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preprocessor.html
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preprocessor.html
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<header>
<h1 class="title">Module <code>ktrain.text.preprocessor</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">from transformers import (
AutoConfig,
AutoTokenizer,
TFAutoModel,
TFAutoModelForSequenceClassification,
)
from .. import utils as U
from ..imports import *
from ..preprocessor import Preprocessor
from . import textutils as TU
DISTILBERT = "distilbert"
NOSPACE_LANGS = ["zh-cn", "zh-tw", "ja"]
def is_nospace_lang(lang):
return lang in NOSPACE_LANGS
def fname_from_url(url):
return os.path.split(url)[-1]
# ------------------------------------------------------------------------------
# Word Vectors
# ------------------------------------------------------------------------------
WV_URL = "https://dl.fbaipublicfiles.com/fasttext/vectors-english/crawl-300d-2M.vec.zip"
# WV_URL = 'http://nlp.stanford.edu/data/glove.6B.zip
def get_wv_path(wv_path_or_url=WV_URL):
# process if file path given
if os.path.isfile(wv_path_or_url) and wv_path_or_url.endswith("vec"):
return wv_path_or_url
elif os.path.isfile(wv_path_or_url):
raise ValueError(
"wv_path_or_url must either be URL .vec.zip or .vec.gz file or file path to .vec file"
)
# process if URL is given
fasttext_url = "https://dl.fbaipublicfiles.com/fasttext"
if not wv_path_or_url.startswith(fasttext_url):
raise ValueError("selected word vector file must be from %s" % (fasttext_url))
if not wv_path_or_url.endswith(".vec.zip") and not wv_path_or_url.endswith(
"vec.gz"
):
raise ValueError(
"If wv_path_or_url is URL, must be .vec.zip filea from Facebook fasttext site."
)
ktrain_data = U.get_ktrain_data()
zip_fpath = os.path.join(ktrain_data, fname_from_url(wv_path_or_url))
wv_path = os.path.join(
ktrain_data, os.path.splitext(fname_from_url(wv_path_or_url))[0]
)
if not os.path.isfile(wv_path):
# download zip
print("downloading pretrained word vectors to %s ..." % (ktrain_data))
U.download(wv_path_or_url, zip_fpath)
# unzip
print("\nextracting pretrained word vectors...")
if wv_path_or_url.endswith(".vec.zip"):
with zipfile.ZipFile(zip_fpath, "r") as zip_ref:
zip_ref.extractall(ktrain_data)
else: # .vec.gz
with gzip.open(zip_fpath, "rb") as f_in:
with open(wv_path, "wb") as f_out:
shutil.copyfileobj(f_in, f_out)
print("done.\n")
# cleanup
print("cleanup downloaded zip...")
try:
os.remove(zip_fpath)
print("done.\n")
except OSError:
print("failed to cleanup/remove %s" % (zip_fpath))
return wv_path
def get_coefs(word, *arr):
return word, np.asarray(arr, dtype="float32")
# def load_wv(wv_path=None, verbose=1):
# if verbose: print('Loading pretrained word vectors...this may take a few moments...')
# if wv_path is None: wv_path = get_wv_path()
# embeddings_index = dict(get_coefs(*o.rstrip().rsplit(' ')) for o in open(wv_path, encoding='utf-8'))
# if verbose: print('Done.')
# return embeddings_index
def file_len(fname):
with open(fname, encoding="utf-8") as f:
for i, l in enumerate(f):
pass
return i + 1
def load_wv(wv_path_or_url=WV_URL, verbose=1):
wv_path = get_wv_path(wv_path_or_url)
if verbose:
print("loading pretrained word vectors...this may take a few moments...")
