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# bert-base-uncased-squad

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown 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: 9e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 2048
- total_eval_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.25
- num_epochs: 3
- training precision: Mixed Precision

### Training results



### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cpu
- Datasets 2.11.0
- Tokenizers 0.13.3

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




# bert-base-uncased-squad

This model is a fine-tuned version of bert-base-uncased on an unknown 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: 9e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 2048
- total_eval_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.25
- num_epochs: 3
- training precision: Mixed Precision

### Training results



### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cpu
- Datasets 2.11.0
- Tokenizers 0.13.3

# bert-base-uncased-squad

This model is a fine-tuned version of bert-base-uncased on an unknown 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: 9e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 2048
- total_eval_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.25
- num_epochs: 3
- training precision: Mixed Precision ### Training results ### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cpu
- Datasets 2.11.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #optimum_graphcore #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
 # bert-base-uncased-squad

This model is a fine-tuned version of bert-base-uncased on an unknown 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: 9e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 2048
- total_eval_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.25
- num_epochs: 3
- training precision: Mixed Precision ### Training results ### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cpu
- Datasets 2.11.0
- Tokenizers 0.13.3
TAGS
#transformers #pytorch #optimum_graphcore #bert #question-answering #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us 
# bert-base-uncased-squad

This model is a fine-tuned version of bert-base-uncased on an unknown 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: 9e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 2048
- total_eval_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.25
- num_epochs: 3
- training precision: Mixed Precision### Training results### Framework versions

- Transformers 4.25.1
- Pytorch 1.13.1+cpu
- Datasets 2.11.0
- Tokenizers 0.13.3