INSTRUCTION stringlengths 1 46.3k | RESPONSE stringlengths 75 80.2k |
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wrapper for client for dataset_status, with additional
dataset_opt to get the status of a dataset from the API
Parameters
==========
dataset_opt: an alternative to dataset | def dataset_status_cli(self, dataset, dataset_opt=None):
""" wrapper for client for dataset_status, with additional
dataset_opt to get the status of a dataset from the API
Parameters
==========
dataset_opt: an alternative to dataset
"""
dataset = ... |
download a single file for a dataset
Parameters
==========
dataset: the string identified of the dataset
should be in format [owner]/[dataset-name]
file_name: the dataset configuration file
path: if defined, download to this location
... | def dataset_download_file(self,
dataset,
file_name,
path=None,
force=False,
quiet=True):
""" download a single file for a dataset
Parameters
... |
download all files for a dataset
Parameters
==========
dataset: the string identified of the dataset
should be in format [owner]/[dataset-name]
path: the path to download the dataset to
force: force the download if the file already exists... | def dataset_download_files(self,
dataset,
path=None,
force=False,
quiet=True,
unzip=False):
""" download all files for a dataset
Parameters
... |
client wrapper for dataset_download_files and download dataset file,
either for a specific file (when file_name is provided),
or all files for a dataset (plural)
Parameters
==========
dataset: the string identified of the dataset
should b... | def dataset_download_cli(self,
dataset,
dataset_opt=None,
file_name=None,
path=None,
unzip=False,
force=False,
quiet=... |
create a version of a dataset
Parameters
==========
folder: the folder with the dataset configuration / data files
version_notes: notes to add for the version
quiet: suppress verbose output (default is False)
convert_to_csv: on upload, if data sho... | def dataset_create_version(self,
folder,
version_notes,
quiet=False,
convert_to_csv=True,
delete_old_versions=False,
dir_mode='skip'):... |
upload a dataset file
Parameters
==========
path: the complete path to upload
quiet: suppress verbose output (default is False) | def dataset_upload_file(self, path, quiet):
""" upload a dataset file
Parameters
==========
path: the complete path to upload
quiet: suppress verbose output (default is False)
"""
file_name = os.path.basename(path)
content_length = os.path... |
client wrapper for creating a version of a dataset
Parameters
==========
folder: the folder with the dataset configuration / data files
version_notes: notes to add for the version
quiet: suppress verbose output (default is False)
convert_to_csv: o... | def dataset_create_version_cli(self,
folder,
version_notes,
quiet=False,
convert_to_csv=True,
delete_old_versions=False,
... |
initialize a folder with a a dataset configuration (metadata) file
Parameters
==========
folder: the folder to initialize the metadata file in | def dataset_initialize(self, folder):
""" initialize a folder with a a dataset configuration (metadata) file
Parameters
==========
folder: the folder to initialize the metadata file in
"""
if not os.path.isdir(folder):
raise ValueError('Invalid fo... |
create a new dataset, meaning the same as creating a version but
with extra metadata like license and user/owner.
Parameters
==========
folder: the folder to initialize the metadata file in
public: should the dataset be public?
quiet: suppress ver... | def dataset_create_new(self,
folder,
public=False,
quiet=False,
convert_to_csv=True,
dir_mode='skip'):
""" create a new dataset, meaning the same as creating a version but
... |
client wrapper for creating a new dataset
Parameters
==========
folder: the folder to initialize the metadata file in
public: should the dataset be public?
quiet: suppress verbose output (default is False)
convert_to_csv: if True, convert data to ... | def dataset_create_new_cli(self,
folder=None,
public=False,
quiet=False,
convert_to_csv=True,
dir_mode='skip'):
""" client wrapper for creating a new dataset... |
download a file to an output file based on a chunk size
Parameters
==========
response: the response to download
outfile: the output file to download to
quiet: suppress verbose output (default is True)
chunk_size: the size of the chunk to stream | def download_file(self, response, outfile, quiet=True, chunk_size=1048576):
""" download a file to an output file based on a chunk size
Parameters
==========
response: the response to download
outfile: the output file to download to
quiet: suppress ve... |
list kernels based on a set of search criteria
Parameters
==========
page: the page of results to return (default is 1)
page_size: results per page (default is 20)
dataset: if defined, filter to this dataset (default None)
competition: if defined,... | def kernels_list(self,
page=1,
page_size=20,
dataset=None,
competition=None,
parent_kernel=None,
search=None,
mine=False,
user=None,
... |
client wrapper for kernels_list, see this function for arguments.
Additional arguments are provided here.
Parameters
==========
csv_display: if True, print comma separated values instead of table | def kernels_list_cli(self,
mine=False,
page=1,
page_size=20,
search=None,
csv_display=False,
parent=None,
competition=None,
... |
create a new kernel in a specified folder from template, including
json metadata that grabs values from the configuration.
Parameters
==========
folder: the path of the folder | def kernels_initialize(self, folder):
""" create a new kernel in a specified folder from template, including
json metadata that grabs values from the configuration.