length = file_len(wv_path)
tups = []
mb = master_bar(range(1))
for i in mb:
f = open(wv_path, encoding="utf-8")
for o in progress_bar(range(length), parent=mb):
o = f.readline()
tups.append(get_coefs(*o.rstrip().rsplit(" ")))
f.close()
# if verbose: mb.write('done.')
return dict(tups)
# ------------------------------------------------------------------------------
# BERT
# ------------------------------------------------------------------------------
# BERT_PATH = os.path.join(os.path.dirname(os.path.abspath(localbert.__file__)), 'uncased_L-12_H-768_A-12')
BERT_URL = (
"https://storage.googleapis.com/bert_models/2018_10_18/uncased_L-12_H-768_A-12.zip"
)
BERT_URL_MULTI = "https://storage.googleapis.com/bert_models/2018_11_23/multi_cased_L-12_H-768_A-12.zip"
BERT_URL_CN = (
"https://storage.googleapis.com/bert_models/2018_11_03/chinese_L-12_H-768_A-12.zip"
)
def get_bert_path(lang="en"):
if lang == "en":
bert_url = BERT_URL
elif lang.startswith("zh-"):
bert_url = BERT_URL_CN
else:
bert_url = BERT_URL_MULTI
ktrain_data = U.get_ktrain_data()
zip_fpath = os.path.join(ktrain_data, fname_from_url(bert_url))
bert_path = os.path.join(ktrain_data, os.path.splitext(fname_from_url(bert_url))[0])
if (
not os.path.isdir(bert_path)
or not os.path.isfile(os.path.join(bert_path, "bert_config.json"))
or not os.path.isfile(
os.path.join(bert_path, "bert_model.ckpt.data-00000-of-00001")
)
or not os.path.isfile(os.path.join(bert_path, "bert_model.ckpt.index"))
or not os.path.isfile(os.path.join(bert_path, "bert_model.ckpt.meta"))
or not os.path.isfile(os.path.join(bert_path, "vocab.txt"))
):
# download zip
print("downloading pretrained BERT model (%s)..." % (fname_from_url(bert_url)))
U.download(bert_url, zip_fpath)
# unzip
print("\nextracting pretrained BERT model...")
with zipfile.ZipFile(zip_fpath, "r") as zip_ref:
zip_ref.extractall(ktrain_data)
print("done.\n")
# cleanup
print("cleanup downloaded zip...")
try:
os.remove(zip_fpath)
print("done.\n")
except OSError:
print("failed to cleanup/remove %s" % (zip_fpath))
return bert_path
def bert_tokenize(docs, tokenizer, max_length, verbose=1):
if verbose:
mb = master_bar(range(1))
pb = progress_bar(docs, parent=mb)
else:
mb = range(1)
pb = docs
indices = []
for i in mb:
for doc in pb:
# https://stackoverflow.com/questions/67360987/bert-model-bug-encountered-during-training/67375675#67375675
doc = str(doc) if isinstance(doc, (float, int)) else doc
ids, segments = tokenizer.encode(doc, max_len=max_length)
indices.append(ids)
if verbose:
mb.write("done.")
zeros = np.zeros_like(indices)
return [np.array(indices), np.array(zeros)]
# ------------------------------------------------------------------------------
# Transformers UTILITIES
# ------------------------------------------------------------------------------
# def convert_to_tfdataset(csv):
# def gen():
# for ex in csv:
# yield {'idx': ex[0],
#'sentence': ex[1],
#'label': str(ex[2])}
# return tf.data.Dataset.from_generator(gen,
# {'idx': tf.int64,
#'sentence': tf.string,
#'label': tf.int64})
# def features_to_tfdataset(features):
# def gen():
# for ex in features:
# yield ({'input_ids': ex.input_ids,
# 'attention_mask': ex.attention_mask,
# 'token_type_ids': ex.token_type_ids},
# ex.label)
# return tf.data.Dataset.from_generator(gen,
# ({'input_ids': tf.int32,
# 'attention_mask': tf.int32,
# 'token_type_ids': tf.int32},
# tf.int64),
# ({'input_ids': tf.TensorShape([None]),
# 'attention_mask': tf.TensorShape([None]),
# 'token_type_ids': tf.TensorShape([None])},
# tf.TensorShape([None])))
# #tf.TensorShape(])))
def _is_sentence_pair(tup):
if (
isinstance(tup, (tuple))
and len(tup) == 2
and isinstance(tup[0], str)
and isinstance(tup[1], str)
):
return True
else:
