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dmlc/gluon-nlp | src/gluonnlp/model/language_model.py | standard_lstm_lm_200 | def standard_lstm_lm_200(dataset_name=None, vocab=None, pretrained=False, ctx=cpu(),
root=os.path.join(get_home_dir(), 'models'), **kwargs):
r"""Standard 2-layer LSTM language model with tied embedding and output weights.
Both embedding and hidden dimensions are 200.
Parameters
... | python | def standard_lstm_lm_200(dataset_name=None, vocab=None, pretrained=False, ctx=cpu(),
root=os.path.join(get_home_dir(), 'models'), **kwargs):
r"""Standard 2-layer LSTM language model with tied embedding and output weights.
Both embedding and hidden dimensions are 200.
Parameters
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dmlc/gluon-nlp | src/gluonnlp/model/language_model.py | big_rnn_lm_2048_512 | def big_rnn_lm_2048_512(dataset_name=None, vocab=None, pretrained=False, ctx=cpu(),
root=os.path.join(get_home_dir(), 'models'), **kwargs):
r"""Big 1-layer LSTMP language model.
Both embedding and projection size are 512. Hidden size is 2048.
Parameters
----------
dataset_n... | python | def big_rnn_lm_2048_512(dataset_name=None, vocab=None, pretrained=False, ctx=cpu(),
root=os.path.join(get_home_dir(), 'models'), **kwargs):
r"""Big 1-layer LSTMP language model.
Both embedding and projection size are 512. Hidden size is 2048.
Parameters
----------
dataset_n... | [
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dmlc/gluon-nlp | src/gluonnlp/model/language_model.py | AWDRNN.forward | def forward(self, inputs, begin_state=None): # pylint: disable=arguments-differ
"""Implement forward computation.
Parameters
-----------
inputs : NDArray
input tensor with shape `(sequence_length, batch_size)`
when `layout` is "TNC".
begin_state : list
... | python | def forward(self, inputs, begin_state=None): # pylint: disable=arguments-differ
"""Implement forward computation.
Parameters
-----------
inputs : NDArray
input tensor with shape `(sequence_length, batch_size)`
when `layout` is "TNC".
begin_state : list
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dmlc/gluon-nlp | src/gluonnlp/model/language_model.py | BigRNN.forward | def forward(self, inputs, begin_state): # pylint: disable=arguments-differ
"""Implement forward computation.
Parameters
-----------
inputs : NDArray
input tensor with shape `(sequence_length, batch_size)`
when `layout` is "TNC".
begin_state : list
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"""Implement forward computation.
Parameters
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inputs : NDArray
input tensor with shape `(sequence_length, batch_size)`
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dmlc/gluon-nlp | src/gluonnlp/model/seq2seq_encoder_decoder.py | _get_cell_type | def _get_cell_type(cell_type):
"""Get the object type of the cell by parsing the input
Parameters
----------
cell_type : str or type
Returns
-------
cell_constructor: type
The constructor of the RNNCell
"""
if isinstance(cell_type, str):
if cell_type == 'lstm':
... | python | def _get_cell_type(cell_type):
"""Get the object type of the cell by parsing the input
Parameters
----------
cell_type : str or type
Returns
-------
cell_constructor: type
The constructor of the RNNCell
"""
if isinstance(cell_type, str):
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dmlc/gluon-nlp | src/gluonnlp/data/batchify/embedding.py | _get_context | def _get_context(center_idx, sentence_boundaries, window_size,
random_window_size, seed):
"""Compute the context with respect to a center word in a sentence.
Takes an numpy array of sentences boundaries.
"""
random.seed(seed + center_idx)
sentence_index = np.searchsorted(sentence... | python | def _get_context(center_idx, sentence_boundaries, window_size,
random_window_size, seed):
"""Compute the context with respect to a center word in a sentence.
Takes an numpy array of sentences boundaries.
