File size: 1,313 Bytes
af0a405
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
from __gin__ import dynamic_registration
import tasks

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/small.gin"
include "t5x/configs/runs/finetune.gin"

MIXTURE_OR_TASK_NAME = %gin.REQUIRED
TASK_FEATURE_LENGTHS = {"inputs": 512, "targets": 2}
INITIAL_CHECKPOINT_PATH = %gin.REQUIRED #"gs://t5-data/pretrained_models/t5x/mt5_small/checkpoint_1000000"
TRAIN_STEPS = %gin.REQUIRED #1_010_000  # 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


# Pere: Only necessary if we load a t5 model. We can start with an t5x model here
# `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 = 234496

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