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
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