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

This model is a fine-tuned version of [intfloat/e5-small](https://huggingface.co/intfloat/e5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5430
- Accuracy: 0.7845
- Precision: 0.7994
- Recall: 0.7845
- F1: 0.7886

## 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     |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.0961        | 0.7463 | 50   | 1.0715          | 0.3853   | 0.7241    | 0.3853 | 0.2241 |
| 1.0181        | 1.4925 | 100  | 0.9009          | 0.6875   | 0.7524    | 0.6875 | 0.6122 |
| 0.8328        | 2.2388 | 150  | 0.7633          | 0.7052   | 0.7815    | 0.7052 | 0.6315 |
| 0.7492        | 2.9851 | 200  | 0.6860          | 0.7295   | 0.6952    | 0.7295 | 0.6858 |
| 0.6931        | 3.7313 | 250  | 0.6982          | 0.7369   | 0.7275    | 0.7369 | 0.7209 |
| 0.6623        | 4.4776 | 300  | 0.6326          | 0.7705   | 0.7544    | 0.7705 | 0.7485 |
| 0.6107        | 5.2239 | 350  | 0.6350          | 0.7556   | 0.7696    | 0.7556 | 0.7530 |
| 0.5789        | 5.9701 | 400  | 0.5892          | 0.7649   | 0.7924    | 0.7649 | 0.7713 |
| 0.5593        | 6.7164 | 450  | 0.5449          | 0.7985   | 0.7946    | 0.7985 | 0.7963 |
| 0.5343        | 7.4627 | 500  | 0.5486          | 0.7845   | 0.8008    | 0.7845 | 0.7897 |
| 0.5177        | 8.2090 | 550  | 0.5373          | 0.8013   | 0.8038    | 0.8013 | 0.8016 |
| 0.5201        | 8.9552 | 600  | 0.5370          | 0.7882   | 0.7996    | 0.7882 | 0.7915 |
| 0.4962        | 9.7015 | 650  | 0.5357          | 0.7882   | 0.7994    | 0.7882 | 0.7917 |


### Framework versions

- Transformers 4.48.2
- Pytorch 2.6.0+cu124
- Datasets 3.3.1
- Tokenizers 0.21.0