
# Sparse BERT mini model (uncased)

Finetuned model pruned to 1:4 structured sparsity.
The model is a pruned version of the [BERT mini model](https://huggingface.co/prajjwal1/bert-mini).

## Intended Use

The model can be used for inference with sparsity optimization.
For further details on the model and its usage will be soon available.

## Evaluation Results
We get the following results on the sst2 tasks development set:

| Task | SST-2 (Acc) |
|------|-------------|
|      |     87.2    |
Better than dense [bert mini](https://huggingface.co/M-FAC/bert-mini-finetuned-sst2) which is 84.74%.

{"license": "mit"}
transformers pytorch onnx bert text-classification license:mit autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #onnx #bert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us 

Sparse BERT mini model (uncased)
================================


Finetuned model pruned to 1:4 structured sparsity.
The model is a pruned version of the BERT mini model.


Intended Use
------------


The model can be used for inference with sparsity optimization.
For further details on the model and its usage will be soon available.


Evaluation Results
------------------


We get the following results on the sst2 tasks development set:




TAGS
#transformers #pytorch #onnx #bert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us 

passage: TAGS
#transformers #pytorch #onnx #bert #text-classification #license-mit #autotrain_compatible #endpoints_compatible #region-us 
