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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 |
### Framework versions
- Transformers 5.15.0
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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