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dmlc/gluon-nlp | scripts/sentiment_analysis/text_cnn.py | model | def model(dropout, vocab, model_mode, output_size):
"""Construct the model."""
textCNN = SentimentNet(dropout=dropout, vocab_size=len(vocab), model_mode=model_mode,\
output_size=output_size)
textCNN.hybridize()
return textCNN | python | def model(dropout, vocab, model_mode, output_size):
"""Construct the model."""
textCNN = SentimentNet(dropout=dropout, vocab_size=len(vocab), model_mode=model_mode,\
output_size=output_size)
textCNN.hybridize()
return textCNN | [
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dmlc/gluon-nlp | scripts/sentiment_analysis/text_cnn.py | init | def init(textCNN, vocab, model_mode, context, lr):
"""Initialize parameters."""
textCNN.initialize(mx.init.Xavier(), ctx=context, force_reinit=True)
if model_mode != 'rand':
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textCNN.embedding_... | python | def init(textCNN, vocab, model_mode, context, lr):
"""Initialize parameters."""
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dmlc/gluon-nlp | scripts/bert/bert_qa_dataset.py | preprocess_dataset | def preprocess_dataset(dataset, transform, num_workers=8):
"""Use multiprocessing to perform transform for dataset.
Parameters
----------
dataset: dataset-like object
Source dataset.
transform: callable
Transformer function.
num_workers: int, default 8
The number of mult... | python | def preprocess_dataset(dataset, transform, num_workers=8):
"""Use multiprocessing to perform transform for dataset.
Parameters
----------
dataset: dataset-like object
Source dataset.
transform: callable
Transformer function.
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dmlc/gluon-nlp | src/gluonnlp/model/__init__.py | get_model | def get_model(name, dataset_name='wikitext-2', **kwargs):
"""Returns a pre-defined model by name.
Parameters
----------
name : str
Name of the model.
dataset_name : str or None, default 'wikitext-2'.
The dataset name on which the pre-trained model is trained.
For language mo... | python | def get_model(name, dataset_name='wikitext-2', **kwargs):
"""Returns a pre-defined model by name.
Parameters
----------
name : str
Name of the model.
dataset_name : str or None, default 'wikitext-2'.
The dataset name on which the pre-trained model is trained.
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dmlc/gluon-nlp | src/gluonnlp/model/attention_cell.py | _masked_softmax | def _masked_softmax(F, att_score, mask, dtype):
"""Ignore the masked elements when calculating the softmax
Parameters
----------
F : symbol or ndarray
att_score : Symborl or NDArray
Shape (batch_size, query_length, memory_length)
mask : Symbol or NDArray or None
Shape (batch_siz... | python | def _masked_softmax(F, att_score, mask, dtype):
"""Ignore the masked elements when calculating the softmax
Parameters
----------
F : symbol or ndarray
att_score : Symborl or NDArray
Shape (batch_size, query_length, memory_length)
mask : Symbol or NDArray or None
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dmlc/gluon-nlp | src/gluonnlp/model/attention_cell.py | AttentionCell._read_by_weight | def _read_by_weight(self, F, att_weights, value):
"""Read from the value matrix given the attention weights.
Parameters
----------
F : symbol or ndarray
att_weights : Symbol or NDArray
Attention weights.
For single-head attention,
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Parameters
----------
F : symbol or ndarray
att_weights : Symbol or NDArray
Attention weights.
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dmlc/gluon-nlp | scripts/machine_translation/translation.py | BeamSearchTranslator.translate | def translate(self, src_seq, src_valid_length):
"""Get the translation result given the input sentence.
Parameters
----------
src_seq : mx.nd.NDArray
Shape (batch_size, length)
src_valid_length : mx.nd.NDArray
Shape (batch_size,)
Returns
... | python | def translate(self, src_seq, src_valid_length):
"""Get the translation result given the input sentence.
