---
base_model: aubmindlab/bert-base-arabertv02-twitter
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: unfortified_arabert
  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. -->

# unfortified_arabert

This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02-twitter](https://huggingface.co/aubmindlab/bert-base-arabertv02-twitter) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2946
- Accuracy: 0.92

## 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: 16
- 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 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| No log        | 0.0546 | 50   | 0.4109          | 0.84     |
| No log        | 0.1092 | 100  | 0.3536          | 0.87     |
| No log        | 0.1638 | 150  | 0.3609          | 0.88     |
| No log        | 0.2183 | 200  | 0.3567          | 0.88     |
| No log        | 0.2729 | 250  | 0.2934          | 0.91     |
| No log        | 0.3275 | 300  | 0.2243          | 0.95     |
| No log        | 0.3821 | 350  | 0.2257          | 0.92     |
| No log        | 0.4367 | 400  | 0.2350          | 0.94     |
| No log        | 0.4913 | 450  | 0.2917          | 0.91     |
| 0.2619        | 0.5459 | 500  | 0.3450          | 0.9      |
| 0.2619        | 0.6004 | 550  | 0.2562          | 0.91     |
| 0.2619        | 0.6550 | 600  | 0.2258          | 0.93     |
| 0.2619        | 0.7096 | 650  | 0.2705          | 0.9      |
| 0.2619        | 0.7642 | 700  | 0.2250          | 0.92     |
| 0.2619        | 0.8188 | 750  | 0.2246          | 0.92     |
| 0.2619        | 0.8734 | 800  | 0.2566          | 0.92     |
| 0.2619        | 0.9279 | 850  | 0.1948          | 0.92     |
| 0.2619        | 0.9825 | 900  | 0.2673          | 0.91     |
| 0.2619        | 1.0371 | 950  | 0.3450          | 0.91     |
| 0.2108        | 1.0917 | 1000 | 0.2748          | 0.92     |
| 0.2108        | 1.1463 | 1050 | 0.2977          | 0.91     |
| 0.2108        | 1.2009 | 1100 | 0.3209          | 0.91     |
| 0.2108        | 1.2555 | 1150 | 0.2821          | 0.91     |
| 0.2108        | 1.3100 | 1200 | 0.3070          | 0.92     |
| 0.2108        | 1.3646 | 1250 | 0.2946          | 0.92     |


### Framework versions

- Transformers 4.42.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1