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<main>
<article id="content">
<header>
<h1 class="title">Module <code>ktrain.text</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">import pickle
from . import shallownlp, textutils
from .data import texts_from_array, texts_from_csv, texts_from_df, texts_from_folder
from .eda import get_topic_model
from .models import (
print_text_classifiers,
print_text_regression_models,
text_classifier,
text_regression_model,
)
from .ner.data import (
entities_from_array,
entities_from_conll2003,
entities_from_df,
entities_from_gmb,
entities_from_txt,
)
from .ner.models import print_sequence_taggers, sequence_tagger
from .preprocessor import Transformer, TransformerEmbedding
from .qa import AnswerExtractor, SimpleQA
from .summarization import TransformerSummarizer
from .textextractor import TextExtractor
from .textutils import extract_filenames, filter_by_id, load_text_files
from .translation import EnglishTranslator, Translator
from .zsl import ZeroShotClassifier
__all__ = [
"text_classifier",
"text_regression_model",
"print_text_classifiers",
"print_text_regression_models",
"texts_from_folder",
"texts_from_csv",
"texts_from_df",
"texts_from_array",
"entities_from_gmb",
"entities_from_conll2003",
"entities_from_txt",
"entities_from_array",
"entities_from_df",
"sequence_tagger",
"print_sequence_taggers",
"get_topic_model",
"Transformer",
"TransformerEmbedding",
"shallownlp",
"TransformerSummarizer",
"ZeroShotClassifier",
"EnglishTranslator",
"Translator",
"SimpleQA",
"AnswerExtractor",
"TextExtractor",
"extract_filenames",
"load_text_files",
]
def load_topic_model(fname):
"""
Load saved TopicModel object
Args:
fname(str): base filename for all saved files
"""
with open(fname + ".tm_vect", "rb") as f:
vectorizer = pickle.load(f)
with open(fname + ".tm_model", "rb") as f:
model = pickle.load(f)
with open(fname + ".tm_params", "rb") as f:
params = pickle.load(f)
tm = get_topic_model(
n_topics=params["n_topics"],
n_features=params["n_features"],
verbose=params["verbose"],
)
tm.model = model
tm.vectorizer = vectorizer
return tm
seqlen_stats = Transformer.seqlen_stats</code></pre>
</details>
</section>
<section>
<h2 class="section-title" id="header-submodules">Sub-modules</h2>
<dl>
<dt><code class="name"><a title="ktrain.text.data" href="data.html">ktrain.text.data</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.text.dataset" href="dataset.html">ktrain.text.dataset</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.text.eda" href="eda.html">ktrain.text.eda</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.text.kw" href="kw/index.html">ktrain.text.kw</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.text.learner" href="learner.html">ktrain.text.learner</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.text.models" href="models.html">ktrain.text.models</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.text.ner" href="ner/index.html">ktrain.text.ner</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.text.predictor" href="predictor.html">ktrain.text.predictor</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.text.preprocessor" href="preprocessor.html">ktrain.text.preprocessor</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.text.qa" href="qa/index.html">ktrain.text.qa</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.text.shallownlp" href="shallownlp/index.html">ktrain.text.shallownlp</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.text.speech" href="speech/index.html">ktrain.text.speech</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.text.summarization" href="summarization/index.html">ktrain.text.summarization</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.text.textextractor" href="textextractor.html">ktrain.text.textextractor</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.text.textutils" href="textutils.html">ktrain.text.textutils</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.text.translation" href="translation/index.html">ktrain.text.translation</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
