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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
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