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---
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: AraBERT-finetuned-CrossVal-fnd
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. -->
# AraBERT-finetuned-CrossVal-fnd
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1603
- Macro F1: 0.9387
- Accuracy: 0.9410
- Precision: 0.9415
- Recall: 0.9363
## 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: 32
- seed: 123
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Accuracy | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:------:|
| 0.3283 | 1.0 | 798 | 0.1603 | 0.9387 | 0.9410 | 0.9415 | 0.9363 |
| 0.2274 | 2.0 | 1596 | 0.1826 | 0.9254 | 0.9271 | 0.9225 | 0.9298 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
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