<dt><code class="name"><a title="ktrain.text.zsl" href="zsl/index.html">ktrain.text.zsl</a></code></dt>
<dd>
<div class="desc"></div>
</dd>
</dl>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-functions">Functions</h2>
<dl>
<dt id="ktrain.text.entities_from_array"><code class="name flex">
<span>def <span class="ident">entities_from_array</span></span>(<span>x_train, y_train, x_test=None, y_test=None, use_char=False, val_pct=0.1, verbose=1)</span>
</code></dt>
<dd>
<div class="desc"><p>Load entities from arrays</p>
<h2 id="args">Args</h2>
<p>x_train(list): list of list of entity tokens for training
Example: x_train = [['Hello', 'world'], ['Hello', 'Cher'], ['I', 'love', 'Chicago']]
y_train(list): list of list of tokens representing entity labels
Example:
y_train = [['O', 'O'], ['O', 'B-PER'], ['O', 'O', 'B-LOC']]
x_test(list): list of list of entity tokens for validation
Example: x_train = [['Hello', 'world'], ['Hello', 'Cher'], ['I', 'love', 'Chicago']]
y_test(list): list of list of tokens representing entity labels
Example:
y_train = [['O', 'O'], ['O', 'B-PER'], ['O', 'O', 'B-LOC']]
use_char(bool):
If True, data will be preprocessed to use character embeddings
in addition to word embeddings
val_pct(float):
percentage of training to use for validation if no validation data is supplied
verbose (boolean): verbosity</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def entities_from_array(
x_train, y_train, x_test=None, y_test=None, use_char=False, val_pct=0.1, verbose=1
):
"""
Load entities from arrays
Args:
x_train(list): list of list of entity tokens for training
Example: x_train = [['Hello', 'world'], ['Hello', 'Cher'], ['I', 'love', 'Chicago']]
y_train(list): list of list of tokens representing entity labels
Example: y_train = [['O', 'O'], ['O', 'B-PER'], ['O', 'O', 'B-LOC']]
x_test(list): list of list of entity tokens for validation
Example: x_train = [['Hello', 'world'], ['Hello', 'Cher'], ['I', 'love', 'Chicago']]
y_test(list): list of list of tokens representing entity labels
Example: y_train = [['O', 'O'], ['O', 'B-PER'], ['O', 'O', 'B-LOC']]
use_char(bool): If True, data will be preprocessed to use character embeddings in addition to word embeddings
val_pct(float): percentage of training to use for validation if no validation data is supplied
verbose (boolean): verbosity
"""
# TODO: converting to df to use entities_from_df - needs to be refactored
train_df = pp.array_to_df(x_train, y_train)
val_df = None
if x_test is not None and y_test is not None:
val_df = pp.array_to_df(x_test, y_test)
if verbose:
print("training data sample:")
print(train_df.head())
if val_df is not None:
print("validation data sample:")
print(val_df.head())
return entities_from_df(
train_df, val_df=val_df, val_pct=val_pct, use_char=use_char, verbose=verbose
)</code></pre>
</details>
</dd>
<dt id="ktrain.text.entities_from_conll2003"><code class="name flex">
<span>def <span class="ident">entities_from_conll2003</span></span>(<span>train_filepath, val_filepath=None, use_char=False, encoding=None, val_pct=0.1, verbose=1)</span>
</code></dt>
<dd>
<div class="desc"><p>Loads sequence-labeled data from a file in CoNLL2003 format.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def entities_from_conll2003(
train_filepath,
val_filepath=None,
use_char=False,
encoding=None,
val_pct=0.1,
verbose=1,
):
"""
Loads sequence-labeled data from a file in CoNLL2003 format.
