partition stringclasses 3
values | func_name stringlengths 1 134 | docstring stringlengths 1 46.9k | path stringlengths 4 223 | original_string stringlengths 75 104k | code stringlengths 75 104k | docstring_tokens listlengths 1 1.97k | repo stringlengths 7 55 | language stringclasses 1
value | url stringlengths 87 315 | code_tokens listlengths 19 28.4k | sha stringlengths 40 40 |
|---|---|---|---|---|---|---|---|---|---|---|---|
train | TokenEmbedding._load_embedding_serialized | Load embedding vectors from a pre-trained token embedding file.
For every unknown token, if its representation `self.unknown_token` is encountered in the
pre-trained token embedding file, index 0 of `self.idx_to_vec` maps to the pre-trained token
embedding vector loaded from the file; otherwise... | src/gluonnlp/embedding/token_embedding.py | def _load_embedding_serialized(self, pretrained_file_path):
"""Load embedding vectors from a pre-trained token embedding file.
For every unknown token, if its representation `self.unknown_token` is encountered in the
pre-trained token embedding file, index 0 of `self.idx_to_vec` maps to the pre... | def _load_embedding_serialized(self, pretrained_file_path):
"""Load embedding vectors from a pre-trained token embedding file.
For every unknown token, if its representation `self.unknown_token` is encountered in the
pre-trained token embedding file, index 0 of `self.idx_to_vec` maps to the pre... | [
"Load",
"embedding",
"vectors",
"from",
"a",
"pre",
"-",
"trained",
"token",
"embedding",
"file",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/embedding/token_embedding.py#L323-L380 | [
"def",
"_load_embedding_serialized",
"(",
"self",
",",
"pretrained_file_path",
")",
":",
"deserialized_embedding",
"=",
"TokenEmbedding",
".",
"deserialize",
"(",
"pretrained_file_path",
")",
"if",
"deserialized_embedding",
".",
"unknown_token",
":",
"# Some .npz files on S... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | TokenEmbedding._check_vector_update | Check that tokens and embedding are in the format for __setitem__. | src/gluonnlp/embedding/token_embedding.py | def _check_vector_update(self, tokens, new_embedding):
"""Check that tokens and embedding are in the format for __setitem__."""
assert self._idx_to_vec is not None, '`idx_to_vec` has not been initialized.'
if not isinstance(tokens, (list, tuple)) or len(tokens) == 1:
assert isinstan... | def _check_vector_update(self, tokens, new_embedding):
"""Check that tokens and embedding are in the format for __setitem__."""
assert self._idx_to_vec is not None, '`idx_to_vec` has not been initialized.'
if not isinstance(tokens, (list, tuple)) or len(tokens) == 1:
assert isinstan... | [
"Check",
"that",
"tokens",
"and",
"embedding",
"are",
"in",
"the",
"format",
"for",
"__setitem__",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/embedding/token_embedding.py#L553-L576 | [
"def",
"_check_vector_update",
"(",
"self",
",",
"tokens",
",",
"new_embedding",
")",
":",
"assert",
"self",
".",
"_idx_to_vec",
"is",
"not",
"None",
",",
"'`idx_to_vec` has not been initialized.'",
"if",
"not",
"isinstance",
"(",
"tokens",
",",
"(",
"list",
","... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | TokenEmbedding._check_source | Checks if a pre-trained token embedding source name is valid.
Parameters
----------
source : str
The pre-trained token embedding source. | src/gluonnlp/embedding/token_embedding.py | def _check_source(cls, source_file_hash, source):
"""Checks if a pre-trained token embedding source name is valid.
Parameters
----------
source : str
The pre-trained token embedding source.
"""
embedding_name = cls.__name__.lower()
if source not in s... | def _check_source(cls, source_file_hash, source):
"""Checks if a pre-trained token embedding source name is valid.
Parameters
----------
source : str
The pre-trained token embedding source.
"""
embedding_name = cls.__name__.lower()
if source not in s... | [
"Checks",
"if",
"a",
"pre",
"-",
"trained",
"token",
"embedding",
"source",
"name",
"is",
"valid",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/embedding/token_embedding.py#L643-L657 | [
"def",
"_check_source",
"(",
"cls",
",",
"source_file_hash",
",",
"source",
")",
":",
"embedding_name",
"=",
"cls",
".",
"__name__",
".",
"lower",
"(",
")",
"if",
"source",
"not",
"in",
"source_file_hash",
":",
"raise",
"KeyError",
"(",
"'Cannot find pre-train... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | TokenEmbedding.from_file | Creates a user-defined token embedding from a pre-trained embedding file.
This is to load embedding vectors from a user-defined pre-trained token embedding file.
For example, if `elem_delim` = ' ', the expected format of a custom pre-trained token
embedding file may look like:
'hello ... | src/gluonnlp/embedding/token_embedding.py | def from_file(file_path, elem_delim=' ', encoding='utf8', **kwargs):
"""Creates a user-defined token embedding from a pre-trained embedding file.
This is to load embedding vectors from a user-defined pre-trained token embedding file.
For example, if `elem_delim` = ' ', the expected format of a... | def from_file(file_path, elem_delim=' ', encoding='utf8', **kwargs):
"""Creates a user-defined token embedding from a pre-trained embedding file.
This is to load embedding vectors from a user-defined pre-trained token embedding file.
For example, if `elem_delim` = ' ', the expected format of a... | [
"Creates",
"a",
"user",
"-",
"defined",
"token",
"embedding",
"from",
"a",
"pre",
"-",
"trained",
"embedding",
"file",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/embedding/token_embedding.py#L660-L694 | [
"def",
"from_file",
"(",
"file_path",
",",
"elem_delim",
"=",
"' '",
",",
"encoding",
"=",
"'utf8'",
",",
"*",
"*",
"kwargs",
")",
":",
"embedding",
"=",
"TokenEmbedding",
"(",
"*",
"*",
"kwargs",
")",
"embedding",
".",
"_load_embedding",
"(",
"file_path",... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | TokenEmbedding.serialize | Serializes the TokenEmbedding to a file specified by file_path.
TokenEmbedding is serialized by converting the list of tokens, the
array of word embeddings and other metadata to numpy arrays, saving all
in a single (optionally compressed) Zipfile. See
https://docs.scipy.org/doc/numpy-1.... | src/gluonnlp/embedding/token_embedding.py | def serialize(self, file_path, compress=True):
"""Serializes the TokenEmbedding to a file specified by file_path.
TokenEmbedding is serialized by converting the list of tokens, the
array of word embeddings and other metadata to numpy arrays, saving all
in a single (optionally compressed... | def serialize(self, file_path, compress=True):
"""Serializes the TokenEmbedding to a file specified by file_path.
TokenEmbedding is serialized by converting the list of tokens, the
array of word embeddings and other metadata to numpy arrays, saving all
in a single (optionally compressed... | [
"Serializes",
"the",
"TokenEmbedding",
"to",
"a",
"file",
"specified",
"by",
"file_path",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/embedding/token_embedding.py#L696-L737 | [
"def",
"serialize",
"(",
"self",
",",
"file_path",
",",
"compress",
"=",
"True",
")",
":",
"if",
"self",
".",
"unknown_lookup",
"is",
"not",
"None",
":",
"warnings",
".",
"warn",
"(",
"'Serialization of `unknown_lookup` is not supported. '",
"'Save it manually and p... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | TokenEmbedding.deserialize | Create a new TokenEmbedding from a serialized one.
TokenEmbedding is serialized by converting the list of tokens, the
array of word embeddings and other metadata to numpy arrays, saving all
in a single (optionally compressed) Zipfile. See
https://docs.scipy.org/doc/numpy-1.14.2/neps/npy... | src/gluonnlp/embedding/token_embedding.py | def deserialize(cls, file_path, **kwargs):
"""Create a new TokenEmbedding from a serialized one.
TokenEmbedding is serialized by converting the list of tokens, the
array of word embeddings and other metadata to numpy arrays, saving all
in a single (optionally compressed) Zipfile. See
... | def deserialize(cls, file_path, **kwargs):
"""Create a new TokenEmbedding from a serialized one.
TokenEmbedding is serialized by converting the list of tokens, the
array of word embeddings and other metadata to numpy arrays, saving all
in a single (optionally compressed) Zipfile. See
... | [
"Create",
"a",
"new",
"TokenEmbedding",
"from",
"a",
"serialized",
"one",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/embedding/token_embedding.py#L740-L784 | [
"def",
"deserialize",
"(",
"cls",
",",
"file_path",
",",
"*",
"*",
"kwargs",
")",
":",
"# idx_to_token is of dtype 'O' so we need to allow pickle",
"npz_dict",
"=",
"np",
".",
"load",
"(",
"file_path",
",",
"allow_pickle",
"=",
"True",
")",
"unknown_token",
"=",
... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | evaluate | Evaluate the model on a mini-batch. | scripts/bert/staticbert/static_export_squad.py | def evaluate(data_source):
"""Evaluate the model on a mini-batch.
"""
log.info('Start predict')
tic = time.time()
for batch in data_source:
inputs, token_types, valid_length = batch
out = net(inputs.astype('float32').as_in_context(ctx),
token_types.astype('float32')... | def evaluate(data_source):
"""Evaluate the model on a mini-batch.
"""
log.info('Start predict')
tic = time.time()
for batch in data_source:
inputs, token_types, valid_length = batch
out = net(inputs.astype('float32').as_in_context(ctx),
token_types.astype('float32')... | [
"Evaluate",
"the",
"model",
"on",
"a",
"mini",
"-",
"batch",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/bert/staticbert/static_export_squad.py#L210-L223 | [
"def",
"evaluate",
"(",
"data_source",
")",
":",
"log",
".",
"info",
"(",
"'Start predict'",
")",
"tic",
"=",
"time",
".",
"time",
"(",
")",
"for",
"batch",
"in",
"data_source",
":",
"inputs",
",",
"token_types",
",",
"valid_length",
"=",
"batch",
"out",... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | register | Registers a dataset with segment specific hyperparameters.
When passing keyword arguments to `register`, they are checked to be valid
keyword arguments for the registered Dataset class constructor and are
saved in the registry. Registered keyword arguments can be retrieved with
the `list_datasets` func... | src/gluonnlp/data/registry.py | def register(class_=None, **kwargs):
"""Registers a dataset with segment specific hyperparameters.
When passing keyword arguments to `register`, they are checked to be valid
keyword arguments for the registered Dataset class constructor and are
saved in the registry. Registered keyword arguments can be... | def register(class_=None, **kwargs):
"""Registers a dataset with segment specific hyperparameters.
When passing keyword arguments to `register`, they are checked to be valid
keyword arguments for the registered Dataset class constructor and are
saved in the registry. Registered keyword arguments can be... | [
"Registers",
"a",
"dataset",
"with",
"segment",
"specific",
"hyperparameters",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/data/registry.py#L34-L97 | [
"def",
"register",
"(",
"class_",
"=",
"None",
",",
"*",
"*",
"kwargs",
")",
":",
"def",
"_real_register",
"(",
"class_",
")",
":",
"# Assert that the passed kwargs are meaningful",
"for",
"kwarg_name",
",",
"values",
"in",
"kwargs",
".",
"items",
"(",
")",
... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | create | Creates an instance of a registered dataset.
Parameters
----------
name : str
The dataset name (case-insensitive).
Returns
-------
An instance of :class:`mxnet.gluon.data.Dataset` constructed with the
keyword arguments passed to the create function. | src/gluonnlp/data/registry.py | def create(name, **kwargs):
"""Creates an instance of a registered dataset.
Parameters
----------
name : str
The dataset name (case-insensitive).
Returns
-------
An instance of :class:`mxnet.gluon.data.Dataset` constructed with the
keyword arguments passed to the create functio... | def create(name, **kwargs):
"""Creates an instance of a registered dataset.
Parameters
----------
name : str
The dataset name (case-insensitive).
Returns
-------
An instance of :class:`mxnet.gluon.data.Dataset` constructed with the
keyword arguments passed to the create functio... | [
"Creates",
"an",
"instance",
"of",
"a",
"registered",
"dataset",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/data/registry.py#L100-L115 | [
"def",
"create",
"(",
"name",
",",
"*",
"*",
"kwargs",
")",
":",
"create_",
"=",
"registry",
".",
"get_create_func",
"(",
"Dataset",
",",
"'dataset'",
")",
"return",
"create_",
"(",
"name",
",",
"*",
"*",
"kwargs",
")"
] | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | list_datasets | Get valid datasets and registered parameters.
Parameters
----------
name : str or None, default None
Return names and registered parameters of registered datasets. If name
is specified, only registered parameters of the respective dataset are
returned.
Returns
-------
d... | src/gluonnlp/data/registry.py | def list_datasets(name=None):
"""Get valid datasets and registered parameters.
Parameters
----------
name : str or None, default None
Return names and registered parameters of registered datasets. If name
is specified, only registered parameters of the respective dataset are
ret... | def list_datasets(name=None):
"""Get valid datasets and registered parameters.
Parameters
----------
name : str or None, default None
Return names and registered parameters of registered datasets. If name
is specified, only registered parameters of the respective dataset are
ret... | [
"Get",
"valid",
"datasets",
"and",
"registered",
"parameters",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/data/registry.py#L118-L146 | [
"def",
"list_datasets",
"(",
"name",
"=",
"None",
")",
":",
"reg",
"=",
"registry",
".",
"get_registry",
"(",
"Dataset",
")",
"if",
"name",
"is",
"not",
"None",
":",
"class_",
"=",
"reg",
"[",
"name",
".",
"lower",
"(",
")",
"]",
"return",
"_REGSITRY... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | parse_args | Parse command line arguments. | scripts/word_embeddings/extract_vocab.py | def parse_args():
"""Parse command line arguments."""
parser = argparse.ArgumentParser(
description='Vocabulary extractor.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--max-size', type=int, default=None)
parser.add_argument('--min-freq', type=int, defau... | def parse_args():
"""Parse command line arguments."""
parser = argparse.ArgumentParser(
description='Vocabulary extractor.',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--max-size', type=int, default=None)
parser.add_argument('--min-freq', type=int, defau... | [
"Parse",
"command",
"line",
"arguments",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/word_embeddings/extract_vocab.py#L32-L44 | [
"def",
"parse_args",
"(",
")",
":",
"parser",
"=",
"argparse",
".",
"ArgumentParser",
"(",
"description",
"=",
"'Vocabulary extractor.'",
",",
"formatter_class",
"=",
"argparse",
".",
"ArgumentDefaultsHelpFormatter",
")",
"parser",
".",
"add_argument",
"(",
"'--max-... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | get_vocab | Compute the vocabulary. | scripts/word_embeddings/extract_vocab.py | def get_vocab(args):
"""Compute the vocabulary."""
counter = nlp.data.Counter()
start = time.time()
for filename in args.files:
print('Starting processing of {} after {:.1f} seconds.'.format(
filename,
time.time() - start))
with open(filename, 'r') as f:
... | def get_vocab(args):
"""Compute the vocabulary."""
counter = nlp.data.Counter()
start = time.time()
for filename in args.files:
print('Starting processing of {} after {:.1f} seconds.'.format(
filename,
time.time() - start))
with open(filename, 'r') as f:
... | [
"Compute",
"the",
"vocabulary",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/word_embeddings/extract_vocab.py#L47-L87 | [
"def",
"get_vocab",
"(",
"args",
")",
":",
"counter",
"=",
"nlp",
".",
"data",
".",
"Counter",
"(",
")",
"start",
"=",
"time",
".",
"time",
"(",
")",
"for",
"filename",
"in",
"args",
".",
"files",
":",
"print",
"(",
"'Starting processing of {} after {:.1... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | BERTClassifier.forward | Generate the unnormalized score for the given the input sequences.
Parameters
----------
inputs : NDArray, shape (batch_size, seq_length)
Input words for the sequences.
token_types : NDArray, shape (batch_size, seq_length)
Token types for the sequences, used to i... | scripts/bert/bert.py | def forward(self, inputs, token_types, valid_length=None): # pylint: disable=arguments-differ
"""Generate the unnormalized score for the given the input sequences.
Parameters
----------
inputs : NDArray, shape (batch_size, seq_length)
Input words for the sequences.
