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intfloat-e5-base-arabic-fp16-allagree
abdulrahman-nuzha/intfloat-e5-base-arabic-fp16-allagree
intfloat-e5-base-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
- 3f566a52
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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-base-arabic-fp16-allagree
This model is a fine-tuned version of intfloat/e5-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4020
- Accuracy: 0.8554
- Precision: 0.8542
- Recall: 0.8554
- F1: 0.8547
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 |
|---|---|---|---|---|---|---|---|
| 1.0217 | 0.7463 | 50 | 0.8492 | 0.6707 | 0.7400 | 0.6707 | 0.5976 |
| 0.774 | 1.4925 | 100 | 0.6322 | 0.7612 | 0.7671 | 0.7612 | 0.7222 |
| 0.6181 | 2.2388 | 150 | 0.5103 | 0.8190 | 0.8151 | 0.8190 | 0.8113 |
| 0.5497 | 2.9851 | 200 | 0.4699 | 0.8218 | 0.8175 | 0.8218 | 0.8182 |
| 0.4594 | 3.7313 | 250 | 0.4475 | 0.8330 | 0.8330 | 0.8330 | 0.8304 |
| 0.4155 | 4.4776 | 300 | 0.4371 | 0.8218 | 0.8337 | 0.8218 | 0.8241 |
| 0.3722 | 5.2239 | 350 | 0.4166 | 0.8461 | 0.8423 | 0.8461 | 0.8427 |
| 0.3382 | 5.9701 | 400 | 0.4301 | 0.8340 | 0.8483 | 0.8340 | 0.8381 |
| 0.3087 | 6.7164 | 450 | 0.3906 | 0.8489 | 0.8476 | 0.8489 | 0.8461 |
| 0.2695 | 7.4627 | 500 | 0.4020 | 0.8601 | 0.8585 | 0.8601 | 0.8588 |
| 0.2685 | 8.2090 | 550 | 0.4136 | 0.8601 | 0.8601 | 0.8601 | 0.8592 |
| 0.2451 | 8.9552 | 600 | 0.4020 | 0.8554 | 0.8542 | 0.8554 | 0.8547 |
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