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@ -448,9 +448,9 @@ The result will be as follows:
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| Parameters | Ascend | GPU |
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| -------------------------- | ---------------------------------------------------------- | ------------------------- |
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| Model Version | BERT_base | BERT_base |
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| Resource | Ascend 910, cpu:2.60GHz 192cores, memory:755G | NV SMX2 V100-32G |
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| Resource | Ascend 910, cpu:2.60GHz 192cores, memory:755G | NV SMX2 V100-32G |
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| uploaded Date | 08/22/2020 | 05/06/2020 |
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| MindSpore Version | 0.6.0 | 0.3.0 |
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| MindSpore Version | 1.0.0 | 1.0.0 |
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| Dataset | cn-wiki-128(4000w) | ImageNet |
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| Training Parameters | src/config.py | src/config.py |
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| Optimizer | Lamb | Momentum |
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@ -463,14 +463,14 @@ The result will be as follows:
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| Total time | 73h | |
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| Params (M) | 110M | |
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| Checkpoint for Fine tuning | 1.2G(.ckpt file) | |
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| Scripts | [BERT_base](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/nlp/bert) | |
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| Parameters | Ascend | GPU |
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| -------------------------- | ---------------------------------------------------------- | ------------------------- |
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| Model Version | BERT_NEZHA | BERT_NEZHA |
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| Resource | Ascend 910, cpu:2.60GHz 192cores, memory:755G | NV SMX2 V100-32G |
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| Resource | Ascend 910, cpu:2.60GHz 192cores, memory:755G | NV SMX2 V100-32G |
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| uploaded Date | 08/20/2020 | 05/06/2020 |
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| MindSpore Version | 0.6.0 | 0.3.0 |
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| MindSpore Version | 1.0.0 | 1.0.0 |
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| Dataset | cn-wiki-128(4000w) | ImageNet |
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| Training Parameters | src/config.py | src/config.py |
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| Optimizer | Lamb | Momentum |
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@ -483,6 +483,7 @@ The result will be as follows:
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| Total time | 200h | |
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| Params (M) | 340M | |
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| Checkpoint for Fine tuning | 3.2G(.ckpt file) | |
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| Scripts | [BERT_NEZHA](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/nlp/bert) | |
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#### Inference Performance
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@ -491,7 +492,7 @@ The result will be as follows:
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| Model Version | | |
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| Resource | Ascend 910 | NV SMX2 V100-32G |
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| uploaded Date | 08/22/2020 | 05/22/2020 |
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| MindSpore Version | 0.6.0 | 0.2.0 |
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| MindSpore Version | 1.0.0 | 1.0.0 |
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| Dataset | cola, 1.2W | ImageNet, 1.2W |
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| batch_size | 32(1P) | 130(8P) |
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| Accuracy | 0.588986 | ACC1[72.07%] ACC5[90.90%] |
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@ -354,7 +354,7 @@ The best acc is 0.891176
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| Model Version | TinyBERT | TinyBERT |
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| Resource | Ascend 910, cpu:2.60GHz 192cores, memory:755G | NV SMX2 V100-32G, cpu:2.10GHz 64cores, memory:251G |
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| uploaded Date | 08/20/2020 | 08/24/2020 |
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| MindSpore Version | 0.6.0 | 0.7.0 |
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| MindSpore Version | 1.0.0 | 1.0.0 |
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| Dataset | cn-wiki-128 | cn-wiki-128 |
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| Training Parameters | src/gd_config.py | src/gd_config.py |
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| Optimizer | AdamWeightDecay | AdamWeightDecay |
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@ -365,6 +365,7 @@ The best acc is 0.891176
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| Total time | 17.3h(3poch, 8p) | 48h(3poch, 8p) |
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| Params (M) | 15M | 15M |
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| Checkpoint for task distill| 74M(.ckpt file) | 74M(.ckpt file) |
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| Scripts | [TinyBERT](https://gitee.com/mindspore/mindspore/tree/master/model_zoo/official/nlp/tinybert) | |
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#### Inference Performance
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@ -373,7 +374,7 @@ The best acc is 0.891176
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| Model Version | | |
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| Resource | Ascend 910 | NV SMX2 V100-32G |
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| uploaded Date | 08/20/2020 | 08/24/2020 |
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| MindSpore Version | 0.6.0 | 0.7.0 |
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| MindSpore Version | 1.0.0 | 1.0.0 |
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| Dataset | SST-2, | SST-2 |
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| batch_size | 32 | 32 |
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| Accuracy | 0.902777 | 0.9086 |
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