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# 01-tinybert-local

This model is a fine-tuned version of [prajjwal1/bert-tiny](https://huggingface.co/prajjwal1/bert-tiny) on the dialdoc 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: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 30
- total_train_batch_size: 60
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 2.0

### Training results



### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cpu
- Datasets 2.11.0
- Tokenizers 0.13.3

{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["dialdoc"], "model-index": [{"name": "01-tinybert-local", "results": []}]}
transformers pytorch tensorboard bert question-answering generated_from_trainer dataset:dialdoc license:mit endpoints_compatible region:us
TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-dialdoc #license-mit #endpoints_compatible #region-us 




# 01-tinybert-local

This model is a fine-tuned version of prajjwal1/bert-tiny on the dialdoc 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: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 30
- total_train_batch_size: 60
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 2.0

### Training results



### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cpu
- Datasets 2.11.0
- Tokenizers 0.13.3

# 01-tinybert-local

This model is a fine-tuned version of prajjwal1/bert-tiny on the dialdoc 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: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 30
- total_train_batch_size: 60
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 2.0 ### Training results ### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cpu
- Datasets 2.11.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-dialdoc #license-mit #endpoints_compatible #region-us 
 # 01-tinybert-local

This model is a fine-tuned version of prajjwal1/bert-tiny on the dialdoc 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: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 30
- total_train_batch_size: 60
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 2.0 ### Training results ### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cpu
- Datasets 2.11.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-dialdoc #license-mit #endpoints_compatible #region-us 
# 01-tinybert-local

This model is a fine-tuned version of prajjwal1/bert-tiny on the dialdoc 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: 3e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 30
- total_train_batch_size: 60
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 2.0### Training results### Framework versions

- Transformers 4.28.1
- Pytorch 2.0.0+cpu
- Datasets 2.11.0
- Tokenizers 0.13.3