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# bert-large-cased-whole-word-masking-sst2

This model is a fine-tuned version of [bert-large-cased-whole-word-masking](https://huggingface.co/bert-large-cased-whole-word-masking) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1725
- Accuracy: 0.9438

## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: sagemaker_data_parallel
- num_devices: 8
- total_train_batch_size: 128
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP


### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "metrics": ["accuracy"], "model-index": [{"name": "bert-large-cased-whole-word-masking-sst2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "glue", "type": "glue", "args": "sst2"}, "metrics": [{"type": "accuracy", "value": 0.9438073394495413, "name": "Accuracy"}]}]}]}
transformers pytorch tensorboard bert text-classification generated_from_trainer dataset:glue license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 




# bert-large-cased-whole-word-masking-sst2

This model is a fine-tuned version of bert-large-cased-whole-word-masking on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1725
- Accuracy: 0.9438

## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: sagemaker_data_parallel
- num_devices: 8
- total_train_batch_size: 128
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP


### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6

# bert-large-cased-whole-word-masking-sst2

This model is a fine-tuned version of bert-large-cased-whole-word-masking on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1725
- Accuracy: 0.9438 ## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: sagemaker_data_parallel
- num_devices: 8
- total_train_batch_size: 128
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6
TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
 # bert-large-cased-whole-word-masking-sst2

This model is a fine-tuned version of bert-large-cased-whole-word-masking on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1725
- Accuracy: 0.9438 ## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: sagemaker_data_parallel
- num_devices: 8
- total_train_batch_size: 128
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP ### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6
passage: TAGS
#transformers #pytorch #tensorboard #bert #text-classification #generated_from_trainer #dataset-glue #license-apache-2.0 #model-index #autotrain_compatible #endpoints_compatible #region-us 
# bert-large-cased-whole-word-masking-sst2

This model is a fine-tuned version of bert-large-cased-whole-word-masking on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1725
- Accuracy: 0.9438## 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: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: sagemaker_data_parallel
- num_devices: 8
- total_train_batch_size: 128
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- mixed_precision_training: Native AMP### Framework versions

- Transformers 4.17.0
- Pytorch 1.10.2+cu113
- Datasets 1.18.4
- Tokenizers 0.11.6