
# Sparse BERT base model (uncased)

Pretrained model pruned to 70% sparsity.
The model is a pruned version of the [BERT base model](https://huggingface.co/bert-base-uncased).

## Intended Use

The model can be used for fine-tuning to downstream tasks with sparsity already embeded to the model.
To keep the sparsity a mask should be added to each sparse weight blocking the optimizer from updating the zeros.
{"language": "en"}
transformers pytorch bert fill-mask en autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #fill-mask #en #autotrain_compatible #endpoints_compatible #region-us 


# Sparse BERT base model (uncased)

Pretrained model pruned to 70% sparsity.
The model is a pruned version of the BERT base model.

## Intended Use

The model can be used for fine-tuning to downstream tasks with sparsity already embeded to the model.
To keep the sparsity a mask should be added to each sparse weight blocking the optimizer from updating the zeros.
# Sparse BERT base model (uncased)

Pretrained model pruned to 70% sparsity.
The model is a pruned version of the BERT base model. ## Intended Use

The model can be used for fine-tuning to downstream tasks with sparsity already embeded to the model.
To keep the sparsity a mask should be added to each sparse weight blocking the optimizer from updating the zeros.
TAGS
#transformers #pytorch #bert #fill-mask #en #autotrain_compatible #endpoints_compatible #region-us 
 # Sparse BERT base model (uncased)

Pretrained model pruned to 70% sparsity.
The model is a pruned version of the BERT base model. ## Intended Use

The model can be used for fine-tuning to downstream tasks with sparsity already embeded to the model.
To keep the sparsity a mask should be added to each sparse weight blocking the optimizer from updating the zeros.
passage: TAGS
#transformers #pytorch #bert #fill-mask #en #autotrain_compatible #endpoints_compatible #region-us 
# Sparse BERT base model (uncased)

Pretrained model pruned to 70% sparsity.
The model is a pruned version of the BERT base model.## Intended Use

The model can be used for fine-tuning to downstream tasks with sparsity already embeded to the model.
To keep the sparsity a mask should be added to each sparse weight blocking the optimizer from updating the zeros.