# 80% 1x4 Block Sparse BERT-Large (uncased) Fine Tuned on SQuADv1.1
This model is a result of fine-tuning a Prune OFA 80% 1x4 block sparse pre-trained BERT-Large combined with knowledge distillation.
This model yields the following results on SQuADv1.1 development set:<br>
`{"exact_match": 84.673, "f1": 91.174}`

For further details see our paper, [Prune Once for All: Sparse Pre-Trained Language Models](https://arxiv.org/abs/2111.05754), and our open source implementation available [here](https://github.com/IntelLabs/Model-Compression-Research-Package/tree/main/research/prune-once-for-all).

{"language": "en", "license": "apache-2.0"}
transformers pytorch onnx bert question-answering en arxiv:2111.05754 license:apache-2.0 endpoints_compatible region:us
TAGS
#transformers #pytorch #onnx #bert #question-answering #en #arxiv-2111.05754 #license-apache-2.0 #endpoints_compatible #region-us 

# 80% 1x4 Block Sparse BERT-Large (uncased) Fine Tuned on SQuADv1.1
This model is a result of fine-tuning a Prune OFA 80% 1x4 block sparse pre-trained BERT-Large combined with knowledge distillation.
This model yields the following results on SQuADv1.1 development set:<br>
'{"exact_match": 84.673, "f1": 91.174}'

For further details see our paper, Prune Once for All: Sparse Pre-Trained Language Models, and our open source implementation available here.

# 80% 1x4 Block Sparse BERT-Large (uncased) Fine Tuned on SQuADv1.1
This model is a result of fine-tuning a Prune OFA 80% 1x4 block sparse pre-trained BERT-Large combined with knowledge distillation.
This model yields the following results on SQuADv1.1 development set:<br>
'{"exact_match": 84.673, "f1": 91.174}'

For further details see our paper, Prune Once for All: Sparse Pre-Trained Language Models, and our open source implementation available here.
TAGS
#transformers #pytorch #onnx #bert #question-answering #en #arxiv-2111.05754 #license-apache-2.0 #endpoints_compatible #region-us 
 # 80% 1x4 Block Sparse BERT-Large (uncased) Fine Tuned on SQuADv1.1
This model is a result of fine-tuning a Prune OFA 80% 1x4 block sparse pre-trained BERT-Large combined with knowledge distillation.
This model yields the following results on SQuADv1.1 development set:<br>
'{"exact_match": 84.673, "f1": 91.174}'

For further details see our paper, Prune Once for All: Sparse Pre-Trained Language Models, and our open source implementation available here.
passage: TAGS
#transformers #pytorch #onnx #bert #question-answering #en #arxiv-2111.05754 #license-apache-2.0 #endpoints_compatible #region-us 
# 80% 1x4 Block Sparse BERT-Large (uncased) Fine Tuned on SQuADv1.1
This model is a result of fine-tuning a Prune OFA 80% 1x4 block sparse pre-trained BERT-Large combined with knowledge distillation.
This model yields the following results on SQuADv1.1 development set:<br>
'{"exact_match": 84.673, "f1": 91.174}'

For further details see our paper, Prune Once for All: Sparse Pre-Trained Language Models, and our open source implementation available here.