"""
return entities_from_txt(
train_filepath=train_filepath,
val_filepath=val_filepath,
use_char=use_char,
data_format="conll2003",
encoding=encoding,
val_pct=val_pct,
verbose=verbose,
)</code></pre>
</details>
</dd>
<dt id="ktrain.text.entities_from_df"><code class="name flex">
<span>def <span class="ident">entities_from_df</span></span>(<span>train_df, val_df=None, word_column='Word', tag_column='Tag', sentence_column='SentenceID', use_char=False, val_pct=0.1, verbose=1)</span>
</code></dt>
<dd>
<div class="desc"><p>Load entities from pandas DataFrame</p>
<h2 id="args">Args</h2>
<dl>
<dt>train_df(pd.DataFrame): training data</dt>
<dt>val_df(pdf.DataFrame): validation data</dt>
<dt>word_column(str): name of column containing the text</dt>
<dt>tag_column(str): name of column containing lael</dt>
<dt>sentence_column(str): name of column containing Sentence IDs</dt>
<dt>use_char(bool):
If True, data will be preprocessed to use character embeddings
in addition to word embeddings</dt>
<dt><strong><code>verbose</code></strong> : <code>boolean</code></dt>
<dd>verbosity</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def entities_from_df(
train_df,
val_df=None,
word_column=WORD_COL,
tag_column=TAG_COL,
sentence_column=SENT_COL,
use_char=False,
val_pct=0.1,
verbose=1,
):
"""
Load entities from pandas DataFrame
Args:
train_df(pd.DataFrame): training data
val_df(pdf.DataFrame): validation data
word_column(str): name of column containing the text
tag_column(str): name of column containing lael
sentence_column(str): name of column containing Sentence IDs
use_char(bool): If True, data will be preprocessed to use character embeddings in addition to word embeddings
verbose (boolean): verbosity
"""
# process dataframe and instantiate NERPreprocessor
x, y = pp.process_df(
train_df,
word_column=word_column,
tag_column=tag_column,
sentence_column=sentence_column,
verbose=verbose,
)
# get validation set
if val_df is None:
x_train, x_valid, y_train, y_valid = train_test_split(x, y, test_size=val_pct)
else:
x_train, y_train = x, y
(x_valid, y_valid) = pp.process_df(
val_df,
word_column=word_column,
tag_column=tag_column,
sentence_column=sentence_column,
verbose=0,
)
# preprocess and convert to generator
from .anago.preprocessing import IndexTransformer
p = IndexTransformer(use_char=use_char)
preproc = NERPreprocessor(p)
preproc.fit(x_train, y_train)
from .dataset import NERSequence
trn = NERSequence(x_train, y_train, batch_size=U.DEFAULT_BS, p=p)
val = NERSequence(x_valid, y_valid, batch_size=U.DEFAULT_BS, p=p)
return (trn, val, preproc)</code></pre>
</details>
</dd>
<dt id="ktrain.text.entities_from_gmb"><code class="name flex">
<span>def <span class="ident">entities_from_gmb</span></span>(<span>train_filepath, val_filepath=None, use_char=False, word_column='Word', tag_column='Tag', sentence_column='SentenceID', encoding=None, val_pct=0.1, verbose=1)</span>
</code></dt>
<dd>
<div class="desc"><p>Loads sequence-labeled data from text file in the
Groningen
Meaning Bank
(GMB) format.</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def entities_from_gmb(
train_filepath,
val_filepath=None,
use_char=False,
word_column=WORD_COL,
tag_column=TAG_COL,
sentence_column=SENT_COL,
encoding=None,
val_pct=0.1,
verbose=1,
):
"""
Loads sequence-labeled data from text file in the Groningen
Meaning Bank (GMB) format.
"""
return entities_from_txt(
train_filepath=train_filepath,
val_filepath=val_filepath,
use_char=use_char,
word_column=word_column,
tag_column=tag_column,
sentence_column=sentence_column,
data_format="gmb",
encoding=encoding,
val_pct=val_pct,
verbose=verbose,
)</code></pre>
</details>
</dd>
<dt id="ktrain.text.entities_from_txt"><code class="name flex">
<span>def <span class="ident">entities_from_txt</span></span>(<span>train_filepath, val_filepath=None, use_char=False, word_column='Word', tag_column='Tag', sentence_column='SentenceID', data_format='conll2003', encoding=None, val_pct=0.1, verbose=1)</span>
</code></dt>
<dd>
<div class="desc"><p>Loads sequence-labeled data from comma or tab-delmited text file.
Format of file is either the CoNLL2003 format or Groningen Meaning
Bank (GMB) format - specified with data_format parameter.</p>
<p>In both formats, each word appars on a separate line along with
its associated tag (or label).
The last item on each line should be the tag or label assigned to word.</p>
<p>In the CoNLL2003 format, there is an empty line after
each sentence.
