pere
/

t5-nynorsk-norbench

Model card Files Files and versions Community

t5-nynorsk-norbench / norbench_nocola_bs128.gin

pere's picture

nocola

4865eda 8 months ago

raw history blame contribute delete

No virus

1.23 kB

	from __gin__ import dynamic_registration
	import tasks
	import seqio
	import optax

	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/base.gin'
	include "t5x/configs/runs/finetune.gin"

	MIXTURE_OR_TASK_NAME = %gin.REQUIRED
	TASK_FEATURE_LENGTHS = {"inputs": 256, "targets": 256}
	INITIAL_CHECKPOINT_PATH = %gin.REQUIRED
	LR = %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
	BATCH_SIZE = 128
	EVAL_PERIOD = 1000

	#Fixing a small error
	infer_eval/utils.DatasetConfig:
	task_feature_lengths = %TASK_FEATURE_LENGTHS

	#Saving every 500 steps
	utils.SaveCheckpointConfig:
	period = 1000
	keep = 1 # number of checkpoints to keep


	#optax.adamw.weight_decay = 0.1
	#OPTIMIZER = @optax.adamw
	#import t5x.optimizers
	#OPTIMIZER = @optax.adamw
	#optax.adamw.learning_rate = %LR
	#optax.adamw.weight_decay = 0.1


	utils.create_learning_rate_scheduler:
	factors = 'constant'
	base_learning_rate = %LR
	warmup_steps = 1000

	# Might have to ba changed based on architecture
	# partitioning.PjitPartitioner.num_partitions = 1