---
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