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intfloat-e5-large-v2-arabic-fp16-allagree
abdulrahman-nuzha/intfloat-e5-large-v2-arabic-fp16-allagree
intfloat-e5-large-v2-arabic-fp16-allagree is a text classification model from abdulrahman-nuzha. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Task
- Text Classification
- Library
- transformers
- License
- mit
- Model type
- bert
- Access
- Public
- Created
- Apr 15, 2025
- Updated
- Apr 15, 2025
- SHA
- 5da02bcf
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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. -->
intfloat-e5-large-v2-arabic-fp16-allagree
This model is a fine-tuned version of intfloat/e5-large-v2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3722
- Accuracy: 0.8554
- Precision: 0.8589
- Recall: 0.8554
- F1: 0.8568
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: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.3
- num_epochs: 10
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.9692 | 0.7463 | 50 | 0.6804 | 0.7379 | 0.7407 | 0.7379 | 0.6924 |
| 0.6066 | 1.4925 | 100 | 0.4802 | 0.8116 | 0.8069 | 0.8116 | 0.8041 |
| 0.4754 | 2.2388 | 150 | 0.4500 | 0.8330 | 0.8333 | 0.8330 | 0.8314 |
| 0.4672 | 2.9851 | 200 | 0.4106 | 0.8386 | 0.8406 | 0.8386 | 0.8384 |
| 0.3954 | 3.7313 | 250 | 0.3969 | 0.8479 | 0.8470 | 0.8479 | 0.8471 |
| 0.3643 | 4.4776 | 300 | 0.3722 | 0.8554 | 0.8589 | 0.8554 | 0.8568 |
| 0.3274 | 5.2239 | 350 | 0.3880 | 0.8657 | 0.8651 | 0.8657 | 0.8652 |
| 0.2922 | 5.9701 | 400 | 0.3938 | 0.8591 | 0.8633 | 0.8591 | 0.8607 |
| 0.2379 | 6.7164 | 450 | 0.4070 | 0.8573 | 0.8626 | 0.8573 | 0.8593 |
Framework versions
- Transformers 4.51.1
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Apr 15, 2025
- Updated
- Apr 15, 2025