File size: 2,713 Bytes
8499711 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 |
---
base_model: aubmindlab/bert-base-arabert
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [aubmindlab/bert-base-arabert](https://huggingface.co/aubmindlab/bert-base-arabert) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4485
- Accuracy: 0.7656
- Precision: 0.7688
- Recall: 0.7656
- F1: 0.7650
- Mrr: 0.8440
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 320
- num_epochs: 12
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Mrr |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| 0.9496 | 1.0 | 2250 | 0.9448 | 0.69 | 0.7197 | 0.69 | 0.6896 | 0.8003 |
| 0.7839 | 2.0 | 4500 | 0.8385 | 0.7 | 0.7302 | 0.7 | 0.7032 | 0.8101 |
| 0.4602 | 3.0 | 6750 | 0.9599 | 0.745 | 0.7524 | 0.745 | 0.7421 | 0.8346 |
| 0.4453 | 4.0 | 9000 | 0.9992 | 0.7325 | 0.7474 | 0.7325 | 0.7353 | 0.8342 |
| 0.3919 | 5.0 | 11250 | 1.2636 | 0.7425 | 0.7551 | 0.7425 | 0.7413 | 0.8312 |
| 0.313 | 6.0 | 13500 | 1.3639 | 0.7625 | 0.7679 | 0.7625 | 0.7628 | 0.8442 |
| 0.2186 | 7.0 | 15750 | 1.6281 | 0.745 | 0.7566 | 0.745 | 0.7461 | 0.8369 |
| 0.1942 | 8.0 | 18000 | 1.5611 | 0.775 | 0.7822 | 0.775 | 0.7752 | 0.8486 |
| 0.128 | 9.0 | 20250 | 1.7601 | 0.74 | 0.7504 | 0.74 | 0.7412 | 0.8341 |
| 0.0598 | 10.0 | 22500 | 1.6894 | 0.7725 | 0.7761 | 0.7725 | 0.7725 | 0.8548 |
| 0.0699 | 11.0 | 24750 | 1.8025 | 0.765 | 0.7698 | 0.765 | 0.7645 | 0.8460 |
| 0.0292 | 12.0 | 27000 | 1.8754 | 0.76 | 0.7621 | 0.76 | 0.7592 | 0.8451 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|