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# klue-bert-finetuned-nsmc

This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the nsmc dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2402
- eval_accuracy: 0.9029
- eval_f1: 0.9029
- eval_runtime: 343.6707
- eval_samples_per_second: 145.488
- eval_steps_per_second: 4.548
- epoch: 1.0
- step: 4688

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Framework versions

- Transformers 4.13.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.10.3

{"license": "cc-by-sa-4.0", "tags": ["generated_from_trainer"], "datasets": ["nsmc"], "model-index": [{"name": "klue-bert-finetuned-nsmc", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer dataset:nsmc license:cc-by-sa-4.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-nsmc #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 




# klue-bert-finetuned-nsmc

This model is a fine-tuned version of klue/bert-base on the nsmc dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2402
- eval_accuracy: 0.9029
- eval_f1: 0.9029
- eval_runtime: 343.6707
- eval_samples_per_second: 145.488
- eval_steps_per_second: 4.548
- epoch: 1.0
- step: 4688

## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2

### Framework versions

- Transformers 4.13.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.10.3

# klue-bert-finetuned-nsmc

This model is a fine-tuned version of klue/bert-base on the nsmc dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2402
- eval_accuracy: 0.9029
- eval_f1: 0.9029
- eval_runtime: 343.6707
- eval_samples_per_second: 145.488
- eval_steps_per_second: 4.548
- epoch: 1.0
- step: 4688 ## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2 ### Framework versions

- Transformers 4.13.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-nsmc #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
 # klue-bert-finetuned-nsmc

This model is a fine-tuned version of klue/bert-base on the nsmc dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2402
- eval_accuracy: 0.9029
- eval_f1: 0.9029
- eval_runtime: 343.6707
- eval_samples_per_second: 145.488
- eval_steps_per_second: 4.548
- epoch: 1.0
- step: 4688 ## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2 ### Framework versions

- Transformers 4.13.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.10.3
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-nsmc #license-cc-by-sa-4.0 #autotrain_compatible #endpoints_compatible #region-us 
# klue-bert-finetuned-nsmc

This model is a fine-tuned version of klue/bert-base on the nsmc dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2402
- eval_accuracy: 0.9029
- eval_f1: 0.9029
- eval_runtime: 343.6707
- eval_samples_per_second: 145.488
- eval_steps_per_second: 4.548
- epoch: 1.0
- step: 4688## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2### Framework versions

- Transformers 4.13.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.10.3