In the GMB format, sentences are deliniated
with a third column denoting the Sentence ID.</p>
<p>More information on CoNLL2003 format:
<a href="https://www.aclweb.org/anthology/W03-0419">https://www.aclweb.org/anthology/W03-0419</a></p>
<p>CoNLL Example (each column is typically separated by space or tab)
and
no column headings:</p>
<p>Paul
B-PER
Newman
I-PER
is
O
a
O
great
O
actor
O
!
O</p>
<p>More information on GMB format:
Refer to ner_dataset.csv on Kaggle here:
<a href="https://www.kaggle.com/abhinavwalia95/entity-annotated-corpus/version/2">https://www.kaggle.com/abhinavwalia95/entity-annotated-corpus/version/2</a></p>
<p>GMB example (each column separated by comma or tab)
with column headings:</p>
<p>SentenceID
Word
Tag
1
Paul
B-PER
1
Newman
I-PER
1
is
O
1
a
O
1
great
O
1
actor
O
1
!
O</p>
<h2 id="args">Args</h2>
<dl>
<dt>train_filepath(str): file path to training CSV</dt>
<dt><strong><code>val_filepath</code></strong> : <code>str</code></dt>
<dd>file path to validation dataset</dd>
<dt>use_char(bool):
If True, data will be preprocessed to use character embeddings in addition to word embeddings</dt>
<dt>word_column(str): name of column containing the text</dt>
<dt>tag_column(str): name of column containing lael</dt>
<dt>sentence_column(str): name of column containing Sentence IDs</dt>
<dt>data_format(str): one of colnll2003 or gmb</dt>
<dt>word_column, tag_column, and sentence_column</dt>
<dt>ignored if 'conll2003'</dt>
<dt>encoding(str): the encoding to use.
If None, encoding is discovered automatically</dt>
<dt>val_pct(float): Proportion of training to use for validation.</dt>
<dt><strong><code>verbose</code></strong> : <code>boolean</code></dt>
<dd>verbosity</dd>
</dl></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def entities_from_txt(
train_filepath,
val_filepath=None,
use_char=False,
word_column=WORD_COL,
tag_column=TAG_COL,
sentence_column=SENT_COL,
data_format="conll2003",
encoding=None,
val_pct=0.1,
verbose=1,
):
"""
Loads sequence-labeled data from comma or tab-delmited text file.
Format of file is either the CoNLL2003 format or Groningen Meaning
Bank (GMB) format - specified with data_format parameter.
In both formats, each word appars on a separate line along with
its associated tag (or label).
The last item on each line should be the tag or label assigned to word.
In the CoNLL2003 format, there is an empty line after
each sentence. In the GMB format, sentences are deliniated
with a third column denoting the Sentence ID.
More information on CoNLL2003 format:
https://www.aclweb.org/anthology/W03-0419
CoNLL Example (each column is typically separated by space or tab)
and no column headings:
Paul B-PER
Newman I-PER
is O
a O
great O
actor O
! O
More information on GMB format:
Refer to ner_dataset.csv on Kaggle here:
https://www.kaggle.com/abhinavwalia95/entity-annotated-corpus/version/2
GMB example (each column separated by comma or tab)
with column headings:
SentenceID Word Tag
1 Paul B-PER
1 Newman I-PER
1 is O
1 a O
1 great O
1 actor O
1 ! O
Args:
train_filepath(str): file path to training CSV
val_filepath (str): file path to validation dataset
use_char(bool): If True, data will be preprocessed to use character embeddings in addition to word embeddings
word_column(str): name of column containing the text
tag_column(str): name of column containing lael
sentence_column(str): name of column containing Sentence IDs
data_format(str): one of colnll2003 or gmb
word_column, tag_column, and sentence_column
ignored if 'conll2003'
encoding(str): the encoding to use. If None, encoding is discovered automatically
val_pct(float): Proportion of training to use for validation.
