---
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: Type_of_relation
  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. -->

# Type_of_relation

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: 1.3733
- Macro F1: 0.7820
- Precision: 0.7773
- Recall: 0.7917
- Kappa: 0.6875
- Accuracy: 0.7917

## 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: 128
- seed: 25
- 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: 15

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Macro F1 | Precision | Recall | Kappa  | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:--------:|
| 1.1229        | 1.0   | 697   | 0.8030          | 0.7500   | 0.7330    | 0.7852 | 0.6631 | 0.7852   |
| 0.8056        | 2.0   | 1395  | 0.7428          | 0.7698   | 0.7571    | 0.7974 | 0.6888 | 0.7974   |
| 0.64          | 3.0   | 2092  | 0.7285          | 0.7731   | 0.7653    | 0.7958 | 0.6867 | 0.7958   |
| 0.5323        | 4.0   | 2790  | 0.7310          | 0.7848   | 0.7758    | 0.8019 | 0.7010 | 0.8019   |
| 0.4717        | 5.0   | 3487  | 0.8139          | 0.7777   | 0.7736    | 0.7924 | 0.6849 | 0.7924   |
| 0.312         | 6.0   | 4185  | 0.8625          | 0.7835   | 0.7761    | 0.7974 | 0.6950 | 0.7974   |
| 0.2707        | 7.0   | 4882  | 0.9528          | 0.7824   | 0.7804    | 0.7910 | 0.6869 | 0.7910   |
| 0.1907        | 8.0   | 5580  | 1.0535          | 0.7814   | 0.7749    | 0.7962 | 0.6902 | 0.7962   |
| 0.1494        | 9.0   | 6277  | 1.1044          | 0.7791   | 0.7761    | 0.7863 | 0.6825 | 0.7863   |
| 0.1408        | 10.0  | 6975  | 1.1593          | 0.7818   | 0.7790    | 0.7879 | 0.6845 | 0.7879   |
| 0.0949        | 11.0  | 7672  | 1.2428          | 0.7846   | 0.7791    | 0.7954 | 0.6920 | 0.7954   |
| 0.0815        | 12.0  | 8370  | 1.2998          | 0.7834   | 0.7770    | 0.7963 | 0.6926 | 0.7963   |
| 0.0657        | 13.0  | 9067  | 1.3431          | 0.7827   | 0.7784    | 0.7929 | 0.6889 | 0.7929   |
| 0.0509        | 14.0  | 9765  | 1.3687          | 0.7813   | 0.7773    | 0.7910 | 0.6863 | 0.7910   |
| 0.0488        | 14.99 | 10455 | 1.3733          | 0.7820   | 0.7773    | 0.7917 | 0.6875 | 0.7917   |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.1.0+cu118
- Tokenizers 0.15.0