---
library_name: transformers
license: mit
base_model: Davlan/afro-xlmr-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: STL-afroxlmr-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. -->

# STL-afroxlmr-base-sentiment

This model is a fine-tuned version of [Davlan/afro-xlmr-base](https://huggingface.co/Davlan/afro-xlmr-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8290
- Sentiment Precision: 0.7115
- Sentiment Recall: 0.7115
- F1: 0.7113
- Sentiment Acc: 0.7157

## 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: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Sentiment Precision | Sentiment Recall | F1     | Sentiment Acc |
|:-------------:|:-----:|:----:|:---------------:|:-------------------:|:----------------:|:------:|:-------------:|
| 0.7934        | 1.0   | 377  | 0.7107          | 0.7023              | 0.6982           | 0.6995 | 0.7045        |
| 0.6659        | 2.0   | 754  | 0.6630          | 0.7249              | 0.7250           | 0.7177 | 0.7269        |
| 0.5482        | 3.0   | 1131 | 0.6687          | 0.7192              | 0.7215           | 0.7181 | 0.7257        |
| 0.4400        | 4.0   | 1508 | 0.7441          | 0.7226              | 0.7171           | 0.7179 | 0.7195        |
| 0.3982        | 5.0   | 1885 | 0.7949          | 0.7109              | 0.7101           | 0.7081 | 0.7170        |
| 0.2832        | 6.0   | 2262 | 0.8290          | 0.7115              | 0.7115           | 0.7113 | 0.7157        |


### Framework versions

- Transformers 5.16.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.23.1