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---
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
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