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# bert-base-uncased-sst2-unstructured-sparsity-80

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the GLUE SST2 dataset.
The sparsity on linear layers is 80%.

It achieves the following results on the evaluation set:
- eval_loss: 0.4133
- eval_accuracy: 0.9128
- eval_runtime: 31.5327
- eval_samples_per_second: 27.654
- eval_steps_per_second: 3.457

## Model description

- eval config： max_seq_length 128

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
{"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["glue"], "model-index": [{"name": "bert-base-uncased-sst2-unstructured-sparsity-80", "results": []}]}
transformers pytorch bert text-classification generated_from_trainer en dataset:glue license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 




# bert-base-uncased-sst2-unstructured-sparsity-80

This model is a fine-tuned version of bert-base-uncased on the GLUE SST2 dataset.
The sparsity on linear layers is 80%.

It achieves the following results on the evaluation set:
- eval_loss: 0.4133
- eval_accuracy: 0.9128
- eval_runtime: 31.5327
- eval_samples_per_second: 27.654
- eval_steps_per_second: 3.457

## Model description

- eval config： max_seq_length 128

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
# bert-base-uncased-sst2-unstructured-sparsity-80

This model is a fine-tuned version of bert-base-uncased on the GLUE SST2 dataset.
The sparsity on linear layers is 80%.

It achieves the following results on the evaluation set:
- eval_loss: 0.4133
- eval_accuracy: 0.9128
- eval_runtime: 31.5327
- eval_samples_per_second: 27.654
- eval_steps_per_second: 3.457 ## Model description

- eval config： max_seq_length 128 ## Intended uses & limitations

More information needed ## Training and evaluation data

More information needed ## Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # bert-base-uncased-sst2-unstructured-sparsity-80

This model is a fine-tuned version of bert-base-uncased on the GLUE SST2 dataset.
The sparsity on linear layers is 80%.

It achieves the following results on the evaluation set:
- eval_loss: 0.4133
- eval_accuracy: 0.9128
- eval_runtime: 31.5327
- eval_samples_per_second: 27.654
- eval_steps_per_second: 3.457 ## Model description

- eval config： max_seq_length 128 ## Intended uses & limitations

More information needed ## Training and evaluation data

More information needed ## Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #en #dataset-glue #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# bert-base-uncased-sst2-unstructured-sparsity-80

This model is a fine-tuned version of bert-base-uncased on the GLUE SST2 dataset.
The sparsity on linear layers is 80%.

It achieves the following results on the evaluation set:
- eval_loss: 0.4133
- eval_accuracy: 0.9128
- eval_runtime: 31.5327
- eval_samples_per_second: 27.654
- eval_steps_per_second: 3.457## Model description

- eval config： max_seq_length 128## Intended uses & limitations

More information needed## Training and evaluation data

More information needed## Framework versions

- Transformers 4.26.0
- Pytorch 1.13.1+cu116
- Datasets 2.8.0
- Tokenizers 0.13.2