... | def forward(self, inputs, token_types, valid_length=None): # pylint: disable=arguments-differ
"""Generate the unnormalized score for the given the input sequences.
Parameters
----------
inputs : NDArray, shape (batch_size, seq_length)
Input words for the sequences.
... | [
"Generate",
"the",
"unnormalized",
"score",
"for",
"the",
"given",
"the",
"input",
"sequences",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/bert/bert.py#L111-L130 | [
"def",
"forward",
"(",
"self",
",",
"inputs",
",",
"token_types",
",",
"valid_length",
"=",
"None",
")",
":",
"# pylint: disable=arguments-differ",
"_",
",",
"pooler_out",
"=",
"self",
".",
"bert",
"(",
"inputs",
",",
"token_types",
",",
"valid_length",
")",
... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | add_parameters | Add evaluation specific parameters to parser. | scripts/word_embeddings/evaluation.py | def add_parameters(parser):
"""Add evaluation specific parameters to parser."""
group = parser.add_argument_group('Evaluation arguments')
group.add_argument('--eval-batch-size', type=int, default=1024)
# Datasets
group.add_argument(
'--similarity-datasets', type=str,
default=nlp.da... | def add_parameters(parser):
"""Add evaluation specific parameters to parser."""
group = parser.add_argument_group('Evaluation arguments')
group.add_argument('--eval-batch-size', type=int, default=1024)
# Datasets
group.add_argument(
'--similarity-datasets', type=str,
default=nlp.da... | [
"Add",
"evaluation",
"specific",
"parameters",
"to",
"parser",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/word_embeddings/evaluation.py#L38-L70 | [
"def",
"add_parameters",
"(",
"parser",
")",
":",
"group",
"=",
"parser",
".",
"add_argument_group",
"(",
"'Evaluation arguments'",
")",
"group",
".",
"add_argument",
"(",
"'--eval-batch-size'",
",",
"type",
"=",
"int",
",",
"default",
"=",
"1024",
")",
"# Dat... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | validate_args | Validate provided arguments and act on --help. | scripts/word_embeddings/evaluation.py | def validate_args(args):
"""Validate provided arguments and act on --help."""
# Check correctness of similarity dataset names
for dataset_name in args.similarity_datasets:
if dataset_name.lower() not in map(
str.lower,
nlp.data.word_embedding_evaluation.word_similarit... | def validate_args(args):
"""Validate provided arguments and act on --help."""
# Check correctness of similarity dataset names
for dataset_name in args.similarity_datasets:
if dataset_name.lower() not in map(
str.lower,
nlp.data.word_embedding_evaluation.word_similarit... | [
"Validate",
"provided",
"arguments",
"and",
"act",
"on",
"--",
"help",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/word_embeddings/evaluation.py#L73-L89 | [
"def",
"validate_args",
"(",
"args",
")",
":",
"# Check correctness of similarity dataset names",
"for",
"dataset_name",
"in",
"args",
".",
"similarity_datasets",
":",
"if",
"dataset_name",
".",
"lower",
"(",
")",
"not",
"in",
"map",
"(",
"str",
".",
"lower",
",... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | iterate_similarity_datasets | Generator over all similarity evaluation datasets.
Iterates over dataset names, keyword arguments for their creation and the
created dataset. | scripts/word_embeddings/evaluation.py | def iterate_similarity_datasets(args):
"""Generator over all similarity evaluation datasets.
Iterates over dataset names, keyword arguments for their creation and the
created dataset.
"""
for dataset_name in args.similarity_datasets:
parameters = nlp.data.list_datasets(dataset_name)
... | def iterate_similarity_datasets(args):
"""Generator over all similarity evaluation datasets.
Iterates over dataset names, keyword arguments for their creation and the
created dataset.
"""
for dataset_name in args.similarity_datasets:
parameters = nlp.data.list_datasets(dataset_name)
... | [
"Generator",
"over",
"all",
"similarity",
"evaluation",
"datasets",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/word_embeddings/evaluation.py#L92-L103 | [
"def",
"iterate_similarity_datasets",
"(",
"args",
")",
":",
"for",
"dataset_name",
"in",
"args",
".",
"similarity_datasets",
":",
"parameters",
"=",
"nlp",
".",
"data",
".",
"list_datasets",
"(",
"dataset_name",
")",
"for",
"key_values",
"in",
"itertools",
".",... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | iterate_analogy_datasets | Generator over all analogy evaluation datasets.
Iterates over dataset names, keyword arguments for their creation and the
created dataset. | scripts/word_embeddings/evaluation.py | def iterate_analogy_datasets(args):
"""Generator over all analogy evaluation datasets.
Iterates over dataset names, keyword arguments for their creation and the
created dataset.
"""
for dataset_name in args.analogy_datasets:
parameters = nlp.data.list_datasets(dataset_name)
for key... | def iterate_analogy_datasets(args):
"""Generator over all analogy evaluation datasets.
Iterates over dataset names, keyword arguments for their creation and the
created dataset.
"""
for dataset_name in args.analogy_datasets:
parameters = nlp.data.list_datasets(dataset_name)
for key... | [
"Generator",
"over",
"all",
"analogy",
"evaluation",
"datasets",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/word_embeddings/evaluation.py#L106-L117 | [
"def",
"iterate_analogy_datasets",
"(",
"args",
")",
":",
"for",
"dataset_name",
"in",
"args",
".",
"analogy_datasets",
":",
"parameters",
"=",
"nlp",
".",
"data",
".",
"list_datasets",
"(",
"dataset_name",
")",
"for",
"key_values",
"in",
"itertools",
".",
"pr... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | get_similarity_task_tokens | Returns a set of all tokens occurring the evaluation datasets. | scripts/word_embeddings/evaluation.py | def get_similarity_task_tokens(args):
"""Returns a set of all tokens occurring the evaluation datasets."""
tokens = set()
for _, _, dataset in iterate_similarity_datasets(args):
tokens.update(
itertools.chain.from_iterable((d[0], d[1]) for d in dataset))
return tokens | def get_similarity_task_tokens(args):
"""Returns a set of all tokens occurring the evaluation datasets."""
tokens = set()
for _, _, dataset in iterate_similarity_datasets(args):
tokens.update(
itertools.chain.from_iterable((d[0], d[1]) for d in dataset))
return tokens | [
"Returns",
"a",
"set",
"of",
"all",
"tokens",
"occurring",
"the",
"evaluation",
"datasets",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/word_embeddings/evaluation.py#L120-L126 | [
"def",
"get_similarity_task_tokens",
"(",
"args",
")",
":",
"tokens",
"=",
"set",
"(",
")",
"for",
"_",
",",
"_",
",",
"dataset",
"in",
"iterate_similarity_datasets",
"(",
"args",
")",
":",
"tokens",
".",
"update",
"(",
"itertools",
".",
"chain",
".",
"f... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | get_analogy_task_tokens | Returns a set of all tokens occuring the evaluation datasets. | scripts/word_embeddings/evaluation.py | def get_analogy_task_tokens(args):
"""Returns a set of all tokens occuring the evaluation datasets."""
tokens = set()
for _, _, dataset in iterate_analogy_datasets(args):
tokens.update(
itertools.chain.from_iterable(
(d[0], d[1], d[2], d[3]) for d in dataset))
return ... | def get_analogy_task_tokens(args):
"""Returns a set of all tokens occuring the evaluation datasets."""
tokens = set()
for _, _, dataset in iterate_analogy_datasets(args):
tokens.update(
itertools.chain.from_iterable(
(d[0], d[1], d[2], d[3]) for d in dataset))
return ... | [
"Returns",
"a",
"set",
"of",
"all",
"tokens",
"occuring",
"the",
"evaluation",
"datasets",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/word_embeddings/evaluation.py#L129-L136 | [
"def",
"get_analogy_task_tokens",
"(",
"args",
")",
":",
"tokens",
"=",
"set",
"(",
")",
"for",
"_",
",",
"_",
",",
"dataset",
"in",
"iterate_analogy_datasets",
"(",
"args",
")",
":",
"tokens",
".",
"update",
"(",
"itertools",
".",
"chain",
".",
"from_it... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | evaluate_similarity | Evaluate on specified similarity datasets. | scripts/word_embeddings/evaluation.py | def evaluate_similarity(args, token_embedding, ctx, logfile=None,
global_step=0):
"""Evaluate on specified similarity datasets."""
results = []
for similarity_function in args.similarity_functions:
evaluator = nlp.embedding.evaluation.WordEmbeddingSimilarity(
idx... | def evaluate_similarity(args, token_embedding, ctx, logfile=None,
global_step=0):
"""Evaluate on specified similarity datasets."""
results = []
for similarity_function in args.similarity_functions:
evaluator = nlp.embedding.evaluation.WordEmbeddingSimilarity(
idx... | [
"Evaluate",
"on",
"specified",
"similarity",
"datasets",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/word_embeddings/evaluation.py#L145-L197 | [
"def",
"evaluate_similarity",
"(",
"args",
",",
"token_embedding",
",",
"ctx",
",",
"logfile",
"=",
"None",
",",
"global_step",
"=",
"0",
")",
":",
"results",
"=",
"[",
"]",
"for",
"similarity_function",
"in",
"args",
".",
"similarity_functions",
":",
"evalu... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | evaluate_analogy | Evaluate on specified analogy datasets.
The analogy task is an open vocabulary task, make sure to pass a
token_embedding with a sufficiently large number of supported tokens. | scripts/word_embeddings/evaluation.py | def evaluate_analogy(args, token_embedding, ctx, logfile=None, global_step=0):
"""Evaluate on specified analogy datasets.
The analogy task is an open vocabulary task, make sure to pass a
token_embedding with a sufficiently large number of supported tokens.
"""
results = []
exclude_question_wor... | def evaluate_analogy(args, token_embedding, ctx, logfile=None, global_step=0):
"""Evaluate on specified analogy datasets.
The analogy task is an open vocabulary task, make sure to pass a
token_embedding with a sufficiently large number of supported tokens.
"""
results = []
exclude_question_wor... | [
"Evaluate",
"on",
"specified",
"analogy",
"datasets",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/word_embeddings/evaluation.py#L200-L259 | [
"def",
"evaluate_analogy",
"(",
"args",
",",
"token_embedding",
",",
"ctx",
",",
"logfile",
"=",
"None",
",",
"global_step",
"=",
"0",
")",
":",
"results",
"=",
"[",
"]",
"exclude_question_words",
"=",
"not",
"args",
".",
"analogy_dont_exclude_question_words",
... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | log_similarity_result | Log a similarity evaluation result dictionary as TSV to logfile. | scripts/word_embeddings/evaluation.py | def log_similarity_result(logfile, result):
"""Log a similarity evaluation result dictionary as TSV to logfile."""
assert result['task'] == 'similarity'
if not logfile:
return
with open(logfile, 'a') as f:
f.write('\t'.join([
str(result['global_step']),
result['... | def log_similarity_result(logfile, result):
"""Log a similarity evaluation result dictionary as TSV to logfile."""
assert result['task'] == 'similarity'
if not logfile:
return
with open(logfile, 'a') as f:
f.write('\t'.join([
str(result['global_step']),
result['... | [
"Log",
"a",
"similarity",
"evaluation",
"result",
"dictionary",
"as",
"TSV",
"to",
"logfile",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/word_embeddings/evaluation.py#L262-L280 | [
"def",
"log_similarity_result",
"(",
"logfile",
",",
"result",
")",
":",
"assert",
"result",
"[",
"'task'",
"]",
"==",
"'similarity'",
"if",
"not",
"logfile",
":",
"return",
"with",
"open",
"(",
"logfile",
",",
"'a'",
")",
"as",
"f",
":",
"f",
".",
"wr... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | get_model_loss | Get model for pre-training. | scripts/bert/pretraining_utils.py | def get_model_loss(ctx, model, pretrained, dataset_name, dtype, ckpt_dir=None, start_step=None):
"""Get model for pre-training."""
# model
model, vocabulary = nlp.model.get_model(model,
dataset_name=dataset_name,
pretrai... | def get_model_loss(ctx, model, pretrained, dataset_name, dtype, ckpt_dir=None, start_step=None):
"""Get model for pre-training."""
# model
model, vocabulary = nlp.model.get_model(model,
dataset_name=dataset_name,
pretrai... | [
"Get",
"model",
"for",
"pre",
"-",
"training",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/bert/pretraining_utils.py#L36-L60 | [
"def",
"get_model_loss",
"(",
"ctx",
",",
"model",
",",
"pretrained",
",",
"dataset_name",
",",
"dtype",
",",
"ckpt_dir",
"=",
"None",
",",
"start_step",
"=",
"None",
")",
":",
"# model",
"model",
",",
"vocabulary",
"=",
"nlp",
".",
"model",
".",
"get_mo... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | get_pretrain_dataset | create dataset for pretraining. | scripts/bert/pretraining_utils.py | def get_pretrain_dataset(data, batch_size, num_ctxes, shuffle, use_avg_len,
num_buckets, num_parts=1, part_idx=0, prefetch=True):
"""create dataset for pretraining."""
num_files = len(glob.glob(os.path.expanduser(data)))
logging.debug('%d files found.', num_files)
assert num_fil... | def get_pretrain_dataset(data, batch_size, num_ctxes, shuffle, use_avg_len,
num_buckets, num_parts=1, part_idx=0, prefetch=True):
"""create dataset for pretraining."""
num_files = len(glob.glob(os.path.expanduser(data)))
logging.debug('%d files found.', num_files)
assert num_fil... | [
"create",
"dataset",
"for",
"pretraining",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/bert/pretraining_utils.py#L62-L107 | [
"def",
"get_pretrain_dataset",
"(",
"data",
",",
"batch_size",
",",
"num_ctxes",
",",
"shuffle",
",",
"use_avg_len",
",",
"num_buckets",
",",
"num_parts",
"=",
"1",
",",
"part_idx",
"=",
"0",
",",
"prefetch",
"=",
"True",
")",
":",
"num_files",
"=",
"len",... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | get_dummy_dataloader | Return a dummy data loader which returns a fixed data batch of target shape | scripts/bert/pretraining_utils.py | def get_dummy_dataloader(dataloader, target_shape):
"""Return a dummy data loader which returns a fixed data batch of target shape"""
data_iter = enumerate(dataloader)
_, data_batch = next(data_iter)
logging.debug('Searching target batch shape: %s', target_shape)
while data_batch[0].shape != target_... | def get_dummy_dataloader(dataloader, target_shape):
"""Return a dummy data loader which returns a fixed data batch of target shape"""
data_iter = enumerate(dataloader)
_, data_batch = next(data_iter)
logging.debug('Searching target batch shape: %s', target_shape)
while data_batch[0].shape != target_... | [
"Return",
"a",
"dummy",
"data",
"loader",
"which",
"returns",
"a",
"fixed",
"data",
"batch",
"of",
"target",
"shape"
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/bert/pretraining_utils.py#L109-L127 | [
"def",
"get_dummy_dataloader",
"(",
"dataloader",
",",
"target_shape",
")",
":",
"data_iter",
"=",
"enumerate",
"(",
"dataloader",
")",
"_",
",",
"data_batch",
"=",
"next",
"(",
"data_iter",
")",
"logging",
".",
"debug",
"(",
"'Searching target batch shape: %s'",
... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | save_params | Save the model parameter, marked by step_num. | scripts/bert/pretraining_utils.py | def save_params(step_num, model, trainer, ckpt_dir):
"""Save the model parameter, marked by step_num."""
param_path = os.path.join(ckpt_dir, '%07d.params'%step_num)
trainer_path = os.path.join(ckpt_dir, '%07d.states'%step_num)
logging.info('[step %d] Saving checkpoints to %s, %s.',
step... | def save_params(step_num, model, trainer, ckpt_dir):
"""Save the model parameter, marked by step_num."""
