metadata
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: fine-tuned-arabert_mixdata_latest
results: []
fine-tuned-arabert_mixdata_latest
This model is a fine-tuned version of aubmindlab/bert-base-arabertv02 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1937
- Accuracy: 0.9572
- Precision: 0.9695
- Recall: 0.9612
- F1: 0.9653
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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.159 | 1.0 | 64776 | 0.1417 | 0.9537 | 0.9620 | 0.9633 | 0.9627 |
| 0.1303 | 2.0 | 129552 | 0.1743 | 0.9551 | 0.9697 | 0.9575 | 0.9636 |
| 0.1135 | 3.0 | 194328 | 0.1615 | 0.9570 | 0.9652 | 0.9654 | 0.9653 |
| 0.0957 | 4.0 | 259104 | 0.1937 | 0.9572 | 0.9695 | 0.9612 | 0.9653 |
Framework versions
- Transformers 4.19.3
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1