# 80% 1x4 Block Sparse BERT-Base (uncased) Prune OFA
This model is was created using Prune OFA method described in [Prune Once for All: Sparse Pre-Trained Language Models](https://arxiv.org/abs/2111.05754) presented in ENLSP NeurIPS Workshop 2021.

For further details on the model and its result, see our paper and our implementation available [here](https://github.com/IntelLabs/Model-Compression-Research-Package/tree/main/research/prune-once-for-all).

{"language": "en", "license": "apache-2.0", "tags": ["fill-mask"], "datasets": ["wikipedia", "bookcorpus"]}
transformers pytorch bert pretraining fill-mask en dataset:wikipedia dataset:bookcorpus arxiv:2111.05754 license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #pretraining #fill-mask #en #dataset-wikipedia #dataset-bookcorpus #arxiv-2111.05754 #license-apache-2.0 #endpoints_compatible #region-us 

# 80% 1x4 Block Sparse BERT-Base (uncased) Prune OFA
This model is was created using Prune OFA method described in Prune Once for All: Sparse Pre-Trained Language Models presented in ENLSP NeurIPS Workshop 2021.

For further details on the model and its result, see our paper and our implementation available here.

# 80% 1x4 Block Sparse BERT-Base (uncased) Prune OFA
This model is was created using Prune OFA method described in Prune Once for All: Sparse Pre-Trained Language Models presented in ENLSP NeurIPS Workshop 2021.

For further details on the model and its result, see our paper and our implementation available here.
TAGS
#transformers #pytorch #bert #pretraining #fill-mask #en #dataset-wikipedia #dataset-bookcorpus #arxiv-2111.05754 #license-apache-2.0 #endpoints_compatible #region-us 
 # 80% 1x4 Block Sparse BERT-Base (uncased) Prune OFA
This model is was created using Prune OFA method described in Prune Once for All: Sparse Pre-Trained Language Models presented in ENLSP NeurIPS Workshop 2021.

For further details on the model and its result, see our paper and our implementation available here.
passage: TAGS
#transformers #pytorch #bert #pretraining #fill-mask #en #dataset-wikipedia #dataset-bookcorpus #arxiv-2111.05754 #license-apache-2.0 #endpoints_compatible #region-us 
# 80% 1x4 Block Sparse BERT-Base (uncased) Prune OFA
This model is was created using Prune OFA method described in Prune Once for All: Sparse Pre-Trained Language Models presented in ENLSP NeurIPS Workshop 2021.

For further details on the model and its result, see our paper and our implementation available here.