1066	0.9099	0.6120
0.8269	2.0	2132	0.7536	0.6627
0.7239	3.0	3198	0.7641	0.6871

Framework versions

• Transformers 4.35.2
• Pytorch 2.1.0+cu121
• Datasets 2.16.1
• Tokenizers 0.15.1