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dmlc/gluon-nlp | scripts/sentiment_analysis/process_data.py | load_dataset | def load_dataset(data_name):
"""Load sentiment dataset."""
if data_name == 'MR' or data_name == 'Subj':
train_dataset, output_size = _load_file(data_name)
vocab, max_len = _build_vocab(data_name, train_dataset, [])
train_dataset, train_data_lengths = _preprocess_dataset(train_dataset, vo... | python | def load_dataset(data_name):
"""Load sentiment dataset."""
if data_name == 'MR' or data_name == 'Subj':
train_dataset, output_size = _load_file(data_name)
vocab, max_len = _build_vocab(data_name, train_dataset, [])
train_dataset, train_data_lengths = _preprocess_dataset(train_dataset, vo... | [
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dmlc/gluon-nlp | src/gluonnlp/base.py | get_home_dir | def get_home_dir():
"""Get home directory for storing datasets/models/pre-trained word embeddings"""
_home_dir = os.environ.get('MXNET_HOME', os.path.join('~', '.mxnet'))
# expand ~ to actual path
_home_dir = os.path.expanduser(_home_dir)
return _home_dir | python | def get_home_dir():
"""Get home directory for storing datasets/models/pre-trained word embeddings"""
_home_dir = os.environ.get('MXNET_HOME', os.path.join('~', '.mxnet'))
# expand ~ to actual path
_home_dir = os.path.expanduser(_home_dir)
return _home_dir | [
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dmlc/gluon-nlp | scripts/natural_language_inference/dataset.py | read_dataset | def read_dataset(args, dataset):
"""
Read dataset from tokenized files.
"""
path = os.path.join(vars(args)[dataset])
logger.info('reading data from {}'.format(path))
examples = [line.strip().split('\t') for line in open(path)]
if args.max_num_examples > 0:
examples = examples[:args.m... | python | def read_dataset(args, dataset):
"""
Read dataset from tokenized files.
"""
path = os.path.join(vars(args)[dataset])
logger.info('reading data from {}'.format(path))
examples = [line.strip().split('\t') for line in open(path)]
if args.max_num_examples > 0:
examples = examples[:args.m... | [
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dmlc/gluon-nlp | scripts/natural_language_inference/dataset.py | build_vocab | def build_vocab(dataset):
"""
Build vocab given a dataset.
"""
counter = nlp.data.count_tokens([w for e in dataset for s in e[:2] for w in s],
to_lower=True)
vocab = nlp.Vocab(counter)
return vocab | python | def build_vocab(dataset):
"""
Build vocab given a dataset.
"""
counter = nlp.data.count_tokens([w for e in dataset for s in e[:2] for w in s],
to_lower=True)
vocab = nlp.Vocab(counter)
return vocab | [
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dmlc/gluon-nlp | scripts/natural_language_inference/dataset.py | prepare_data_loader | def prepare_data_loader(args, dataset, vocab, test=False):
"""
Read data and build data loader.
"""
# Preprocess
dataset = dataset.transform(lambda s1, s2, label: (vocab(s1), vocab(s2), label),
lazy=False)
# Batching
batchify_fn = btf.Tuple(btf.Pad(), btf.Pad... | python | def prepare_data_loader(args, dataset, vocab, test=False):
"""
Read data and build data loader.
"""
# Preprocess
dataset = dataset.transform(lambda s1, s2, label: (vocab(s1), vocab(s2), label),
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# Batching
batchify_fn = btf.Tuple(btf.Pad(), btf.Pad... | [
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dmlc/gluon-nlp | scripts/parsing/common/utils.py | mxnet_prefer_gpu | def mxnet_prefer_gpu():
"""If gpu available return gpu, else cpu
Returns
-------
context : Context
The preferable GPU context.
"""
gpu = int(os.environ.get('MXNET_GPU', default=0))
if gpu in mx.test_utils.list_gpus():
return mx.gpu(gpu)
return mx.cpu() | python | def mxnet_prefer_gpu():
"""If gpu available return gpu, else cpu
Returns
-------
context : Context
The preferable GPU context.