verbose (boolean): verbosity
"""
# set dataframe converter
if data_format == "gmb":
data_to_df = pp.gmb_to_df
else:
data_to_df = pp.conll2003_to_df
word_column, tag_column, sentence_column = WORD_COL, TAG_COL, SENT_COL
# detect encoding
if encoding is None:
with open(train_filepath, "rb") as f:
encoding = TU.detect_encoding(f.read())
U.vprint(
"detected encoding: %s (if wrong, set manually)" % (encoding),
verbose=verbose,
)
# create dataframe
train_df = data_to_df(train_filepath, encoding=encoding)
val_df = (
None if val_filepath is None else data_to_df(val_filepath, encoding=encoding)
)
return entities_from_df(
train_df,
val_df=val_df,
word_column=word_column,
tag_column=tag_column,
sentence_column=sentence_column,
use_char=use_char,
val_pct=val_pct,
verbose=verbose,
)</code></pre>
</details>
</dd>
<dt id="ktrain.text.extract_filenames"><code class="name flex">
<span>def <span class="ident">extract_filenames</span></span>(<span>corpus_path, follow_links=False)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def extract_filenames(corpus_path, follow_links=False):
if os.listdir(corpus_path) == []:
raise ValueError("%s: path is empty" % corpus_path)
walk = os.walk
for root, dirs, filenames in walk(corpus_path, followlinks=follow_links):
for filename in filenames:
try:
yield os.path.join(root, filename)
except:
continue</code></pre>
</details>
</dd>
<dt id="ktrain.text.load_text_files"><code class="name flex">
<span>def <span class="ident">load_text_files</span></span>(<span>corpus_path, truncate_len=None, clean=True, return_fnames=False)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>load text files
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def load_text_files(corpus_path, truncate_len=None, clean=True, return_fnames=False):
"""
```
load text files
```
"""
texts = []
filenames = []
mb = master_bar(range(1))
for i in mb:
for filename in progress_bar(list(extract_filenames(corpus_path)), parent=mb):
with open(filename, "r") as f:
text = f.read()
if clean:
text = strip_control_characters(text)
text = to_ascii(text)
if truncate_len is not None:
text = " ".join(text.split()[:truncate_len])
texts.append(text)
filenames.append(filename)
mb.write("done.")
if return_fnames:
return (texts, filenames)
else:
return texts</code></pre>
</details>
</dd>
<dt id="ktrain.text.print_sequence_taggers"><code class="name flex">
<span>def <span class="ident">print_sequence_taggers</span></span>(<span>)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def print_sequence_taggers():
for k, v in SEQUENCE_TAGGERS.items():
print("%s: %s" % (k, v))</code></pre>
</details>
</dd>
<dt id="ktrain.text.print_text_classifiers"><code class="name flex">
<span>def <span class="ident">print_text_classifiers</span></span>(<span>)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def print_text_classifiers():
for k, v in TEXT_CLASSIFIERS.items():
print("%s: %s" % (k, v))</code></pre>
</details>
</dd>
<dt id="ktrain.text.print_text_regression_models"><code class="name flex">
<span>def <span class="ident">print_text_regression_models</span></span>(<span>)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def print_text_regression_models():
for k, v in TEXT_REGRESSION_MODELS.items():
print("%s: %s" % (k, v))</code></pre>
</details>
</dd>
<dt id="ktrain.text.sequence_tagger"><code class="name flex">
<span>def <span class="ident">sequence_tagger</span></span>(<span>name, preproc, wv_path_or_url=None, transformer_model='bert-base-multilingual-cased', transformer_layers_to_use=[-2], bert_model=None, word_embedding_dim=100, char_embedding_dim=25, word_lstm_size=100, char_lstm_size=25, fc_dim=100, dropout=0.5, verbose=1)</span>
</code></dt>
<dd>
<div class="desc"><p>Build and return a sequence tagger (i.e., named entity recognizer).</p>
<h2 id="args">Args</h2>
<dl>
<dt><strong><code>name</code></strong> : <code>string</code></dt>
<dd>one of:
- 'bilstm-crf' for Bidirectional LSTM-CRF model
- 'bilstm' for Bidirectional LSTM (no CRF layer)</dd>
</dl>
<p>preproc(NERPreprocessor):
an instance of NERPreprocessor
wv_path_or_url(str): either a URL or file path toa fasttext word vector file (.vec or .vec.zip or .vec.gz)
Example valid values for wv_path_or_url:</p>
<pre><code> Randomly-initialized word embeeddings:
set wv_path_or_url=None
English pretrained word vectors:
<https://dl.fbaipublicfiles.com/fasttext/vectors-english/crawl-300d-2M.vec.zip>
Chinese pretrained word vectors:
<https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.zh.300.vec.gz>
Russian pretrained word vectors:
<https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.ru.300.vec.gz>
Dutch pretrained word vectors:
<https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.nl.300.vec.gz>
See these two Web pages for a full list of URLs to word vector files for
different languages:
1. <https://fasttext.cc/docs/en/english-vectors.html> (for English)
2. <https://fasttext.cc/docs/en/crawl-vectors.html> (for non-English langages)
Default:None (randomly-initialized word embeddings are used)
</code></pre>
<p>transformer_model_name(str):
the name of the transformer model.
