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t5-political-evaluator

t5-political-evaluator / finetune_classification_large_mt5.gin

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ea44c7b about 1 year ago

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	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