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# checkpoint-24453

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None 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: 2e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 48
- optimizer: Adafactor
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 6000
- num_epochs: 30

### Framework versions

- Transformers 4.18.0
- Pytorch 1.5.0
- Datasets 2.4.0
- Tokenizers 0.12.1

{"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "checkpoint-24453", "results": []}]}
transformers pytorch bert question-answering generated_from_trainer license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 




# checkpoint-24453

This model is a fine-tuned version of bert-base-uncased on the None 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: 2e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 48
- optimizer: Adafactor
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 6000
- num_epochs: 30

### Framework versions

- Transformers 4.18.0
- Pytorch 1.5.0
- Datasets 2.4.0
- Tokenizers 0.12.1

# checkpoint-24453

This model is a fine-tuned version of bert-base-uncased on the None 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: 2e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 48
- optimizer: Adafactor
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 6000
- num_epochs: 30 ### Framework versions

- Transformers 4.18.0
- Pytorch 1.5.0
- Datasets 2.4.0
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 # checkpoint-24453

This model is a fine-tuned version of bert-base-uncased on the None 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: 2e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 48
- optimizer: Adafactor
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 6000
- num_epochs: 30 ### Framework versions

- Transformers 4.18.0
- Pytorch 1.5.0
- Datasets 2.4.0
- Tokenizers 0.12.1
TAGS
#transformers #pytorch #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
# checkpoint-24453

This model is a fine-tuned version of bert-base-uncased on the None 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: 2e-05
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 48
- optimizer: Adafactor
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 6000
- num_epochs: 30### Framework versions

- Transformers 4.18.0
- Pytorch 1.5.0
- Datasets 2.4.0
- Tokenizers 0.12.1