reuters21578's Introduction
Uses deep learning for the multi-label classification task on Reuters data NN architecture: BERT transformer (pretrained-model) + sigmoid layer (fine-tuning layer) Run script ``` python run_multilabel_classifier.py \ --reuters_data_dir=C:\Users\vivin\Documents\Projects\fidelity\reuters21578 \ --task_name=reuters \ --vocab_file=C:\Users\vivin\Documents\Projects\fidelity\models\uncased_L-12_H-768_A-12\uncased_L-12_H-768_A-12\vocab.txt \ --bert_config_file=C:\Users\vivin\Documents\Projects\fidelity\models\uncased_L-12_H-768_A-12\uncased_L-12_H-768_A-12\bert_config.json \ --output_dir=C:\Users\vivin\Documents\Projects\fidelity\models\model_20220317 --do_train=False --do_eval=False --do_predict=False ``` EVAL result I0320 00:12:38.979363 139628830963584 <ipython-input-10-2fb718a642df>:152] ***** Eval results ***** INFO:tensorflow: eval_accuracy = 0.037902974 I0320 00:12:38.989170 139628830963584 <ipython-input-10-2fb718a642df>:154] eval_accuracy = 0.037902974 INFO:tensorflow: eval_loss = 0.0015029485 I0320 00:12:39.004969 139628830963584 <ipython-input-10-2fb718a642df>:154] eval_loss = 0.0015029485 INFO:tensorflow: global_step = 1293 I0320 00:12:39.011782 139628830963584 <ipython-input-10-2fb718a642df>:154] global_step = 1293 INFO:tensorflow: loss = 0.001502916 I0320 00:12:39.015124 139628830963584 <ipython-input-10-2fb718a642df>:154] loss = 0.001502916
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