---
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: 0.7828
- Macro F1: 0.9002
- Precision: 0.8999
- Recall: 0.9007
- Kappa: 0.8082
- Accuracy: 0.9007

## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:--------:|
| 0.492         | 1.0   | 697   | 0.3226          | 0.8904   | 0.8920    | 0.8923 | 0.7872 | 0.8923   |
| 0.3283        | 2.0   | 1395  | 0.3107          | 0.9000   | 0.9001    | 0.9010 | 0.8071 | 0.9010   |
| 0.2371        | 3.0   | 2092  | 0.3224          | 0.8959   | 0.8973    | 0.8949 | 0.8020 | 0.8949   |
| 0.1817        | 4.0   | 2790  | 0.3469          | 0.9000   | 0.9007    | 0.9000 | 0.8082 | 0.9000   |
| 0.1372        | 5.0   | 3487  | 0.4185          | 0.8966   | 0.8980    | 0.8962 | 0.8034 | 0.8962   |
| 0.0779        | 6.0   | 4185  | 0.4717          | 0.8989   | 0.8991    | 0.8992 | 0.8059 | 0.8992   |
| 0.0676        | 7.0   | 4882  | 0.5415          | 0.8962   | 0.8972    | 0.8958 | 0.8014 | 0.8958   |
| 0.042         | 8.0   | 5580  | 0.6031          | 0.8984   | 0.8988    | 0.8982 | 0.8057 | 0.8982   |
| 0.0335        | 9.0   | 6277  | 0.6551          | 0.9017   | 0.9019    | 0.9025 | 0.8106 | 0.9025   |
| 0.0239        | 10.0  | 6975  | 0.7116          | 0.8975   | 0.8974    | 0.8980 | 0.8029 | 0.8980   |
| 0.0168        | 11.0  | 7672  | 0.7130          | 0.8976   | 0.8976    | 0.8984 | 0.8029 | 0.8984   |
| 0.019         | 12.0  | 8370  | 0.7464          | 0.9011   | 0.9007    | 0.9018 | 0.8101 | 0.9018   |
| 0.0121        | 13.0  | 9067  | 0.7709          | 0.9006   | 0.9003    | 0.9010 | 0.8091 | 0.9010   |
| 0.0089        | 14.0  | 9765  | 0.7790          | 0.9002   | 0.9002    | 0.9007 | 0.8081 | 0.9007   |
| 0.0068        | 14.99 | 10455 | 0.7828          | 0.9002   | 0.8999    | 0.9007 | 0.8082 | 0.9007   |


### Framework versions

- Transformers 4.36.0
- Pytorch 2.1.0+cu118
- Tokenizers 0.15.0