default: 'bert-base-multilingual-cased'
This parameter is only used if bilstm-transformer is selected for name parameter.
The value of this parameter is a name of transformer model from here:
<a href="https://huggingface.co/transformers/pretrained_models.html">https://huggingface.co/transformers/pretrained_models.html</a>
or a community-uploaded BERT model from here:
<a href="https://huggingface.co/models">https://huggingface.co/models</a>
Example values:
bert-base-multilingual-cased:
Multilingual BERT (157 languages) - this is the default
bert-base-cased:
English BERT
bert-base-chinese: Chinese BERT
distilbert-base-german-cased: German DistilBert
albert-base-v2: English ALBERT model
monologg/biobert_v1.1_pubmed: community uploaded BioBERT (pretrained on PubMed)</p>
<dl>
<dt>transformer_layers_to_use(list): indices of hidden layers to use.
default:[-2] # second-to-last layer</dt>
<dt>To use the concatenation of last 4 layers: use [-1, -2, -3, -4]</dt>
<dt>bert_model(str): alias for transformer_model</dt>
<dt><strong><code>word_embedding_dim</code></strong> : <code>int</code></dt>
<dd>word embedding dimensions.</dd>
<dt><strong><code>char_embedding_dim</code></strong> : <code>int</code></dt>
<dd>character embedding dimensions.</dd>
<dt><strong><code>word_lstm_size</code></strong> : <code>int</code></dt>
<dd>character LSTM feature extractor output dimensions.</dd>
<dt><strong><code>char_lstm_size</code></strong> : <code>int</code></dt>
<dd>word tagger LSTM output dimensions.</dd>
<dt><strong><code>fc_dim</code></strong> : <code>int</code></dt>
<dd>output fully-connected layer size.</dd>
<dt><strong><code>dropout</code></strong> : <code>float</code></dt>
<dd>dropout rate.</dd>
<dt><strong><code>verbose</code></strong> : <code>boolean</code></dt>
<dd>verbosity of output</dd>
</dl>
<h2 id="return">Return</h2>
<p>model (Model): A Keras Model instance</p></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def sequence_tagger(
name,
preproc,
wv_path_or_url=None,
transformer_model="bert-base-multilingual-cased",
transformer_layers_to_use=U.DEFAULT_TRANSFORMER_LAYERS,
bert_model=None,
word_embedding_dim=100,
char_embedding_dim=25,
word_lstm_size=100,
char_lstm_size=25,
fc_dim=100,
dropout=0.5,
verbose=1,
):
"""
Build and return a sequence tagger (i.e., named entity recognizer).
