from __gin__ import dynamic_registration import tasks import seqio import __main__ as train_script from t5.data import mixtures from t5x import models from t5x import partitioning from t5x import utils include 't5x/examples/t5/mt5/large.gin' include "t5x/configs/runs/finetune.gin" MIXTURE_OR_TASK_NAME = %gin.REQUIRED TASK_FEATURE_LENGTHS = {"inputs": 512, "targets": 512} INITIAL_CHECKPOINT_PATH = %gin.REQUIRED TRAIN_STEPS = %gin.REQUIRED # 1000000 pre-trained steps + 10000 fine-tuning steps. USE_CACHED_TASKS = False DROPOUT_RATE = 0.1 RANDOM_SEED = 0 #Fixing a small error infer_eval/utils.DatasetConfig: task_feature_lengths = %TASK_FEATURE_LENGTHS #Saving every 1000 steps utils.SaveCheckpointConfig: period = 1000 keep = 1 # number of checkpoints to keep # Might have to ba changed based on architecture partitioning.PjitPartitioner.num_partitions = 1