Parameters
==========
folder: the path of the folder
"""
if not os.path.isdir(fold... |
client wrapper for kernels_initialize, takes same arguments but
sets default folder to be None. If None, defaults to present
working directory.
Parameters
==========
folder: the path of the folder (None defaults to ${PWD}) | def kernels_initialize_cli(self, folder=None):
""" client wrapper for kernels_initialize, takes same arguments but
sets default folder to be None. If None, defaults to present
working directory.
Parameters
==========
folder: the path of the folder (No... |
read the metadata file and kernel files from a notebook, validate
both, and use Kernel API to push to Kaggle if all is valid.
Parameters
==========
folder: the path of the folder | def kernels_push(self, folder):
""" read the metadata file and kernel files from a notebook, validate
both, and use Kernel API to push to Kaggle if all is valid.
Parameters
==========
folder: the path of the folder
"""
if not os.path.isdir(folder)... |
client wrapper for kernels_push, with same arguments. | def kernels_push_cli(self, folder):
""" client wrapper for kernels_push, with same arguments.
"""
folder = folder or os.getcwd()
result = self.kernels_push(folder)
if result is None:
print('Kernel push error: see previous output')
elif not result.error:
... |
pull a kernel, including a metadata file (if metadata is True)
and associated files to a specified path.
Parameters
==========
kernel: the kernel to pull
path: the path to pull files to on the filesystem
metadata: if True, also pull metadata
... | def kernels_pull(self, kernel, path, metadata=False, quiet=True):
""" pull a kernel, including a metadata file (if metadata is True)
and associated files to a specified path.
Parameters
==========
kernel: the kernel to pull
path: the path to pull file... |
client wrapper for kernels_pull | def kernels_pull_cli(self,
kernel,
kernel_opt=None,
path=None,
metadata=False):
""" client wrapper for kernels_pull
"""
kernel = kernel or kernel_opt
effective_path = self.kernels_pull(
... |
client wrapper for kernels_output, with same arguments. Extra
arguments are described below, and see kernels_output for others.
Parameters
==========
kernel_opt: option from client instead of kernel, if not defined | def kernels_output_cli(self,
kernel,
kernel_opt=None,
path=None,
force=False,
quiet=False):
""" client wrapper for kernels_output, with same arguments. Extra
argumen... |
call to the api to get the status of a kernel.
Parameters
==========
kernel: the kernel to get the status for | def kernels_status(self, kernel):
""" call to the api to get the status of a kernel.
Parameters
==========
kernel: the kernel to get the status for
"""
if kernel is None:
raise ValueError('A kernel must be specified')
if '/' in kernel:
... |
determine if a download is needed based on timestamp. Return True
if needed (remote is newer) or False if local is newest.
Parameters
==========
response: the response from the API
outfile: the output file to write to
quiet: suppress verbose outpu... | def download_needed(self, response, outfile, quiet=True):
""" determine if a download is needed based on timestamp. Return True
if needed (remote is newer) or False if local is newest.
Parameters
==========
response: the response from the API
outfile:... |
client wrapper for kernel_status
Parameters
==========
kernel_opt: additional option from the client, if kernel not defined | def kernels_status_cli(self, kernel, kernel_opt=None):
""" client wrapper for kernel_status
Parameters
==========
kernel_opt: additional option from the client, if kernel not defined
"""
kernel = kernel or kernel_opt
response = self.kernels_status(ker... |
print a table of items, for a set of fields defined
Parameters
==========
items: a list of items to print
fields: a list of fields to select from items | def print_table(self, items, fields):
""" print a table of items, for a set of fields defined
Parameters
==========
items: a list of items to print
fields: a list of fields to select from items
"""
formats = []
borders = []
for f i... |
print a set of fields in a set of items using a csv.writer
Parameters
==========
items: a list of items to print
fields: a list of fields to select from items | def print_csv(self, items, fields):
""" print a set of fields in a set of items using a csv.writer
Parameters
==========
items: a list of items to print
fields: a list of fields to select from items
"""
writer = csv.writer(sys.stdout)
writ... |
process a response from the API. We check the API version against
the client's to see if it's old, and give them a warning (once)
Parameters
==========
result: the result from the API | def process_response(self, result):
""" process a response from the API. We check the API version against
the client's to see if it's old, and give them a warning (once)
Parameters
==========
result: the result from the API
"""
if len(result) == 3... |
determine if a client (on the local user's machine) is up to date
with the version provided on the server. Return a boolean with True
or False
Parameters
==========
server_version: the server version string to compare to the host | def is_up_to_date(self, server_version):
""" determine if a client (on the local user's machine) is up to date
with the version provided on the server. Return a boolean with True
or False
Parameters
==========
server_version: the server version string... |
upload files in a folder
Parameters
==========
request: the prepared request
resources: the files to upload
folder: the folder to upload from
quiet: suppress verbose output (default is False) | def upload_files(self,
request,
resources,
folder,
quiet=False,
dir_mode='skip'):
""" upload files in a folder
Parameters
==========
request: the prepared request
... |
Helper function to upload a single file
Parameters
==========
file_name: name of the file to upload
full_path: path to the file to upload
request: the prepared request
resources: optional file metadata
quiet: suppress verbose output
... | def _upload_file(self, file_name, full_path, quiet, request, resources):
""" Helper function to upload a single file
Parameters
==========
file_name: name of the file to upload
full_path: path to the file to upload
request: the prepared request
... |
process a column, check for the type, and return the processed
column
Parameters
==========
column: a list of values in a column to be processed | def process_column(self, column):
""" process a column, check for the type, and return the processed
column
Parameters
==========
column: a list of values in a column to be processed
"""
processed_column = DatasetColumn(
name=self.get_... |
function to complete an upload to retrieve a path from a url
Parameters
==========
path: the path for the upload that is read in
url: the url to send the POST to
quiet: suppress verbose output (default is False) | def upload_complete(self, path, url, quiet):
""" function to complete an upload to retrieve a path from a url
Parameters
==========
path: the path for the upload that is read in
url: the url to send the POST to
quiet: suppress verbose output (default ... |
determine if a dataset string is valid, meaning it is in the format
of {username}/{dataset-slug}.
