---
library_name: transformers
license: mit
base_model: intfloat/e5-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: intfloat-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-e5-base-arabic-fp16

This model is a fine-tuned version of [intfloat/e5-base](https://huggingface.co/intfloat/e5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7482
- Accuracy: 0.6909
- Precision: 0.6879
- Recall: 0.6909
- F1: 0.6881

## 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     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.0832        | 0.3636 | 50   | 1.0122          | 0.49     | 0.6672    | 0.49   | 0.3741 |
| 0.9697        | 0.7273 | 100  | 0.8935          | 0.6073   | 0.5817    | 0.6073 | 0.5493 |
| 0.8744        | 1.0873 | 150  | 0.8016          | 0.6636   | 0.6552    | 0.6636 | 0.6272 |
| 0.8115        | 1.4509 | 200  | 0.7482          | 0.6909   | 0.6879    | 0.6909 | 0.6881 |
| 0.7757        | 1.8145 | 250  | 0.8217          | 0.6482   | 0.6747    | 0.6482 | 0.6500 |
| 0.7566        | 2.1745 | 300  | 0.7877          | 0.6518   | 0.6874    | 0.6518 | 0.6610 |
| 0.7325        | 2.5382 | 350  | 0.8127          | 0.6436   | 0.6968    | 0.6436 | 0.6553 |


### Framework versions

- Transformers 4.51.1
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1