Fah-d/xlm-yoruba-tweets-classifications

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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
• —trainbatchsize: 8
• —evalbatchsize: 8
• —seed: 42
• —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• —lrschedulertype: linear
• —num_epochs: 3

Training results

Framework versions

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