---
base_model: Anwaarma/Improved-Arabert-twitter-sentiment2
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: robust-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. -->

# robust-arabert

This model is a fine-tuned version of [Anwaarma/Improved-Arabert-twitter-sentiment2](https://huggingface.co/Anwaarma/Improved-Arabert-twitter-sentiment2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2072
- Accuracy: 0.94

## 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   | 0.2677          | 0.91     |
| No log        | 0.1092 | 100  | 0.2432          | 0.93     |
| No log        | 0.1638 | 150  | 0.3328          | 0.91     |
| No log        | 0.2183 | 200  | 0.2894          | 0.92     |
| No log        | 0.2729 | 250  | 0.3008          | 0.91     |
| No log        | 0.3275 | 300  | 0.1951          | 0.93     |
| No log        | 0.3821 | 350  | 0.2176          | 0.94     |
| No log        | 0.4367 | 400  | 0.2291          | 0.95     |
| No log        | 0.4913 | 450  | 0.2678          | 0.93     |
| 0.2068        | 0.5459 | 500  | 0.2975          | 0.9      |
| 0.2068        | 0.6004 | 550  | 0.2245          | 0.92     |
| 0.2068        | 0.6550 | 600  | 0.2014          | 0.94     |
| 0.2068        | 0.7096 | 650  | 0.2189          | 0.93     |
| 0.2068        | 0.7642 | 700  | 0.2272          | 0.94     |
| 0.2068        | 0.8188 | 750  | 0.2148          | 0.92     |
| 0.2068        | 0.8734 | 800  | 0.2082          | 0.94     |
| 0.2068        | 0.9279 | 850  | 0.1869          | 0.93     |
| 0.2068        | 0.9825 | 900  | 0.2166          | 0.95     |
| 0.2068        | 1.0371 | 950  | 0.2095          | 0.94     |
| 0.1852        | 1.0917 | 1000 | 0.2948          | 0.92     |
| 0.1852        | 1.1463 | 1050 | 0.2586          | 0.93     |
| 0.1852        | 1.2009 | 1100 | 0.2669          | 0.93     |
| 0.1852        | 1.2555 | 1150 | 0.2635          | 0.94     |
| 0.1852        | 1.3100 | 1200 | 0.2600          | 0.94     |
| 0.1852        | 1.3646 | 1250 | 0.2290          | 0.95     |
| 0.1852        | 1.4192 | 1300 | 0.2248          | 0.95     |
| 0.1852        | 1.4738 | 1350 | 0.1840          | 0.96     |
| 0.1852        | 1.5284 | 1400 | 0.2366          | 0.95     |
| 0.1852        | 1.5830 | 1450 | 0.2322          | 0.95     |
| 0.1137        | 1.6376 | 1500 | 0.1797          | 0.96     |
| 0.1137        | 1.6921 | 1550 | 0.1662          | 0.96     |
| 0.1137        | 1.7467 | 1600 | 0.1762          | 0.96     |
| 0.1137        | 1.8013 | 1650 | 0.1508          | 0.96     |
| 0.1137        | 1.8559 | 1700 | 0.1996          | 0.95     |
| 0.1137        | 1.9105 | 1750 | 0.2072          | 0.94     |


### Framework versions

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