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Model_train_predict/example/output/model_re.pth filter=lfs diff=lfs merge=lfs -text |
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**/.idea | ||
**/__pycache__ | ||
conf/conf.yaml | ||
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Model_train_predict/example/ner/standard/checkpoints/* | ||
Model_train_predict/example/re/standard/checkpoints/* | ||
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Model_train_predict/build/lib/deepke/name_entity_re/standard/models/__init__.py | ||
Model_train_predict/dist | ||
Model_train_predict/example/ner/standard/logs | ||
Model_train_predict/example/ner/standard/wandb | ||
Model_train_predict/example/re/standard/logs | ||
Model_train_predict/example/re/standard/neo4j_relation.csv | ||
Model_train_predict/src/deepke.egg-info |
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FROM python:3.8 | ||
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RUN apt-get update | ||
RUN apt-get install vim --yes | ||
#RUN cat /proc/version | ||
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WORKDIR /kg | ||
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COPY . . | ||
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# 安装环境 | ||
RUN pip install -r requirements.txt --no-cache-dir | ||
RUN cd Model_train_predict && python setup.py install && python setup.py develop |
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2 changes: 1 addition & 1 deletion
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...er/standard/conf/hydra/model/lstmcrf.yaml → ...er/standard/conf/hydra/model/lstmcrf.yaml
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predict_model_path: 'Model_train_predict/example/output/model_ner.pth' |
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adam_epsilon: 1e-8 | ||
data_dir: "data/" | ||
do_eval: True | ||
do_train: True | ||
eval_batch_size: 2 # 16 | ||
eval_on: "dev" | ||
gpu_id: 0 | ||
gradient_accumulation_steps: 1 | ||
learning_rate: 1e-3 | ||
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num_train_epochs: 2 # 150 # the number of training epochs | ||
output_dir: "checkpoints" | ||
seed: 42 | ||
train_batch_size: 2 # 128 | ||
use_gpu: True # use gpu or not | ||
warmup_proportion: 0.1 | ||
weight_decay: 0.01 | ||
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# For StepLR Optimizer | ||
lr_step : 5 | ||
lr_gamma : 0.8 | ||
beta1: 0.9 | ||
beta2: 0.999 | ||
labels: ['TOO','LIN','SYS','NUM','COM','NOU','SUB','MET','PER'] | ||
# labels: ['YAS','TOJ', 'NGS', 'QCV', 'OKB', 'BQF', 'CAR', 'ZFM', 'EMT', 'UER', 'QEE', 'UFT', 'GJS', 'SVA', 'ANO', 'KEJ', 'ZDI', 'CAT', 'GCK', 'FQK', 'BAK', 'RET', 'QZP', 'QAQ', 'ZRE', 'TDZ', 'CVC', 'PMN'] |
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