Parameters
==========
dataset: the dataset name to validate | def validate_dataset_string(self, dataset):
""" determine if a dataset string is valid, meaning it is in the format
of {username}/{dataset-slug}.
Parameters
==========
dataset: the dataset name to validate
"""
if dataset:
if '/' not in... |
determine if a kernel string is valid, meaning it is in the format
of {username}/{kernel-slug}.
Parameters
==========
kernel: the kernel name to validate | def validate_kernel_string(self, kernel):
""" determine if a kernel string is valid, meaning it is in the format
of {username}/{kernel-slug}.
Parameters
==========
kernel: the kernel name to validate
"""
if kernel:
if '/' not in kernel... |
validate resources is a wrapper to validate the existence of files
and that there are no duplicates for a folder and set of resources.
Parameters
==========
folder: the folder to validate
resources: one or more resources to validate within the folder | def validate_resources(self, folder, resources):
""" validate resources is a wrapper to validate the existence of files
and that there are no duplicates for a folder and set of resources.
Parameters
==========
folder: the folder to validate
resources:... |
ensure that one or more resource files exist in a folder
Parameters
==========
folder: the folder to validate
resources: one or more resources to validate within the folder | def validate_files_exist(self, folder, resources):
""" ensure that one or more resource files exist in a folder
Parameters
==========
folder: the folder to validate
resources: one or more resources to validate within the folder
"""
for item in res... |
ensure that the user has not provided duplicate paths in
a list of resources.
Parameters
==========
resources: one or more resources to validate not duplicated | def validate_no_duplicate_paths(self, resources):
""" ensure that the user has not provided duplicate paths in
a list of resources.
Parameters
==========
resources: one or more resources to validate not duplicated
"""
paths = set()
for ite... |
convert a set of file_data to a metadata file at path
Parameters
==========
file_data: a dictionary of file data to write to file
path: the path to write the metadata to | def convert_to_dataset_file_metadata(self, file_data, path):
""" convert a set of file_data to a metadata file at path
Parameters
==========
file_data: a dictionary of file data to write to file
path: the path to write the metadata to
"""
as_metad... |
read the buffer, passing named and non named arguments to the
io.BufferedReader function. | def read(self, *args, **kwargs):
""" read the buffer, passing named and non named arguments to the
io.BufferedReader function.
"""
buf = io.BufferedReader.read(self, *args, **kwargs)
self.increment(len(buf))
return buf |
Get parameters as list of tuples, formatting collections.
:param params: Parameters as dict or list of two-tuples
:param dict collection_formats: Parameter collection formats
:return: Parameters as list of tuples, collections formatted | def parameters_to_tuples(self, params, collection_formats):
"""Get parameters as list of tuples, formatting collections.
:param params: Parameters as dict or list of two-tuples
:param dict collection_formats: Parameter collection formats
:return: Parameters as list of tuples, collection... |
Builds form parameters.
:param post_params: Normal form parameters.
:param files: File parameters.
:return: Form parameters with files. | def prepare_post_parameters(self, post_params=None, files=None):
"""Builds form parameters.
:param post_params: Normal form parameters.
:param files: File parameters.
:return: Form parameters with files.
"""
params = []
if post_params:
params = post_... |
Deserializes body to file
Saves response body into a file in a temporary folder,
using the filename from the `Content-Disposition` header if provided.
:param response: RESTResponse.
:return: file path. | def __deserialize_file(self, response):
"""Deserializes body to file
Saves response body into a file in a temporary folder,
using the filename from the `Content-Disposition` header if provided.
:param response: RESTResponse.
:return: file path.
"""
fd, path = t... |
Deserializes string to primitive type.
:param data: str.
:param klass: class literal.
:return: int, long, float, str, bool. | def __deserialize_primitive(self, data, klass):
"""Deserializes string to primitive type.
:param data: str.
:param klass: class literal.
:return: int, long, float, str, bool.
