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library_name: transformers
license: mit
base_model: intfloat/multilingual-e5-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: intfloat-multilingual-e5-base-arabic-fp16
results: []
---
<!-- 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-multilingual-e5-base-arabic-fp16
This model is a fine-tuned version of [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4961
- Accuracy: 0.7986
- Precision: 0.7991
- Recall: 0.7986
- F1: 0.7988
## 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 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.0686 | 0.3636 | 50 | 1.0146 | 0.5495 | 0.7252 | 0.5495 | 0.4582 |
| 0.9589 | 0.7273 | 100 | 0.8046 | 0.6777 | 0.7234 | 0.6777 | 0.6081 |
| 0.7431 | 1.0873 | 150 | 0.6238 | 0.7595 | 0.7565 | 0.7595 | 0.7530 |
| 0.6066 | 1.4509 | 200 | 0.5485 | 0.7945 | 0.7947 | 0.7945 | 0.7906 |
| 0.5558 | 1.8145 | 250 | 0.5530 | 0.7827 | 0.7860 | 0.7827 | 0.7837 |
| 0.5343 | 2.1745 | 300 | 0.5430 | 0.7973 | 0.8009 | 0.7973 | 0.7983 |
| 0.4965 | 2.5382 | 350 | 0.5178 | 0.7986 | 0.7993 | 0.7986 | 0.7988 |
| 0.5017 | 2.9018 | 400 | 0.4961 | 0.7986 | 0.7991 | 0.7986 | 0.7988 |
| 0.4525 | 3.2618 | 450 | 0.5441 | 0.7932 | 0.7991 | 0.7932 | 0.7950 |
| 0.4194 | 3.6255 | 500 | 0.5147 | 0.8027 | 0.8051 | 0.8027 | 0.8027 |
| 0.4353 | 3.9891 | 550 | 0.4918 | 0.8118 | 0.8109 | 0.8118 | 0.8110 |
| 0.3635 | 4.3491 | 600 | 0.5659 | 0.7977 | 0.8058 | 0.7977 | 0.7980 |
| 0.3529 | 4.7127 | 650 | 0.5493 | 0.8023 | 0.8066 | 0.8023 | 0.8029 |
| 0.3574 | 5.0727 | 700 | 0.5438 | 0.8023 | 0.8043 | 0.8023 | 0.8031 |
### Framework versions
- Transformers 4.51.1
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
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