# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("Rogendo/afribert-mlm")
model = AutoModelForMaskedLM.from_pretrained("Rogendo/afribert-mlm")Quick Links
afribert-mlm
This model is a fine-tuned version of castorini/afriberta_large on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.9959
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: 8
- eval_batch_size: 8
- 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 100 | 1.9814 |
| No log | 2.0 | 200 | 1.9049 |
| No log | 3.0 | 300 | 1.9402 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for Rogendo/afribert-mlm
Base model
castorini/afriberta_large
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Rogendo/afribert-mlm")