"""
try:
return klass(data)
except UnicodeEncodeError:
return s... |
The logger file.
If the logger_file is None, then add stream handler and remove file
handler. Otherwise, add file handler and remove stream handler.
:param value: The logger_file path.
:type: str | def logger_file(self, value):
"""The logger file.
If the logger_file is None, then add stream handler and remove file
handler. Otherwise, add file handler and remove stream handler.
:param value: The logger_file path.
:type: str
"""
self.__logger_file = value
... |
Sets the license_name of this DatasetNewRequest.
The license that should be associated with the dataset # noqa: E501
:param license_name: The license_name of this DatasetNewRequest. # noqa: E501
:type: str | def license_name(self, license_name):
"""Sets the license_name of this DatasetNewRequest.
The license that should be associated with the dataset # noqa: E501
:param license_name: The license_name of this DatasetNewRequest. # noqa: E501
:type: str
"""
allowed_values = ... |
Train textCNN model for sentiment analysis. | def train(net, train_data, test_data):
"""Train textCNN model for sentiment analysis."""
start_pipeline_time = time.time()
net, trainer = text_cnn.init(net, vocab, args.model_mode, context, args.lr)
random.shuffle(train_data)
sp = int(len(train_data)*0.9)
train_dataloader = DataLoader(dataset=tr... |
Get tokens, tokens embedding
Parameters
----------
sentences : List[str]
sentences for encoding.
oov_way : str, default avg.
use **avg**, **sum** or **last** to get token embedding for those out of
vocabulary words
Returns
-------
... | def embedding(self, sentences, oov_way='avg'):
"""
Get tokens, tokens embedding
Parameters
----------
sentences : List[str]
sentences for encoding.
oov_way : str, default avg.
use **avg**, **sum** or **last** to get token embedding for those out o... |
Load, tokenize and prepare the input sentences. | def data_loader(self, sentences, shuffle=False):
"""Load, tokenize and prepare the input sentences."""
dataset = BertEmbeddingDataset(sentences, self.transform)
return DataLoader(dataset=dataset, batch_size=self.batch_size, shuffle=shuffle) |
How to handle oov. Also filter out [CLS], [SEP] tokens.
Parameters
----------
batches : List[(tokens_id,
sequence_outputs,
pooled_output].
batch token_ids (max_seq_length, ),
sequence_outputs (max_seq_length, dim,... | def oov(self, batches, oov_way='avg'):
"""
How to handle oov. Also filter out [CLS], [SEP] tokens.
Parameters
----------
batches : List[(tokens_id,
sequence_outputs,
pooled_output].
batch token_ids (max_seq_length, ),... |
Any BERT pretrained model.
Parameters
----------
model_name : str or None, default None
Options include 'bert_24_1024_16' and 'bert_12_768_12'.
dataset_name : str or None, default None
Options include 'book_corpus_wiki_en_cased', 'book_corpus_wiki_en_uncased'
for both bert_24_10... | def get_bert_model(model_name=None, dataset_name=None, vocab=None,
pretrained=True, ctx=mx.cpu(),
use_pooler=True, use_decoder=True, use_classifier=True,
output_attention=False, output_all_encodings=False,
root=os.path.join(get_home_dir(), 'mod... |
forward computation. | def hybrid_forward(self, F, data, gamma, beta):
"""forward computation."""
# TODO(haibin): LayerNorm does not support fp16 safe reduction. Issue is tracked at:
# https://github.com/apache/incubator-mxnet/issues/14073
if self._dtype:
data = data.astype('float32')
g... |
Construct a decoder for the next sentence prediction task | def _get_classifier(self, prefix):
""" Construct a decoder for the next sentence prediction task """
with self.name_scope():
classifier = nn.Dense(2, prefix=prefix)
return classifier |
Construct a decoder for the masked language model task | def _get_decoder(self, units, vocab_size, embed, prefix):
""" Construct a decoder for the masked language model task """
with self.name_scope():
decoder = nn.HybridSequential(prefix=prefix)
decoder.add(nn.Dense(units, flatten=False))
decoder.add(GELU())
de... |
Construct an embedding block. | def _get_embed(self, embed, vocab_size, embed_size, initializer, dropout, prefix):
""" Construct an embedding block. """
if embed is None:
assert embed_size is not None, '"embed_size" cannot be None if "word_embed" or ' \
'token_type_embed is not gi... |
Construct pooler.
The pooler slices and projects the hidden output of first token
in the sequence for segment level classification. | def _get_pooler(self, units, prefix):
""" Construct pooler.
The pooler slices and projects the hidden output of first token
in the sequence for segment level classification.
"""
with self.name_scope():
pooler = nn.Dense(units=units, flatten=False, activation='tanh',... |
Generate the representation given the input sequences.
This is used for pre-training or fine-tuning a BERT model. | def _encode_sequence(self, inputs, token_types, valid_length=None):
"""Generate the representation given the input sequences.
This is used for pre-training or fine-tuning a BERT model.
"""
# embedding
word_embedding = self.word_embed(inputs)
type_embedding = self.token_t... |
Generate unnormalized prediction for the masked language model task.
