---
base_model: aubmindlab/bert-base-arabertv2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Improved-Arabert-twitter-sentiment-No-dropout
  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. -->

# Improved-Arabert-twitter-sentiment-No-dropout

This model is a fine-tuned version of [aubmindlab/bert-base-arabertv2](https://huggingface.co/aubmindlab/bert-base-arabertv2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5434
- Accuracy: 0.9

## 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5764        | 0.55  | 50   | 0.4925          | 0.79     |
| 0.3357        | 1.1   | 100  | 0.3094          | 0.88     |
| 0.2183        | 1.65  | 150  | 0.2971          | 0.87     |
| 0.2042        | 2.2   | 200  | 0.3013          | 0.89     |
| 0.1258        | 2.75  | 250  | 0.3038          | 0.9      |
| 0.1359        | 3.3   | 300  | 0.3114          | 0.89     |
| 0.0893        | 3.85  | 350  | 0.3108          | 0.91     |
| 0.0816        | 4.4   | 400  | 0.3569          | 0.9      |
| 0.071         | 4.95  | 450  | 0.3574          | 0.9      |
| 0.1043        | 5.49  | 500  | 0.4332          | 0.89     |
| 0.0407        | 6.04  | 550  | 0.4232          | 0.9      |
| 0.0378        | 6.59  | 600  | 0.4273          | 0.91     |
| 0.0341        | 7.14  | 650  | 0.4671          | 0.91     |
| 0.0226        | 7.69  | 700  | 0.5174          | 0.9      |
| 0.0215        | 8.24  | 750  | 0.4786          | 0.89     |
| 0.0329        | 8.79  | 800  | 0.4853          | 0.9      |
| 0.021         | 9.34  | 850  | 0.5430          | 0.9      |
| 0.0219        | 9.89  | 900  | 0.5510          | 0.89     |
| 0.0154        | 10.44 | 950  | 0.5518          | 0.9      |
| 0.0119        | 10.99 | 1000 | 0.5473          | 0.9      |
| 0.0108        | 11.54 | 1050 | 0.5285          | 0.9      |
| 0.0138        | 12.09 | 1100 | 0.5239          | 0.91     |
| 0.0133        | 12.64 | 1150 | 0.5584          | 0.89     |
| 0.0121        | 13.19 | 1200 | 0.5334          | 0.9      |
| 0.0063        | 13.74 | 1250 | 0.5325          | 0.91     |
| 0.0061        | 14.29 | 1300 | 0.5429          | 0.9      |
| 0.0105        | 14.84 | 1350 | 0.5434          | 0.9      |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.7
- Tokenizers 0.14.1