Args:
name (string): one of:
- 'bilstm-crf' for Bidirectional LSTM-CRF model
- 'bilstm' for Bidirectional LSTM (no CRF layer)
preproc(NERPreprocessor): an instance of NERPreprocessor
wv_path_or_url(str): either a URL or file path toa fasttext word vector file (.vec or .vec.zip or .vec.gz)
Example valid values for wv_path_or_url:
Randomly-initialized word embeeddings:
set wv_path_or_url=None
English pretrained word vectors:
https://dl.fbaipublicfiles.com/fasttext/vectors-english/crawl-300d-2M.vec.zip
Chinese pretrained word vectors:
https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.zh.300.vec.gz
Russian pretrained word vectors:
https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.ru.300.vec.gz
Dutch pretrained word vectors:
https://dl.fbaipublicfiles.com/fasttext/vectors-crawl/cc.nl.300.vec.gz
See these two Web pages for a full list of URLs to word vector files for
different languages:
1. https://fasttext.cc/docs/en/english-vectors.html (for English)
2. https://fasttext.cc/docs/en/crawl-vectors.html (for non-English langages)
Default:None (randomly-initialized word embeddings are used)
transformer_model_name(str): the name of the transformer model. default: 'bert-base-multilingual-cased'
This parameter is only used if bilstm-transformer is selected for name parameter.
The value of this parameter is a name of transformer model from here:
https://huggingface.co/transformers/pretrained_models.html
or a community-uploaded BERT model from here:
https://huggingface.co/models
Example values:
bert-base-multilingual-cased: Multilingual BERT (157 languages) - this is the default
bert-base-cased: English BERT
bert-base-chinese: Chinese BERT
distilbert-base-german-cased: German DistilBert
albert-base-v2: English ALBERT model
monologg/biobert_v1.1_pubmed: community uploaded BioBERT (pretrained on PubMed)
transformer_layers_to_use(list): indices of hidden layers to use. default:[-2] # second-to-last layer
To use the concatenation of last 4 layers: use [-1, -2, -3, -4]
bert_model(str): alias for transformer_model
word_embedding_dim (int): word embedding dimensions.
char_embedding_dim (int): character embedding dimensions.
word_lstm_size (int): character LSTM feature extractor output dimensions.
char_lstm_size (int): word tagger LSTM output dimensions.
fc_dim (int): output fully-connected layer size.
dropout (float): dropout rate.
verbose (boolean): verbosity of output
Return:
model (Model): A Keras Model instance
"""
# backwards compatibility
name = BILSTM_TRANSFORMER if name == "bilstm-bert" else name
if bert_model is not None:
transformer_model = bert_model
warnings.warn(
"The bert_model argument is deprecated - please use transformer_model instead.",
DeprecationWarning,
stacklevel=2,
)
if name not in SEQUENCE_TAGGERS:
raise ValueError(
f"Invalid model name {name}. {'Did you mean bilstm-transformer?' if name == 'bilstm-bert' else ''}"
)
# check BERT
if name in TRANSFORMER_MODELS and not transformer_model:
raise ValueError(
f"transformer_model is required for {BILSTM_TRANSFORMER} models"
)
if name in TRANSFORMER_MODELS and DISABLE_V2_BEHAVIOR:
raise ValueError(
"BERT and other transformer models cannot be used with DISABLE_v2_BEHAVIOR"
)
# check CRF
if not DISABLE_V2_BEHAVIOR and name in V1_ONLY_MODELS:
warnings.warn(
"Falling back to BiLSTM (no CRF) because DISABLE_V2_BEHAVIOR=False"
)
msg = (
"\nIMPORTANT NOTE: ktrain uses the CRF module from keras_contrib, which is not yet\n"
+ "fully compatible with TensorFlow 2. You can still use the BiLSTM-CRF model\n"
+ "in ktrain for sequence tagging with TensorFlow 2, but you must add the\n"
+ "following to the top of your script or notebook BEFORE you import ktrain:\n\n"
+ "import os\n"
+ "os.environ['DISABLE_V2_BEHAVIOR'] = '1'\n\n"
+ "For this run, a vanilla BiLSTM model (with no CRF layer) will be used.\n"
)
print(msg)
name = BILSTM if name == BILSTM_CRF else BILSTM_ELMO
# check for use_char=True
if not DISABLE_V2_BEHAVIOR and preproc.p._use_char:
# turn off masking due to open TF2 issue ##33148: https://github.com/tensorflow/tensorflow/issues/33148
warnings.warn(
"Setting use_char=False: character embeddings cannot be used in TF2 due to open TensorFlow 2 bug (#33148).\n"
+ 'Add os.environ["DISABLE_V2_BEHAVIOR"] = "1" to the top of script if you really want to use it.'
