---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
model-index:
- name: train
  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. -->

# train

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: 0.9948
- Macro F1: 0.7856
- Precision: 0.7820
- Recall: 0.7956
- Kappa: 0.6940
- Accuracy: 0.7956

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:--------:|
| No log        | 1.0   | 101  | 1.1562          | 0.6031   | 0.5561    | 0.7044 | 0.4967 | 0.7044   |
| No log        | 2.0   | 203  | 0.9119          | 0.7151   | 0.7107    | 0.7672 | 0.6236 | 0.7672   |
| No log        | 3.0   | 304  | 0.8493          | 0.7280   | 0.7139    | 0.7734 | 0.6381 | 0.7734   |
| No log        | 4.0   | 406  | 0.8087          | 0.7455   | 0.7632    | 0.7648 | 0.6421 | 0.7648   |
| 0.9431        | 5.0   | 507  | 0.7735          | 0.7779   | 0.7741    | 0.7931 | 0.6858 | 0.7931   |
| 0.9431        | 6.0   | 609  | 0.8201          | 0.7753   | 0.7735    | 0.7869 | 0.6797 | 0.7869   |
| 0.9431        | 7.0   | 710  | 0.8564          | 0.7886   | 0.7883    | 0.8017 | 0.7004 | 0.8017   |
| 0.9431        | 8.0   | 812  | 0.8712          | 0.7799   | 0.7754    | 0.7894 | 0.6854 | 0.7894   |
| 0.9431        | 9.0   | 913  | 0.9142          | 0.7775   | 0.7751    | 0.7869 | 0.6811 | 0.7869   |
| 0.2851        | 10.0  | 1015 | 0.9007          | 0.7820   | 0.7764    | 0.7943 | 0.6913 | 0.7943   |
| 0.2851        | 11.0  | 1116 | 0.9425          | 0.7859   | 0.7825    | 0.7956 | 0.6940 | 0.7956   |
| 0.2851        | 12.0  | 1218 | 0.9798          | 0.7815   | 0.7797    | 0.7906 | 0.6869 | 0.7906   |
| 0.2851        | 13.0  | 1319 | 0.9895          | 0.7895   | 0.7860    | 0.7993 | 0.7003 | 0.7993   |
| 0.2851        | 14.0  | 1421 | 0.9872          | 0.7854   | 0.7813    | 0.7943 | 0.6935 | 0.7943   |
| 0.1273        | 14.93 | 1515 | 0.9948          | 0.7856   | 0.7820    | 0.7956 | 0.6940 | 0.7956   |


### Framework versions

- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Tokenizers 0.13.3