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fine_tuned_mix40k_arabert
aomar85/fine_tuned_mix40k_arabert
fine_tuned_mix40k_arabert is a text classification model from aomar85. Use it when you need a label for a piece of text. It is set up for transformers.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- May 28, 2022
- Updated
- May 28, 2022
- SHA
- fe452df6
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Model card
From the Hugging Face model README
<!-- 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. -->
fine_tuned_mix40k_arabert
This model is a fine-tuned version of aubmindlab/bert-base-arabertv02 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4165
- Accuracy: 0.8909
- Precision: 0.9146
- Recall: 0.8610
- F1: 0.8870
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: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.3133 | 1.0 | 10794 | 0.2683 | 0.8812 | 0.8845 | 0.8752 | 0.8798 |
| 0.2326 | 2.0 | 21588 | 0.2838 | 0.8862 | 0.9133 | 0.8519 | 0.8815 |
| 0.1855 | 3.0 | 32382 | 0.3199 | 0.8865 | 0.9247 | 0.8399 | 0.8803 |
| 0.149 | 4.0 | 43176 | 0.4165 | 0.8909 | 0.9146 | 0.8610 | 0.8870 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Created
- May 28, 2022
- Updated
- May 28, 2022