param_path = os.path.join(ckpt_dir, '%07d.params'%step_num)
trainer_path = os.path.join(ckpt_dir, '%07d.states'%step_num)
logging.info('[step %d] Saving checkpoints to %s, %s.',
step... | [
"Save",
"the",
"model",
"parameter",
"marked",
"by",
"step_num",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/bert/pretraining_utils.py#L129-L136 | [
"def",
"save_params",
"(",
"step_num",
",",
"model",
",",
"trainer",
",",
"ckpt_dir",
")",
":",
"param_path",
"=",
"os",
".",
"path",
".",
"join",
"(",
"ckpt_dir",
",",
"'%07d.params'",
"%",
"step_num",
")",
"trainer_path",
"=",
"os",
".",
"path",
".",
... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | log | Log training progress. | scripts/bert/pretraining_utils.py | def log(begin_time, running_num_tks, running_mlm_loss, running_nsp_loss, step_num,
mlm_metric, nsp_metric, trainer, log_interval):
"""Log training progress."""
end_time = time.time()
duration = end_time - begin_time
throughput = running_num_tks / duration / 1000.0
running_mlm_loss = running_... | def log(begin_time, running_num_tks, running_mlm_loss, running_nsp_loss, step_num,
mlm_metric, nsp_metric, trainer, log_interval):
"""Log training progress."""
end_time = time.time()
duration = end_time - begin_time
throughput = running_num_tks / duration / 1000.0
running_mlm_loss = running_... | [
"Log",
"training",
"progress",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/bert/pretraining_utils.py#L138-L150 | [
"def",
"log",
"(",
"begin_time",
",",
"running_num_tks",
",",
"running_mlm_loss",
",",
"running_nsp_loss",
",",
"step_num",
",",
"mlm_metric",
",",
"nsp_metric",
",",
"trainer",
",",
"log_interval",
")",
":",
"end_time",
"=",
"time",
".",
"time",
"(",
")",
"... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | split_and_load | split and load arrays to a list of contexts | scripts/bert/pretraining_utils.py | def split_and_load(arrs, ctx):
"""split and load arrays to a list of contexts"""
assert isinstance(arrs, (list, tuple))
# split and load
loaded_arrs = [mx.gluon.utils.split_and_load(arr, ctx, even_split=False) for arr in arrs]
return zip(*loaded_arrs) | def split_and_load(arrs, ctx):
"""split and load arrays to a list of contexts"""
assert isinstance(arrs, (list, tuple))
# split and load
loaded_arrs = [mx.gluon.utils.split_and_load(arr, ctx, even_split=False) for arr in arrs]
return zip(*loaded_arrs) | [
"split",
"and",
"load",
"arrays",
"to",
"a",
"list",
"of",
"contexts"
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/bert/pretraining_utils.py#L153-L158 | [
"def",
"split_and_load",
"(",
"arrs",
",",
"ctx",
")",
":",
"assert",
"isinstance",
"(",
"arrs",
",",
"(",
"list",
",",
"tuple",
")",
")",
"# split and load",
"loaded_arrs",
"=",
"[",
"mx",
".",
"gluon",
".",
"utils",
".",
"split_and_load",
"(",
"arr",
... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | forward | forward computation for evaluation | scripts/bert/pretraining_utils.py | def forward(data, model, mlm_loss, nsp_loss, vocab_size, dtype):
"""forward computation for evaluation"""
(input_id, masked_id, masked_position, masked_weight, \
next_sentence_label, segment_id, valid_length) = data
num_masks = masked_weight.sum() + 1e-8
valid_length = valid_length.reshape(-1)
... | def forward(data, model, mlm_loss, nsp_loss, vocab_size, dtype):
"""forward computation for evaluation"""
(input_id, masked_id, masked_position, masked_weight, \
next_sentence_label, segment_id, valid_length) = data
num_masks = masked_weight.sum() + 1e-8
valid_length = valid_length.reshape(-1)
... | [
"forward",
"computation",
"for",
"evaluation"
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/bert/pretraining_utils.py#L161-L179 | [
"def",
"forward",
"(",
"data",
",",
"model",
",",
"mlm_loss",
",",
"nsp_loss",
",",
"vocab_size",
",",
"dtype",
")",
":",
"(",
"input_id",
",",
"masked_id",
",",
"masked_position",
",",
"masked_weight",
",",
"next_sentence_label",
",",
"segment_id",
",",
"va... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | evaluate | Evaluation function. | scripts/bert/pretraining_utils.py | def evaluate(data_eval, model, nsp_loss, mlm_loss, vocab_size, ctx, log_interval, dtype):
"""Evaluation function."""
mlm_metric = MaskedAccuracy()
nsp_metric = MaskedAccuracy()
mlm_metric.reset()
nsp_metric.reset()
eval_begin_time = time.time()
begin_time = time.time()
step_num = 0
... | def evaluate(data_eval, model, nsp_loss, mlm_loss, vocab_size, ctx, log_interval, dtype):
"""Evaluation function."""
mlm_metric = MaskedAccuracy()
nsp_metric = MaskedAccuracy()
mlm_metric.reset()
nsp_metric.reset()
eval_begin_time = time.time()
begin_time = time.time()
step_num = 0
... | [
"Evaluation",
"function",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/bert/pretraining_utils.py#L182-L238 | [
"def",
"evaluate",
"(",
"data_eval",
",",
"model",
",",
"nsp_loss",
",",
"mlm_loss",
",",
"vocab_size",
",",
"ctx",
",",
"log_interval",
",",
"dtype",
")",
":",
"mlm_metric",
"=",
"MaskedAccuracy",
"(",
")",
"nsp_metric",
"=",
"MaskedAccuracy",
"(",
")",
"... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | get_argparser | Argument parser | scripts/bert/pretraining_utils.py | def get_argparser():
"""Argument parser"""
parser = argparse.ArgumentParser(description='BERT pretraining example.')
parser.add_argument('--num_steps', type=int, default=20, help='Number of optimization steps')
parser.add_argument('--num_buckets', type=int, default=1,
help='Numbe... | def get_argparser():
"""Argument parser"""
parser = argparse.ArgumentParser(description='BERT pretraining example.')
parser.add_argument('--num_steps', type=int, default=20, help='Number of optimization steps')
parser.add_argument('--num_buckets', type=int, default=1,
help='Numbe... | [
"Argument",
"parser"
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/bert/pretraining_utils.py#L240-L285 | [
"def",
"get_argparser",
"(",
")",
":",
"parser",
"=",
"argparse",
".",
"ArgumentParser",
"(",
"description",
"=",
"'BERT pretraining example.'",
")",
"parser",
".",
"add_argument",
"(",
"'--num_steps'",
",",
"type",
"=",
"int",
",",
"default",
"=",
"20",
",",
... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | _cache_dataset | Cache the processed npy dataset the dataset into a npz
Parameters
----------
dataset : SimpleDataset
file_path : str | scripts/machine_translation/dataprocessor.py | def _cache_dataset(dataset, prefix):
"""Cache the processed npy dataset the dataset into a npz
Parameters
----------
dataset : SimpleDataset
file_path : str
"""
if not os.path.exists(_constants.CACHE_PATH):
os.makedirs(_constants.CACHE_PATH)
src_data = np.concatenate([e[0] for e... | def _cache_dataset(dataset, prefix):
"""Cache the processed npy dataset the dataset into a npz
Parameters
----------
dataset : SimpleDataset
file_path : str
"""
if not os.path.exists(_constants.CACHE_PATH):
os.makedirs(_constants.CACHE_PATH)
src_data = np.concatenate([e[0] for e... | [
"Cache",
"the",
"processed",
"npy",
"dataset",
"the",
"dataset",
"into",
"a",
"npz"
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/machine_translation/dataprocessor.py#L33-L49 | [
"def",
"_cache_dataset",
"(",
"dataset",
",",
"prefix",
")",
":",
"if",
"not",
"os",
".",
"path",
".",
"exists",
"(",
"_constants",
".",
"CACHE_PATH",
")",
":",
"os",
".",
"makedirs",
"(",
"_constants",
".",
"CACHE_PATH",
")",
"src_data",
"=",
"np",
".... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | load_translation_data | Load translation dataset
Parameters
----------
dataset : str
args : argparse result
Returns
------- | scripts/machine_translation/dataprocessor.py | def load_translation_data(dataset, bleu, args):
"""Load translation dataset
Parameters
----------
dataset : str
args : argparse result
Returns
-------
"""
src_lang, tgt_lang = args.src_lang, args.tgt_lang
if dataset == 'IWSLT2015':
common_prefix = 'IWSLT2015_{}_{}_{}_{... | def load_translation_data(dataset, bleu, args):
"""Load translation dataset
Parameters
----------
dataset : str
args : argparse result
Returns
-------
"""
src_lang, tgt_lang = args.src_lang, args.tgt_lang
if dataset == 'IWSLT2015':
common_prefix = 'IWSLT2015_{}_{}_{}_{... | [
"Load",
"translation",
"dataset"
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/machine_translation/dataprocessor.py#L117-L198 | [
"def",
"load_translation_data",
"(",
"dataset",
",",
"bleu",
",",
"args",
")",
":",
"src_lang",
",",
"tgt_lang",
"=",
"args",
".",
"src_lang",
",",
"args",
".",
"tgt_lang",
"if",
"dataset",
"==",
"'IWSLT2015'",
":",
"common_prefix",
"=",
"'IWSLT2015_{}_{}_{}_{... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | make_dataloader | Create data loaders for training/validation/test. | scripts/machine_translation/dataprocessor.py | def make_dataloader(data_train, data_val, data_test, args,
use_average_length=False, num_shards=0, num_workers=8):
"""Create data loaders for training/validation/test."""
data_train_lengths = get_data_lengths(data_train)
data_val_lengths = get_data_lengths(data_val)
data_test_lengths... | def make_dataloader(data_train, data_val, data_test, args,
use_average_length=False, num_shards=0, num_workers=8):
"""Create data loaders for training/validation/test."""
data_train_lengths = get_data_lengths(data_train)
data_val_lengths = get_data_lengths(data_val)
data_test_lengths... | [
"Create",
"data",
"loaders",
"for",
"training",
"/",
"validation",
"/",
"test",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/machine_translation/dataprocessor.py#L206-L265 | [
"def",
"make_dataloader",
"(",
"data_train",
",",
"data_val",
",",
"data_test",
",",
"args",
",",
"use_average_length",
"=",
"False",
",",
"num_shards",
"=",
"0",
",",
"num_workers",
"=",
"8",
")",
":",
"data_train_lengths",
"=",
"get_data_lengths",
"(",
"data... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | _Prefetcher.run | Method representing the process’s activity. | src/gluonnlp/data/stream.py | def run(self):
"""Method representing the process’s activity."""
random.seed(self.seed)
np.random.seed(self.np_seed)
if not isinstance(self, multiprocessing.Process):
# Calling mxnet methods in a subprocess will raise an exception if
# mxnet is built with GPU supp... | def run(self):
"""Method representing the process’s activity."""
random.seed(self.seed)
np.random.seed(self.np_seed)
if not isinstance(self, multiprocessing.Process):
# Calling mxnet methods in a subprocess will raise an exception if
# mxnet is built with GPU supp... | [
"Method",
"representing",
"the",
"process’s",
"activity",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/data/stream.py#L228-L270 | [
"def",
"run",
"(",
"self",
")",
":",
"random",
".",
"seed",
"(",
"self",
".",
"seed",
")",
"np",
".",
"random",
".",
"seed",
"(",
"self",
".",
"np_seed",
")",
"if",
"not",
"isinstance",
"(",
"self",
",",
"multiprocessing",
".",
"Process",
")",
":",... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | RNNCellLayer.forward | Defines the forward computation. Arguments can be either
:py:class:`NDArray` or :py:class:`Symbol`. | src/gluonnlp/model/block.py | def forward(self, inputs, states=None): # pylint: disable=arguments-differ
"""Defines the forward computation. Arguments can be either
:py:class:`NDArray` or :py:class:`Symbol`."""
batch_size = inputs.shape[self._batch_axis]
skip_states = states is None
if skip_states:
... | def forward(self, inputs, states=None): # pylint: disable=arguments-differ
"""Defines the forward computation. Arguments can be either
:py:class:`NDArray` or :py:class:`Symbol`."""
batch_size = inputs.shape[self._batch_axis]
skip_states = states is None
if skip_states:
... | [
"Defines",
"the",
"forward",
"computation",
".",
"Arguments",
"can",
"be",
"either",
":",
"py",
":",
"class",
":",
"NDArray",
"or",
":",
"py",
":",
"class",
":",
"Symbol",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/model/block.py#L48-L69 | [
"def",
"forward",
"(",
"self",
",",
"inputs",
",",
"states",
"=",
"None",
")",
":",
"# pylint: disable=arguments-differ",
"batch_size",
"=",
"inputs",
".",
"shape",
"[",
"self",
".",
"_batch_axis",
"]",
"skip_states",
"=",
"states",
"is",
"None",
"if",
"skip... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | CSREmbeddingModel.hybrid_forward | Compute embedding of words in batch.
Parameters
----------
words : mx.nd.NDArray
Array of token indices. | src/gluonnlp/model/train/embedding.py | def hybrid_forward(self, F, words, weight):
"""Compute embedding of words in batch.
Parameters
----------
words : mx.nd.NDArray
Array of token indices.
"""
#pylint: disable=arguments-differ
embeddings = F.sparse.dot(words, weight)
return embe... | def hybrid_forward(self, F, words, weight):
"""Compute embedding of words in batch.
Parameters
----------
words : mx.nd.NDArray
Array of token indices.
"""
#pylint: disable=arguments-differ
embeddings = F.sparse.dot(words, weight)
return embe... | [
"Compute",
"embedding",
"of",
"words",
"in",
"batch",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/model/train/embedding.py#L120-L131 | [
"def",
"hybrid_forward",
"(",
"self",
",",
"F",
",",
"words",
",",
"weight",
")",
":",
"#pylint: disable=arguments-differ",
"embeddings",
"=",
"F",
".",
"sparse",
".",
"dot",
"(",
"words",
",",
"weight",
")",
"return",
"embeddings"
] | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | FasttextEmbeddingModel.load_fasttext_format | Create an instance of the class and load weights.
Load the weights from the fastText binary format created by
https://github.com/facebookresearch/fastText
Parameters
----------
path : str
Path to the .bin model file.
ctx : mx.Context, default mx.cpu()
... | src/gluonnlp/model/train/embedding.py | def load_fasttext_format(cls, path, ctx=cpu(), **kwargs):
"""Create an instance of the class and load weights.
Load the weights from the fastText binary format created by
https://github.com/facebookresearch/fastText
Parameters
----------
path : str
Path to t... | def load_fasttext_format(cls, path, ctx=cpu(), **kwargs):
"""Create an instance of the class and load weights.
Load the weights from the fastText binary format created by
https://github.com/facebookresearch/fastText
Parameters
----------
path : str
Path to t... | [
"Create",
"an",
"instance",
"of",
"the",
"class",
"and",
"load",
"weights",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/model/train/embedding.py#L232-L280 | [
"def",
"load_fasttext_format",
"(",
"cls",
",",
"path",
",",
"ctx",
"=",
"cpu",
"(",
")",
",",
"*",
"*",
"kwargs",
")",
":",
"with",
"open",
"(",
"path",
",",
"'rb'",
")",
"as",
"f",
":",
"new_format",
",",
"dim",
",",
"bucket",
",",
"minn",
",",... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | logging_config | Config the logging. | scripts/natural_language_inference/utils.py | def logging_config(logpath=None,
level=logging.DEBUG,
console_level=logging.INFO,
no_console=False):
"""
Config the logging.
"""
logger = logging.getLogger('nli')
# Remove all the current handlers
for handler in logger.handlers:
lo... | def logging_config(logpath=None,
level=logging.DEBUG,
console_level=logging.INFO,
no_console=False):
"""
Config the logging.