# if (
# isinstance(tup, (list, np.ndarray))
# and len(tup) == 2
# and isinstance(tup[0], str)
# and isinstance(tup[1], str)
# ):
# warnings.warn(
# "List or array of two texts supplied, so task being treated as text classification. "
# + "If this is a sentence pair classification task, please cast to tuple."
# )
return False
def detect_text_format(texts):
is_pair = False
is_array = False
err_msg = "invalid text format: texts should be list of strings or list of sentence pairs in form of tuples (str, str)"
if _is_sentence_pair(texts):
is_pair = True
is_array = False
elif isinstance(texts, (tuple, list, np.ndarray)):
is_array = True
if len(texts) == 0:
raise ValueError("texts is empty")
peek = texts[0]
is_pair = _is_sentence_pair(peek)
if not is_pair and not isinstance(peek, str):
raise ValueError(err_msg)
return is_array, is_pair
def hf_features_to_tfdataset(features_list, labels):
features_list = np.array(features_list)
labels = np.array(labels) if labels is not None else None
tfdataset = tf.data.Dataset.from_tensor_slices((features_list, labels))
tfdataset = tfdataset.map(
lambda x, y: (
{"input_ids": x[0], "attention_mask": x[1], "token_type_ids": x[2]},
y,
)
)
return tfdataset
def hf_convert_example(
text_a,
text_b=None,
tokenizer=None,
max_length=512,
pad_on_left=False,
pad_token=0,
pad_token_segment_id=0,
mask_padding_with_zero=True,
):
"""
```
convert InputExample to InputFeature for Hugging Face transformer
```
"""
if tokenizer is None:
raise ValueError("tokenizer is required")
inputs = tokenizer.encode_plus(
text_a,
text_b,
add_special_tokens=True,
return_token_type_ids=True,
max_length=max_length,
truncation="longest_first",
)
input_ids, token_type_ids = inputs["input_ids"], inputs["token_type_ids"]
# The mask has 1 for real tokens and 0 for padding tokens. Only real
# tokens are attended to.
attention_mask = [1 if mask_padding_with_zero else 0] * len(input_ids)
# Zero-pad up to the sequence length.
padding_length = max_length - len(input_ids)
if pad_on_left:
input_ids = ([pad_token] * padding_length) + input_ids
attention_mask = (
[0 if mask_padding_with_zero else 1] * padding_length
) + attention_mask
token_type_ids = ([pad_token_segment_id] * padding_length) + token_type_ids
else:
input_ids = input_ids + ([pad_token] * padding_length)
attention_mask = attention_mask + (
[0 if mask_padding_with_zero else 1] * padding_length
)
token_type_ids = token_type_ids + ([pad_token_segment_id] * padding_length)
assert len(input_ids) == max_length, "Error with input length {} vs {}".format(
len(input_ids), max_length
)
assert len(attention_mask) == max_length, "Error with input length {} vs {}".format(
len(attention_mask), max_length
)
assert len(token_type_ids) == max_length, "Error with input length {} vs {}".format(
len(token_type_ids), max_length
)
# if ex_index < 1:
# print("*** Example ***")
# print("guid: %s" % (example.guid))
# print("input_ids: %s" % " ".join([str(x) for x in input_ids]))
# print("attention_mask: %s" % " ".join([str(x) for x in attention_mask]))
# print("token_type_ids: %s" % " ".join([str(x) for x in token_type_ids]))
# print("label: %s (id = %d)" % (example.label, label))
return [input_ids, attention_mask, token_type_ids]
def hf_convert_examples(
texts,
y=None,
tokenizer=None,
max_length=512,
pad_on_left=False,
pad_token=0,
pad_token_segment_id=0,
mask_padding_with_zero=True,
use_dynamic_shape=False,
verbose=1,
):
"""
```
Loads a data file into a list of ``InputFeatures``
Args:
texts: texts of documents or sentence pairs
y: labels for documents
tokenizer: Instance of a tokenizer that will tokenize the examples
max_length: Maximum example length
pad_on_left: If set to ``True``, the examples will be padded on the left rather than on the right (default)
pad_token: Padding token
pad_token_segment_id: The segment ID for the padding token (It is usually 0, but can vary such as for XLNet where it is 4)
mask_padding_with_zero: If set to ``True``, the attention mask will be filled by ``1`` for actual values
and by ``0`` for padded values. If set to ``False``, inverts it (``1`` for padded values, ``0`` for
actual values)
use_dynamic_shape(bool): If True, supplied max_length will be ignored and will be computed
based on provided texts instead.
verbose(bool): verbosity
Returns:
If the ``examples`` input is a ``tf.data.Dataset``, will return a ``tf.data.Dataset``
containing the task-specific features. If the input is a list of ``InputExamples``, will return
a list of task-specific ``InputFeatures`` which can be fed to the model.
