metadata
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: AraElectra-finetuned-fnd
results: []
AraElectra-finetuned-fnd
This model is a fine-tuned version of aubmindlab/araelectra-base-discriminator on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5072
- Macro F1: 0.7679
- Accuracy: 0.7745
- Precision: 0.7680
- Recall: 0.7678
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: 8
- 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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Accuracy | Precision | Recall |
|---|---|---|---|---|---|---|---|
| 0.5316 | 1.0 | 1597 | 0.4992 | 0.7428 | 0.7564 | 0.7535 | 0.7386 |
| 0.4349 | 2.0 | 3194 | 0.4832 | 0.7523 | 0.7677 | 0.7692 | 0.7470 |
| 0.3423 | 3.0 | 4791 | 0.5072 | 0.7679 | 0.7745 | 0.7680 | 0.7678 |
| 0.2826 | 4.0 | 6388 | 0.5890 | 0.7672 | 0.7752 | 0.7693 | 0.7656 |
Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1