This is only used for pre-training the BERT model.
Inputs:
- **sequence**: input tensor of sequence encodings.
Shape (batch_size, seq_length, units).
- **masked_positions**: input tensor of posit... | def _decode(self, sequence, masked_positions):
"""Generate unnormalized prediction for the masked language model task.
This is only used for pre-training the BERT model.
Inputs:
- **sequence**: input tensor of sequence encodings.
Shape (batch_size, seq_length, units).... |
Extracts n-grams from an input segment.
Parameters
----------
segment: list
Text segment from which n-grams will be extracted.
n: int
Order of n-gram.
Returns
-------
ngram_counts: Counter
Contain all the nth n-grams in segment with a count of how many times each n-... | def _ngrams(segment, n):
"""Extracts n-grams from an input segment.
Parameters
----------
segment: list
Text segment from which n-grams will be extracted.
n: int
Order of n-gram.
Returns
-------
ngram_counts: Counter
Contain all the nth n-grams in segment with a... |
Convert a sequence of bpe words into sentence. | def _bpe_to_words(sentence, delimiter='@@'):
"""Convert a sequence of bpe words into sentence."""
words = []
word = ''
delimiter_len = len(delimiter)
for subwords in sentence:
if len(subwords) >= delimiter_len and subwords[-delimiter_len:] == delimiter:
word += subwords[:-delimit... |
r"""
Tokenizes a string following the tokenizer in mteval-v13a.pl.
See https://github.com/moses-smt/mosesdecoder/"
"blob/master/scripts/generic/mteval-v14.pl#L917-L942
Parameters
----------
segment: str
A string to be tokenized
Returns
-------
The tokenized string | def _tokenize_mteval_13a(segment):
r"""
Tokenizes a string following the tokenizer in mteval-v13a.pl.
See https://github.com/moses-smt/mosesdecoder/"
"blob/master/scripts/generic/mteval-v14.pl#L917-L942
Parameters
----------
segment: str
A string to be tokenized
Returns
... |
r"""Tokenize a string following following the international tokenizer in mteval-v14a.pl.
See https://github.com/moses-smt/mosesdecoder/"
"blob/master/scripts/generic/mteval-v14.pl#L954-L983
Parameters
----------
segment: str
A string to be tokenized
Returns
-------
The t... | def _tokenize_mteval_v14_intl(segment):
r"""Tokenize a string following following the international tokenizer in mteval-v14a.pl.
See https://github.com/moses-smt/mosesdecoder/"
"blob/master/scripts/generic/mteval-v14.pl#L954-L983
Parameters
----------
segment: str
A string to be ... |
r"""Compute bleu score of translation against references.
Parameters
----------
reference_corpus_list: list of list(list(str)) or list of list(str)
list of list(list(str)): tokenized references
list of list(str): plain text
List of references for each translation.
translation_co... | def compute_bleu(reference_corpus_list, translation_corpus, tokenized=True,
tokenizer='13a', max_n=4, smooth=False, lower_case=False,
bpe=False, split_compound_word=False):
r"""Compute bleu score of translation against references.
Parameters
----------
reference_corpus... |
Compute ngram precision.
Parameters
----------
references: list(list(str))
A list of references.
translation: list(str)
A translation.
n: int
Order of n-gram.
Returns
-------
matches: int
Number of matched nth order n-grams
candidates
Number ... | def _compute_precision(references, translation, n):
"""Compute ngram precision.
Parameters
----------
references: list(list(str))
A list of references.
translation: list(str)
A translation.
n: int
Order of n-gram.
Returns
-------
matches: int
Number ... |
Calculate brevity penalty.
Parameters
----------
ref_length: int
Sum of all closest references'lengths for every translations in a corpus
trans_length: int
Sum of all translations's lengths in a corpus.
Returns
-------
bleu's brevity penalty: float | def _brevity_penalty(ref_length, trans_length):
"""Calculate brevity penalty.
Parameters
----------
ref_length: int
Sum of all closest references'lengths for every translations in a corpus
trans_length: int
Sum of all translations's lengths in a corpus.
Returns
-------
... |
Find the reference that has the closest length to the translation.
Parameters
----------
references: list(list(str))
A list of references.
trans_length: int
Length of the translation.
Returns
-------
closest_ref_len: int
Length of the reference that is closest to th... | def _closest_ref_length(references, trans_length):
"""Find the reference that has the closest length to the translation.
Parameters
----------
references: list(list(str))
A list of references.
trans_length: int
Length of the translation.
Returns
-------
closest_ref_len:... |
Compute the smoothed precision for all the orders.
Parameters
----------
precision_fractions: list(tuple)
Contain a list of (precision_numerator, precision_denominator) pairs
c: int, default 1
Smoothing constant to use
Returns
-------
ratios: list of floats
Contain ... | def _smoothing(precision_fractions, c=1):
"""Compute the smoothed precision for all the orders.
