Python-基于TensorFlow和BERT的管道式实体及关系抽取

[{"title":"（ 28 个子文件 3.46MB ） Python-基于TensorFlow和BERT的管道式实体及关系抽取","children":[{"title":"Entity-Relation-Extraction-master","children":[{"title":"bert","children":[{"title":"tf_metrics.py <span style='color:#111;'> 8.01KB </span>","children":null,"spread":false},{"title":"tokenization.py <span style='color:#111;'> 13.95KB </span>","children":null,"spread":false},{"title":"run_classifier.py <span style='color:#111;'> 33.97KB </span>","children":null,"spread":false},{"title":"run_pretraining.py <span style='color:#111;'> 18.23KB </span>","children":null,"spread":false},{"title":"modeling.py <span style='color:#111;'> 37.03KB </span>","children":null,"spread":false},{"title":"__init__.py <span style='color:#111;'> 616B </span>","children":null,"spread":false},{"title":"optimization.py <span style='color:#111;'> 6.11KB </span>","children":null,"spread":false},{"title":"__pycache__","children":[{"title":"__init__.cpython-36.pyc <span style='color:#111;'> 165B </span>","children":null,"spread":false},{"title":"tokenization.cpython-36.pyc <span style='color:#111;'> 11.18KB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"run_predicate_classification.py <span style='color:#111;'> 32.21KB </span>","children":null,"spread":false},{"title":"pretrained_model","children":[{"title":"chinese_L-12_H-768_A-12","children":[{"title":"bert_config.json <span style='color:#111;'> 520B </span>","children":null,"spread":false},{"title":"vocab.txt <span style='color:#111;'> 106.97KB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"run_sequnce_labeling.py <span style='color:#111;'> 38.25KB </span>","children":null,"spread":false},{"title":"2019语言与智能技术竞赛信息抽取排行榜.png <span style='color:#111;'> 2.77MB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 7.57KB </span>","children":null,"spread":false},{"title":"produce_submit_json_file.py <span style='color:#111;'> 17.84KB </span>","children":null,"spread":false},{"title":"raw_data","children":[{"title":"DATA_README.md <span style='color:#111;'> 629B </span>","children":null,"spread":false},{"title":"test1_data_postag.json <span style='color:#111;'> 473.44KB </span>","children":null,"spread":false},{"title":"dev_data.json <span style='color:#111;'> 1.27MB </span>","children":null,"spread":false},{"title":"all_50_schemas <span style='color:#111;'> 3.87KB </span>","children":null,"spread":false},{"title":"train_data.json <span style='color:#111;'> 3.30MB </span>","children":null,"spread":false}],"spread":true},{"title":"bin","children":[{"title":"subject_object_labeling","children":[{"title":"sequence_labeling_data_manager.py <span style='color:#111;'> 7.85KB </span>","children":null,"spread":false}],"spread":true},{"title":"predicate_classifiction","children":[{"title":"prepare_data_for_labeling_infer.py <span style='color:#111;'> 6.27KB </span>","children":null,"spread":false},{"title":"predicate_data_manager.py <span style='color:#111;'> 6.40KB </span>","children":null,"spread":false}],"spread":true},{"title":"evaluation","children":[{"title":"evaluate_classifiction.py <span style='color:#111;'> 3.37KB </span>","children":null,"spread":false},{"title":"calc_pr.py <span style='color:#111;'> 8.67KB </span>","children":null,"spread":false},{"title":"evaluate_labeling.py <span style='color:#111;'> 3.83KB </span>","children":null,"spread":false}],"spread":true},{"title":"bert_tokenizer_test.py <span style='color:#111;'> 671B </span>","children":null,"spread":false}],"spread":true}],"spread":true}],"spread":true}]