```
"""
is_array, is_pair = detect_text_format(texts)
if use_dynamic_shape:
sentences = []
for text in texts:
if is_pair:
text_a = text[0]
text_b = text[1]
else:
text_a = text
text_b = None
sentences.append(
tokenizer.convert_ids_to_tokens(tokenizer.encode(text_a, text_b))
)
# sentences.append(tokenizer.tokenize(text_a, text_b)) # only works for Fast tokenizers
maxlen = (
len(
max(
[tokens for tokens in sentences],
key=len,
)
)
+ 2
)
if maxlen < max_length:
max_length = maxlen
data = []
features_list = []
labels = []
if verbose:
mb = master_bar(range(1))
pb = progress_bar(texts, parent=mb)
else:
mb = range(1)
pb = texts
for i in mb:
# for (idx, text) in enumerate(progress_bar(texts, parent=mb)):
for (idx, text) in enumerate(pb):
if is_pair:
text_a = text[0]
text_b = text[1]
else:
text_a = text
text_b = None
features = hf_convert_example(
text_a,
text_b=text_b,
tokenizer=tokenizer,
max_length=max_length,
pad_on_left=pad_on_left,
pad_token=pad_token,
pad_token_segment_id=pad_token_segment_id,
mask_padding_with_zero=mask_padding_with_zero,
)
features_list.append(features)
labels.append(y[idx] if y is not None else None)
# tfdataset = hf_features_to_tfdataset(features_list, labels)
# return tfdataset
# return (features_list, labels)
# HF_EXCEPTION
# due to issues in transormers library and TF2 tf.Datasets, arrays are converted
# to iterators on-the-fly
# return TransformerSequence(np.array(features_list), np.array(labels))
from .dataset import TransformerDataset
return TransformerDataset(np.array(features_list), np.array(labels))
# ------------------------------------------------------------------------------
class TextPreprocessor(Preprocessor):
"""
```
Text preprocessing base class
```
"""
def __init__(self, maxlen, class_names, lang="en", multilabel=None):
self.set_classes(class_names) # converts to list of necessary
self.maxlen = maxlen
self.lang = lang
self.multilabel = multilabel # currently, this is always initially set None until set by set_multilabel
self.preprocess_train_called = False
# self.label_encoder = None # only set if y is in string format
self.ytransform = None
self.c = self.c.tolist() if isinstance(self.c, np.ndarray) else self.c
def migrate_classes(self, class_names, classes):
# NOTE: this method transforms to np.ndarray to list.
# If removed and "if class_names" is issued prior to set_classes(), an error will occur.
class_names = (
class_names.tolist() if isinstance(class_names, np.ndarray) else class_names
)
classes = classes.tolist() if isinstance(classes, np.ndarray) else classes
if not class_names and classes:
class_names = classes
warnings.warn(
"The class_names argument is replacing the classes argument. Please update your code."
)
return class_names
def get_tokenizer(self):
raise NotImplementedError("This method was not overridden in subclass")
def check_trained(self):
if not self.preprocess_train_called:
warnings.warn(
"The method preprocess_train was never called. You can disable this warning by setting preprocess_train_called=True."
)
# raise Exception('preprocess_train must be called')
def get_preprocessor(self):
raise NotImplementedError
def get_classes(self):
return self.c
def set_classes(self, class_names):
self.c = (
class_names.tolist() if isinstance(class_names, np.ndarray) else class_names
)
def preprocess(self, texts):
raise NotImplementedError
def set_multilabel(self, data, mode, verbose=1):
if mode == "train" and self.get_classes():
original_multilabel = self.multilabel
discovered_multilabel = U.is_multilabel(data)
if original_multilabel is None:
self.multilabel = discovered_multilabel
elif original_multilabel is True and discovered_multilabel is False:
warnings.warn(
"The multilabel=True argument was supplied, but labels do not indicate "
+ "a multilabel problem (labels appear to be mutually-exclusive). Using multilabel=True anyways."
