training_args = TrainingArguments(\n output_dir=resume_from_checkpoint,\n evaluation_strategy="epoch",\n per_device_train_batch_size=1,\n)\ndefcompute_metrics(pred: EvalPrediction):\n labels= pred.label_ids\n preds = pred.predictions.argmax(-1)\n f1 = f1_score(labels, preds, average=...
strategy = hub.AdamWeightDecayStrategy( weight_decay=0.01, warmup_proportion=0.1, learning_rate=5e-5) //运行配置 config = hub.RunConfig( use_cuda=False, num_epoch=1, checkpoint_dir="model_all2", batch_size=32, eval_interval=100, strategy=strategy) %env CPU_NUM=15 //Finetune Task inpu...
( output_dir='./results', num_train_epochs=3, per_device_train_batch_size=batch_size, per_device_eval_batch_size=batch_size, learning_rate=learning_rate, evaluation_strategy="epoch") trainer = Trainer(model=model, args=training_args, train_dataset=(X_train, y_train) eval_dataset=(X_...
# The checkpoint save strategy to adopt during training. Possible values are: # "no": No save is done during training. # "epoch": Save is done at the end of each epoch. # "steps": Save is done every save_steps (default 500). save_strategy="steps", # save_steps (default: 500):...
save_steps, evaluation_strategy="steps" if cli_args.eval_steps is not None else "epoch", eval_steps=cli_args.eval_steps, metric_for_best_model="loss", learning_rate=cli_args.learning_rate, per_device_train_batch_size=cli_args.batch_size, per_device_eval_batch_size=cli_args.batch_size...
这段代码很适合用Google Colab TPU来跑. 用Colab TPUs, 每个epoch大概花5-6分钟即可. use_tpu=Trueifuse_tpu: #Createdistributionstrategytpu=tf.distribute.cluster_resolver.TPUClusterResolver() tf.config.experimental_connect_to_cluster(tpu) tf.tpu.experimental.initialize_tpu_system(tpu) ...
save_file: "./ckpt_strategy.ckpt" only_trainable_params: False # 设置成 False,才能在策略文件中保存所有参数 parallel_config: data_parallel: 1 model_parallel: 1 pipeline_stage: 1 expert_parallel: 1 micro_batch_num: 1 vocab_emb_dp: True ...
eval_throttle_secs: Do not re-evaluate unless the last evaluation was started at least this many seconds ago Returns: None """ n_gpus = num_gpus() strategy = tf.contrib.distribute.MirroredStrategy(num_gpus=n_gpus) run_config = tf.estimator.RunConfig( model_dir=model_dir, save_checkpoints...
{\prime}-h+1}is therefore a concatenation of the convolution operator over all possible window of words in the tweet. Note that because of the zero-padding strategy we use, we are effectively applying wide convolutionsKalchbrenner et al. (2014). We can use multiple filtering matrices to ...
-text_column src \ --summary_column tgt \ --output_dir ./inter_model \ --per_device_train_batch_size 3 \ --gradient_accumulation_steps 4 \ --max_source_length 1024 \ --max_target_length 16 \ --save_strategy epoch \ --num_train_epochs 10 \ --ddp_find_unused_parameters False \...