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# bert-base-arabic-camelbert-msa-sixteenth-xnli-finetuned

This model is a fine-tuned version of [CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth](https://huggingface.co/CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth) on the xnli dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6446
- eval_accuracy: 0.7295
- eval_f1: 0.7286
- eval_runtime: 72.1081
- eval_samples_per_second: 69.479
- eval_steps_per_second: 69.479
- epoch: 1.0
- step: 12271

## 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: 2
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["xnli"], "model-index": [{"name": "bert-base-arabic-camelbert-msa-sixteenth-xnli-finetuned", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer dataset:xnli license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-xnli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 




# bert-base-arabic-camelbert-msa-sixteenth-xnli-finetuned

This model is a fine-tuned version of CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth on the xnli dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6446
- eval_accuracy: 0.7295
- eval_f1: 0.7286
- eval_runtime: 72.1081
- eval_samples_per_second: 69.479
- eval_steps_per_second: 69.479
- epoch: 1.0
- step: 12271

## 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: 2
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2

# bert-base-arabic-camelbert-msa-sixteenth-xnli-finetuned

This model is a fine-tuned version of CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth on the xnli dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6446
- eval_accuracy: 0.7295
- eval_f1: 0.7286
- eval_runtime: 72.1081
- eval_samples_per_second: 69.479
- eval_steps_per_second: 69.479
- epoch: 1.0
- step: 12271 ## 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: 2
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-xnli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # bert-base-arabic-camelbert-msa-sixteenth-xnli-finetuned

This model is a fine-tuned version of CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth on the xnli dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6446
- eval_accuracy: 0.7295
- eval_f1: 0.7286
- eval_runtime: 72.1081
- eval_samples_per_second: 69.479
- eval_steps_per_second: 69.479
- epoch: 1.0
- step: 12271 ## 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: 2
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-xnli #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# bert-base-arabic-camelbert-msa-sixteenth-xnli-finetuned

This model is a fine-tuned version of CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth on the xnli dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6446
- eval_accuracy: 0.7295
- eval_f1: 0.7286
- eval_runtime: 72.1081
- eval_samples_per_second: 69.479
- eval_steps_per_second: 69.479
- epoch: 1.0
- step: 12271## 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: 2
- mixed_precision_training: Native AMP### Framework versions

- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.9.0
- Tokenizers 0.13.2