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add bert4rec #624

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Dec 4, 2021
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add bert4rec #624

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7fbdf7e
add bert4rec
wangzhen38 Dec 4, 2021
1cf9bff
add bert4rec model
wangzhen38 Dec 4, 2021
5ae4e7f
modify readme
wangzhen38 Dec 4, 2021
80f1564
add pre-commit
wangzhen38 Dec 4, 2021
c083e65
update commit
wangzhen38 Dec 4, 2021
42c48bd
fix code style
wangzhen38 Dec 4, 2021
7859916
fix code style
wangzhen38 Dec 4, 2021
353c5f7
fix code style handly
wangzhen38 Dec 4, 2021
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1 change: 1 addition & 0 deletions README.md
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Expand Up @@ -124,6 +124,7 @@ python -u tools/static_trainer.py -m models/rank/dnn/config.yaml # 静态图训
| 排序 | [Logistic Regression](models/rank/logistic_regression/) | ✓ | ✓ | ✓ | x | >=2.1.0 | / |
| 排序 | [Dnn](models/rank/dnn/) | ✓ | ✓ | ✓ | ✓ | >=2.1.0 | / |
| 排序 | [FM](models/rank/fm/) | ✓ | ✓ | ✓ | x | >=2.1.0 | [IEEE Data Mining 2010][Factorization machines](https://analyticsconsultores.com.mx/wp-content/uploads/2019/03/Factorization-Machines-Steffen-Rendle-Osaka-University-2010.pdf) |
| 排序 | [BERT4REC](models/rank/bert4rec/) | ✓ | ✓ | ✓ | x | >=2.1.0 | [CIKM 2019][BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://arxiv.org/pdf/1904.06690.pdf) |
| 排序 | [FFM](models/rank/ffm/) | ✓ | ✓ | ✓ | x | >=2.1.0 | [RECSYS 2016][Field-aware Factorization Machines for CTR Prediction](https://dl.acm.org/doi/pdf/10.1145/2959100.2959134) |
| 排序 | [FNN](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/fnn/) | ✓ | ✓ | ✓ | x | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [ECIR 2016][Deep Learning over Multi-field Categorical Data](https://arxiv.org/pdf/1601.02376.pdf) |
| 排序 | [Deep Crossing](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/deep_crossing/) | ✓ | ✓ | ✓ | x | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [ACM 2016][Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features](https://www.kdd.org/kdd2016/papers/files/adf0975-shanA.pdf) |
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1 change: 1 addition & 0 deletions README_EN.md
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Expand Up @@ -114,6 +114,7 @@ python -u tools/static_trainer.py -m models/rank/dnn/config.yaml # Training wit
| Rank | [Logistic Regression](models/rank/logistic_regression/) | ✓ | ✓ | ✓ | x | >=2.1.0 | / |
| Rank | [Dnn](models/rank/dnn/) | ✓ | ✓ | ✓ | ✓ | >=2.1.0 | / |
| Rank | [FM](models/rank/fm/) | ✓ | ✓ | ✓ | x | >=2.1.0 | [IEEE Data Mining 2010][Factorization machines](https://analyticsconsultores.com.mx/wp-content/uploads/2019/03/Factorization-Machines-Steffen-Rendle-Osaka-University-2010.pdf) |
| Rank | [BERT4REC](models/rank/bert4rec/) | ✓ | ✓ | ✓ | x | >=2.1.0 | [CIKM 2019][BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://arxiv.org/pdf/1904.06690.pdf) |
| Rank | [FFM](models/rank/ffm/) | ✓ | ✓ | ✓ | x | >=2.1.0 | [RECSYS 2016][Field-aware Factorization Machines for CTR Prediction](https://dl.acm.org/doi/pdf/10.1145/2959100.2959134) |
| Rank | [FNN](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/fnn/) | ✓ | ✓ | ✓ | x | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [ECIR 2016][Deep Learning over Multi-field Categorical Data](https://arxiv.org/pdf/1601.02376.pdf) |
| Rank | [Deep Crossing](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/rank/deep_crossing/) | ✓ | ✓ | ✓ | x | [1.8.5](https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5) | [ACM 2016][Deep Crossing: Web-Scale Modeling without Manually Crafted Combinatorial Features](https://www.kdd.org/kdd2016/papers/files/adf0975-shanA.pdf) |
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53 changes: 53 additions & 0 deletions models/rank/bert4rec/config_bigdata.yaml
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# global settings

runner:
train_data_dir: "data/train"
train_reader_path: "data_reader" # importlib format
use_gpu: True
train_batch_size: 1
data_batch_size: 256
epochs: 10
print_interval: 100

model_save_path: "output_model_bert4rec"
test_data_dir: "data/test"
infer_reader_path: "data_reader" # importlib format
infer_batch_size: 1
infer_load_path: "output_model_bert4rec"
infer_start_epoch: 9
infer_end_epoch: 10


# hyper parameters of user-defined network
hyper_parameters:
# optimizer config
optimizer:
learning_rate: 0.0001
weight_decay: 0.01

_emb_size: 64
_n_layer: 2
_n_head: 2
_voc_size: 54546
_max_position_seq_len: 50
_sent_types: 2
hidden_act: "gelu"
_dropout: 0.5
_attention_dropout: 0.2
_param_initializer: 0.02
num_test_user: 40226

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