)
elif original_multilabel is False and discovered_multilabel is True:
warnings.warn(
"The multilabel=False argument was supplied, but labels inidcate that "
+ "this is a multilabel problem (labels are not mutually-exclusive). Using multilabel=False anyways."
)
U.vprint("Is Multi-Label? %s" % (self.multilabel), verbose=verbose)
def undo(self, doc):
"""
```
undoes preprocessing and returns raw data by:
converting a list or array of Word IDs back to words
```
"""
raise NotImplementedError
def is_chinese(self):
return TU.is_chinese(self.lang)
def is_nospace_lang(self):
return TU.is_nospace_lang(self.lang)
def process_chinese(self, texts, lang=None):
# if lang is None: lang = langdetect.detect(texts[0])
if lang is None:
lang = TU.detect_lang(texts)
if not TU.is_nospace_lang(lang):
return texts
return TU.split_chinese(texts)
@classmethod
def seqlen_stats(cls, list_of_texts):
"""
```
compute sequence length stats from
list of texts in any spaces-segmented language
Args:
list_of_texts: list of strings
Returns:
dict: dictionary with keys: mean, 95percentile, 99percentile
```
"""
counts = []
for text in list_of_texts:
if isinstance(text, (list, np.ndarray)):
lst = text
else:
lst = text.split()
counts.append(len(lst))
p95 = np.percentile(counts, 95)
p99 = np.percentile(counts, 99)
avg = sum(counts) / len(counts)
return {"mean": avg, "95percentile": p95, "99percentile": p99}
def print_seqlen_stats(self, texts, mode, verbose=1):
"""
```
prints stats about sequence lengths
```
"""
if verbose and not self.is_nospace_lang():
stat_dict = TextPreprocessor.seqlen_stats(texts)
print("%s sequence lengths:" % mode)
for k in stat_dict:
print("\t%s : %s" % (k, int(round(stat_dict[k]))))
def _transform_y(self, y_data, train=False, verbose=1):
"""
```
preprocess y
If shape of y is 1, then task is considered classification if self.c exists
or regression if not.
```
"""
if self.ytransform is None:
self.ytransform = U.YTransform(class_names=self.get_classes())
y = self.ytransform.apply(y_data, train=train)
if train:
self.c = self.ytransform.get_classes()
return y
class StandardTextPreprocessor(TextPreprocessor):
"""
```
Standard text preprocessing
```
"""
def __init__(
self,
maxlen,
max_features,
class_names=[],
classes=[],
lang="en",
ngram_range=1,
multilabel=None,
):
class_names = self.migrate_classes(class_names, classes)
super().__init__(maxlen, class_names, lang=lang, multilabel=multilabel)
self.tok = None
self.tok_dct = {}
self.max_features = max_features
self.ngram_range = ngram_range
def get_tokenizer(self):
return self.tok
def __getstate__(self):
return {k: v for k, v in self.__dict__.items()}
def __setstate__(self, state):
"""
```
For backwards compatibility with pre-ytransform versions
```
"""
self.__dict__.update(state)
if not hasattr(self, "ytransform"):
le = self.label_encoder if hasattr(self, "label_encoder") else None
self.ytransform = U.YTransform(
class_names=self.get_classes(), label_encoder=le
)
def get_preprocessor(self):
return (self.tok, self.tok_dct)
def preprocess(self, texts):
return self.preprocess_test(texts, verbose=0)[0]
def undo(self, doc):
"""
```
undoes preprocessing and returns raw data by:
converting a list or array of Word IDs back to words
```
"""
dct = self.tok.index_word
return " ".join([dct[wid] for wid in doc if wid != 0 and wid in dct])
def preprocess_train(self, train_text, y_train, verbose=1):
"""
```
preprocess training set
```
"""
if self.lang is None:
self.lang = TU.detect_lang(train_text)
U.vprint("language: %s" % (self.lang), verbose=verbose)
# special processing if Chinese
train_text = self.process_chinese(train_text, lang=self.lang)
# extract vocabulary
self.tok = keras.preprocessing.text.Tokenizer(num_words=self.max_features)
self.tok.fit_on_texts(train_text)
U.vprint("Word Counts: {}".format(len(self.tok.word_counts)), verbose=verbose)
U.vprint("Nrows: {}".format(len(train_text)), verbose=verbose)
# convert to word IDs
x_train = self.tok.texts_to_sequences(train_text)
U.vprint("{} train sequences".format(len(x_train)), verbose=verbose)
self.print_seqlen_stats(x_train, "train", verbose=verbose)
# add ngrams
x_train = self._fit_ngrams(x_train, verbose=verbose)
# pad sequences
x_train = keras.preprocessing.sequence.pad_sequences(
x_train, maxlen=self.maxlen
)
U.vprint(
"x_train shape: ({},{})".format(x_train.shape[0], x_train.shape[1]),
verbose=verbose,
)
# transform y
y_train = self._transform_y(y_train, train=True, verbose=verbose)
if y_train is not None and verbose:
print("y_train shape: %s" % (y_train.shape,))
# return
result = (x_train, y_train)
self.set_multilabel(result, "train")
self.preprocess_train_called = True
return result
def preprocess_test(self, test_text, y_test=None, verbose=1):
"""
```
preprocess validation or test dataset
```
"""
self.check_trained()
if self.tok is None or self.lang is None:
raise Exception(
"Unfitted tokenizer or missing language. Did you run preprocess_train first?"
