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# distilbert-base-uncased-finetuned-sst2

This model is a fine-tuned version of [Telstema/distilbert-base-uncased-finetuned-sst2](https://huggingface.co/Telstema/distilbert-base-uncased-finetuned-sst2) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.7080
- eval_accuracy: 0.7218
- eval_runtime: 13.1083
- eval_samples_per_second: 10.146
- eval_steps_per_second: 0.687
- epoch: 2.0
- step: 100

## 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: 3.909275911638729e-06
- train_batch_size: 8
- eval_batch_size: 16
- seed: 23
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

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




# distilbert-base-uncased-finetuned-sst2

This model is a fine-tuned version of Telstema/distilbert-base-uncased-finetuned-sst2 on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.7080
- eval_accuracy: 0.7218
- eval_runtime: 13.1083
- eval_samples_per_second: 10.146
- eval_steps_per_second: 0.687
- epoch: 2.0
- step: 100

## 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: 3.909275911638729e-06
- train_batch_size: 8
- eval_batch_size: 16
- seed: 23
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3

# distilbert-base-uncased-finetuned-sst2

This model is a fine-tuned version of Telstema/distilbert-base-uncased-finetuned-sst2 on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.7080
- eval_accuracy: 0.7218
- eval_runtime: 13.1083
- eval_samples_per_second: 10.146
- eval_steps_per_second: 0.687
- epoch: 2.0
- step: 100 ## 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: 3.909275911638729e-06
- train_batch_size: 8
- eval_batch_size: 16
- seed: 23
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2 ### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # distilbert-base-uncased-finetuned-sst2

This model is a fine-tuned version of Telstema/distilbert-base-uncased-finetuned-sst2 on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.7080
- eval_accuracy: 0.7218
- eval_runtime: 13.1083
- eval_samples_per_second: 10.146
- eval_steps_per_second: 0.687
- epoch: 2.0
- step: 100 ## 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: 3.909275911638729e-06
- train_batch_size: 8
- eval_batch_size: 16
- seed: 23
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2 ### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3