Parameters
----------
precision_fractions: list(tuple)
Contain a list of (precision_numerator, precision_denominator) pairs
c: int, default 1
Smoothing constant to use
Returns
--... |
Draw samples from log uniform distribution and returns sampled candidates,
expected count for true classes and sampled classes.
Parameters
----------
true_classes: NDArray
The true classes.
Returns
-------
samples: NDArray
The sampled can... | def forward(self, true_classes):
"""Draw samples from log uniform distribution and returns sampled candidates,
expected count for true classes and sampled classes.
Parameters
----------
true_classes: NDArray
The true classes.
Returns
-------
... |
Dataset preprocessing helper.
Parameters
----------
data : mx.data.Dataset
Input Dataset. For example gluonnlp.data.Text8 or gluonnlp.data.Fil9
min_freq : int, default 5
Minimum token frequency for a token to be included in the vocabulary
and returned DataStream.
max_vocab_s... | def preprocess_dataset(data, min_freq=5, max_vocab_size=None):
"""Dataset preprocessing helper.
Parameters
----------
data : mx.data.Dataset
Input Dataset. For example gluonnlp.data.Text8 or gluonnlp.data.Fil9
min_freq : int, default 5
Minimum token frequency for a token to be inclu... |
Wikipedia dump helper.
Parameters
----------
wiki_root : str
Parameter for WikiDumpStream
wiki_date : str
Parameter for WikiDumpStream
wiki_language : str
Parameter for WikiDumpStream
max_vocab_size : int, optional
Specifies a maximum size for the vocabulary.
... | def wiki(wiki_root, wiki_date, wiki_language, max_vocab_size=None):
"""Wikipedia dump helper.
Parameters
----------
wiki_root : str
Parameter for WikiDumpStream
wiki_date : str
Parameter for WikiDumpStream
wiki_language : str
Parameter for WikiDumpStream
max_vocab_si... |
Transform a DataStream of coded DataSets to a DataStream of batches.
Parameters
----------
data : gluonnlp.data.DataStream
DataStream where each sample is a valid input to
gluonnlp.data.EmbeddingCenterContextBatchify.
vocab : gluonnlp.Vocab
Vocabulary containing all tokens whose... | def transform_data_fasttext(data, vocab, idx_to_counts, cbow, ngram_buckets,
ngrams, batch_size, window_size,
frequent_token_subsampling=1E-4, dtype='float32',
index_dtype='int64'):
"""Transform a DataStream of coded DataSets to a D... |
Transform a DataStream of coded DataSets to a DataStream of batches.
Parameters
----------
data : gluonnlp.data.DataStream
DataStream where each sample is a valid input to
gluonnlp.data.EmbeddingCenterContextBatchify.
vocab : gluonnlp.Vocab
Vocabulary containing all tokens whose... | def transform_data_word2vec(data, vocab, idx_to_counts, cbow, batch_size,
window_size, frequent_token_subsampling=1E-4,
dtype='float32', index_dtype='int64'):
"""Transform a DataStream of coded DataSets to a DataStream of batches.
Parameters
---------... |
Create a batch for CBOW training objective with subwords. | def cbow_fasttext_batch(centers, contexts, num_tokens, subword_lookup, dtype,
index_dtype):
"""Create a batch for CBOW training objective with subwords."""
_, contexts_row, contexts_col = contexts
data, row, col = subword_lookup(contexts_row, contexts_col)
centers = mx.nd.array(c... |
Create a batch for SG training objective with subwords. | def skipgram_fasttext_batch(centers, contexts, num_tokens, subword_lookup,
dtype, index_dtype):
"""Create a batch for SG training objective with subwords."""
contexts = mx.nd.array(contexts[2], dtype=index_dtype)
data, row, col = subword_lookup(centers)
centers = mx.nd.array(... |
Create a batch for CBOW training objective. | def cbow_batch(centers, contexts, num_tokens, dtype, index_dtype):
"""Create a batch for CBOW training objective."""
contexts_data, contexts_row, contexts_col = contexts
centers = mx.nd.array(centers, dtype=index_dtype)
contexts = mx.nd.sparse.csr_matrix(
(contexts_data, (contexts_row, contexts_... |
Create a batch for SG training objective. | def skipgram_batch(centers, contexts, num_tokens, dtype, index_dtype):
"""Create a batch for SG training objective."""
contexts = mx.nd.array(contexts[2], dtype=index_dtype)
indptr = mx.nd.arange(len(centers) + 1)
centers = mx.nd.array(centers, dtype=index_dtype)
centers_csr = mx.nd.sparse.csr_matri... |
Get a sparse COO array of words and subwords for SkipGram.
Parameters
----------
indices : numpy.ndarray
Array containing numbers in [0, vocabulary_size). The element at
position idx is taken to be the word that occurs at row idx in the
SkipGram batch.
offset : int
Offse... | def skipgram_lookup(indices, subwordidxs, subwordidxsptr, offset=0):
"""Get a sparse COO array of words and subwords for SkipGram.
Parameters
----------
indices : numpy.ndarray
Array containing numbers in [0, vocabulary_size). The element at
position idx is taken to be the word that occ... |
Get a sparse COO array of words and subwords for CBOW.
