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# BERT_Mod_2

This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the glue dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5659
- eval_accuracy: 0.9037
- eval_runtime: 0.3838
- eval_samples_per_second: 2271.724
- eval_steps_per_second: 143.285
- epoch: 0.01
- step: 49

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Framework versions

- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "model-index": [{"name": "BERT_Mod_2", "results": []}]}
transformers pytorch distilbert text-classification generated_from_trainer dataset:glue license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 




# BERT_Mod_2

This model is a fine-tuned version of distilbert-base-cased on the glue dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5659
- eval_accuracy: 0.9037
- eval_runtime: 0.3838
- eval_samples_per_second: 2271.724
- eval_steps_per_second: 143.285
- epoch: 0.01
- step: 49

## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Framework versions

- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.4.0
- Tokenizers 0.12.1

# BERT_Mod_2

This model is a fine-tuned version of distilbert-base-cased on the glue dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5659
- eval_accuracy: 0.9037
- eval_runtime: 0.3838
- eval_samples_per_second: 2271.724
- eval_steps_per_second: 143.285
- epoch: 0.01
- step: 49 ## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5 ### Framework versions

- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.4.0
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # BERT_Mod_2

This model is a fine-tuned version of distilbert-base-cased on the glue dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5659
- eval_accuracy: 0.9037
- eval_runtime: 0.3838
- eval_samples_per_second: 2271.724
- eval_steps_per_second: 143.285
- epoch: 0.01
- step: 49 ## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5 ### Framework versions

- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.4.0
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# BERT_Mod_2

This model is a fine-tuned version of distilbert-base-cased on the glue dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5659
- eval_accuracy: 0.9037
- eval_runtime: 0.3838
- eval_samples_per_second: 2271.724
- eval_steps_per_second: 143.285
- epoch: 0.01
- step: 49## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5### Framework versions

- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.4.0
- Tokenizers 0.12.1