afro-xlmr-base-finetuned-ner
This model is a fine-tuned version of Davlan/afro-xlmr-base on the masakhaner dataset. It achieves the following results on the evaluation set:
- Loss: 0.1729
- Precision: 0.6378
- Recall: 0.7409
- F1: 0.6855
- Accuracy: 0.9539
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 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 | Precision | Recall | F1 | Accuracy |
---|---|---|---|---|---|---|---|
No log | 1.0 | 219 | 0.2089 | 0.55 | 0.6707 | 0.6044 | 0.9310 |
No log | 2.0 | 438 | 0.1602 | 0.6305 | 0.7439 | 0.6825 | 0.9536 |
0.2795 | 3.0 | 657 | 0.1729 | 0.6378 | 0.7409 | 0.6855 | 0.9539 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
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Model tree for Elu-dan/afro-xlmr-base-finetuned-ner
Base model
Davlan/afro-xlmr-baseDataset used to train Elu-dan/afro-xlmr-base-finetuned-ner
Evaluation results
- Precision on masakhanervalidation set self-reported0.638
- Recall on masakhanervalidation set self-reported0.741
- F1 on masakhanervalidation set self-reported0.685
- Accuracy on masakhanervalidation set self-reported0.954