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include 't5x/examples/t5/byt5/base.gin'
include 'pretrain_cont.gin'
#include 't5x/configs/runs/pretrain.gin'
#iinclude 't5x/configs/runs/finetune.gin'
# Register necessary SeqIO Tasks/Mixtures.
import t5.data.mixtures
import tasks
MIXTURE_OR_TASK_NAME = "byt5_ncc_english_span_corruption_stream"
TASK_FEATURE_LENGTHS = {"inputs": 512, "targets": 512}
TRAIN_STEPS = 1_500_000
DROPOUT_RATE = 0.0 # Changed from the default since T5-1.1 recomments this.
INITIAL_CHECKPOINT_PATH = "gs://t5-data/pretrained_models/byt5/base/model.ckpt-1000000"
PjitPartitioner.num_partitions = 1
# `LOSS_NORMALIZING_FACTOR`: When fine-tuning a model that was pre-trained
# # using Mesh Tensorflow (e.g. the public T5 / mT5 / ByT5 models), this should be
# # set to `pretraining batch_size` * `target_token_length`. For T5 and T5.1.1:
# # `2048 * 114`. For mT5: `1024 * 229`. For ByT5: `1024 * 189`.
# The instructions above is from T5X. We here have to convert the Mesh Tensorflow byt5-model, so this needs to be set
LOSS_NORMALIZING_FACTOR = 193536
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