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intfloat-e5-small-v2-arabic-fp16-allagree
abdulrahman-nuzha/intfloat-e5-small-v2-arabic-fp16-allagree
intfloat-e5-small-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
- de74bd1a
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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-v2-arabic-fp16-allagree
This model is a fine-tuned version of intfloat/e5-small-v2 on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4486
- Accuracy: 0.8451
- Precision: 0.8477
- Recall: 0.8451
- F1: 0.8458
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 |
|---|---|---|---|---|---|---|---|
| 1.0892 | 0.7463 | 50 | 1.0692 | 0.4198 | 0.7313 | 0.4198 | 0.2920 |
| 1.0058 | 1.4925 | 100 | 0.8471 | 0.7155 | 0.7762 | 0.7155 | 0.6375 |
| 0.7971 | 2.2388 | 150 | 0.7135 | 0.7295 | 0.7177 | 0.7295 | 0.6688 |
| 0.7025 | 2.9851 | 200 | 0.6560 | 0.7603 | 0.7719 | 0.7603 | 0.7341 |
| 0.6405 | 3.7313 | 250 | 0.5768 | 0.8060 | 0.8070 | 0.8060 | 0.8047 |
| 0.5794 | 4.4776 | 300 | 0.5331 | 0.8209 | 0.8213 | 0.8209 | 0.8108 |
| 0.5474 | 5.2239 | 350 | 0.5227 | 0.8116 | 0.8265 | 0.8116 | 0.8156 |
| 0.5228 | 5.9701 | 400 | 0.5046 | 0.8237 | 0.8291 | 0.8237 | 0.8246 |
| 0.472 | 6.7164 | 450 | 0.4832 | 0.8302 | 0.8315 | 0.8302 | 0.8260 |
| 0.4527 | 7.4627 | 500 | 0.4739 | 0.8368 | 0.8435 | 0.8368 | 0.8391 |
| 0.4347 | 8.2090 | 550 | 0.4526 | 0.8414 | 0.8443 | 0.8414 | 0.8425 |
| 0.4322 | 8.9552 | 600 | 0.4486 | 0.8451 | 0.8477 | 0.8451 | 0.8458 |
| 0.4111 | 9.7015 | 650 | 0.4545 | 0.8405 | 0.8488 | 0.8405 | 0.8435 |
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