"""
logger = logging.getLogger('nli')
# Remove all the current handlers
for handler in logger.handlers:
lo... | [
"Config",
"the",
"logging",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/natural_language_inference/utils.py#L27-L56 | [
"def",
"logging_config",
"(",
"logpath",
"=",
"None",
",",
"level",
"=",
"logging",
".",
"DEBUG",
",",
"console_level",
"=",
"logging",
".",
"INFO",
",",
"no_console",
"=",
"False",
")",
":",
"logger",
"=",
"logging",
".",
"getLogger",
"(",
"'nli'",
")",... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | parse_args | Parse command line arguments. | scripts/word_embeddings/train_glove.py | def parse_args():
"""Parse command line arguments."""
parser = argparse.ArgumentParser(
description='GloVe with GluonNLP',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# Data options
group = parser.add_argument_group('Data arguments')
group.add_argument(
'cooccurr... | def parse_args():
"""Parse command line arguments."""
parser = argparse.ArgumentParser(
description='GloVe with GluonNLP',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# Data options
group = parser.add_argument_group('Data arguments')
group.add_argument(
'cooccurr... | [
"Parse",
"command",
"line",
"arguments",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/word_embeddings/train_glove.py#L59-L127 | [
"def",
"parse_args",
"(",
")",
":",
"parser",
"=",
"argparse",
".",
"ArgumentParser",
"(",
"description",
"=",
"'GloVe with GluonNLP'",
",",
"formatter_class",
"=",
"argparse",
".",
"ArgumentDefaultsHelpFormatter",
")",
"# Data options",
"group",
"=",
"parser",
".",... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | get_train_data | Helper function to get training data. | scripts/word_embeddings/train_glove.py | def get_train_data(args):
"""Helper function to get training data."""
counter = dict()
with io.open(args.vocab, 'r', encoding='utf-8') as f:
for line in f:
token, count = line.split('\t')
counter[token] = int(count)
vocab = nlp.Vocab(counter, unknown_token=None, padding_t... | def get_train_data(args):
"""Helper function to get training data."""
counter = dict()
with io.open(args.vocab, 'r', encoding='utf-8') as f:
for line in f:
token, count = line.split('\t')
counter[token] = int(count)
vocab = nlp.Vocab(counter, unknown_token=None, padding_t... | [
"Helper",
"function",
"to",
"get",
"training",
"data",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/word_embeddings/train_glove.py#L130-L161 | [
"def",
"get_train_data",
"(",
"args",
")",
":",
"counter",
"=",
"dict",
"(",
")",
"with",
"io",
".",
"open",
"(",
"args",
".",
"vocab",
",",
"'r'",
",",
"encoding",
"=",
"'utf-8'",
")",
"as",
"f",
":",
"for",
"line",
"in",
"f",
":",
"token",
",",... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | train | Training helper. | scripts/word_embeddings/train_glove.py | def train(args):
"""Training helper."""
vocab, row, col, counts = get_train_data(args)
model = GloVe(token_to_idx=vocab.token_to_idx, output_dim=args.emsize,
dropout=args.dropout, x_max=args.x_max, alpha=args.alpha,
weight_initializer=mx.init.Uniform(scale=1 / args.emsize... | def train(args):
"""Training helper."""
vocab, row, col, counts = get_train_data(args)
model = GloVe(token_to_idx=vocab.token_to_idx, output_dim=args.emsize,
dropout=args.dropout, x_max=args.x_max, alpha=args.alpha,
weight_initializer=mx.init.Uniform(scale=1 / args.emsize... | [
"Training",
"helper",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/word_embeddings/train_glove.py#L273-L357 | [
"def",
"train",
"(",
"args",
")",
":",
"vocab",
",",
"row",
",",
"col",
",",
"counts",
"=",
"get_train_data",
"(",
"args",
")",
"model",
"=",
"GloVe",
"(",
"token_to_idx",
"=",
"vocab",
".",
"token_to_idx",
",",
"output_dim",
"=",
"args",
".",
"emsize"... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | log | Log to a file. | scripts/word_embeddings/train_glove.py | def log(args, kwargs):
"""Log to a file."""
logfile = os.path.join(args.logdir, 'log.tsv')
if 'log_created' not in globals():
if os.path.exists(logfile):
logging.error('Logfile %s already exists.', logfile)
sys.exit(1)
global log_created
log_created = sorte... | def log(args, kwargs):
"""Log to a file."""
logfile = os.path.join(args.logdir, 'log.tsv')
if 'log_created' not in globals():
if os.path.exists(logfile):
logging.error('Logfile %s already exists.', logfile)
sys.exit(1)
global log_created
log_created = sorte... | [
"Log",
"to",
"a",
"file",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/word_embeddings/train_glove.py#L396-L416 | [
"def",
"log",
"(",
"args",
",",
"kwargs",
")",
":",
"logfile",
"=",
"os",
".",
"path",
".",
"join",
"(",
"args",
".",
"logdir",
",",
"'log.tsv'",
")",
"if",
"'log_created'",
"not",
"in",
"globals",
"(",
")",
":",
"if",
"os",
".",
"path",
".",
"ex... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | GloVe.hybrid_forward | Compute embedding of words in batch.
Parameters
----------
row : mxnet.nd.NDArray or mxnet.sym.Symbol
Array of token indices for source words. Shape (batch_size, ).
row : mxnet.nd.NDArray or mxnet.sym.Symbol
Array of token indices for context words. Shape (batch_... | scripts/word_embeddings/train_glove.py | def hybrid_forward(self, F, row, col, counts):
"""Compute embedding of words in batch.
Parameters
----------
row : mxnet.nd.NDArray or mxnet.sym.Symbol
Array of token indices for source words. Shape (batch_size, ).
row : mxnet.nd.NDArray or mxnet.sym.Symbol
... | def hybrid_forward(self, F, row, col, counts):
"""Compute embedding of words in batch.
Parameters
----------
row : mxnet.nd.NDArray or mxnet.sym.Symbol
Array of token indices for source words. Shape (batch_size, ).
row : mxnet.nd.NDArray or mxnet.sym.Symbol
... | [
"Compute",
"embedding",
"of",
"words",
"in",
"batch",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/word_embeddings/train_glove.py#L201-L235 | [
"def",
"hybrid_forward",
"(",
"self",
",",
"F",
",",
"row",
",",
"col",
",",
"counts",
")",
":",
"emb_in",
"=",
"self",
".",
"source_embedding",
"(",
"row",
")",
"emb_out",
"=",
"self",
".",
"context_embedding",
"(",
"col",
")",
"if",
"self",
".",
"_... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | MaskedAccuracy.update | Updates the internal evaluation result.
Parameters
----------
labels : list of `NDArray`
The labels of the data with class indices as values, one per sample.
preds : list of `NDArray`
Prediction values for samples. Each prediction value can either be the class in... | src/gluonnlp/metric/masked_accuracy.py | def update(self, labels, preds, masks=None):
# pylint: disable=arguments-differ
"""Updates the internal evaluation result.
Parameters
----------
labels : list of `NDArray`
The labels of the data with class indices as values, one per sample.
preds : list of `N... | def update(self, labels, preds, masks=None):
# pylint: disable=arguments-differ
"""Updates the internal evaluation result.
Parameters
----------
labels : list of `NDArray`
The labels of the data with class indices as values, one per sample.
preds : list of `N... | [
"Updates",
"the",
"internal",
"evaluation",
"result",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/metric/masked_accuracy.py#L232-L275 | [
"def",
"update",
"(",
"self",
",",
"labels",
",",
"preds",
",",
"masks",
"=",
"None",
")",
":",
"# pylint: disable=arguments-differ",
"labels",
",",
"preds",
"=",
"check_label_shapes",
"(",
"labels",
",",
"preds",
",",
"True",
")",
"masks",
"=",
"[",
"None... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | NLIModel.hybrid_forward | Predict the relation of two sentences.
Parameters
----------
sentence1 : NDArray
Shape (batch_size, length)
sentence2 : NDArray
Shape (batch_size, length)
Returns
-------
pred : NDArray
Shape (batch_size, num_classes). num_cla... | scripts/natural_language_inference/decomposable_attention.py | def hybrid_forward(self, F, sentence1, sentence2):
"""
Predict the relation of two sentences.
Parameters
----------
sentence1 : NDArray
Shape (batch_size, length)
sentence2 : NDArray
Shape (batch_size, length)
Returns
-------
... | def hybrid_forward(self, F, sentence1, sentence2):
"""
Predict the relation of two sentences.
Parameters
----------
sentence1 : NDArray
Shape (batch_size, length)
sentence2 : NDArray
Shape (batch_size, length)
Returns
-------
... | [
"Predict",
"the",
"relation",
"of",
"two",
"sentences",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/natural_language_inference/decomposable_attention.py#L55-L78 | [
"def",
"hybrid_forward",
"(",
"self",
",",
"F",
",",
"sentence1",
",",
"sentence2",
")",
":",
"feature1",
"=",
"self",
".",
"lin_proj",
"(",
"self",
".",
"word_emb",
"(",
"sentence1",
")",
")",
"feature2",
"=",
"self",
".",
"lin_proj",
"(",
"self",
"."... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | IntraSentenceAttention.hybrid_forward | Compute intra-sentence attention given embedded words.
Parameters
----------
feature_a : NDArray
Shape (batch_size, length, hidden_size)
Returns
-------
alpha : NDArray
Shape (batch_size, length, hidden_size) | scripts/natural_language_inference/decomposable_attention.py | def hybrid_forward(self, F, feature_a):
"""
Compute intra-sentence attention given embedded words.
Parameters
----------
feature_a : NDArray
Shape (batch_size, length, hidden_size)
Returns
-------
alpha : NDArray
Shape (batch_size... | def hybrid_forward(self, F, feature_a):
"""
Compute intra-sentence attention given embedded words.
Parameters
----------
feature_a : NDArray
Shape (batch_size, length, hidden_size)
Returns
-------
alpha : NDArray
Shape (batch_size... | [
"Compute",
"intra",
"-",
"sentence",
"attention",
"given",
"embedded",
"words",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/natural_language_inference/decomposable_attention.py#L98-L115 | [
"def",
"hybrid_forward",
"(",
"self",
",",
"F",
",",
"feature_a",
")",
":",
"tilde_a",
"=",
"self",
".",
"intra_attn_emb",
"(",
"feature_a",
")",
"e_matrix",
"=",
"F",
".",
"batch_dot",
"(",
"tilde_a",
",",
"tilde_a",
",",
"transpose_b",
"=",
"True",
")"... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | DecomposableAttention.hybrid_forward | Forward of Decomposable Attention layer | scripts/natural_language_inference/decomposable_attention.py | def hybrid_forward(self, F, a, b):
"""
Forward of Decomposable Attention layer
"""
# a.shape = [B, L1, H]
# b.shape = [B, L2, H]
# extract features
tilde_a = self.f(a) # shape = [B, L1, H]
tilde_b = self.f(b) # shape = [B, L2, H]
# attention
... | def hybrid_forward(self, F, a, b):
"""
Forward of Decomposable Attention layer
"""
# a.shape = [B, L1, H]
# b.shape = [B, L2, H]
# extract features
tilde_a = self.f(a) # shape = [B, L1, H]
tilde_b = self.f(b) # shape = [B, L2, H]
# attention
... | [
"Forward",
"of",
"Decomposable",
"Attention",
"layer"
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/natural_language_inference/decomposable_attention.py#L144-L166 | [
"def",
"hybrid_forward",
"(",
"self",
",",
"F",
",",
"a",
",",
"b",
")",
":",
"# a.shape = [B, L1, H]",
"# b.shape = [B, L2, H]",
"# extract features",
"tilde_a",
"=",
"self",
".",
"f",
"(",
"a",
")",
"# shape = [B, L1, H]",
"tilde_b",
"=",
"self",
".",
"f",
... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | count_tokens | r"""Counts tokens in the specified string.
For token_delim='(td)' and seq_delim='(sd)', a specified string of two sequences of tokens may
look like::
(td)token1(td)token2(td)token3(td)(sd)(td)token4(td)token5(td)(sd)
Parameters
----------
tokens : list of str
A source list of tok... | src/gluonnlp/data/utils.py | def count_tokens(tokens, to_lower=False, counter=None):
r"""Counts tokens in the specified string.
For token_delim='(td)' and seq_delim='(sd)', a specified string of two sequences of tokens may
look like::
(td)token1(td)token2(td)token3(td)(sd)(td)token4(td)token5(td)(sd)
Parameters
----... | def count_tokens(tokens, to_lower=False, counter=None):
r"""Counts tokens in the specified string.
For token_delim='(td)' and seq_delim='(sd)', a specified string of two sequences of tokens may
look like::
(td)token1(td)token2(td)token3(td)(sd)(td)token4(td)token5(td)(sd)
Parameters
----... | [
"r",
"Counts",
"tokens",
"in",
"the",
"specified",
"string",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/data/utils.py#L92-L133 | [
"def",
"count_tokens",
"(",
"tokens",
",",
"to_lower",
"=",
"False",
",",
"counter",
"=",
"None",
")",
":",
"if",
"to_lower",
":",
"tokens",
"=",
"[",
"t",
".",
"lower",
"(",
")",
"for",
"t",
"in",
"tokens",
"]",
"if",
"counter",
"is",
"None",
":",... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | slice_sequence | Slice a flat sequence of tokens into sequences tokens, with each
inner sequence's length equal to the specified `length`, taking into account the requested
sequence overlap.
Parameters
----------
sequence : list of object
A flat list of tokens.
length : int
The length of each of... | src/gluonnlp/data/utils.py | def slice_sequence(sequence, length, pad_last=False, pad_val=C.PAD_TOKEN, overlap=0):
"""Slice a flat sequence of tokens into sequences tokens, with each
inner sequence's length equal to the specified `length`, taking into account the requested
sequence overlap.
Parameters
----------
sequence :... | def slice_sequence(sequence, length, pad_last=False, pad_val=C.PAD_TOKEN, overlap=0):
"""Slice a flat sequence of tokens into sequences tokens, with each
inner sequence's length equal to the specified `length`, taking into account the requested
sequence overlap.
Parameters
----------
sequence :... | [
"Slice",
"a",
"flat",
"sequence",
"of",
"tokens",
"into",
"sequences",
"tokens",
"with",
"each",
"inner",
"sequence",
"s",
"length",
"equal",
"to",
"the",
"specified",
"length",
"taking",
"into",
"account",
"the",
"requested",
"sequence",
"overlap",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/data/utils.py#L152-L187 | [
"def",
"slice_sequence",
"(",
"sequence",
",",
"length",
",",
"pad_last",
"=",
"False",
",",
"pad_val",
"=",
"C",
".",
"PAD_TOKEN",
",",
"overlap",
"=",
"0",
")",
":",
"if",
"length",
"<=",
"overlap",
":",
"raise",
"ValueError",
"(",
"'length needs to be l... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | _slice_pad_length | Calculate the padding length needed for sliced samples in order not to discard data.
Parameters
----------
num_items : int
Number of items in dataset before collating.
length : int
The length of each of the samples.
overlap : int, default 0
The extra number of items in curre... | src/gluonnlp/data/utils.py | def _slice_pad_length(num_items, length, overlap=0):
"""Calculate the padding length needed for sliced samples in order not to discard data.
Parameters
----------
num_items : int
Number of items in dataset before collating.
length : int
The length of each of the samples.
overlap... | def _slice_pad_length(num_items, length, overlap=0):
"""Calculate the padding length needed for sliced samples in order not to discard data.
Parameters
----------
num_items : int
Number of items in dataset before collating.
length : int
The length of each of the samples.
overlap... | [
"Calculate",
"the",
"padding",
"length",
"needed",
"for",
"sliced",
"samples",
"in",
"order",
"not",
"to",
"discard",
"data",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/data/utils.py#L190-L217 | [
"def",
"_slice_pad_length",
"(",
"num_items",
",",
"length",
",",
"overlap",
"=",
"0",
")",
":",
"if",
"length",
"<=",
"overlap",
":",
"raise",
"ValueError",
"(",
"'length needs to be larger than overlap'",
")",
"step",
"=",
"length",
"-",
"overlap",
"span",
"... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | train_valid_split | Split the dataset into training and validation sets.
Parameters
----------
dataset : list
A list of training samples.
valid_ratio : float, default 0.05
Proportion of training samples to use for validation set
range: [0, 1]
Returns
-------
train : SimpleDataset
v... | src/gluonnlp/data/utils.py | def train_valid_split(dataset, valid_ratio=0.05):
"""Split the dataset into training and validation sets.
Parameters
----------
dataset : list
A list of training samples.
valid_ratio : float, default 0.05
Proportion of training samples to use for validation set
range: [0, 1]... | def train_valid_split(dataset, valid_ratio=0.05):
"""Split the dataset into training and validation sets.
