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# bert-large-uncased-prefix-tuning-squad

This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on the squad dataset.

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0

### Training results



### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-large-uncased-prefix-tuning-squad", "results": []}]}
tensorboard generated_from_trainer dataset:squad license:apache-2.0 region:us
TAGS
#tensorboard #generated_from_trainer #dataset-squad #license-apache-2.0 #region-us 




# bert-large-uncased-prefix-tuning-squad

This model is a fine-tuned version of bert-large-uncased on the squad dataset.

## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0

### Training results



### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6

# bert-large-uncased-prefix-tuning-squad

This model is a fine-tuned version of bert-large-uncased on the squad dataset. ## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0 ### Training results ### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
TAGS
#tensorboard #generated_from_trainer #dataset-squad #license-apache-2.0 #region-us 
 # bert-large-uncased-prefix-tuning-squad

This model is a fine-tuned version of bert-large-uncased on the squad dataset. ## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0 ### Training results ### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6
passage: TAGS
#tensorboard #generated_from_trainer #dataset-squad #license-apache-2.0 #region-us 
# bert-large-uncased-prefix-tuning-squad

This model is a fine-tuned version of bert-large-uncased on the squad dataset.## 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: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0### Training results### Framework versions

- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.0.0
- Tokenizers 0.11.6