Parameters
----------
context_row : numpy.ndarray of dtype int64
Array of same length as context_col containing numbers in [0,
batch_size). For each idx, context_row[idx] specifies the row that
context_col[idx] occurs in a spars... | def cbow_lookup(context_row, context_col, subwordidxs, subwordidxsptr,
offset=0):
"""Get a sparse COO array of words and subwords for CBOW.
Parameters
----------
context_row : numpy.ndarray of dtype int64
Array of same length as context_col containing numbers in [0,
batc... |
Source Vocabulary of the Dataset.
Returns
-------
src_vocab : Vocab
Source vocabulary. | def src_vocab(self):
"""Source Vocabulary of the Dataset.
Returns
-------
src_vocab : Vocab
Source vocabulary.
"""
if self._src_vocab is None:
src_vocab_file_name, src_vocab_hash = \
self._data_file[self._pair_key]['vocab' + '_' + ... |
Target Vocabulary of the Dataset.
Returns
-------
tgt_vocab : Vocab
Target vocabulary. | def tgt_vocab(self):
"""Target Vocabulary of the Dataset.
Returns
-------
tgt_vocab : Vocab
Target vocabulary.
"""
if self._tgt_vocab is None:
tgt_vocab_file_name, tgt_vocab_hash = \
self._data_file[self._pair_key]['vocab' + '_' + ... |
Evaluate given the data loader
Parameters
----------
data_loader : DataLoader
Returns
-------
avg_loss : float
Average loss
real_translation_out : list of list of str
The translation output | def evaluate(data_loader):
"""Evaluate given the data loader
Parameters
----------
data_loader : DataLoader
Returns
-------
avg_loss : float
Average loss
real_translation_out : list of list of str
The translation output
"""
translation_out = []
all_inst_ids ... |
Training function. | def train():
"""Training function."""
trainer = gluon.Trainer(model.collect_params(), args.optimizer, {'learning_rate': args.lr})
train_data_loader, val_data_loader, test_data_loader \
= dataprocessor.make_dataloader(data_train, data_val, data_test, args)
best_valid_bleu = 0.0
for epoch_id... |
r"""Returns a cache model using a pre-trained language model.
We implement the neural cache language model proposed in the following work::
@article{grave2016improving,
title={Improving neural language models with a continuous cache},
author={Grave, Edouard and Joulin, Armand and Usunier, ... | def get_cache_model(name, dataset_name='wikitext-2', window=2000,
theta=0.6, lambdas=0.2, ctx=mx.cpu(), **kwargs):
r"""Returns a cache model using a pre-trained language model.
We implement the neural cache language model proposed in the following work::
@article{grave2016improving... |
Training helper. | def train(args):
"""Training helper."""
if not args.model.lower() in ['cbow', 'skipgram']:
logging.error('Unsupported model %s.', args.model)
sys.exit(1)
if args.data.lower() == 'toy':
data = mx.gluon.data.SimpleDataset(nlp.data.Text8(segment='train')[:2])
data, vocab, idx_t... |
Evaluation helper | def evaluate(args, embedding, vocab, global_step, eval_analogy=False):
"""Evaluation helper"""
if 'eval_tokens' not in globals():
global eval_tokens
eval_tokens_set = evaluation.get_tokens_in_evaluation_datasets(args)
if not args.no_eval_analogy:
eval_tokens_set.update(vocab... |
Return the dataset corresponds to the provided key.
Example::
a = np.ones((2,2))
b = np.zeros((2,2))
np.savez('data.npz', a=a, b=b)
dataset = NumpyDataset('data.npz')
data_a = dataset.get_field('a')
data_b = dataset.get_field('b')
... | def get_field(self, field):
"""Return the dataset corresponds to the provided key.
Example::
a = np.ones((2,2))
b = np.zeros((2,2))
np.savez('data.npz', a=a, b=b)
dataset = NumpyDataset('data.npz')
data_a = dataset.get_field('a')
d... |
Project the tokenized prediction back to the original text. | def get_final_text(pred_text, orig_text, tokenizer):
"""Project the tokenized prediction back to the original text."""
# When we created the data, we kept track of the alignment between original
# (whitespace tokenized) tokens and our WordPiece tokenized tokens. So
# now `orig_text` contains the span o... |
Get prediction results
Parameters
----------
dev_dataset: dataset
Examples of transform.
all_results: dict
A dictionary containing model prediction results.
tokenizer: callable
Tokenizer function.
max_answer_length: int, default 64
Maximum length of the answer to... | def predictions(dev_dataset,
all_results,
tokenizer,
max_answer_length=64,
null_score_diff_threshold=0.0,
n_best_size=10,
version_2=False):
"""Get prediction results
Parameters
----------
dev_dataset: datase... |
Calculate the F1 and EM scores of the predicted results.
Use only with the SQuAD1.1 dataset.
Parameters
----------
dataset_file: string
Path to the data file.
predict_data: dict
All final predictions.