Parameters
----------
src_seq : mx.nd.NDArray
Shape (batch_size, length)
src_valid_length : mx.nd.NDArray
Shape (batch_size,)
Returns
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dmlc/gluon-nlp | scripts/parsing/parser/evaluate/evaluate.py | evaluate_official_script | def evaluate_official_script(parser, vocab, num_buckets_test, test_batch_size, test_file, output_file,
debug=False):
"""Evaluate parser on a data set
Parameters
----------
parser : BiaffineParser
biaffine parser
vocab : ParserVocabulary
vocabulary built ... | python | def evaluate_official_script(parser, vocab, num_buckets_test, test_batch_size, test_file, output_file,
debug=False):
"""Evaluate parser on a data set
Parameters
----------
parser : BiaffineParser
biaffine parser
vocab : ParserVocabulary
vocabulary built ... | [
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dmlc/gluon-nlp | scripts/parsing/parser/biaffine_parser.py | BiaffineParser.parameter_from_numpy | def parameter_from_numpy(self, name, array):
""" Create parameter with its value initialized according to a numpy tensor
Parameters
----------
name : str
parameter name
array : np.ndarray
initiation value
Returns
-------
mxnet.glu... | python | def parameter_from_numpy(self, name, array):
""" Create parameter with its value initialized according to a numpy tensor
Parameters
----------
name : str
parameter name
array : np.ndarray
initiation value
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dmlc/gluon-nlp | scripts/parsing/parser/biaffine_parser.py | BiaffineParser.forward | def forward(self, word_inputs, tag_inputs, arc_targets=None, rel_targets=None):
"""Run decoding
Parameters
----------
word_inputs : mxnet.ndarray.NDArray
word indices of seq_len x batch_size
tag_inputs : mxnet.ndarray.NDArray
tag indices of seq_len x batc... | python | def forward(self, word_inputs, tag_inputs, arc_targets=None, rel_targets=None):
"""Run decoding
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word indices of seq_len x batch_size
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dmlc/gluon-nlp | scripts/parsing/parser/biaffine_parser.py | BiaffineParser.save_parameters | def save_parameters(self, filename):
"""Save model
Parameters
----------
filename : str
path to model file
"""
params = self._collect_params_with_prefix()
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params.pop('pret_... | python | def save_parameters(self, filename):
"""Save model
Parameters
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filename : str
path to model file
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dmlc/gluon-nlp | src/gluonnlp/data/dataloader.py | _worker_fn | def _worker_fn(samples, batchify_fn, dataset=None):
"""Function for processing data in worker process."""
# pylint: disable=unused-argument
# it is required that each worker process has to fork a new MXIndexedRecordIO handle
# preserving dataset as global variable can save tons of overhead and is safe i... | python | def _worker_fn(samples, batchify_fn, dataset=None):
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# pylint: disable=unused-argument
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dmlc/gluon-nlp | src/gluonnlp/data/dataloader.py | _thread_worker_fn | def _thread_worker_fn(samples, batchify_fn, dataset):
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dmlc/gluon-nlp | src/gluonnlp/embedding/token_embedding.py | create | def create(embedding_name, **kwargs):
"""Creates an instance of token embedding.
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dmlc/gluon-nlp | src/gluonnlp/embedding/token_embedding.py | list_sources | def list_sources(embedding_name=None):
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dmlc/gluon-nlp | src/gluonnlp/embedding/token_embedding.py | TokenEmbedding._load_embedding | def _load_embedding(self, pretrained_file_path, elem_delim,
encoding='utf8'):
"""Load embedding vectors from a pre-trained token embedding file.
Both text files and TokenEmbedding serialization files are supported.
elem_delim and encoding are ignored for non-text files.
... | python | def _load_embedding(self, pretrained_file_path, elem_delim,
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dmlc/gluon-nlp | src/gluonnlp/embedding/token_embedding.py | TokenEmbedding._load_embedding_txt | def _load_embedding_txt(self, pretrained_file_path, elem_delim, encoding='utf8'):
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dmlc/gluon-nlp | src/gluonnlp/embedding/token_embedding.py | TokenEmbedding._load_embedding_serialized | def _load_embedding_serialized(self, pretrained_file_path):
"""Load embedding vectors from a pre-trained token embedding file.
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dmlc/gluon-nlp | src/gluonnlp/embedding/token_embedding.py | TokenEmbedding._check_vector_update | 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... | python | 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.'
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dmlc/gluon-nlp | src/gluonnlp/embedding/token_embedding.py | TokenEmbedding._check_source | 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... | python | def _check_source(cls, source_file_hash, source):
"""Checks if a pre-trained token embedding source name is valid.
