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M1-arabert-similiraty-without_unique-values_40
aomar85/M1-arabert-similiraty-without_unique-values_40
M1-arabert-similiraty-without_unique-values_40 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
- Sep 7, 2022
- Updated
- Sep 7, 2022
- SHA
- 987d7f2a
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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. -->
M1-arabert-similiraty-without_unique-values_40
This model is a fine-tuned version of aubmindlab/bert-base-arabertv2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.1745
- Accuracy: 0.9573
- Precision: 0.9203
- Recall: 0.9312
- F1: 0.9257
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 |
|---|---|---|---|---|---|---|---|
| 0.1917 | 1.0 | 7850 | 0.1560 | 0.9532 | 0.9112 | 0.9262 | 0.9186 |
| 0.1364 | 2.0 | 15700 | 0.1568 | 0.9545 | 0.9012 | 0.9440 | 0.9221 |
| 0.1148 | 3.0 | 23550 | 0.1705 | 0.9567 | 0.9045 | 0.9486 | 0.9260 |
| 0.0963 | 4.0 | 31400 | 0.1745 | 0.9573 | 0.9203 | 0.9312 | 0.9257 |
Framework versions
- Transformers 4.21.3
- Pytorch 1.12.1+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- Created
- Sep 7, 2022
- Updated
- Sep 7, 2022