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| Model Name | Stub | Description |
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| bert-pruned-moderate | zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/pruned-moderate |This model is the result of pruning BERT base uncased on the SQuAD dataset. The sparsity level is 90% uniformly applied to all encoder layers. Distillation was used with the teacher being the BERT model fine-tuned on the dataset for two epochs.|
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| bert-6layers-aggressive-pruned| zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/pruned_6layers-aggressive_96 |This model is the result of pruning a modified BERT base uncased with 6 layers on the SQuAD dataset. The sparsity level is 95% uniformly applied to all encoder layers. Distillation was used with the teacher being the BERT model fine-tuned on the dataset for two epochs.|
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| bert-6layers-aggressive-pruned-96| zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/pruned_6layers-aggressive_96 |This model is the result of pruning a modified BERT base uncased with 6 layers on the SQuAD dataset. The sparsity level is 95% uniformly applied to all encoder layers. Distillation was used with the teacher being the BERT model fine-tuned on the dataset for two epochs.|
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| bert-pruned-conservative| zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/pruned-conservative |This model is the result of pruning BERT base uncased on the SQuAD dataset. The sparsity level is 80% uniformly applied to all encoder layers. Distillation was used with the teacher being the BERT model fine-tuned on the dataset for two epochs.|
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| pruned_6layers-moderate | zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/pruned_6layers-moderate |This model is the result of pruning a modified BERT base uncased with 6 layers on the SQuAD dataset. The sparsity level is 90% uniformly applied to all encoder layers. Distillation was used with the teacher being the BERT model fine-tuned on the dataset for two epochs. The integration with Hugging Face's Transformers can be found [here](https://github.com/neuralmagic/sparseml/tree/main/integrations/huggingface-transformers).|
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| pruned-aggressive_94 | zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/pruned-aggressive_94|This model is the result of pruning BERT base uncased on the SQuAD dataset. The sparsity level is 95% uniformly applied to all encoder layers. Distillation was used with the teacher being the BERT model fine-tuned on the dataset for two epochs.|
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| pruned_6layers-conservative| zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/pruned_6layers-conservative|This model is the result of pruning a modified BERT base uncased with 6 layers on the SQuAD dataset. The sparsity level is 80% uniformly applied to all encoder layers. Distillation was used with the teacher being the BERT model fine-tuned on the dataset for two epochs.|
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| bert-base|zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/base-none |This model is the result of a BERT base uncased model fine-tuned on the SQuAD dataset for two epochs.|
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| pruned-aggressive_94 | zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/pruned-aggressive_94|This model is the result of pruning a modified BERT base uncased with 6 layers on the SQuAD dataset. The sparsity level is 90% uniformly applied to all encoder layers. Distillation was used with the teacher being the BERT model fine-tuned on the dataset for two epochs.|
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| bert-3layers-pruned-aggressive-89| zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/pruned_3layers-aggressive_89|This model is the result of pruning a modified BERT base uncased with 6 layers on the SQuAD dataset. The sparsity level is 89% uniformly applied to all encoder layers. Distillation was used with the teacher being the BERT model fine-tuned on the dataset for two epochs.|
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| bert-base|zoo:nlp/question_answering/bert-base/pytorch/huggingface/squad/base-none |This model is the result of a BERT base uncased model fine-tuned on the SQuAD dataset for two epochs.|
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