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Model5_arabertv2_large_T1_WOS
SMG0/Model5_arabertv2_large_T1_WOS
Model5_arabertv2_large_T1_WOS is a text classification model from SMG0. 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
- Aug 15, 2023
- Updated
- Aug 15, 2023
- SHA
- e1e8f647
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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. -->
Model5_arabertv2_large_T1_WOS
This model is a fine-tuned version of aubmindlab/bert-large-arabertv02-twitter on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.4466
- F1: 0.5436
- F1 Macro: 0.1187
- Roc Auc: 0.7222
- Accuracy: 0.5531
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: 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: 15
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | F1 Macro | Roc Auc | Accuracy |
|---|---|---|---|---|---|---|---|
| 0.263 | 1.0 | 507 | 0.3805 | 0.5436 | 0.1187 | 0.7222 | 0.5531 |
| 0.377 | 2.0 | 1014 | 0.3819 | 0.5436 | 0.1187 | 0.7222 | 0.5531 |
| 0.3716 | 3.0 | 1521 | 0.4092 | 0.5436 | 0.1187 | 0.7222 | 0.5531 |
| 0.3705 | 4.0 | 2028 | 0.4030 | 0.5436 | 0.1187 | 0.7222 | 0.5531 |
| 0.3664 | 5.0 | 2535 | 0.4466 | 0.5436 | 0.1187 | 0.7222 | 0.5531 |
Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Created
- Aug 15, 2023
- Updated
- Aug 15, 2023