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intfloat-e5-large-arabic-fp16-allagree
abdulrahman-nuzha/intfloat-e5-large-arabic-fp16-allagree
intfloat-e5-large-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
- 1d3a89d8
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Base models
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-arabic-fp16-allagree
This model is a fine-tuned version of intfloat/e5-large on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4301
- Accuracy: 0.8405
- Precision: 0.8470
- Recall: 0.8405
- F1: 0.8429
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 |
|---|---|---|---|---|---|---|---|
| 1.0133 | 0.7463 | 50 | 0.6945 | 0.7127 | 0.7471 | 0.7127 | 0.6558 |
| 0.6316 | 1.4925 | 100 | 0.5125 | 0.8013 | 0.7942 | 0.8013 | 0.7899 |
| 0.5287 | 2.2388 | 150 | 0.4711 | 0.8041 | 0.8037 | 0.8041 | 0.8034 |
| 0.4814 | 2.9851 | 200 | 0.4799 | 0.7985 | 0.8288 | 0.7985 | 0.8071 |
| 0.4304 | 3.7313 | 250 | 0.4373 | 0.8256 | 0.8256 | 0.8256 | 0.8256 |
| 0.3706 | 4.4776 | 300 | 0.4595 | 0.8293 | 0.8314 | 0.8293 | 0.8283 |
| 0.3455 | 5.2239 | 350 | 0.4738 | 0.8293 | 0.8455 | 0.8293 | 0.8341 |
| 0.3053 | 5.9701 | 400 | 0.4301 | 0.8405 | 0.8470 | 0.8405 | 0.8429 |
| 0.2437 | 6.7164 | 450 | 0.4713 | 0.8358 | 0.8526 | 0.8358 | 0.8409 |
| 0.223 | 7.4627 | 500 | 0.4894 | 0.8414 | 0.8503 | 0.8414 | 0.8431 |
| 0.2093 | 8.2090 | 550 | 0.4576 | 0.8554 | 0.8572 | 0.8554 | 0.8562 |
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