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intfloat-e5-small-arabic-fp16-allagree
abdulrahman-nuzha/intfloat-e5-small-arabic-fp16-allagree
intfloat-e5-small-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
- 5063a1b7
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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-small-arabic-fp16-allagree
This model is a fine-tuned version of intfloat/e5-small on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5430
- Accuracy: 0.7845
- Precision: 0.7994
- Recall: 0.7845
- F1: 0.7886
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 |
|---|---|---|---|---|---|---|---|
| 1.0961 | 0.7463 | 50 | 1.0715 | 0.3853 | 0.7241 | 0.3853 | 0.2241 |
| 1.0181 | 1.4925 | 100 | 0.9009 | 0.6875 | 0.7524 | 0.6875 | 0.6122 |
| 0.8328 | 2.2388 | 150 | 0.7633 | 0.7052 | 0.7815 | 0.7052 | 0.6315 |
| 0.7492 | 2.9851 | 200 | 0.6860 | 0.7295 | 0.6952 | 0.7295 | 0.6858 |
| 0.6931 | 3.7313 | 250 | 0.6982 | 0.7369 | 0.7275 | 0.7369 | 0.7209 |
| 0.6623 | 4.4776 | 300 | 0.6326 | 0.7705 | 0.7544 | 0.7705 | 0.7485 |
| 0.6107 | 5.2239 | 350 | 0.6350 | 0.7556 | 0.7696 | 0.7556 | 0.7530 |
| 0.5789 | 5.9701 | 400 | 0.5892 | 0.7649 | 0.7924 | 0.7649 | 0.7713 |
| 0.5593 | 6.7164 | 450 | 0.5449 | 0.7985 | 0.7946 | 0.7985 | 0.7963 |
| 0.5343 | 7.4627 | 500 | 0.5486 | 0.7845 | 0.8008 | 0.7845 | 0.7897 |
| 0.5177 | 8.2090 | 550 | 0.5373 | 0.8013 | 0.8038 | 0.8013 | 0.8016 |
| 0.5201 | 8.9552 | 600 | 0.5370 | 0.7882 | 0.7996 | 0.7882 | 0.7915 |
| 0.4962 | 9.7015 | 650 | 0.5357 | 0.7882 | 0.7994 | 0.7882 | 0.7917 |
Framework versions
- Transformers 4.48.2
- Pytorch 2.6.0+cu124
- Datasets 3.3.1
- Tokenizers 0.21.0
- Task
- Text Classification
- Library
- transformers
- Type
- bert
- License
- mit
- Created
- Apr 15, 2025
- Updated
- Apr 15, 2025