| from __gin__ import dynamic_registration | |
| 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/t5_1_1/small.gin" | |
| include "t5x/configs/runs/finetune.gin" | |
| MIXTURE_OR_TASK_NAME = "wmt_t2t_ende_v003" | |
| TASK_FEATURE_LENGTHS = {"inputs": 256, "targets": 256} | |
| TRAIN_STEPS = 1_020_000 # 1000000 pre-trained steps + 20000 fine-tuning steps. | |
| DROPOUT_RATE = 0.0 | |
| INITIAL_CHECKPOINT_PATH = "gs://t5-data/pretrained_models/t5x/t5_1_1_small/checkpoint_1000000" | |
| # `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`. | |
| LOSS_NORMALIZING_FACTOR = 233472 | |