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# adapted from https://huggingface.co/pere/pk-nb-t5x/blob/main/tasks.py
import functools
import seqio
import tensorflow as tf
import t5.data
from datasets import load_dataset, load_from_disk
from t5.data import postprocessors
from t5.data import preprocessors
from t5.evaluation import metrics
from seqio import FunctionDataSource, utils
TaskRegistry = seqio.TaskRegistry
vocabulary = seqio.SentencePieceVocabulary('spiece.model', extra_ids=0)
DEFAULT_OUTPUT_FEATURES = {
"inputs": seqio.Feature(
vocabulary=vocabulary, add_eos=True,
required=False),
"targets": seqio.Feature(
vocabulary=vocabulary, add_eos=True)
}
def gen_dataset(split, shuffle=False, seed=None, column="text", dataset=None):
if shuffle:
if seed:
dataset = dataset.shuffle(seed=seed)
else:
dataset = dataset.shuffle()
while True:
for item in dataset[str(split)]:
yield item[column]
def dataset_fn(split, shuffle_files, seed=None, dataset=None):
return tf.data.Dataset.from_generator(
functools.partial(gen_dataset, split, shuffle_files, seed, dataset=dataset),
output_signature=tf.TensorSpec(shape=(), dtype=tf.string, name=dataset_name)
)
@utils.map_over_dataset
def target_to_key(x, key_map, target_key):
"""Assign the value from the dataset to target_key in key_map"""
return {**key_map, target_key: x}
# Final pretraining task used in Raffel et al., 2019 adaptated to our use
dataset_name = "/researchdisk/lm_training_dataset_full"
dataset_params = {"from_disk_path": dataset_name}
if "from_disk_path" in dataset_params:
dataset = load_from_disk(dataset_params.get("from_disk_path"))
else:
dataset = load_dataset(**dataset_params)
dataset_shapes = {"train": dataset["train"].num_rows, "validation": dataset["validation"].num_rows}
TaskRegistry.add(
"pretrain_finnish",
source=seqio.FunctionDataSource(
dataset_fn=functools.partial(dataset_fn, dataset=dataset),
splits=("train", "validation"),
caching_permitted=False,
num_input_examples=dataset_shapes,
),
preprocessors=[
functools.partial(
target_to_key, key_map={
"inputs": None,
"targets": None,
}, target_key="targets"),
seqio.preprocessors.tokenize,
# seqio.CacheDatasetPlaceholder(),
preprocessors.span_corruption,
seqio.preprocessors.append_eos_after_trim,
],
output_features={"targets": DEFAULT_OUTPUT_FEATURES["targets"]},
metric_fns=[metrics.accuracy]
) |