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source : str
The pre-trained token embedding source.
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dmlc/gluon-nlp | src/gluonnlp/embedding/token_embedding.py | TokenEmbedding.from_file | 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.
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dmlc/gluon-nlp | src/gluonnlp/embedding/token_embedding.py | TokenEmbedding.serialize | def serialize(self, file_path, compress=True):
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dmlc/gluon-nlp | src/gluonnlp/embedding/token_embedding.py | TokenEmbedding.deserialize | 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
... | python | def deserialize(cls, file_path, **kwargs):
"""Create a new TokenEmbedding from a serialized one.
TokenEmbedding is serialized by converting the list of tokens, the
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dmlc/gluon-nlp | scripts/bert/staticbert/static_export_squad.py | evaluate | def evaluate(data_source):
"""Evaluate the model on a mini-batch.
"""
log.info('Start predict')
tic = time.time()
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inputs, token_types, valid_length = batch
out = net(inputs.astype('float32').as_in_context(ctx),
token_types.astype('float32')... | python | 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
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dmlc/gluon-nlp | src/gluonnlp/data/registry.py | register | def register(class_=None, **kwargs):
"""Registers a dataset with segment specific hyperparameters.
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dmlc/gluon-nlp | src/gluonnlp/data/registry.py | create | def create(name, **kwargs):
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name : str
The dataset name (case-insensitive).
Returns
-------
An instance of :class:`mxnet.gluon.data.Dataset` constructed with the
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Parameters
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name : str
The dataset name (case-insensitive).
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dmlc/gluon-nlp | src/gluonnlp/data/registry.py | list_datasets | 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... | python | 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
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dmlc/gluon-nlp | scripts/word_embeddings/extract_vocab.py | parse_args | 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... | python | 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)
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dmlc/gluon-nlp | scripts/word_embeddings/extract_vocab.py | get_vocab | 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:
... | python | 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:
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dmlc/gluon-nlp | scripts/bert/bert.py | BERTClassifier.forward | 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.
... | python | def forward(self, inputs, token_types, valid_length=None): # pylint: disable=arguments-differ
"""Generate the unnormalized score for the given the input sequences.
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inputs : NDArray, shape (batch_size, seq_length)
Input words for the sequences.
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dmlc/gluon-nlp | scripts/word_embeddings/evaluation.py | add_parameters | 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... | python | 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(
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dmlc/gluon-nlp | scripts/word_embeddings/evaluation.py | validate_args | 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... | python | 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(
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dmlc/gluon-nlp | scripts/word_embeddings/evaluation.py | iterate_similarity_datasets | 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)
... | python | 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)
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dmlc/gluon-nlp | scripts/word_embeddings/evaluation.py | iterate_analogy_datasets | 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... | python | def iterate_analogy_datasets(args):
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Iterates over dataset names, keyword arguments for their creation and the
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dmlc/gluon-nlp | scripts/word_embeddings/evaluation.py | get_similarity_task_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 | python | 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))
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dmlc/gluon-nlp | scripts/word_embeddings/evaluation.py | evaluate_similarity | 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(
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global_step=0):
"""Evaluate on specified similarity datasets."""