Parameters
----------
dataset : list
A list of training samples.
valid_ratio : float, default 0.05
Proportion of training samples to use for validation set
range: [0, 1]... | [
"Split",
"the",
"dataset",
"into",
"training",
"and",
"validation",
"sets",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/data/utils.py#L236-L262 | [
"def",
"train_valid_split",
"(",
"dataset",
",",
"valid_ratio",
"=",
"0.05",
")",
":",
"if",
"not",
"0.0",
"<=",
"valid_ratio",
"<=",
"1.0",
":",
"raise",
"ValueError",
"(",
"'valid_ratio should be in [0, 1]'",
")",
"num_train",
"=",
"len",
"(",
"dataset",
")"... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | _load_pretrained_vocab | Load the accompanying vocabulary object for pre-trained model.
Parameters
----------
name : str
Name of the vocabulary, usually the name of the dataset.
root : str, default '$MXNET_HOME/models'
Location for keeping the model parameters.
MXNET_HOME defaults to '~/.mxnet'.
cls... | src/gluonnlp/data/utils.py | def _load_pretrained_vocab(name, root=os.path.join(get_home_dir(), 'models'), cls=None):
"""Load the accompanying vocabulary object for pre-trained model.
Parameters
----------
name : str
Name of the vocabulary, usually the name of the dataset.
root : str, default '$MXNET_HOME/models'
... | def _load_pretrained_vocab(name, root=os.path.join(get_home_dir(), 'models'), cls=None):
"""Load the accompanying vocabulary object for pre-trained model.
Parameters
----------
name : str
Name of the vocabulary, usually the name of the dataset.
root : str, default '$MXNET_HOME/models'
... | [
"Load",
"the",
"accompanying",
"vocabulary",
"object",
"for",
"pre",
"-",
"trained",
"model",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/data/utils.py#L271-L318 | [
"def",
"_load_pretrained_vocab",
"(",
"name",
",",
"root",
"=",
"os",
".",
"path",
".",
"join",
"(",
"get_home_dir",
"(",
")",
",",
"'models'",
")",
",",
"cls",
"=",
"None",
")",
":",
"file_name",
"=",
"'{name}-{short_hash}'",
".",
"format",
"(",
"name",... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | _extract_archive | Extract archive file
Parameters
----------
file : str
Absolute path of the archive file.
target_dir : str
Target directory of the archive to be uncompressed | src/gluonnlp/data/utils.py | def _extract_archive(file, target_dir):
"""Extract archive file
Parameters
----------
file : str
Absolute path of the archive file.
target_dir : str
Target directory of the archive to be uncompressed
"""
if file.endswith('.gz') or file.endswith('.tar') or file.endswith('.tg... | def _extract_archive(file, target_dir):
"""Extract archive file
Parameters
----------
file : str
Absolute path of the archive file.
target_dir : str
Target directory of the archive to be uncompressed
"""
if file.endswith('.gz') or file.endswith('.tar') or file.endswith('.tg... | [
"Extract",
"archive",
"file"
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/data/utils.py#L330-L348 | [
"def",
"_extract_archive",
"(",
"file",
",",
"target_dir",
")",
":",
"if",
"file",
".",
"endswith",
"(",
"'.gz'",
")",
"or",
"file",
".",
"endswith",
"(",
"'.tar'",
")",
"or",
"file",
".",
"endswith",
"(",
"'.tgz'",
")",
":",
"archive",
"=",
"tarfile",... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | Counter.discard | Discards tokens with frequency below min_frequency and represents them
as `unknown_token`.
Parameters
----------
min_freq: int
Tokens whose frequency is under min_freq is counted as `unknown_token` in
the Counter returned.
unknown_token: str
T... | src/gluonnlp/data/utils.py | def discard(self, min_freq, unknown_token):
"""Discards tokens with frequency below min_frequency and represents them
as `unknown_token`.
Parameters
----------
min_freq: int
Tokens whose frequency is under min_freq is counted as `unknown_token` in
the Cou... | def discard(self, min_freq, unknown_token):
"""Discards tokens with frequency below min_frequency and represents them
as `unknown_token`.
Parameters
----------
min_freq: int
Tokens whose frequency is under min_freq is counted as `unknown_token` in
the Cou... | [
"Discards",
"tokens",
"with",
"frequency",
"below",
"min_frequency",
"and",
"represents",
"them",
"as",
"unknown_token",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/data/utils.py#L45-L75 | [
"def",
"discard",
"(",
"self",
",",
"min_freq",
",",
"unknown_token",
")",
":",
"freq",
"=",
"0",
"ret",
"=",
"Counter",
"(",
"{",
"}",
")",
"for",
"token",
",",
"count",
"in",
"self",
".",
"items",
"(",
")",
":",
"if",
"count",
"<",
"min_freq",
... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | train | Training function. | scripts/machine_translation/train_transformer.py | def train():
"""Training function."""
trainer = gluon.Trainer(model.collect_params(), args.optimizer,
{'learning_rate': args.lr, 'beta2': 0.98, 'epsilon': 1e-9})
train_data_loader, val_data_loader, test_data_loader \
= dataprocessor.make_dataloader(data_train, data_val, ... | def train():
"""Training function."""
trainer = gluon.Trainer(model.collect_params(), args.optimizer,
{'learning_rate': args.lr, 'beta2': 0.98, 'epsilon': 1e-9})
train_data_loader, val_data_loader, test_data_loader \
= dataprocessor.make_dataloader(data_train, data_val, ... | [
"Training",
"function",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/machine_translation/train_transformer.py#L262-L408 | [
"def",
"train",
"(",
")",
":",
"trainer",
"=",
"gluon",
".",
"Trainer",
"(",
"model",
".",
"collect_params",
"(",
")",
",",
"args",
".",
"optimizer",
",",
"{",
"'learning_rate'",
":",
"args",
".",
"lr",
",",
"'beta2'",
":",
"0.98",
",",
"'epsilon'",
... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | Highway.hybrid_forward | r"""
Forward computation for highway layer
Parameters
----------
inputs: NDArray
The input tensor is of shape `(..., input_size)`.
Returns
----------
outputs: NDArray
The output tensor is of the same shape with input tensor `(..., input_s... | src/gluonnlp/model/highway.py | def hybrid_forward(self, F, inputs, **kwargs):
# pylint: disable=unused-argument
r"""
Forward computation for highway layer
Parameters
----------
inputs: NDArray
The input tensor is of shape `(..., input_size)`.
Returns
----------
out... | def hybrid_forward(self, F, inputs, **kwargs):
# pylint: disable=unused-argument
r"""
Forward computation for highway layer
Parameters
----------
inputs: NDArray
The input tensor is of shape `(..., input_size)`.
Returns
----------
out... | [
"r",
"Forward",
"computation",
"for",
"highway",
"layer"
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/model/highway.py#L102-L126 | [
"def",
"hybrid_forward",
"(",
"self",
",",
"F",
",",
"inputs",
",",
"*",
"*",
"kwargs",
")",
":",
"# pylint: disable=unused-argument",
"current_input",
"=",
"inputs",
"for",
"layer",
"in",
"self",
".",
"hnet",
":",
"projected_input",
"=",
"layer",
"(",
"curr... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | BertForQA.forward | Generate the unnormalized score for the given the input sequences.
Parameters
----------
inputs : NDArray, shape (batch_size, seq_length)
Input words for the sequences.
token_types : NDArray, shape (batch_size, seq_length)
Token types for the sequences, used to i... | scripts/bert/bert_qa_model.py | def forward(self, inputs, token_types, valid_length=None): # pylint: disable=arguments-differ
"""Generate the unnormalized score for the given the input sequences.
Parameters
----------
inputs : NDArray, shape (batch_size, seq_length)
Input words for the sequences.
... | def forward(self, inputs, token_types, valid_length=None): # pylint: disable=arguments-differ
"""Generate the unnormalized score for the given the input sequences.
Parameters
----------
inputs : NDArray, shape (batch_size, seq_length)
Input words for the sequences.
... | [
"Generate",
"the",
"unnormalized",
"score",
"for",
"the",
"given",
"the",
"input",
"sequences",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/bert/bert_qa_model.py#L49-L69 | [
"def",
"forward",
"(",
"self",
",",
"inputs",
",",
"token_types",
",",
"valid_length",
"=",
"None",
")",
":",
"# pylint: disable=arguments-differ",
"bert_output",
"=",
"self",
".",
"bert",
"(",
"inputs",
",",
"token_types",
",",
"valid_length",
")",
"output",
... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | BiLMEncoder.hybrid_forward | Defines the forward computation for cache cell. Arguments can be either
:py:class:`NDArray` or :py:class:`Symbol`.
Parameters
----------
inputs : NDArray
The input data layout='TNC'.
states : Tuple[List[List[NDArray]]]
The states. including:
s... | src/gluonnlp/model/bilm_encoder.py | def hybrid_forward(self, F, inputs, states=None, mask=None):
# pylint: disable=arguments-differ
# pylint: disable=unused-argument
"""Defines the forward computation for cache cell. Arguments can be either
:py:class:`NDArray` or :py:class:`Symbol`.
Parameters
----------
... | def hybrid_forward(self, F, inputs, states=None, mask=None):
# pylint: disable=arguments-differ
# pylint: disable=unused-argument
"""Defines the forward computation for cache cell. Arguments can be either
:py:class:`NDArray` or :py:class:`Symbol`.
Parameters
----------
... | [
"Defines",
"the",
"forward",
"computation",
"for",
"cache",
"cell",
".",
"Arguments",
"can",
"be",
"either",
":",
"py",
":",
"class",
":",
"NDArray",
"or",
":",
"py",
":",
"class",
":",
"Symbol",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/model/bilm_encoder.py#L132-L205 | [
"def",
"hybrid_forward",
"(",
"self",
",",
"F",
",",
"inputs",
",",
"states",
"=",
"None",
",",
"mask",
"=",
"None",
")",
":",
"# pylint: disable=arguments-differ",
"# pylint: disable=unused-argument",
"states_forward",
",",
"states_backward",
"=",
"states",
"if",
... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | main | Read tokens from the provided parse tree in the SNLI dataset.
Illegal examples are removed. | scripts/natural_language_inference/preprocess.py | def main(args):
"""
Read tokens from the provided parse tree in the SNLI dataset.
Illegal examples are removed.
"""
examples = []
with open(args.input, 'r') as fin:
reader = csv.DictReader(fin, delimiter='\t')
for cols in reader:
s1 = read_tokens(cols['sentence1_parse... | def main(args):
"""
Read tokens from the provided parse tree in the SNLI dataset.
Illegal examples are removed.
"""
examples = []
with open(args.input, 'r') as fin:
reader = csv.DictReader(fin, delimiter='\t')
for cols in reader:
s1 = read_tokens(cols['sentence1_parse... | [
"Read",
"tokens",
"from",
"the",
"provided",
"parse",
"tree",
"in",
"the",
"SNLI",
"dataset",
".",
"Illegal",
"examples",
"are",
"removed",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/natural_language_inference/preprocess.py#L42-L58 | [
"def",
"main",
"(",
"args",
")",
":",
"examples",
"=",
"[",
"]",
"with",
"open",
"(",
"args",
".",
"input",
",",
"'r'",
")",
"as",
"fin",
":",
"reader",
"=",
"csv",
".",
"DictReader",
"(",
"fin",
",",
"delimiter",
"=",
"'\\t'",
")",
"for",
"cols"... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | KMeans._recenter | one iteration of k-means | scripts/parsing/common/k_means.py | def _recenter(self):
"""
one iteration of k-means
"""
for split_idx in range(len(self._splits)):
split = self._splits[split_idx]
len_idx = self._split2len_idx[split]
if split == self._splits[-1]:
continue
right_split = self.... | def _recenter(self):
"""
one iteration of k-means
"""
for split_idx in range(len(self._splits)):
split = self._splits[split_idx]
len_idx = self._split2len_idx[split]
if split == self._splits[-1]:
continue
right_split = self.... | [
"one",
"iteration",
"of",
"k",
"-",
"means"
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/parsing/common/k_means.py#L108-L145 | [
"def",
"_recenter",
"(",
"self",
")",
":",
"for",
"split_idx",
"in",
"range",
"(",
"len",
"(",
"self",
".",
"_splits",
")",
")",
":",
"split",
"=",
"self",
".",
"_splits",
"[",
"split_idx",
"]",
"len_idx",
"=",
"self",
".",
"_split2len_idx",
"[",
"sp... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | KMeans._reindex | Index every sentence into a cluster | scripts/parsing/common/k_means.py | def _reindex(self):
"""
Index every sentence into a cluster
"""
self._len2split_idx = {}
last_split = -1
for split_idx, split in enumerate(self._splits):
self._len2split_idx.update(
dict(list(zip(list(range(last_split + 1, split)), [split_idx] ... | def _reindex(self):
"""
Index every sentence into a cluster
"""
self._len2split_idx = {}
last_split = -1
for split_idx, split in enumerate(self._splits):
self._len2split_idx.update(
dict(list(zip(list(range(last_split + 1, split)), [split_idx] ... | [
"Index",
"every",
"sentence",
"into",
"a",
"cluster"
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/parsing/common/k_means.py#L147-L155 | [
"def",
"_reindex",
"(",
"self",
")",
":",
"self",
".",
"_len2split_idx",
"=",
"{",
"}",
"last_split",
"=",
"-",
"1",
"for",
"split_idx",
",",
"split",
"in",
"enumerate",
"(",
"self",
".",
"_splits",
")",
":",
"self",
".",
"_len2split_idx",
".",
"update... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | get_gnmt_encoder_decoder | Build a pair of GNMT encoder/decoder
Parameters
----------
cell_type : str or type
attention_cell : str or AttentionCell
num_layers : int
num_bi_layers : int
hidden_size : int
dropout : float
use_residual : bool
i2h_weight_initializer : mx.init.Initializer or None
h2h_weight... | scripts/machine_translation/gnmt.py | def get_gnmt_encoder_decoder(cell_type='lstm', attention_cell='scaled_luong', num_layers=2,
num_bi_layers=1, hidden_size=128, dropout=0.0, use_residual=False,
i2h_weight_initializer=None, h2h_weight_initializer=None,
i2h_bias_initial... | def get_gnmt_encoder_decoder(cell_type='lstm', attention_cell='scaled_luong', num_layers=2,
num_bi_layers=1, hidden_size=128, dropout=0.0, use_residual=False,
i2h_weight_initializer=None, h2h_weight_initializer=None,
i2h_bias_initial... | [
"Build",
"a",
"pair",
"of",
"GNMT",
"encoder",
"/",
"decoder"
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/machine_translation/gnmt.py#L407-L455 | [
"def",
"get_gnmt_encoder_decoder",
"(",
"cell_type",
"=",
"'lstm'",
",",
"attention_cell",
"=",
"'scaled_luong'",
",",
"num_layers",
"=",
"2",
",",
"num_bi_layers",
"=",
"1",
",",
"hidden_size",
"=",
"128",
",",
"dropout",
"=",
"0.0",
",",
"use_residual",
"=",... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | GNMTDecoder.init_state_from_encoder | Initialize the state from the encoder outputs.
Parameters
----------
encoder_outputs : list
encoder_valid_length : NDArray or None
Returns
-------
decoder_states : list
The decoder states, includes:
- rnn_states : NDArray
- a... | scripts/machine_translation/gnmt.py | def init_state_from_encoder(self, encoder_outputs, encoder_valid_length=None):
"""Initialize the state from the encoder outputs.
Parameters
----------
encoder_outputs : list
encoder_valid_length : NDArray or None
Returns
-------
decoder_states : list
... | def init_state_from_encoder(self, encoder_outputs, encoder_valid_length=None):
"""Initialize the state from the encoder outputs.
