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# electra-small-finetuned-sst2

This model is a fine-tuned version of [google/electra-small-discriminator](https://huggingface.co/google/electra-small-discriminator) on the sst2 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2875
- eval_accuracy: 0.9014
- eval_runtime: 0.5682
- eval_samples_per_second: 1534.758
- eval_steps_per_second: 3.52
- epoch: 3.0
- step: 393

## 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: 6.68561343998775e-05
- train_batch_size: 512
- eval_batch_size: 512
- seed: 39
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.27.1
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "electra-small-finetuned-sst2", "results": []}]}
transformers pytorch tensorboard electra text-classification generated_from_trainer license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 




# electra-small-finetuned-sst2

This model is a fine-tuned version of google/electra-small-discriminator on the sst2 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2875
- eval_accuracy: 0.9014
- eval_runtime: 0.5682
- eval_samples_per_second: 1534.758
- eval_steps_per_second: 3.52
- epoch: 3.0
- step: 393

## 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: 6.68561343998775e-05
- train_batch_size: 512
- eval_batch_size: 512
- seed: 39
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP

### Framework versions

- Transformers 4.27.1
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.12.1

# electra-small-finetuned-sst2

This model is a fine-tuned version of google/electra-small-discriminator on the sst2 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2875
- eval_accuracy: 0.9014
- eval_runtime: 0.5682
- eval_samples_per_second: 1534.758
- eval_steps_per_second: 3.52
- epoch: 3.0
- step: 393 ## 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: 6.68561343998775e-05
- train_batch_size: 512
- eval_batch_size: 512
- seed: 39
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.27.1
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # electra-small-finetuned-sst2

This model is a fine-tuned version of google/electra-small-discriminator on the sst2 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2875
- eval_accuracy: 0.9014
- eval_runtime: 0.5682
- eval_samples_per_second: 1534.758
- eval_steps_per_second: 3.52
- epoch: 3.0
- step: 393 ## 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: 6.68561343998775e-05
- train_batch_size: 512
- eval_batch_size: 512
- seed: 39
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.27.1
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #tensorboard #electra #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# electra-small-finetuned-sst2

This model is a fine-tuned version of google/electra-small-discriminator on the sst2 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2875
- eval_accuracy: 0.9014
- eval_runtime: 0.5682
- eval_samples_per_second: 1534.758
- eval_steps_per_second: 3.52
- epoch: 3.0
- step: 393## 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: 6.68561343998775e-05
- train_batch_size: 512
- eval_batch_size: 512
- seed: 39
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP### Framework versions

- Transformers 4.27.1
- Pytorch 2.0.0+cu117
- Datasets 2.10.1
- Tokenizers 0.12.1