---
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: ArBERT-finetuned-fnd
  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. -->

# ArBERT-finetuned-fnd

This model is a fine-tuned version of [UBC-NLP/ARBERT](https://huggingface.co/UBC-NLP/ARBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4896
- Macro F1: 0.7637
- Accuracy: 0.7738
- Precision: 0.7695
- Recall: 0.7604

## 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: 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: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Accuracy | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------:|:------:|
| 0.5031        | 1.0   | 1597 | 0.4754          | 0.7547   | 0.7606   | 0.7538    | 0.7559 |
| 0.3832        | 2.0   | 3194 | 0.4896          | 0.7637   | 0.7738   | 0.7695    | 0.7604 |
| 0.2571        | 3.0   | 4791 | 0.5890          | 0.7605   | 0.7692   | 0.7634    | 0.7585 |


### Framework versions

- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1