license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlm-yoruba-tweets-classifications
results: []
datasets:
- shmuhammad/AfriSenti-twitter-sentiment
language:
- yo
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
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:
- 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