results = []
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dmlc/gluon-nlp | scripts/word_embeddings/evaluation.py | evaluate_analogy | 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... | python | 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 = []
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dmlc/gluon-nlp | scripts/word_embeddings/evaluation.py | log_similarity_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['... | python | 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([
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dmlc/gluon-nlp | scripts/bert/pretraining_utils.py | get_model_loss | 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... | python | 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,
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dmlc/gluon-nlp | scripts/bert/pretraining_utils.py | get_pretrain_dataset | 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... | python | 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... | [
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dmlc/gluon-nlp | scripts/bert/pretraining_utils.py | get_dummy_dataloader | 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_... | python | 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)
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dmlc/gluon-nlp | scripts/bert/pretraining_utils.py | save_params | 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... | python | 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.',
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dmlc/gluon-nlp | scripts/bert/pretraining_utils.py | log | 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_... | python | 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
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dmlc/gluon-nlp | scripts/bert/pretraining_utils.py | split_and_load | 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) | python | 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) | [
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dmlc/gluon-nlp | scripts/bert/pretraining_utils.py | forward | 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)
... | python | 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
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dmlc/gluon-nlp | scripts/bert/pretraining_utils.py | evaluate | 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
... | python | 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()
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begin_time = time.time()
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dmlc/gluon-nlp | scripts/bert/pretraining_utils.py | get_argparser | 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... | python | 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,
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dmlc/gluon-nlp | scripts/machine_translation/dataprocessor.py | _cache_dataset | 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... | python | 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... | [
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dmlc/gluon-nlp | scripts/machine_translation/dataprocessor.py | load_translation_data | 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_{}_{}_{}_{... | python | 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_{}_{}_{}_{... | [
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dmlc/gluon-nlp | scripts/machine_translation/dataprocessor.py | make_dataloader | 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... | python | 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)
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dmlc/gluon-nlp | src/gluonnlp/data/stream.py | _Prefetcher.run | 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... | python | 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):
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dmlc/gluon-nlp | src/gluonnlp/model/block.py | RNNCellLayer.forward | 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:
... | python | 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:
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dmlc/gluon-nlp | src/gluonnlp/model/train/embedding.py | CSREmbeddingModel.hybrid_forward | 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... | python | 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)
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dmlc/gluon-nlp | src/gluonnlp/model/train/embedding.py | FasttextEmbeddingModel.load_fasttext_format | 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... | python | 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
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dmlc/gluon-nlp | scripts/natural_language_inference/utils.py | logging_config | 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... | python | 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:
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dmlc/gluon-nlp | scripts/word_embeddings/train_glove.py | parse_args | 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... | python | 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(
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dmlc/gluon-nlp | scripts/word_embeddings/train_glove.py | get_train_data | 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... | python | 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... | [
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dmlc/gluon-nlp | scripts/word_embeddings/train_glove.py | train | 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... | python | 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,
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dmlc/gluon-nlp | scripts/word_embeddings/train_glove.py | log | 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... | python | 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):
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dmlc/gluon-nlp | scripts/word_embeddings/train_glove.py | GloVe.hybrid_forward | 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
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"""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, ).
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dmlc/gluon-nlp | src/gluonnlp/metric/masked_accuracy.py | MaskedAccuracy.update | 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... | python | 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... | [
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dmlc/gluon-nlp | scripts/natural_language_inference/decomposable_attention.py | NLIModel.hybrid_forward | 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
-------
... | python | 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
-------
... | [
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dmlc/gluon-nlp | scripts/natural_language_inference/decomposable_attention.py | IntraSentenceAttention.hybrid_forward | 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... | python | 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... | [
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dmlc/gluon-nlp | scripts/natural_language_inference/decomposable_attention.py | DecomposableAttention.hybrid_forward | 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
... | python | def hybrid_forward(self, F, a, b):
"""
Forward of Decomposable Attention layer
"""
# a.shape = [B, L1, H]
# b.shape = [B, L2, H]
# extract features
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# attention
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dmlc/gluon-nlp | src/gluonnlp/data/utils.py | count_tokens | 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
----... | python | 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)
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dmlc/gluon-nlp | src/gluonnlp/data/utils.py | slice_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 :... | python | 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
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dmlc/gluon-nlp | src/gluonnlp/data/utils.py | _slice_pad_length | 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... | python | def _slice_pad_length(num_items, length, overlap=0):
"""Calculate the padding length needed for sliced samples in order not to discard data.
Parameters
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num_items : int
Number of items in dataset before collating.
length : int
The length of each of the samples.
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dmlc/gluon-nlp | src/gluonnlp/data/utils.py | train_valid_split | 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]... | python | def train_valid_split(dataset, valid_ratio=0.05):
"""Split the dataset into training and validation sets.
Parameters
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dataset : list
A list of training samples.
valid_ratio : float, default 0.05
Proportion of training samples to use for validation set
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dmlc/gluon-nlp | src/gluonnlp/data/utils.py | _load_pretrained_vocab | 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 '$MXNET_HOME/models'
... | python | def _load_pretrained_vocab(name, root=os.path.join(get_home_dir(), 'models'), cls=None):
"""Load the accompanying vocabulary object for pre-trained model.