)
# check for and process chinese
test_text = self.process_chinese(test_text, self.lang)
# convert to word IDs
x_test = self.tok.texts_to_sequences(test_text)
U.vprint("{} test sequences".format(len(x_test)), verbose=verbose)
self.print_seqlen_stats(x_test, "test", verbose=verbose)
# add n-grams
x_test = self._add_ngrams(x_test, mode="test", verbose=verbose)
# pad sequences
x_test = keras.preprocessing.sequence.pad_sequences(x_test, maxlen=self.maxlen)
U.vprint(
"x_test shape: ({},{})".format(x_test.shape[0], x_test.shape[1]),
verbose=verbose,
)
# transform y
y_test = self._transform_y(y_test, train=False, verbose=verbose)
if y_test is not None and verbose:
print("y_test shape: %s" % (y_test.shape,))
# return
return (x_test, y_test)
def _fit_ngrams(self, x_train, verbose=1):
self.tok_dct = {}
if self.ngram_range < 2:
return x_train
U.vprint("Adding {}-gram features".format(self.ngram_range), verbose=verbose)
# Create set of unique n-gram from the training set.
ngram_set = set()
for input_list in x_train:
for i in range(2, self.ngram_range + 1):
set_of_ngram = self._create_ngram_set(input_list, ngram_value=i)
ngram_set.update(set_of_ngram)
# Dictionary mapping n-gram token to a unique integer.
# Integer values are greater than max_features in order
# to avoid collision with existing features.
start_index = self.max_features + 1
token_indice = {v: k + start_index for k, v in enumerate(ngram_set)}
indice_token = {token_indice[k]: k for k in token_indice}
self.tok_dct = token_indice
# max_features is the highest integer that could be found in the dataset.
self.max_features = np.max(list(indice_token.keys())) + 1
U.vprint(
"max_features changed to %s with addition of ngrams" % (self.max_features),
verbose=verbose,
)
# Augmenting x_train with n-grams features
x_train = self._add_ngrams(x_train, verbose=verbose, mode="train")
return x_train
def _add_ngrams(self, sequences, verbose=1, mode="test"):
"""
```
Augment the input list of list (sequences) by appending n-grams values.
Example: adding bi-gram
```
"""
token_indice = self.tok_dct
if self.ngram_range < 2:
return sequences
new_sequences = []
for input_list in sequences:
new_list = input_list[:]
for ngram_value in range(2, self.ngram_range + 1):
for i in range(len(new_list) - ngram_value + 1):
ngram = tuple(new_list[i : i + ngram_value])
if ngram in token_indice:
new_list.append(token_indice[ngram])
new_sequences.append(new_list)
U.vprint(
"Average {} sequence length with ngrams: {}".format(
mode, np.mean(list(map(len, new_sequences)), dtype=int)
),
verbose=verbose,
)
self.print_seqlen_stats(new_sequences, "%s (w/ngrams)" % mode, verbose=verbose)
return new_sequences
def _create_ngram_set(self, input_list, ngram_value=2):
"""
```
Extract a set of n-grams from a list of integers.
