| --- |
| library_name: transformers |
| base_model: castorini/afriberta_base |
| tags: |
| - generated_from_trainer |
| metrics: |
| - f1 |
| model-index: |
| - name: afriberta-stl-base-sentiment |
| results: [] |
| --- |
| |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You |
| should probably proofread and complete it, then remove this comment. --> |
|
|
| # afriberta-stl-base-sentiment |
|
|
| This model is a fine-tuned version of [castorini/afriberta_base](https://huggingface.co/castorini/afriberta_base) on the None dataset. |
| It achieves the following results on the evaluation set: |
| - Loss: 0.8213 |
| - Sentiment Precision: 0.7191 |
| - Sentiment Recall: 0.7099 |
| - F1: 0.7111 |
| - Sentiment Acc: 0.7161 |
|
|
| ## Model description |
|
|
| More information needed |
|
|
| ## Intended uses & limitations |
|
|
| More information needed |
|
|
| ## Training and evaluation data |
|
|
| More information needed |
|
|
| ## Training procedure |
|
|
| ### Training hyperparameters |
|
|
| The following hyperparameters were used during training: |
| - learning_rate: 1e-05 |
| - train_batch_size: 16 |
| - eval_batch_size: 32 |
| - seed: 42 |
| - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
| - lr_scheduler_type: cosine |
| - lr_scheduler_warmup_steps: 300 |
| - num_epochs: 6 |
| - mixed_precision_training: Native AMP |
|
|
| ### Training results |
|
|
| | Training Loss | Epoch | Step | Validation Loss | Sentiment Precision | Sentiment Recall | F1 | Sentiment Acc | |
| |:-------------:|:-----:|:----:|:---------------:|:-------------------:|:----------------:|:------:|:-------------:| |
| | 0.7579 | 1.0 | 402 | 0.7556 | 0.6904 | 0.6818 | 0.6729 | 0.6845 | |
| | 0.6418 | 2.0 | 804 | 0.7293 | 0.6981 | 0.6946 | 0.6945 | 0.7032 | |
| | 0.4884 | 3.0 | 1206 | 0.7908 | 0.6974 | 0.6863 | 0.6763 | 0.6933 | |
| | 0.3494 | 4.0 | 1608 | 0.8595 | 0.7064 | 0.6959 | 0.6971 | 0.7032 | |
| | 0.3035 | 5.0 | 2010 | 0.9201 | 0.7120 | 0.7062 | 0.7074 | 0.7120 | |
| | 0.2468 | 6.0 | 2412 | 0.9231 | 0.7012 | 0.6990 | 0.6996 | 0.7057 | |
|
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|
|
| ### Framework versions |
|
|
| - Transformers 5.15.0 |
| - Pytorch 2.11.0+cu128 |
| - Datasets 4.0.0 |
| - Tokenizers 0.22.2 |
|
|