Parameters
----------
encoder_outputs : list
encoder_valid_length : NDArray or None
Returns
-------
decoder_states : list
... | [
"Initialize",
"the",
"state",
"from",
"the",
"encoder",
"outputs",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/machine_translation/gnmt.py#L224-L252 | [
"def",
"init_state_from_encoder",
"(",
"self",
",",
"encoder_outputs",
",",
"encoder_valid_length",
"=",
"None",
")",
":",
"mem_value",
",",
"rnn_states",
"=",
"encoder_outputs",
"batch_size",
",",
"_",
",",
"mem_size",
"=",
"mem_value",
".",
"shape",
"attention_v... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | GNMTDecoder.decode_seq | Decode the decoder inputs. This function is only used for training.
Parameters
----------
inputs : NDArray, Shape (batch_size, length, C_in)
states : list of NDArrays or None
Initial states. The list of initial decoder states
valid_length : NDArray or None
... | scripts/machine_translation/gnmt.py | def decode_seq(self, inputs, states, valid_length=None):
"""Decode the decoder inputs. This function is only used for training.
Parameters
----------
inputs : NDArray, Shape (batch_size, length, C_in)
states : list of NDArrays or None
Initial states. The list of init... | def decode_seq(self, inputs, states, valid_length=None):
"""Decode the decoder inputs. This function is only used for training.
Parameters
----------
inputs : NDArray, Shape (batch_size, length, C_in)
states : list of NDArrays or None
Initial states. The list of init... | [
"Decode",
"the",
"decoder",
"inputs",
".",
"This",
"function",
"is",
"only",
"used",
"for",
"training",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/machine_translation/gnmt.py#L254-L304 | [
"def",
"decode_seq",
"(",
"self",
",",
"inputs",
",",
"states",
",",
"valid_length",
"=",
"None",
")",
":",
"length",
"=",
"inputs",
".",
"shape",
"[",
"1",
"]",
"output",
"=",
"[",
"]",
"additional_outputs",
"=",
"[",
"]",
"inputs",
"=",
"_as_list",
... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | transform | Transform instance to inputs for MLM and NSP. | scripts/bert/create_pretraining_data.py | def transform(instance, tokenizer, max_seq_length, max_predictions_per_seq, do_pad=True):
"""Transform instance to inputs for MLM and NSP."""
pad = tokenizer.convert_tokens_to_ids(['[PAD]'])[0]
input_ids = tokenizer.convert_tokens_to_ids(instance.tokens)
input_mask = [1] * len(input_ids)
segment_ids... | def transform(instance, tokenizer, max_seq_length, max_predictions_per_seq, do_pad=True):
"""Transform instance to inputs for MLM and NSP."""
pad = tokenizer.convert_tokens_to_ids(['[PAD]'])[0]
input_ids = tokenizer.convert_tokens_to_ids(instance.tokens)
input_mask = [1] * len(input_ids)
segment_ids... | [
"Transform",
"instance",
"to",
"inputs",
"for",
"MLM",
"and",
"NSP",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/bert/create_pretraining_data.py#L163-L208 | [
"def",
"transform",
"(",
"instance",
",",
"tokenizer",
",",
"max_seq_length",
",",
"max_predictions_per_seq",
",",
"do_pad",
"=",
"True",
")",
":",
"pad",
"=",
"tokenizer",
".",
"convert_tokens_to_ids",
"(",
"[",
"'[PAD]'",
"]",
")",
"[",
"0",
"]",
"input_id... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | write_to_files_np | Write to numpy files from `TrainingInstance`s. | scripts/bert/create_pretraining_data.py | def write_to_files_np(features, tokenizer, max_seq_length,
max_predictions_per_seq, output_files):
# pylint: disable=unused-argument
"""Write to numpy files from `TrainingInstance`s."""
next_sentence_labels = []
valid_lengths = []
assert len(output_files) == 1, 'numpy format o... | def write_to_files_np(features, tokenizer, max_seq_length,
max_predictions_per_seq, output_files):
# pylint: disable=unused-argument
"""Write to numpy files from `TrainingInstance`s."""
next_sentence_labels = []
valid_lengths = []
assert len(output_files) == 1, 'numpy format o... | [
"Write",
"to",
"numpy",
"files",
"from",
"TrainingInstance",
"s",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/bert/create_pretraining_data.py#L218-L242 | [
"def",
"write_to_files_np",
"(",
"features",
",",
"tokenizer",
",",
"max_seq_length",
",",
"max_predictions_per_seq",
",",
"output_files",
")",
":",
"# pylint: disable=unused-argument",
"next_sentence_labels",
"=",
"[",
"]",
"valid_lengths",
"=",
"[",
"]",
"assert",
"... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | write_to_files_rec | Create IndexedRecordIO files from `TrainingInstance`s. | scripts/bert/create_pretraining_data.py | def write_to_files_rec(instances, tokenizer, max_seq_length,
max_predictions_per_seq, output_files):
"""Create IndexedRecordIO files from `TrainingInstance`s."""
writers = []
for output_file in output_files:
writers.append(
mx.recordio.MXIndexedRecordIO(
... | def write_to_files_rec(instances, tokenizer, max_seq_length,
max_predictions_per_seq, output_files):
"""Create IndexedRecordIO files from `TrainingInstance`s."""
writers = []
for output_file in output_files:
writers.append(
mx.recordio.MXIndexedRecordIO(
... | [
"Create",
"IndexedRecordIO",
"files",
"from",
"TrainingInstance",
"s",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/bert/create_pretraining_data.py#L244-L266 | [
"def",
"write_to_files_rec",
"(",
"instances",
",",
"tokenizer",
",",
"max_seq_length",
",",
"max_predictions_per_seq",
",",
"output_files",
")",
":",
"writers",
"=",
"[",
"]",
"for",
"output_file",
"in",
"output_files",
":",
"writers",
".",
"append",
"(",
"mx",... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | create_training_instances | Create `TrainingInstance`s from raw text. | scripts/bert/create_pretraining_data.py | def create_training_instances(x):
"""Create `TrainingInstance`s from raw text."""
(input_files, out, tokenizer, max_seq_length, dupe_factor,
short_seq_prob, masked_lm_prob, max_predictions_per_seq, rng) = x
time_start = time.time()
logging.info('Processing %s', input_files)
all_documents = [[]]... | def create_training_instances(x):
"""Create `TrainingInstance`s from raw text."""
(input_files, out, tokenizer, max_seq_length, dupe_factor,
short_seq_prob, masked_lm_prob, max_predictions_per_seq, rng) = x
time_start = time.time()
logging.info('Processing %s', input_files)
all_documents = [[]]... | [
"Create",
"TrainingInstance",
"s",
"from",
"raw",
"text",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/bert/create_pretraining_data.py#L269-L351 | [
"def",
"create_training_instances",
"(",
"x",
")",
":",
"(",
"input_files",
",",
"out",
",",
"tokenizer",
",",
"max_seq_length",
",",
"dupe_factor",
",",
"short_seq_prob",
",",
"masked_lm_prob",
",",
"max_predictions_per_seq",
",",
"rng",
")",
"=",
"x",
"time_st... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | create_instances_from_document | Creates `TrainingInstance`s for a single document. | scripts/bert/create_pretraining_data.py | def create_instances_from_document(
all_documents, document_index, max_seq_length, short_seq_prob,
masked_lm_prob, max_predictions_per_seq, vocab_words, rng):
"""Creates `TrainingInstance`s for a single document."""
document = all_documents[document_index]
# Account for [CLS], [SEP], [SEP]
... | def create_instances_from_document(
all_documents, document_index, max_seq_length, short_seq_prob,
masked_lm_prob, max_predictions_per_seq, vocab_words, rng):
"""Creates `TrainingInstance`s for a single document."""
document = all_documents[document_index]
# Account for [CLS], [SEP], [SEP]
... | [
"Creates",
"TrainingInstance",
"s",
"for",
"a",
"single",
"document",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/bert/create_pretraining_data.py#L354-L472 | [
"def",
"create_instances_from_document",
"(",
"all_documents",
",",
"document_index",
",",
"max_seq_length",
",",
"short_seq_prob",
",",
"masked_lm_prob",
",",
"max_predictions_per_seq",
",",
"vocab_words",
",",
"rng",
")",
":",
"document",
"=",
"all_documents",
"[",
... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | create_masked_lm_predictions | Creates the predictions for the masked LM objective. | scripts/bert/create_pretraining_data.py | def create_masked_lm_predictions(tokens, masked_lm_prob,
max_predictions_per_seq, vocab_words, rng):
"""Creates the predictions for the masked LM objective."""
cand_indexes = []
for (i, token) in enumerate(tokens):
if token in ['[CLS]', '[SEP]']:
continu... | def create_masked_lm_predictions(tokens, masked_lm_prob,
max_predictions_per_seq, vocab_words, rng):
"""Creates the predictions for the masked LM objective."""
cand_indexes = []
for (i, token) in enumerate(tokens):
if token in ['[CLS]', '[SEP]']:
continu... | [
"Creates",
"the",
"predictions",
"for",
"the",
"masked",
"LM",
"objective",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/bert/create_pretraining_data.py#L479-L530 | [
"def",
"create_masked_lm_predictions",
"(",
"tokens",
",",
"masked_lm_prob",
",",
"max_predictions_per_seq",
",",
"vocab_words",
",",
"rng",
")",
":",
"cand_indexes",
"=",
"[",
"]",
"for",
"(",
"i",
",",
"token",
")",
"in",
"enumerate",
"(",
"tokens",
")",
"... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | truncate_seq_pair | Truncates a pair of sequences to a maximum sequence length. | scripts/bert/create_pretraining_data.py | def truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng):
"""Truncates a pair of sequences to a maximum sequence length."""
while True:
total_length = len(tokens_a) + len(tokens_b)
if total_length <= max_num_tokens:
break
trunc_tokens = tokens_a if len(tokens_a) > len(... | def truncate_seq_pair(tokens_a, tokens_b, max_num_tokens, rng):
"""Truncates a pair of sequences to a maximum sequence length."""
while True:
total_length = len(tokens_a) + len(tokens_b)
if total_length <= max_num_tokens:
break
trunc_tokens = tokens_a if len(tokens_a) > len(... | [
"Truncates",
"a",
"pair",
"of",
"sequences",
"to",
"a",
"maximum",
"sequence",
"length",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/bert/create_pretraining_data.py#L533-L548 | [
"def",
"truncate_seq_pair",
"(",
"tokens_a",
",",
"tokens_b",
",",
"max_num_tokens",
",",
"rng",
")",
":",
"while",
"True",
":",
"total_length",
"=",
"len",
"(",
"tokens_a",
")",
"+",
"len",
"(",
"tokens_b",
")",
"if",
"total_length",
"<=",
"max_num_tokens",... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | main | Main function. | scripts/bert/create_pretraining_data.py | def main():
"""Main function."""
time_start = time.time()
logging.info('loading vocab file from dataset: %s', args.vocab)
vocab_obj = nlp.data.utils._load_pretrained_vocab(args.vocab)
tokenizer = BERTTokenizer(
vocab=vocab_obj, lower='uncased' in args.vocab)
input_files = []
for inp... | def main():
"""Main function."""
time_start = time.time()
logging.info('loading vocab file from dataset: %s', args.vocab)
vocab_obj = nlp.data.utils._load_pretrained_vocab(args.vocab)
tokenizer = BERTTokenizer(
vocab=vocab_obj, lower='uncased' in args.vocab)
input_files = []
for inp... | [
"Main",
"function",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/bert/create_pretraining_data.py#L551-L607 | [
"def",
"main",
"(",
")",
":",
"time_start",
"=",
"time",
".",
"time",
"(",
")",
"logging",
".",
"info",
"(",
"'loading vocab file from dataset: %s'",
",",
"args",
".",
"vocab",
")",
"vocab_obj",
"=",
"nlp",
".",
"data",
".",
"utils",
".",
"_load_pretrained... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | convert_vocab | GluonNLP specific code to convert the original vocabulary to nlp.vocab.BERTVocab. | scripts/bert/utils.py | def convert_vocab(vocab_file):
"""GluonNLP specific code to convert the original vocabulary to nlp.vocab.BERTVocab."""
original_vocab = load_vocab(vocab_file)
token_to_idx = dict(original_vocab)
num_tokens = len(token_to_idx)
idx_to_token = [None] * len(original_vocab)
for word in original_vocab... | def convert_vocab(vocab_file):
"""GluonNLP specific code to convert the original vocabulary to nlp.vocab.BERTVocab."""
original_vocab = load_vocab(vocab_file)
token_to_idx = dict(original_vocab)
num_tokens = len(token_to_idx)
idx_to_token = [None] * len(original_vocab)
for word in original_vocab... | [
"GluonNLP",
"specific",
"code",
"to",
"convert",
"the",
"original",
"vocabulary",
"to",
"nlp",
".",
"vocab",
".",
"BERTVocab",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/bert/utils.py#L33-L83 | [
"def",
"convert_vocab",
"(",
"vocab_file",
")",
":",
"original_vocab",
"=",
"load_vocab",
"(",
"vocab_file",
")",
"token_to_idx",
"=",
"dict",
"(",
"original_vocab",
")",
"num_tokens",
"=",
"len",
"(",
"token_to_idx",
")",
"idx_to_token",
"=",
"[",
"None",
"]"... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | read_tf_checkpoint | read tensorflow checkpoint | scripts/bert/utils.py | def read_tf_checkpoint(path):
"""read tensorflow checkpoint"""
from tensorflow.python import pywrap_tensorflow
tensors = {}
reader = pywrap_tensorflow.NewCheckpointReader(path)
var_to_shape_map = reader.get_variable_to_shape_map()
for key in sorted(var_to_shape_map):
tensor = reader.get_... | def read_tf_checkpoint(path):
"""read tensorflow checkpoint"""
from tensorflow.python import pywrap_tensorflow
tensors = {}
reader = pywrap_tensorflow.NewCheckpointReader(path)
var_to_shape_map = reader.get_variable_to_shape_map()
for key in sorted(var_to_shape_map):
tensor = reader.get_... | [
"read",
"tensorflow",
"checkpoint"
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/bert/utils.py#L97-L106 | [
"def",
"read_tf_checkpoint",
"(",
"path",
")",
":",
"from",
"tensorflow",
".",
"python",
"import",
"pywrap_tensorflow",
"tensors",
"=",
"{",
"}",
"reader",
"=",
"pywrap_tensorflow",
".",
"NewCheckpointReader",
"(",
"path",
")",
"var_to_shape_map",
"=",
"reader",
... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | profile | profile the program between [start_step, end_step). | scripts/bert/utils.py | def profile(curr_step, start_step, end_step, profile_name='profile.json',
early_exit=True):
"""profile the program between [start_step, end_step)."""
if curr_step == start_step:
mx.nd.waitall()
mx.profiler.set_config(profile_memory=False, profile_symbolic=True,
... | def profile(curr_step, start_step, end_step, profile_name='profile.json',
early_exit=True):
"""profile the program between [start_step, end_step)."""
if curr_step == start_step:
mx.nd.waitall()
mx.profiler.set_config(profile_memory=False, profile_symbolic=True,
... | [
"profile",
"the",
"program",
"between",
"[",
"start_step",
"end_step",
")",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/bert/utils.py#L108-L123 | [
"def",
"profile",
"(",
"curr_step",
",",
"start_step",
",",
"end_step",
",",
"profile_name",
"=",
"'profile.json'",
",",
"early_exit",
"=",
"True",
")",
":",
"if",
"curr_step",
"==",
"start_step",
":",
"mx",
".",
"nd",
".",
"waitall",
"(",
")",
"mx",
"."... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | load_vocab | Loads a vocabulary file into a dictionary. | scripts/bert/utils.py | def load_vocab(vocab_file):
"""Loads a vocabulary file into a dictionary."""
vocab = collections.OrderedDict()
index = 0
with io.open(vocab_file, 'r') as reader:
while True:
token = reader.readline()
if not token:
break
token = token.strip()
... | def load_vocab(vocab_file):
"""Loads a vocabulary file into a dictionary."""
