---
base_model: aubmindlab/bert-base-arabertv2
tags:
- generated_from_trainer
datasets:
- arcd
model-index:
- name: rinna-AraBert-qa-ar4
  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. -->

# rinna-AraBert-qa-ar4

This model is a fine-tuned version of [aubmindlab/bert-base-arabertv2](https://huggingface.co/aubmindlab/bert-base-arabertv2) on the arcd dataset.
It achieves the following results on the evaluation set:
- Loss: 7.1639

## 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: 7e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 100

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3751        | 6.88  | 150  | 3.4763          |
| 0.2526        | 13.75 | 300  | 4.7270          |
| 0.1059        | 20.63 | 450  | 5.7927          |
| 0.0604        | 27.51 | 600  | 5.6757          |
| 0.0347        | 34.38 | 750  | 6.0637          |
| 0.0163        | 41.26 | 900  | 6.3835          |
| 0.0116        | 48.14 | 1050 | 6.7934          |
| 0.0024        | 55.01 | 1200 | 6.8119          |
| 0.0021        | 61.89 | 1350 | 6.9426          |
| 0.0042        | 68.77 | 1500 | 6.8997          |
| 0.0033        | 75.64 | 1650 | 6.8969          |
| 0.0055        | 82.52 | 1800 | 7.0831          |
| 0.0012        | 89.4  | 1950 | 7.0766          |
| 0.0014        | 96.28 | 2100 | 7.1639          |


### Framework versions

- Transformers 4.33.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.13.3