Instructions to use Ottneel/afroxlmr_base_moderation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ottneel/afroxlmr_base_moderation with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Ottneel/afroxlmr_base_moderation")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Ottneel/afroxlmr_base_moderation") model = AutoModelForSequenceClassification.from_pretrained("Ottneel/afroxlmr_base_moderation", device_map="auto") - Notebooks
- Google Colab
- Kaggle
afroxlmr_base_moderation
This model is a fine-tuned version of Davlan/afro-xlmr-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.0971
- Accuracy: 0.6849
- Macro F1: 0.6610
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: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- 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: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Macro F1 |
|---|---|---|---|---|---|
| 0.4384 | 1.0 | 688 | 0.7319 | 0.6409 | 0.6203 |
| 0.2864 | 2.0 | 1376 | 0.8637 | 0.6855 | 0.6639 |
| 0.2123 | 3.0 | 2064 | 0.9438 | 0.6855 | 0.6615 |
| 0.1695 | 4.0 | 2752 | 1.0971 | 0.6849 | 0.6610 |
Framework versions
- Transformers 5.16.1
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.23.1
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Model tree for Ottneel/afroxlmr_base_moderation
Base model
Davlan/afro-xlmr-base