>>> create_ngram_set([1, 4, 9, 4, 1, 4], ngram_value=2)
{(4, 9), (4, 1), (1, 4), (9, 4)}
>>> create_ngram_set([1, 4, 9, 4, 1, 4], ngram_value=3)
[(1, 4, 9), (4, 9, 4), (9, 4, 1), (4, 1, 4)]
```
"""
return set(zip(*[input_list[i:] for i in range(ngram_value)]))
def ngram_count(self):
if not self.tok_dct:
return 1
s = set()
for k in self.tok_dct.keys():
s.add(len(k))
return max(list(s))
class BERTPreprocessor(TextPreprocessor):
"""
```
text preprocessing for BERT model
```
"""
def __init__(
self,
maxlen,
max_features,
class_names=[],
classes=[],
lang="en",
ngram_range=1,
multilabel=None,
):
class_names = self.migrate_classes(class_names, classes)
if maxlen > 512:
raise ValueError("BERT only supports maxlen <= 512")
super().__init__(maxlen, class_names, lang=lang, multilabel=multilabel)
vocab_path = os.path.join(get_bert_path(lang=lang), "vocab.txt")
token_dict = {}
with codecs.open(vocab_path, "r", "utf8") as reader:
for line in reader:
token = line.strip()
token_dict[token] = len(token_dict)
from keras_bert import Tokenizer as BERT_Tokenizer
tokenizer = BERT_Tokenizer(token_dict)
self.tok = tokenizer
self.tok_dct = dict((v, k) for k, v in token_dict.items())
self.max_features = max_features # ignored
self.ngram_range = 1 # ignored
def get_tokenizer(self):
return self.tok
def __getstate__(self):
return {k: v for k, v in self.__dict__.items()}
def __setstate__(self, state):
"""
```
For backwards compatibility with pre-ytransform versions
```
"""
self.__dict__.update(state)
if not hasattr(self, "ytransform"):
le = self.label_encoder if hasattr(self, "label_encoder") else None
self.ytransform = U.YTransform(
class_names=self.get_classes(), label_encoder=le
)
def get_preprocessor(self):
return (self.tok, self.tok_dct)
def preprocess(self, texts):
return self.preprocess_test(texts, verbose=0)[0]
def undo(self, doc):
"""
```
undoes preprocessing and returns raw data by:
converting a list or array of Word IDs back to words
```
"""
dct = self.tok_dct
return " ".join([dct[wid] for wid in doc if wid != 0 and wid in dct])
def preprocess_train(self, texts, y=None, mode="train", verbose=1):
"""
```
preprocess training set
```
"""
if mode == "train" and y is None:
raise ValueError("y is required when mode=train")
if self.lang is None and mode == "train":
self.lang = TU.detect_lang(texts)
U.vprint("preprocessing %s..." % (mode), verbose=verbose)
U.vprint("language: %s" % (self.lang), verbose=verbose)
x = bert_tokenize(texts, self.tok, self.maxlen, verbose=verbose)
# transform y
y = self._transform_y(y, train=mode == "train", verbose=verbose)
result = (x, y)
self.set_multilabel(result, mode)
if mode == "train":
self.preprocess_train_called = True
return result
def preprocess_test(self, texts, y=None, mode="test", verbose=1):
self.check_trained()
return self.preprocess_train(texts, y=y, mode=mode, verbose=verbose)
class TransformersPreprocessor(TextPreprocessor):
"""
```
text preprocessing for Hugging Face Transformer models
```
"""
def __init__(
self,
model_name,
maxlen,
max_features,
class_names=[],
classes=[],
lang="en",
ngram_range=1,
multilabel=None,
):
class_names = self.migrate_classes(class_names, classes)
if maxlen > 512:
warnings.warn(
"Transformer models typically only support maxlen <= 512, unless you are using certain models like the Longformer."
)
super().__init__(maxlen, class_names, lang=lang, multilabel=multilabel)
self.model_name = model_name
self.name = model_name.split("-")[0]
if model_name.startswith("xlm-roberta"):
self.name = "xlm_roberta"
self.model_name = "jplu/tf-" + self.model_name
else:
self.name = model_name.split("-")[0]
self.config = AutoConfig.from_pretrained(model_name)
self.model_type = TFAutoModelForSequenceClassification