vocab = collections.OrderedDict()
index = 0
with io.open(vocab_file, 'r') as reader:
while True:
token = reader.readline()
if not token:
break
token = token.strip()
... | [
"Loads",
"a",
"vocabulary",
"file",
"into",
"a",
"dictionary",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/bert/utils.py#L125-L137 | [
"def",
"load_vocab",
"(",
"vocab_file",
")",
":",
"vocab",
"=",
"collections",
".",
"OrderedDict",
"(",
")",
"index",
"=",
"0",
"with",
"io",
".",
"open",
"(",
"vocab_file",
",",
"'r'",
")",
"as",
"reader",
":",
"while",
"True",
":",
"token",
"=",
"r... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | ConvolutionalEncoder.hybrid_forward | r"""
Forward computation for char_encoder
Parameters
----------
inputs: NDArray
The input tensor is of shape `(seq_len, batch_size, embedding_size)` TNC.
mask: NDArray
The mask applied to the input of shape `(seq_len, batch_size)`, the mask will
... | src/gluonnlp/model/convolutional_encoder.py | def hybrid_forward(self, F, inputs, mask=None): # pylint: disable=arguments-differ
r"""
Forward computation for char_encoder
Parameters
----------
inputs: NDArray
The input tensor is of shape `(seq_len, batch_size, embedding_size)` TNC.
mask: NDArray
... | def hybrid_forward(self, F, inputs, mask=None): # pylint: disable=arguments-differ
r"""
Forward computation for char_encoder
Parameters
----------
inputs: NDArray
The input tensor is of shape `(seq_len, batch_size, embedding_size)` TNC.
mask: NDArray
... | [
"r",
"Forward",
"computation",
"for",
"char_encoder"
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/model/convolutional_encoder.py#L135-L166 | [
"def",
"hybrid_forward",
"(",
"self",
",",
"F",
",",
"inputs",
",",
"mask",
"=",
"None",
")",
":",
"# pylint: disable=arguments-differ",
"if",
"mask",
"is",
"not",
"None",
":",
"inputs",
"=",
"F",
".",
"broadcast_mul",
"(",
"inputs",
",",
"mask",
".",
"e... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | _position_encoding_init | Init the sinusoid position encoding table | src/gluonnlp/model/transformer.py | def _position_encoding_init(max_length, dim):
"""Init the sinusoid position encoding table """
position_enc = np.arange(max_length).reshape((-1, 1)) \
/ (np.power(10000, (2. / dim) * np.arange(dim).reshape((1, -1))))
# Apply the cosine to even columns and sin to odds.
position_enc[:, ... | def _position_encoding_init(max_length, dim):
"""Init the sinusoid position encoding table """
position_enc = np.arange(max_length).reshape((-1, 1)) \
/ (np.power(10000, (2. / dim) * np.arange(dim).reshape((1, -1))))
# Apply the cosine to even columns and sin to odds.
position_enc[:, ... | [
"Init",
"the",
"sinusoid",
"position",
"encoding",
"table"
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/model/transformer.py#L46-L53 | [
"def",
"_position_encoding_init",
"(",
"max_length",
",",
"dim",
")",
":",
"position_enc",
"=",
"np",
".",
"arange",
"(",
"max_length",
")",
".",
"reshape",
"(",
"(",
"-",
"1",
",",
"1",
")",
")",
"/",
"(",
"np",
".",
"power",
"(",
"10000",
",",
"(... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | get_transformer_encoder_decoder | Build a pair of Parallel Transformer encoder/decoder
Parameters
----------
num_layers : int
num_heads : int
scaled : bool
units : int
hidden_size : int
dropout : float
use_residual : bool
max_src_length : int
max_tgt_length : int
weight_initializer : mx.init.Initializer ... | src/gluonnlp/model/transformer.py | def get_transformer_encoder_decoder(num_layers=2,
num_heads=8, scaled=True,
units=512, hidden_size=2048, dropout=0.0, use_residual=True,
max_src_length=50, max_tgt_length=50,
w... | def get_transformer_encoder_decoder(num_layers=2,
num_heads=8, scaled=True,
units=512, hidden_size=2048, dropout=0.0, use_residual=True,
max_src_length=50, max_tgt_length=50,
w... | [
"Build",
"a",
"pair",
"of",
"Parallel",
"Transformer",
"encoder",
"/",
"decoder"
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/model/transformer.py#L1123-L1177 | [
"def",
"get_transformer_encoder_decoder",
"(",
"num_layers",
"=",
"2",
",",
"num_heads",
"=",
"8",
",",
"scaled",
"=",
"True",
",",
"units",
"=",
"512",
",",
"hidden_size",
"=",
"2048",
",",
"dropout",
"=",
"0.0",
",",
"use_residual",
"=",
"True",
",",
"... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | transformer_en_de_512 | r"""Transformer pretrained model.
Embedding size is 400, and hidden layer size is 1150.
Parameters
----------
dataset_name : str or None, default None
src_vocab : gluonnlp.Vocab or None, default None
tgt_vocab : gluonnlp.Vocab or None, default None
pretrained : bool, default False
... | src/gluonnlp/model/transformer.py | def transformer_en_de_512(dataset_name=None, src_vocab=None, tgt_vocab=None, pretrained=False,
ctx=cpu(), root=os.path.join(get_home_dir(), 'models'), **kwargs):
r"""Transformer pretrained model.
Embedding size is 400, and hidden layer size is 1150.
Parameters
----------
... | def transformer_en_de_512(dataset_name=None, src_vocab=None, tgt_vocab=None, pretrained=False,
ctx=cpu(), root=os.path.join(get_home_dir(), 'models'), **kwargs):
r"""Transformer pretrained model.
Embedding size is 400, and hidden layer size is 1150.
Parameters
----------
... | [
"r",
"Transformer",
"pretrained",
"model",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/model/transformer.py#L1200-L1251 | [
"def",
"transformer_en_de_512",
"(",
"dataset_name",
"=",
"None",
",",
"src_vocab",
"=",
"None",
",",
"tgt_vocab",
"=",
"None",
",",
"pretrained",
"=",
"False",
",",
"ctx",
"=",
"cpu",
"(",
")",
",",
"root",
"=",
"os",
".",
"path",
".",
"join",
"(",
... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | BasePositionwiseFFN._get_activation | Get activation block based on the name. | src/gluonnlp/model/transformer.py | def _get_activation(self, act):
"""Get activation block based on the name. """
if isinstance(act, str):
if act.lower() == 'gelu':
return GELU()
else:
return gluon.nn.Activation(act)
assert isinstance(act, gluon.Block)
return act | def _get_activation(self, act):
"""Get activation block based on the name. """
if isinstance(act, str):
if act.lower() == 'gelu':
return GELU()
else:
return gluon.nn.Activation(act)
assert isinstance(act, gluon.Block)
return act | [
"Get",
"activation",
"block",
"based",
"on",
"the",
"name",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/model/transformer.py#L116-L124 | [
"def",
"_get_activation",
"(",
"self",
",",
"act",
")",
":",
"if",
"isinstance",
"(",
"act",
",",
"str",
")",
":",
"if",
"act",
".",
"lower",
"(",
")",
"==",
"'gelu'",
":",
"return",
"GELU",
"(",
")",
"else",
":",
"return",
"gluon",
".",
"nn",
".... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | BasePositionwiseFFN.hybrid_forward | Position-wise encoding of the inputs.
Parameters
----------
inputs : Symbol or NDArray
Input sequence. Shape (batch_size, length, C_in)
Returns
-------
outputs : Symbol or NDArray
Shape (batch_size, length, C_out) | src/gluonnlp/model/transformer.py | def hybrid_forward(self, F, inputs): # pylint: disable=arguments-differ
# pylint: disable=unused-argument
"""Position-wise encoding of the inputs.
Parameters
----------
inputs : Symbol or NDArray
Input sequence. Shape (batch_size, length, C_in)
Returns
... | def hybrid_forward(self, F, inputs): # pylint: disable=arguments-differ
# pylint: disable=unused-argument
"""Position-wise encoding of the inputs.
Parameters
----------
inputs : Symbol or NDArray
Input sequence. Shape (batch_size, length, C_in)
Returns
... | [
"Position",
"-",
"wise",
"encoding",
"of",
"the",
"inputs",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/model/transformer.py#L126-L149 | [
"def",
"hybrid_forward",
"(",
"self",
",",
"F",
",",
"inputs",
")",
":",
"# pylint: disable=arguments-differ",
"# pylint: disable=unused-argument",
"outputs",
"=",
"self",
".",
"ffn_1",
"(",
"inputs",
")",
"if",
"self",
".",
"activation",
":",
"outputs",
"=",
"s... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | BaseTransformerEncoderCell.hybrid_forward | Transformer Encoder Attention Cell.
Parameters
----------
inputs : Symbol or NDArray
Input sequence. Shape (batch_size, length, C_in)
mask : Symbol or NDArray or None
Mask for inputs. Shape (batch_size, length, length)
Returns
-------
enc... | src/gluonnlp/model/transformer.py | def hybrid_forward(self, F, inputs, mask=None): # pylint: disable=arguments-differ
# pylint: disable=unused-argument
"""Transformer Encoder Attention Cell.
Parameters
----------
inputs : Symbol or NDArray
Input sequence. Shape (batch_size, length, C_in)
mask... | def hybrid_forward(self, F, inputs, mask=None): # pylint: disable=arguments-differ
# pylint: disable=unused-argument
"""Transformer Encoder Attention Cell.
Parameters
----------
inputs : Symbol or NDArray
Input sequence. Shape (batch_size, length, C_in)
mask... | [
"Transformer",
"Encoder",
"Attention",
"Cell",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/model/transformer.py#L236-L267 | [
"def",
"hybrid_forward",
"(",
"self",
",",
"F",
",",
"inputs",
",",
"mask",
"=",
"None",
")",
":",
"# pylint: disable=arguments-differ",
"# pylint: disable=unused-argument",
"outputs",
",",
"attention_weights",
"=",
"self",
".",
"attention_cell",
"(",
"inputs",
",",... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | TransformerDecoderCell.hybrid_forward | Transformer Decoder Attention Cell.
Parameters
----------
inputs : Symbol or NDArray
Input sequence. Shape (batch_size, length, C_in)
mem_value : Symbol or NDArrays
Memory value, i.e. output of the encoder. Shape (batch_size, mem_length, C_in)
mask : Symb... | src/gluonnlp/model/transformer.py | def hybrid_forward(self, F, inputs, mem_value, mask=None, mem_mask=None): #pylint: disable=unused-argument
# pylint: disable=arguments-differ
"""Transformer Decoder Attention Cell.
Parameters
----------
inputs : Symbol or NDArray
Input sequence. Shape (batch_size, ... | def hybrid_forward(self, F, inputs, mem_value, mask=None, mem_mask=None): #pylint: disable=unused-argument
# pylint: disable=arguments-differ
"""Transformer Decoder Attention Cell.
Parameters
----------
inputs : Symbol or NDArray
Input sequence. Shape (batch_size, ... | [
"Transformer",
"Decoder",
"Attention",
"Cell",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/model/transformer.py#L778-L823 | [
"def",
"hybrid_forward",
"(",
"self",
",",
"F",
",",
"inputs",
",",
"mem_value",
",",
"mask",
"=",
"None",
",",
"mem_mask",
"=",
"None",
")",
":",
"#pylint: disable=unused-argument",
"# pylint: disable=arguments-differ",
"outputs",
",",
"attention_in_outputs",
"=",... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | TransformerDecoder.init_state_from_encoder | Initialize the state from the encoder outputs.
Parameters
----------
encoder_outputs : list
encoder_valid_length : NDArray or None
Returns
-------
decoder_states : list
The decoder states, includes:
- mem_value : NDArray
- me... | src/gluonnlp/model/transformer.py | def init_state_from_encoder(self, encoder_outputs, encoder_valid_length=None):
"""Initialize the state from the encoder outputs.
Parameters
----------
encoder_outputs : list
encoder_valid_length : NDArray or None
Returns
-------
decoder_states : list
... | def init_state_from_encoder(self, encoder_outputs, encoder_valid_length=None):
"""Initialize the state from the encoder outputs.
Parameters
----------
encoder_outputs : list
encoder_valid_length : NDArray or None
Returns
-------
decoder_states : list
... | [
"Initialize",
"the",
"state",
"from",
"the",
"encoder",
"outputs",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/model/transformer.py#L905-L932 | [
"def",
"init_state_from_encoder",
"(",
"self",
",",
"encoder_outputs",
",",
"encoder_valid_length",
"=",
"None",
")",
":",
"mem_value",
"=",
"encoder_outputs",
"decoder_states",
"=",
"[",
"mem_value",
"]",
"mem_length",
"=",
"mem_value",
".",
"shape",
"[",
"1",
... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | TransformerDecoder.decode_seq | Decode the decoder inputs. This function is only used for training.
Parameters
----------
inputs : NDArray, Shape (batch_size, length, C_in)
states : list of NDArrays or None
Initial states. The list of decoder states
valid_length : NDArray or None
Valid ... | src/gluonnlp/model/transformer.py | def decode_seq(self, inputs, states, valid_length=None):
"""Decode the decoder inputs. This function is only used for training.
Parameters
----------
inputs : NDArray, Shape (batch_size, length, C_in)
states : list of NDArrays or None
Initial states. The list of deco... | def decode_seq(self, inputs, states, valid_length=None):
"""Decode the decoder inputs. This function is only used for training.
Parameters
----------
inputs : NDArray, Shape (batch_size, length, C_in)
states : list of NDArrays or None
Initial states. The list of deco... | [
"Decode",
"the",
"decoder",
"inputs",
".",
"This",
"function",
"is",
"only",
"used",
"for",
"training",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/model/transformer.py#L934-L982 | [
"def",
"decode_seq",
"(",
"self",
",",
"inputs",
",",
"states",
",",
"valid_length",
"=",
"None",
")",
":",
"batch_size",
"=",
"inputs",
".",
"shape",
"[",
"0",
"]",
"length",
"=",
"inputs",
".",
"shape",
"[",
"1",
"]",
"length_array",
"=",
"mx",
"."... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | ParallelTransformer.forward_backward | Perform forward and backward computation for a batch of src seq and dst seq | src/gluonnlp/model/transformer.py | def forward_backward(self, x):
"""Perform forward and backward computation for a batch of src seq and dst seq"""
(src_seq, tgt_seq, src_valid_length, tgt_valid_length), batch_size = x
with mx.autograd.record():
out, _ = self._model(src_seq, tgt_seq[:, :-1],
... | def forward_backward(self, x):
"""Perform forward and backward computation for a batch of src seq and dst seq"""
(src_seq, tgt_seq, src_valid_length, tgt_valid_length), batch_size = x
with mx.autograd.record():
out, _ = self._model(src_seq, tgt_seq[:, :-1],
... | [
"Perform",
"forward",
"and",
"backward",
"computation",
"for",
"a",
"batch",
"of",
"src",
"seq",
"and",
"dst",
"seq"
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/model/transformer.py#L1274-L1284 | [
"def",
"forward_backward",
"(",
"self",
",",
"x",
")",
":",
"(",
"src_seq",
",",
"tgt_seq",
",",
"src_valid_length",
",",
"tgt_valid_length",
")",
",",
"batch_size",
"=",
"x",
"with",
"mx",
".",
"autograd",
".",
"record",
"(",
")",
":",
"out",
",",
"_"... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | parse_args | Parse arguments. | scripts/natural_language_inference/main.py | def parse_args():
"""
Parse arguments.
"""
parser = argparse.ArgumentParser()
parser.add_argument('--gpu-id', type=int, default=0,
help='GPU id (-1 means CPU)')
parser.add_argument('--train-file', default='snli_1.0/snli_1.0_train.txt',
help='traini... | def parse_args():
"""
Parse arguments.
"""
parser = argparse.ArgumentParser()
parser.add_argument('--gpu-id', type=int, default=0,
help='GPU id (-1 means CPU)')
parser.add_argument('--train-file', default='snli_1.0/snli_1.0_train.txt',
help='traini... | [
"Parse",
"arguments",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/natural_language_inference/main.py#L53-L97 | [
"def",
"parse_args",
"(",
")",
":",
"parser",
"=",
"argparse",
".",
"ArgumentParser",
"(",
")",
"parser",
".",
"add_argument",
"(",
"'--gpu-id'",
",",
"type",
"=",
"int",
",",
"default",
"=",
"0",
",",
"help",
"=",
"'GPU id (-1 means CPU)'",
")",
"parser",... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | train_model | Train model and validate/save every epoch. | scripts/natural_language_inference/main.py | def train_model(model, train_data_loader, val_data_loader, embedding, ctx, args):
"""
Train model and validate/save every epoch.
