File size: 6,133 Bytes
ad029a3 e957cc1 ad029a3 e957cc1 ad029a3 e957cc1 ad029a3 e957cc1 ad029a3 |
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 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 |
# /home/perk/mymodel/categorisation-mt5x/tasks.py
import functools
import seqio
import tensorflow_datasets as tfds
from t5.evaluation import metrics
import my_metrics
from t5.data import preprocessors
import t5
import tensorflow.compat.v1 as tf
tsv_path = {
"train": "gs://north-t5x/corpus/deuncaser/norwegian/train.tsv",
"validation": "gs://north-t5x/corpus/deuncaser/norwegian/validation.tsv",
"test": "gs://north-t5x/corpus/deuncaser/norwegian/validation.tsv"
}
vocabulary = seqio.SentencePieceVocabulary(
'gs://t5-data/vocabs/mc4.250000.100extra/sentencepiece.model', extra_ids=0)
DEFAULT_OUTPUT_FEATURES = {
"inputs":
seqio.Feature(
vocabulary=vocabulary, add_eos=True),
"targets":
seqio.Feature(
vocabulary=vocabulary, add_eos=True)
}
def categorise_preprocessor(ds):
def normalize_text(text):
"""Lowercase and remove quotes from a TensorFlow string."""
text = tf.strings.regex_replace(text,"'(.*)'", r"\1")
return text
def to_inputs_and_targets(ex):
"""Map {"source": ..., "source": ...}->{"target": ..., "target": ...}."""
return {
"inputs":
tf.strings.join(
[normalize_text(ex["source"])]),
"targets":
tf.strings.join(
[normalize_text(ex["target"])]),
}
return ds.map(to_inputs_and_targets,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
def categorise_fulltext_preprocessor(ds):
def normalize_text(text):
"""Lowercase and remove quotes from a TensorFlow string."""
text = tf.strings.regex_replace(text,"'(.*)'", r"\1")
return text
def fulltext(t):
if t=="0":
t="il testo è favorevole alla vaccinazione"
elif t=="1":
t="il testo è neutro rispetto alla vaccinazione"
elif t=="2":
t="is testo è sfavorevole alla vaccinazione"
return t
def to_inputs_and_targets(ex):
"""Map {"source": ..., "source": ...}->{"target": ..., "target": ...}."""
return {
"inputs":
tf.strings.join(
[normalize_text(ex["source"])]),
"targets":
tf.strings.join(
[fulltext(normalize_text(ex["target"]))]),
}
return ds.map(to_inputs_and_targets,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
def categorise_fulltext_word_preprocessor(ds):
def normalize_text(text):
"""Lowercase and remove quotes from a TensorFlow string."""
text = tf.strings.regex_replace(text,"'(.*)'", r"\1")
return text
def fulltext(t):
if t=="0":
t="promozionale"
elif t=="1":
t="neutro"
elif t=="2":
t="scoraggiante"
return t
def to_inputs_and_targets(ex):
"""Map {"source": ..., "source": ...}->{"target": ..., "target": ...}."""
return {
"inputs":
tf.strings.join(
[normalize_text(ex["source"])]),
"targets":
tf.strings.join(
[fulltext(normalize_text(ex["target"]))]),
}
return ds.map(to_inputs_and_targets,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
def categorise_binary_preprocessor(ds):
def normalize_text(text):
"""Lowercase and remove quotes from a TensorFlow string."""
text = tf.strings.regex_replace(text,"'(.*)'", r"\1")
return text
def fulltext(t):
if t=="0":
t="1"
elif t=="1":
t="1"
elif t=="2":
t="2"
return t
def to_inputs_and_targets(ex):
"""Map {"source": ..., "source": ...}->{"target": ..., "target": ...}."""
return {
"inputs":
tf.strings.join(
[normalize_text(ex["source"])]),
"targets":
tf.strings.join(
[fulltext(normalize_text(ex["target"]))]),
}
return ds.map(to_inputs_and_targets,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
seqio.TaskRegistry.add(
"deuncaser",
source=seqio.TextLineDataSource(
split_to_filepattern=tsv_path,
#num_input_examples=num_nq_examples
),
preprocessors=[
functools.partial(
t5.data.preprocessors.parse_tsv,
field_names=["id","methods","source","target"]),
categorise_preprocessor,
seqio.preprocessors.tokenize_and_append_eos,
],
metric_fns=[metrics.accuracy,metrics.bleu],
output_features=DEFAULT_OUTPUT_FEATURES,
)
seqio.TaskRegistry.add(
"classify_tweets_fulltext",
source=seqio.TextLineDataSource(
split_to_filepattern=tsv_path,
#num_input_examples=num_nq_examples
),
preprocessors=[
functools.partial(
t5.data.preprocessors.parse_tsv,
field_names=["annotator1","annotator2","annotator3","target","source","id"]),
categorise_fulltext_preprocessor,
seqio.preprocessors.tokenize_and_append_eos,
],
metric_fns=[metrics.accuracy,my_metrics.f1_macro],
output_features=DEFAULT_OUTPUT_FEATURES,
)
seqio.TaskRegistry.add(
"classify_tweets_binary",
source=seqio.TextLineDataSource(
split_to_filepattern=tsv_path,
#num_input_examples=num_nq_examples
),
preprocessors=[
functools.partial(
t5.data.preprocessors.parse_tsv,
field_names=["annotator1","annotator2","annotator3","target","source","id"]),
categorise_binary_preprocessor,
seqio.preprocessors.tokenize_and_append_eos,
],
metric_fns=[metrics.accuracy,my_metrics.f1_macro],
output_features=DEFAULT_OUTPUT_FEATURES,
)
seqio.TaskRegistry.add(
"classify_tweets_fulltext_word",
source=seqio.TextLineDataSource(
split_to_filepattern=tsv_path,
#num_input_examples=num_nq_examples
),
preprocessors=[
functools.partial(
t5.data.preprocessors.parse_tsv,
field_names=["annotator1","annotator2","annotator3","target","source","id"]),
categorise_fulltext_word_preprocessor,
seqio.preprocessors.tokenize_and_append_eos,
],
metric_fns=[metrics.accuracy,my_metrics.f1_macro],
output_features=DEFAULT_OUTPUT_FEATURES,
)
|