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# xlm-yoruba-tweets-classifications

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

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:
- 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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