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intfloat-e5-large-arabic-fp16
abdulrahman-nuzha/intfloat-e5-large-arabic-fp16
intfloat-e5-large-arabic-fp16 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 16, 2025
- Updated
- Apr 16, 2025
- SHA
- 41a9efeb
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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
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: 0.6571
- Accuracy: 0.7295
- Precision: 0.7252
- Recall: 0.7295
- F1: 0.7229
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 |
|---|---|---|---|---|---|---|---|
| 1.1453 | 0.3636 | 50 | 0.9382 | 0.5823 | 0.4522 | 0.5823 | 0.5088 |
| 0.9116 | 0.7273 | 100 | 0.8151 | 0.6568 | 0.6543 | 0.6568 | 0.6162 |
| 0.8321 | 1.0873 | 150 | 0.8027 | 0.6645 | 0.6610 | 0.6645 | 0.6379 |
| 0.8035 | 1.4509 | 200 | 0.7924 | 0.6777 | 0.6807 | 0.6777 | 0.6628 |
| 0.7746 | 1.8145 | 250 | 0.9196 | 0.6141 | 0.6605 | 0.6141 | 0.6040 |
| 0.7751 | 2.1745 | 300 | 0.7843 | 0.6677 | 0.6741 | 0.6677 | 0.6650 |
| 0.753 | 2.5382 | 350 | 0.7799 | 0.6568 | 0.6968 | 0.6568 | 0.6672 |
| 0.731 | 2.9018 | 400 | 0.7178 | 0.7123 | 0.7160 | 0.7123 | 0.6950 |
| 0.7133 | 3.2618 | 450 | 0.6932 | 0.71 | 0.7151 | 0.71 | 0.7117 |
| 0.7057 | 3.6255 | 500 | 0.7281 | 0.6986 | 0.7044 | 0.6986 | 0.6988 |
| 0.6831 | 3.9891 | 550 | 0.6745 | 0.7309 | 0.7296 | 0.7309 | 0.7195 |
| 0.6486 | 4.3491 | 600 | 0.6571 | 0.7295 | 0.7252 | 0.7295 | 0.7229 |
| 0.6378 | 4.7127 | 650 | 0.6701 | 0.7232 | 0.7217 | 0.7232 | 0.7223 |
| 0.6281 | 5.0727 | 700 | 0.6627 | 0.7386 | 0.7350 | 0.7386 | 0.7360 |
| 0.5938 | 5.4364 | 750 | 0.6814 | 0.7155 | 0.7229 | 0.7155 | 0.7181 |
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 16, 2025
- Updated
- Apr 16, 2025