huawei-noah
25 models • 1 total models in database
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TinyBERT_General_4L_312D
TinyBERT: Distilling BERT for Natural Language Understanding ======== TinyBERT is 7.5x smaller and 9.4x faster on inference than BERT-base and achieves competitive performances in the tasks of natural language understanding. It performs a novel transformer distillation at both the pre-training and task-specific learning stages. In general distillation, we use the original BERT-base without fine-tuning as the teacher and a large-scale text corpus as the learning data. By performing the Transforme
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83,872
68
TinyBERT_General_6L_768D
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6,195
8
TinyBERT_4L_zh
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1,418
16
EntityCS-39-MLM-xlmr-base
license:apache-2.0
15
0
TinyBERT_6L_zh
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12
7
DynaBERT_MNLI
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9
1
JABERv2
license:apache-2.0
9
0
DynaBERT_SST-2
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4
1
TernaryBERT_MNLI
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4
0
TernaryBERT_SST-2
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4
0
AutoTinyBERT-S4
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3
0
JABERv2-6L
license:apache-2.0
3
0
MOASpec-Llama-3-8B-Instruct
NaNK
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2
5
EntityCS-39-WEP-xlmr-base
license:apache-2.0
2
2
pangu-CodeCLM-300m
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2
1
AT5Sv2
license:apache-2.0
2
0
pycodegpt-CodeCLM-partial-100m
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2
0
pangu-CodeCLM-full-300m
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1
3
AutoTinyBERT-S1
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1
0
AutoTinyBERT-S3
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1
0
AutoTinyBERT-KD-S3
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1
0
AutoTinyBERT-KD-S4
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1
0
pycodegpt-CodeCLM-100m
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1
0
Grad-TTS
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0
2
AT5B
NaNK
license:apache-2.0
0
1