Returns
-------
scores: dict
F1 and EM scores. | def get_F1_EM(dataset, predict_data):
"""Calculate the F1 and EM scores of the predicted results.
Use only with the SQuAD1.1 dataset.
Parameters
----------
dataset_file: string
Path to the data file.
predict_data: dict
All final predictions.
Returns
-------
scores: ... |
Data preparation function. | def preprocess_data(tokenizer, task, batch_size, dev_batch_size, max_len, pad=False):
"""Data preparation function."""
# transformation
trans = BERTDatasetTransform(
tokenizer,
max_len,
labels=task.get_labels(),
pad=pad,
pair=task.is_pair,
label_dtype='float32... |
Evaluate the model on validation dataset. | def evaluate(dataloader_eval, metric):
"""Evaluate the model on validation dataset.
"""
metric.reset()
for _, seqs in enumerate(dataloader_eval):
input_ids, valid_len, type_ids, label = seqs
out = model(
input_ids.as_in_context(ctx), type_ids.as_in_context(ctx),
v... |
Generate and print out the log message for training. | def log_train(batch_id, batch_num, metric, step_loss, log_interval, epoch_id, learning_rate):
"""Generate and print out the log message for training.
"""
metric_nm, metric_val = metric.get()
if not isinstance(metric_nm, list):
metric_nm = [metric_nm]
metric_val = [metric_val]
train_... |
Generate and print out the log message for inference. | def log_inference(batch_id, batch_num, metric, step_loss, log_interval):
"""Generate and print out the log message for inference.
"""
metric_nm, metric_val = metric.get()
if not isinstance(metric_nm, list):
metric_nm = [metric_nm]
metric_val = [metric_val]
eval_str = '[Batch %d/%d] ... |
Training function. | def train(metric):
"""Training function."""
logging.info('Now we are doing BERT classification training on %s!', ctx)
optimizer_params = {'learning_rate': lr, 'epsilon': epsilon, 'wd': 0.01}
try:
trainer = gluon.Trainer(
model.collect_params(),
args.optimizer,
... |
Inference function. | def inference(metric):
"""Inference function."""
logging.info('Now we are doing BERT classification inference on %s!', ctx)
model = BERTClassifier(bert, dropout=0.1, num_classes=len(task.get_labels()))
model.hybridize(static_alloc=True)
model.load_parameters(model_parameters, ctx=ctx)
metric.r... |
Process SQuAD dataset by creating NDArray version of data
:param Dataset dataset: SQuAD dataset
:param int question_max_length: Maximum length of question (padded or trimmed to that size)
:param int context_max_length: Maximum length of context (padded or trimmed to that size)
Returns
-------
... | def preprocess_dataset(dataset, question_max_length, context_max_length):
"""Process SQuAD dataset by creating NDArray version of data
:param Dataset dataset: SQuAD dataset
:param int question_max_length: Maximum length of question (padded or trimmed to that size)
:param int context_max_length: Maximum... |
Find all answer spans from the context, returning start_index and end_index
:param list[str] answer_list: List of all answers
:param list[int] answer_start_list: List of all answers' start indices
Returns
-------
List[Tuple]
list of Tuple(answer_start_index answer_e... | def _get_answer_spans(answer_list, answer_start_list):
"""Find all answer spans from the context, returning start_index and end_index
:param list[str] answer_list: List of all answers
:param list[int] answer_start_list: List of all answers' start indices
Returns
-------
... |
Provides word level vocabulary
Returns
-------
Vocab
Word level vocabulary | def get_word_level_vocab(self):
"""Provides word level vocabulary
Returns
-------
Vocab
Word level vocabulary
"""
def simple_tokenize(source_str, token_delim=' ', seq_delim='\n'):
return list(filter(None, re.split(token_delim + '|' + seq_delim, s... |
Parameters
----------
states : list
the stack outputs from RNN, which consists of output from each time step (TNC).
Returns
--------
loss : NDArray
loss tensor with shape (batch_size,). Dimensions other than batch_axis are averaged out. | def hybrid_forward(self, F, *states): # pylint: disable=arguments-differ
"""
Parameters
----------
states : list
the stack outputs from RNN, which consists of output from each time step (TNC).
Returns
--------
loss : NDArray
loss tensor wi... |
Tokenizes a piece of text. | def _tokenize(self, text):
"""Tokenizes a piece of text."""
text = self._clean_text(text)
# This was added on November 1st, 2018 for the multilingual and Chinese
# models. This is also applied to the English models now, but it doesn't
# matter since the English models were not t... |
Performs invalid character removal and whitespace cleanup on text. | def _clean_text(self, text):
"""Performs invalid character removal and whitespace cleanup on text."""
output = []
for char in text:
cp = ord(char)
if cp in (0, 0xfffd) or self._is_control(char):
continue
if self._is_whitespace(char):
... |
Checks whether `chars` is a control character. | def _is_control(self, char):
"""Checks whether `chars` is a control character."""
# These are technically control characters but we count them as whitespace
# characters.
if char in ['\t', '\n', '\r']:
return False
cat = unicodedata.category(char)
if cat.start... |
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