2.1.1173 Part 1 Section 19.5.65, seq (Sequence Time Node) 2.1.1174 Part 1 Section 19.5.66, set (Set Time Node Behavior) 2.1.1175 Part 1 Section 19.5.67, sldTgt (Slide Target) 2.1.1176 Part 1 Section 19.5.72, spTgt (Shape Target) 2.1.1177 Part 1 Section 19.5.77, subSp...
2.1.1173 Part 1 Section 19.5.65, seq (Sequence Time Node) 2.1.1174 Part 1 Section 19.5.66, set (Set Time Node Behavior) 2.1.1175 Part 1 Section 19.5.67, sldTgt (Slide Target) 2.1.1176 Part 1 Section 19.5.72, spTgt (Shape Target) 2.1.1177 Part 1 Section 19.5.77,...
MedicalGPT: Training Your Own Medical GPT Model with ChatGPT Training Pipeline. 训练医疗大模型,实现了包括增量预训练(PT)、有监督微调(SFT)、RLHF、DPO、ORPO。 - MedicalGPT/pretraining.py at main · shibing624/MedicalGPT
max_seq_length int 128 Maximum number of tokens for each training instance. train_input_file str "" Path of the training dataset in a .db format eval_input_file str "" Path of the validation set in a tsv format continue_from int 0 Resuming the training after a specified number of step...
DTS_E_TERMEXTRACTION_INCORRECTMAXLENOFTERM 字段 DTS_E_TERMEXTRACTION_INCORRECTSCORETYPE 字段 DTS_E_TERMEXTRACTION_INCORRECTTERMTYPE 字段 DTS_E_TERMEXTRACTION_INITFSA 字段 DTS_E_TERMEXTRACTION_INITIALIZE 字段 DTS_E_TERMEXTRACTION_INITPOSPROCESSOR 字段 DTS_E_TERMEXTRACTION_INITPOSTAGVECTOR 字段 DTS_E_...
--train_batch_size=24 \ --learning_rate=3e-5 \ --num_train_epochs=2.0 \ --max_seq_length=384 \ --doc_stride=128 \ --output_dir=gs://some_bucket/squad_large/ \ --use_tpu=True \ --tpu_name=$TPU_NAME \ --version_2_with_negative=True \ --null_score_diff_threshold=$THRESH...
If source is equal to the example output of the data loading example, with a seq_length limit of 2, we'd get the following two Variables for i = 0: ┌ a g m s ┐┌ b h n t ┐ └ b h n t ┘└ c i o u ┘ Note that despite the name of the function, the sub...
本文主要针对HuggingFace开源的 transformers,以BERT为例介绍其源码并进行一些实践。主要以pytorch为例 (tf 2.0 代码风格几乎和pytorch一致),介绍BERT使用的Transformer Encoder,Pre-training Tasks和Fine-tuning Tasks。最后,针对预训练好的BERT进行简单的实践,例如产出语句embeddings,预测目标词以及进行抽取式问答。本文主要面...
max_seq_length 100 \ --per_gpu_eval_batch_size=32 \ --per_gpu_train_batch_size=32 \ --learning_rate 2e-4 \ --num_train_epochs 5.0 \ --output_dir $OUTPUT_PATH \ --evaluate_during_training \ --logging_steps 100 \ --save_steps 4000 \ --warmup_percent 0.1 \ --hidden_dropout...
python -u predict_classifier.py \ --use_cuda true \ --batch_size 32 \ --vocab_path ${MODEL_PATH}/vocab.txt \ --init_checkpoint "./checkpoints/step_100" \ --do_lower_case true \ --max_seq_len 128 \ --ernie_config_path ${MODEL_PATH}/ernie_config.json \ --do_predict true ...