"""
logger.info(vars(args))
# Initialization
model.hybridize()
model.collect_params().initialize(mx.init.Normal(0.01), ctx=ctx)
model.word_emb.weight.set_data... | def train_model(model, train_data_loader, val_data_loader, embedding, ctx, args):
"""
Train model and validate/save every epoch.
"""
logger.info(vars(args))
# Initialization
model.hybridize()
model.collect_params().initialize(mx.init.Normal(0.01), ctx=ctx)
model.word_emb.weight.set_data... | [
"Train",
"model",
"and",
"validate",
"/",
"save",
"every",
"epoch",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/natural_language_inference/main.py#L99-L163 | [
"def",
"train_model",
"(",
"model",
",",
"train_data_loader",
",",
"val_data_loader",
",",
"embedding",
",",
"ctx",
",",
"args",
")",
":",
"logger",
".",
"info",
"(",
"vars",
"(",
"args",
")",
")",
"# Initialization",
"model",
".",
"hybridize",
"(",
")",
... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | main | Entry point: train or test. | scripts/natural_language_inference/main.py | def main(args):
"""
Entry point: train or test.
"""
json.dump(vars(args), open(os.path.join(args.output_dir, 'config.json'), 'w'))
if args.gpu_id == -1:
ctx = mx.cpu()
else:
ctx = mx.gpu(args.gpu_id)
mx.random.seed(args.seed, ctx=ctx)
if args.mode == 'train':
t... | def main(args):
"""
Entry point: train or test.
"""
json.dump(vars(args), open(os.path.join(args.output_dir, 'config.json'), 'w'))
if args.gpu_id == -1:
ctx = mx.cpu()
else:
ctx = mx.gpu(args.gpu_id)
mx.random.seed(args.seed, ctx=ctx)
if args.mode == 'train':
t... | [
"Entry",
"point",
":",
"train",
"or",
"test",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/scripts/natural_language_inference/main.py#L184-L231 | [
"def",
"main",
"(",
"args",
")",
":",
"json",
".",
"dump",
"(",
"vars",
"(",
"args",
")",
",",
"open",
"(",
"os",
".",
"path",
".",
"join",
"(",
"args",
".",
"output_dir",
",",
"'config.json'",
")",
",",
"'w'",
")",
")",
"if",
"args",
".",
"gpu... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | UnigramCandidateSampler.hybrid_forward | Draw samples from uniform distribution and return sampled candidates.
Parameters
----------
candidates_like: mxnet.nd.NDArray or mxnet.sym.Symbol
This input specifies the shape of the to be sampled candidates. #
TODO shape selection is not yet supported. Shape must be sp... | src/gluonnlp/data/candidate_sampler.py | def hybrid_forward(self, F, candidates_like, prob, alias):
# pylint: disable=unused-argument
"""Draw samples from uniform distribution and return sampled candidates.
Parameters
----------
candidates_like: mxnet.nd.NDArray or mxnet.sym.Symbol
This input specifies the ... | def hybrid_forward(self, F, candidates_like, prob, alias):
# pylint: disable=unused-argument
"""Draw samples from uniform distribution and return sampled candidates.
Parameters
----------
candidates_like: mxnet.nd.NDArray or mxnet.sym.Symbol
This input specifies the ... | [
"Draw",
"samples",
"from",
"uniform",
"distribution",
"and",
"return",
"sampled",
"candidates",
"."
] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/data/candidate_sampler.py#L105-L134 | [
"def",
"hybrid_forward",
"(",
"self",
",",
"F",
",",
"candidates_like",
",",
"prob",
",",
"alias",
")",
":",
"# pylint: disable=unused-argument",
"flat_shape",
"=",
"functools",
".",
"reduce",
"(",
"operator",
".",
"mul",
",",
"self",
".",
"_shape",
")",
"id... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba |
train | Logger.add | r"""Add a handler sending log messages to a sink adequately configured.
Parameters
----------
sink : |file-like object|_, |str|, |Path|, |function|_, |Handler| or |class|_
An object in charge of receiving formatted logging messages and propagating them to an
appropriate ... | loguru/_logger.py | def add(
self,
sink,
*,
level=_defaults.LOGURU_LEVEL,
format=_defaults.LOGURU_FORMAT,
filter=_defaults.LOGURU_FILTER,
colorize=_defaults.LOGURU_COLORIZE,
serialize=_defaults.LOGURU_SERIALIZE,
backtrace=_defaults.LOGURU_BACKTRACE,
diagnose=_... | def add(
self,
sink,
*,
level=_defaults.LOGURU_LEVEL,
format=_defaults.LOGURU_FORMAT,
filter=_defaults.LOGURU_FILTER,
colorize=_defaults.LOGURU_COLORIZE,
serialize=_defaults.LOGURU_SERIALIZE,
backtrace=_defaults.LOGURU_BACKTRACE,
diagnose=_... | [
"r",
"Add",
"a",
"handler",
"sending",
"log",
"messages",
"to",
"a",
"sink",
"adequately",
"configured",
"."
] | Delgan/loguru | python | https://github.com/Delgan/loguru/blob/6571879c37904e3a18567e694d70651c6886b860/loguru/_logger.py#L166-L843 | [
"def",
"add",
"(",
"self",
",",
"sink",
",",
"*",
",",
"level",
"=",
"_defaults",
".",
"LOGURU_LEVEL",
",",
"format",
"=",
"_defaults",
".",
"LOGURU_FORMAT",
",",
"filter",
"=",
"_defaults",
".",
"LOGURU_FILTER",
",",
"colorize",
"=",
"_defaults",
".",
"... | 6571879c37904e3a18567e694d70651c6886b860 |
train | Logger.remove | Remove a previously added handler and stop sending logs to its sink.
Parameters
----------
handler_id : |int| or ``None``
The id of the sink to remove, as it was returned by the |add| method. If ``None``, all
handlers are removed. The pre-configured handler is guaranteed... | loguru/_logger.py | def remove(self, handler_id=None):
"""Remove a previously added handler and stop sending logs to its sink.
Parameters
----------
handler_id : |int| or ``None``
The id of the sink to remove, as it was returned by the |add| method. If ``None``, all
handlers are rem... | def remove(self, handler_id=None):
"""Remove a previously added handler and stop sending logs to its sink.
Parameters
----------
handler_id : |int| or ``None``
The id of the sink to remove, as it was returned by the |add| method. If ``None``, all
handlers are rem... | [
"Remove",
"a",
"previously",
"added",
"handler",
"and",
"stop",
"sending",
"logs",
"to",
"its",
"sink",
"."
] | Delgan/loguru | python | https://github.com/Delgan/loguru/blob/6571879c37904e3a18567e694d70651c6886b860/loguru/_logger.py#L845-L883 | [
"def",
"remove",
"(",
"self",
",",
"handler_id",
"=",
"None",
")",
":",
"with",
"self",
".",
"_lock",
":",
"handlers",
"=",
"self",
".",
"_handlers",
".",
"copy",
"(",
")",
"if",
"handler_id",
"is",
"None",
":",
"for",
"handler",
"in",
"handlers",
".... | 6571879c37904e3a18567e694d70651c6886b860 |
train | Logger.catch | Return a decorator to automatically log possibly caught error in wrapped function.
This is useful to ensure unexpected exceptions are logged, the entire program can be
wrapped by this method. This is also very useful to decorate |Thread.run| methods while
using threads to propagate errors to th... | loguru/_logger.py | def catch(
self,
exception=Exception,
*,
level="ERROR",
reraise=False,
message="An error has been caught in function '{record[function]}', "
"process '{record[process].name}' ({record[process].id}), "
"thread '{record[thread].name}' ({record[thread].id}):"... | def catch(
self,
exception=Exception,
*,
level="ERROR",
reraise=False,
message="An error has been caught in function '{record[function]}', "
"process '{record[process].name}' ({record[process].id}), "
"thread '{record[thread].name}' ({record[thread].id}):"... | [
"Return",
"a",
"decorator",
"to",
"automatically",
"log",
"possibly",
"caught",
"error",
"in",
"wrapped",
"function",
"."
] | Delgan/loguru | python | https://github.com/Delgan/loguru/blob/6571879c37904e3a18567e694d70651c6886b860/loguru/_logger.py#L885-L1011 | [
"def",
"catch",
"(",
"self",
",",
"exception",
"=",
"Exception",
",",
"*",
",",
"level",
"=",
"\"ERROR\"",
",",
"reraise",
"=",
"False",
",",
"message",
"=",
"\"An error has been caught in function '{record[function]}', \"",
"\"process '{record[process].name}' ({record[pr... | 6571879c37904e3a18567e694d70651c6886b860 |
train | Logger.opt | r"""Parametrize a logging call to slightly change generated log message.
Parameters
----------
exception : |bool|, |tuple| or |Exception|, optional
If it does not evaluate as ``False``, the passed exception is formatted and added to the
log message. It could be an |Excep... | loguru/_logger.py | def opt(self, *, exception=None, record=False, lazy=False, ansi=False, raw=False, depth=0):
r"""Parametrize a logging call to slightly change generated log message.
Parameters
----------
exception : |bool|, |tuple| or |Exception|, optional
If it does not evaluate as ``False`... | def opt(self, *, exception=None, record=False, lazy=False, ansi=False, raw=False, depth=0):
r"""Parametrize a logging call to slightly change generated log message.
Parameters
----------
exception : |bool|, |tuple| or |Exception|, optional
If it does not evaluate as ``False`... | [
"r",
"Parametrize",
"a",
"logging",
"call",
"to",
"slightly",
"change",
"generated",
"log",
"message",
"."
] | Delgan/loguru | python | https://github.com/Delgan/loguru/blob/6571879c37904e3a18567e694d70651c6886b860/loguru/_logger.py#L1013-L1079 | [
"def",
"opt",
"(",
"self",
",",
"*",
",",
"exception",
"=",
"None",
",",
"record",
"=",
"False",
",",
"lazy",
"=",
"False",
",",
"ansi",
"=",
"False",
",",
"raw",
"=",
"False",
",",
"depth",
"=",
"0",
")",
":",
"return",
"Logger",
"(",
"self",
... | 6571879c37904e3a18567e694d70651c6886b860 |
train | Logger.bind | Bind attributes to the ``extra`` dict of each logged message record.
This is used to add custom context to each logging call.
Parameters
----------
**kwargs
Mapping between keys and values that will be added to the ``extra`` dict.
Returns
-------
:c... | loguru/_logger.py | def bind(_self, **kwargs):
"""Bind attributes to the ``extra`` dict of each logged message record.
This is used to add custom context to each logging call.
Parameters
----------
**kwargs
Mapping between keys and values that will be added to the ``extra`` dict.
... | def bind(_self, **kwargs):
"""Bind attributes to the ``extra`` dict of each logged message record.
This is used to add custom context to each logging call.
Parameters
----------
**kwargs
Mapping between keys and values that will be added to the ``extra`` dict.
... | [
"Bind",
"attributes",
"to",
"the",
"extra",
"dict",
"of",
"each",
"logged",
"message",
"record",
"."
] | Delgan/loguru | python | https://github.com/Delgan/loguru/blob/6571879c37904e3a18567e694d70651c6886b860/loguru/_logger.py#L1081-L1123 | [
"def",
"bind",
"(",
"_self",
",",
"*",
"*",
"kwargs",
")",
":",
"return",
"Logger",
"(",
"{",
"*",
"*",
"_self",
".",
"_extra",
",",
"*",
"*",
"kwargs",
"}",
",",
"_self",
".",
"_exception",
",",
"_self",
".",
"_record",
",",
"_self",
".",
"_lazy... | 6571879c37904e3a18567e694d70651c6886b860 |
train | Logger.level | Add, update or retrieve a logging level.
Logging levels are defined by their ``name`` to which a severity ``no``, an ansi ``color``
and an ``icon`` are associated and possibly modified at run-time. To |log| to a custom
level, you should necessarily use its name, the severity number is not linke... | loguru/_logger.py | def level(self, name, no=None, color=None, icon=None):
"""Add, update or retrieve a logging level.
Logging levels are defined by their ``name`` to which a severity ``no``, an ansi ``color``
and an ``icon`` are associated and possibly modified at run-time. To |log| to a custom
level, you... | def level(self, name, no=None, color=None, icon=None):
"""Add, update or retrieve a logging level.
Logging levels are defined by their ``name`` to which a severity ``no``, an ansi ``color``
and an ``icon`` are associated and possibly modified at run-time. To |log| to a custom
level, you... | [
"Add",
"update",
"or",
"retrieve",
"a",
"logging",
"level",
"."
] | Delgan/loguru | python | https://github.com/Delgan/loguru/blob/6571879c37904e3a18567e694d70651c6886b860/loguru/_logger.py#L1125-L1212 | [
"def",
"level",
"(",
"self",
",",
"name",
",",
"no",
"=",
"None",
",",
"color",
"=",
"None",
",",
"icon",
"=",
"None",
")",
":",
"if",
"not",
"isinstance",
"(",
"name",
",",
"str",
")",
":",
"raise",
"ValueError",
"(",
"\"Invalid level name, it should ... | 6571879c37904e3a18567e694d70651c6886b860 |
train | Logger.configure | Configure the core logger.
It should be noted that ``extra`` values set using this function are available across all
modules, so this is the best way to set overall default values.
Parameters
----------
handlers : |list| of |dict|, optional
A list of each handler to... | loguru/_logger.py | def configure(self, *, handlers=None, levels=None, extra=None, activation=None):
"""Configure the core logger.
It should be noted that ``extra`` values set using this function are available across all
modules, so this is the best way to set overall default values.
Parameters
--... | def configure(self, *, handlers=None, levels=None, extra=None, activation=None):
"""Configure the core logger.
It should be noted that ``extra`` values set using this function are available across all
modules, so this is the best way to set overall default values.
Parameters
--... | [
"Configure",
"the",
"core",
"logger",
"."
] | Delgan/loguru | python | https://github.com/Delgan/loguru/blob/6571879c37904e3a18567e694d70651c6886b860/loguru/_logger.py#L1255-L1330 | [
"def",
"configure",
"(",
"self",
",",
"*",
",",
"handlers",
"=",
"None",
",",
"levels",
"=",
"None",
",",
"extra",
"=",
"None",
",",
"activation",
"=",
"None",
")",
":",
"if",
"handlers",
"is",
"not",
"None",
":",
"self",
".",
"remove",
"(",
")",
... | 6571879c37904e3a18567e694d70651c6886b860 |
train | Logger.parse | Parse raw logs and extract each entry as a |dict|.
The logging format has to be specified as the regex ``pattern``, it will then be
used to parse the ``file`` and retrieve each entries based on the named groups present
in the regex.
Parameters
----------
file : |str|, |... | loguru/_logger.py | def parse(file, pattern, *, cast={}, chunk=2 ** 16):
"""
Parse raw logs and extract each entry as a |dict|.
The logging format has to be specified as the regex ``pattern``, it will then be
used to parse the ``file`` and retrieve each entries based on the named groups present
in ... | def parse(file, pattern, *, cast={}, chunk=2 ** 16):
"""
Parse raw logs and extract each entry as a |dict|.
The logging format has to be specified as the regex ``pattern``, it will then be
used to parse the ``file`` and retrieve each entries based on the named groups present
in ... | [
"Parse",
"raw",
"logs",
"and",
"extract",
"each",
"entry",
"as",
"a",
"|dict|",
"."
] | Delgan/loguru | python | https://github.com/Delgan/loguru/blob/6571879c37904e3a18567e694d70651c6886b860/loguru/_logger.py#L1359-L1450 | [
"def",
"parse",
"(",
"file",
",",
"pattern",
",",
"*",
",",
"cast",
"=",
"{",
"}",
",",
"chunk",
"=",
"2",
"**",
"16",
")",
":",
"if",
"isinstance",
"(",
"file",
",",
"(",
"str",
",",
"PathLike",
")",
")",
":",
"should_close",
"=",
"True",
"fil... | 6571879c37904e3a18567e694d70651c6886b860 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.