transformers pytorch tensorboard electra text-classification generated_from_trainer dataset:xnli model-index autotrain_compatible endpoints_compatible region:us
---
datasets:
- xnli
metrics:
- accuracy
- f1
tags:
- generated_from_trainer
model-index:
- name: bert-base-arabic-electra-xnli-finetuned
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: xnli
      type: xnli
      config: ar
      split: train
      args: ar
    metrics:
    - type: accuracy
      value: 0.7870259481037924
      name: Accuracy
    - type: f1
      value: 0.7875257009692987
      name: F1
---

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# bert-base-arabic-electra-xnli-finetuned

This model is a fine-tuned version of [aubmindlab/araelectra-base-discriminator](https://huggingface.co/aubmindlab/araelectra-base-discriminator) on the xnli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5453
- Accuracy: 0.7870
- F1: 0.7875

## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Accuracy | F1     |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.5541        | 1.0   | 12271 | 0.5453          | 0.7870   | 0.7875 |


### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cu117
- Datasets 2.8.0
- Tokenizers 0.13.2