Parameters
----------
name : str
Name of the vocabulary, usually the name of the dataset.
root : str, default '$MXNET_HOME/models'
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dmlc/gluon-nlp | src/gluonnlp/data/utils.py | _extract_archive | 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... | python | 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
"""
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dmlc/gluon-nlp | src/gluonnlp/data/utils.py | Counter.discard | def discard(self, min_freq, unknown_token):
"""Discards tokens with frequency below min_frequency and represents them
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Parameters
----------
min_freq: int
Tokens whose frequency is under min_freq is counted as `unknown_token` in
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"""Discards tokens with frequency below min_frequency and represents them
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dmlc/gluon-nlp | scripts/machine_translation/train_transformer.py | train | 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, ... | python | 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, ... | [
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dmlc/gluon-nlp | src/gluonnlp/model/highway.py | Highway.hybrid_forward | 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... | python | def hybrid_forward(self, F, inputs, **kwargs):
# pylint: disable=unused-argument
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Forward computation for highway layer
Parameters
----------
inputs: NDArray
The input tensor is of shape `(..., input_size)`.
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dmlc/gluon-nlp | scripts/bert/bert_qa_model.py | BertForQA.forward | 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.
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"""Generate the unnormalized score for the given the input sequences.
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inputs : NDArray, shape (batch_size, seq_length)
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dmlc/gluon-nlp | src/gluonnlp/model/bilm_encoder.py | BiLMEncoder.hybrid_forward | 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
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----------
... | python | def hybrid_forward(self, F, inputs, states=None, mask=None):
# pylint: disable=arguments-differ
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dmlc/gluon-nlp | scripts/natural_language_inference/preprocess.py | main | 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... | python | def main(args):
"""
Read tokens from the provided parse tree in the SNLI dataset.
Illegal examples are removed.
"""
examples = []
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dmlc/gluon-nlp | scripts/parsing/common/k_means.py | KMeans._recenter | 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]
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"""
one iteration of k-means
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dmlc/gluon-nlp | scripts/parsing/common/k_means.py | KMeans._reindex | def _reindex(self):
"""
Index every sentence into a cluster
"""
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"""
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dmlc/gluon-nlp | scripts/machine_translation/gnmt.py | get_gnmt_encoder_decoder | 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... | python | 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,
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dmlc/gluon-nlp | scripts/machine_translation/gnmt.py | GNMTDecoder.init_state_from_encoder | 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
... | python | 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
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dmlc/gluon-nlp | scripts/machine_translation/gnmt.py | GNMTDecoder.decode_seq | 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... | python | 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
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Parameters
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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
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dmlc/gluon-nlp | scripts/bert/create_pretraining_data.py | transform | 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... | python | 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... | [
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dmlc/gluon-nlp | scripts/bert/create_pretraining_data.py | write_to_files_np | 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... | python | 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... | [
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dmlc/gluon-nlp | scripts/bert/create_pretraining_data.py | write_to_files_rec | 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(
... | python | 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(
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dmlc/gluon-nlp | scripts/bert/create_pretraining_data.py | create_training_instances | 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 = [[]]... | python | 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 = [[]]... | [
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dmlc/gluon-nlp | scripts/bert/create_pretraining_data.py | create_instances_from_document | 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]
... | python | 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]
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dmlc/gluon-nlp | scripts/bert/create_pretraining_data.py | create_masked_lm_predictions | 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... | python | 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... | [
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dmlc/gluon-nlp | scripts/bert/create_pretraining_data.py | truncate_seq_pair | 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(... | python | 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(... | [
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dmlc/gluon-nlp | scripts/bert/create_pretraining_data.py | main | 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... | python | 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... | [
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dmlc/gluon-nlp | scripts/bert/utils.py | convert_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... | python | 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... | [
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dmlc/gluon-nlp | scripts/bert/utils.py | read_tf_checkpoint | 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_... | python | 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_... | [
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dmlc/gluon-nlp | scripts/bert/utils.py | profile | 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,
... | python | 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,
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