
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# bert_reviews

This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.8063
- eval_accuracy: 0.647
- eval_runtime: 38.7598
- eval_samples_per_second: 129.0
- eval_steps_per_second: 16.125
- epoch: 0.52
- step: 13000

## 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: 1.2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 20000

### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["amazon_reviews_multi"], "base_model": "bert-base-cased", "model-index": [{"name": "bert_reviews", "results": []}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer dataset:amazon_reviews_multi base_model:bert-base-cased license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 




# bert_reviews

This model is a fine-tuned version of bert-base-cased on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.8063
- eval_accuracy: 0.647
- eval_runtime: 38.7598
- eval_samples_per_second: 129.0
- eval_steps_per_second: 16.125
- epoch: 0.52
- step: 13000

## 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: 1.2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 20000

### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1

# bert_reviews

This model is a fine-tuned version of bert-base-cased on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.8063
- eval_accuracy: 0.647
- eval_runtime: 38.7598
- eval_samples_per_second: 129.0
- eval_steps_per_second: 16.125
- epoch: 0.52
- step: 13000 ## 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: 1.2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 20000 ### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # bert_reviews

This model is a fine-tuned version of bert-base-cased on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.8063
- eval_accuracy: 0.647
- eval_runtime: 38.7598
- eval_samples_per_second: 129.0
- eval_steps_per_second: 16.125
- epoch: 0.52
- step: 13000 ## 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: 1.2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 20000 ### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-amazon_reviews_multi #base_model-bert-base-cased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# bert_reviews

This model is a fine-tuned version of bert-base-cased on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.8063
- eval_accuracy: 0.647
- eval_runtime: 38.7598
- eval_samples_per_second: 129.0
- eval_steps_per_second: 16.125
- epoch: 0.52
- step: 13000## 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: 1.2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 20000### Framework versions

- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1