metadata

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

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