)
preproc.p._use_char = False
if verbose:
emb_names = []
if wv_path_or_url is not None:
emb_names.append(
"word embeddings initialized with fasttext word vectors (%s)"
% (os.path.basename(wv_path_or_url))
)
else:
emb_names.append("word embeddings initialized randomly")
if name in TRANSFORMER_MODELS:
emb_names.append("transformer embeddings with " + transformer_model)
if name in ELMO_MODELS:
emb_names.append("Elmo embeddings for English")
if preproc.p._use_char:
emb_names.append("character embeddings")
if len(emb_names) > 1:
print("Embedding schemes employed (combined with concatenation):")
else:
print("embedding schemes employed:")
for emb_name in emb_names:
print("\t%s" % (emb_name))
print()
# setup embedding
if wv_path_or_url is not None:
wv_model, word_embedding_dim = preproc.get_wv_model(
wv_path_or_url, verbose=verbose
)
else:
wv_model = None
if name == BILSTM_CRF:
use_crf = False if not DISABLE_V2_BEHAVIOR else True # fallback to bilstm
elif name == BILSTM_CRF_ELMO:
use_crf = False if not DISABLE_V2_BEHAVIOR else True # fallback to bilstm
preproc.p.activate_elmo()
elif name == BILSTM:
use_crf = False
elif name == BILSTM_ELMO:
use_crf = False
preproc.p.activate_elmo()
elif name == BILSTM_TRANSFORMER:
use_crf = False
preproc.p.activate_transformer(
transformer_model, layers=transformer_layers_to_use, force=True
)
else:
raise ValueError("Unsupported model name")
from .anago.models import BiLSTMCRF
model = BiLSTMCRF(
char_embedding_dim=char_embedding_dim,
word_embedding_dim=word_embedding_dim,
char_lstm_size=char_lstm_size,
word_lstm_size=word_lstm_size,
fc_dim=fc_dim,
char_vocab_size=preproc.p.char_vocab_size,
word_vocab_size=preproc.p.word_vocab_size,
num_labels=preproc.p.label_size,
dropout=dropout,
use_crf=use_crf,
use_char=preproc.p._use_char,
embeddings=wv_model,
use_elmo=preproc.p.elmo_is_activated(),
use_transformer_with_dim=preproc.p.get_transformer_dim(),
)
model, loss = model.build()
model.compile(loss=loss, optimizer=U.DEFAULT_OPT)
return model</code></pre>
</details>
</dd>
<dt id="ktrain.text.text_classifier"><code class="name flex">
<span>def <span class="ident">text_classifier</span></span>(<span>name, train_data, preproc=None, multilabel=None, metrics=['accuracy'], verbose=1)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Build and return a text classification model.
Args:
name (string): one of:
- 'fasttext' for FastText model
- 'nbsvm' for NBSVM model
- 'logreg' for logistic regression using embedding layers
- 'bigru' for Bidirectional GRU with pretrained word vectors
- 'bert' for BERT Text Classification
- 'distilbert' for Hugging Face DistilBert model
train_data (tuple): a tuple of numpy.ndarrays: (x_train, y_train) or ktrain.Dataset instance
returned from one of the texts_from_* functions
preproc: a ktrain.text.TextPreprocessor instance.
As of v0.8.0, this is required.
multilabel (bool): If True, multilabel model will be returned.
If false, binary/multiclass model will be returned.
If None, multilabel will be inferred from data.
metrics(list): metrics to use
verbose (boolean): verbosity of output
Return:
model (Model): A Keras Model instance
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>