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tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: AraBERT_token_classification_Araieval24_binary
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_token_classification_Araieval24_binary
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv2](https://huggingface.co/aubmindlab/bert-base-arabertv2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0062
- Precision: 0.1851
- Recall: 0.2340
- F1: 0.2067
- Accuracy: 0.7436
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5408 | 1.0 | 776 | 0.5362 | 0.1292 | 0.1665 | 0.1455 | 0.7566 |
| 0.4664 | 2.0 | 1552 | 0.5402 | 0.1399 | 0.2139 | 0.1692 | 0.7462 |
| 0.3855 | 3.0 | 2328 | 0.5915 | 0.1632 | 0.1935 | 0.1771 | 0.7552 |
| 0.2911 | 4.0 | 3104 | 0.6717 | 0.1712 | 0.1953 | 0.1825 | 0.7457 |
| 0.2334 | 5.0 | 3880 | 0.7420 | 0.1826 | 0.2066 | 0.1939 | 0.7473 |
| 0.18 | 6.0 | 4656 | 0.8034 | 0.1835 | 0.2278 | 0.2033 | 0.7447 |
| 0.1611 | 7.0 | 5432 | 0.8849 | 0.1949 | 0.2275 | 0.2099 | 0.7515 |
| 0.1323 | 8.0 | 6208 | 0.9530 | 0.1870 | 0.2337 | 0.2078 | 0.7449 |
| 0.1133 | 9.0 | 6984 | 1.0043 | 0.1909 | 0.2348 | 0.2106 | 0.7434 |
| 0.1007 | 10.0 | 7760 | 1.0062 | 0.1851 | 0.2340 | 0.2067 | 0.7436 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.13.3
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