"""
gpu = int(os.environ.get('MXNET_GPU', default=0))
if gpu in mx.test_utils.list_gpus():
return mx.gpu(gpu)
return mx.cpu() | [
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dmlc/gluon-nlp | scripts/parsing/common/utils.py | init_logger | def init_logger(root_dir, name="train.log"):
"""Initialize a logger
Parameters
----------
root_dir : str
directory for saving log
name : str
name of logger
Returns
-------
logger : logging.Logger
a logger
"""
os.makedirs(root_dir, exist_ok=True)
log_... | python | def init_logger(root_dir, name="train.log"):
"""Initialize a logger
Parameters
----------
root_dir : str
directory for saving log
name : str
name of logger
Returns
-------
logger : logging.Logger
a logger
"""
os.makedirs(root_dir, exist_ok=True)
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dmlc/gluon-nlp | scripts/parsing/common/utils.py | orthonormal_VanillaLSTMBuilder | def orthonormal_VanillaLSTMBuilder(lstm_layers, input_dims, lstm_hiddens, dropout_x=0., dropout_h=0., debug=False):
"""Build a standard LSTM cell, with variational dropout,
with weights initialized to be orthonormal (https://arxiv.org/abs/1312.6120)
Parameters
----------
lstm_layers : int
C... | python | def orthonormal_VanillaLSTMBuilder(lstm_layers, input_dims, lstm_hiddens, dropout_x=0., dropout_h=0., debug=False):
"""Build a standard LSTM cell, with variational dropout,
with weights initialized to be orthonormal (https://arxiv.org/abs/1312.6120)
Parameters
----------
lstm_layers : int
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dmlc/gluon-nlp | scripts/parsing/common/utils.py | biLSTM | def biLSTM(f_lstm, b_lstm, inputs, batch_size=None, dropout_x=0., dropout_h=0.):
"""Feature extraction through BiLSTM
Parameters
----------
f_lstm : VariationalDropoutCell
Forward cell
b_lstm : VariationalDropoutCell
Backward cell
inputs : NDArray
seq_len x batch_size
... | python | def biLSTM(f_lstm, b_lstm, inputs, batch_size=None, dropout_x=0., dropout_h=0.):
"""Feature extraction through BiLSTM
Parameters
----------
f_lstm : VariationalDropoutCell
Forward cell
b_lstm : VariationalDropoutCell
Backward cell
inputs : NDArray
seq_len x batch_size
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dmlc/gluon-nlp | scripts/parsing/common/utils.py | bilinear | def bilinear(x, W, y, input_size, seq_len, batch_size, num_outputs=1, bias_x=False, bias_y=False):
"""Do xWy
Parameters
----------
x : NDArray
(input_size x seq_len) x batch_size
W : NDArray
(num_outputs x ny) x nx
y : NDArray
(input_size x seq_len) x batch_size
inpu... | python | def bilinear(x, W, y, input_size, seq_len, batch_size, num_outputs=1, bias_x=False, bias_y=False):
"""Do xWy
Parameters
----------
x : NDArray
(input_size x seq_len) x batch_size
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(num_outputs x ny) x nx
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dmlc/gluon-nlp | scripts/parsing/common/utils.py | orthonormal_initializer | def orthonormal_initializer(output_size, input_size, debug=False):
"""adopted from Timothy Dozat https://github.com/tdozat/Parser/blob/master/lib/linalg.py
Parameters
----------
output_size : int
input_size : int
debug : bool
Whether to skip this initializer
Returns
-------
... | python | def orthonormal_initializer(output_size, input_size, debug=False):
"""adopted from Timothy Dozat https://github.com/tdozat/Parser/blob/master/lib/linalg.py
Parameters
----------
output_size : int
input_size : int
debug : bool
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dmlc/gluon-nlp | scripts/parsing/common/utils.py | arc_argmax | def arc_argmax(parse_probs, length, tokens_to_keep, ensure_tree=True):
"""MST
Adopted from Timothy Dozat https://github.com/tdozat/Parser/blob/master/lib/models/nn.py
Parameters
----------
parse_probs : NDArray
seq_len x seq_len, the probability of arcs
length : NDArray
real sen... | python | def arc_argmax(parse_probs, length, tokens_to_keep, ensure_tree=True):
"""MST
Adopted from Timothy Dozat https://github.com/tdozat/Parser/blob/master/lib/models/nn.py
Parameters
----------
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seq_len x seq_len, the probability of arcs
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dmlc/gluon-nlp | scripts/parsing/common/utils.py | rel_argmax | def rel_argmax(rel_probs, length, ensure_tree=True):
"""Fix the relation prediction by heuristic rules
Parameters
----------
rel_probs : NDArray
seq_len x rel_size
length :
real sentence length
ensure_tree :
whether to apply rules
Returns
-------
rel_preds : ... | python | def rel_argmax(rel_probs, length, ensure_tree=True):
"""Fix the relation prediction by heuristic rules
Parameters
----------
rel_probs : NDArray
seq_len x rel_size
length :
real sentence length
ensure_tree :
whether to apply rules
Returns
-------
rel_preds : ... | [
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dmlc/gluon-nlp | scripts/parsing/common/utils.py | reshape_fortran | def reshape_fortran(tensor, shape):
"""The missing Fortran reshape for mx.NDArray
Parameters
----------
tensor : NDArray
source tensor
shape : NDArray
desired shape
Returns
-------
output : NDArray
reordered result
"""
return tensor.T.reshape(tuple(rever... | python | def reshape_fortran(tensor, shape):
"""The missing Fortran reshape for mx.NDArray
Parameters
----------
tensor : NDArray
source tensor
shape : NDArray
desired shape
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dmlc/gluon-nlp | scripts/parsing/common/utils.py | Progbar.update | def update(self, current, values=[], exact=[], strict=[]):
"""
Updates the progress bar.
# Arguments
current: Index of current step.
values: List of tuples (name, value_for_last_step).
The progress bar will display averages for these values.
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Updates the progress bar.
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current: Index of current step.
values: List of tuples (name, value_for_last_step).
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dmlc/gluon-nlp | scripts/language_model/word_language_model.py | get_batch | def get_batch(data_source, i, seq_len=None):
"""Get mini-batches of the dataset.
Parameters
----------
data_source : NDArray
The dataset is evaluated on.
i : int
The index of the batch, starting from 0.
seq_len : int
The length of each sample in the batch.
Returns
... | python | def get_batch(data_source, i, seq_len=None):
"""Get mini-batches of the dataset.
Parameters
----------
data_source : NDArray
The dataset is evaluated on.
i : int
The index of the batch, starting from 0.
seq_len : int
The length of each sample in the batch.
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dmlc/gluon-nlp | scripts/language_model/word_language_model.py | evaluate | def evaluate(data_source, batch_size, params_file_name, ctx=None):
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Parameters
----------
data_source : NDArray
The dataset is evaluated on.
batch_size : int
The size of the mini-batch.
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"""Evaluate the model on the dataset.
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----------
data_source : NDArray
The dataset is evaluated on.
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The size of the mini-batch.
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dmlc/gluon-nlp | scripts/language_model/word_language_model.py | train | def train():
"""Training loop for awd language model.
"""
ntasgd = False
best_val = float('Inf')
start_train_time = time.time()
parameters = model.collect_params()
param_dict_avg = None
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valid_losses = []
for epoch in range(args.epochs):
... | python | def train():
"""Training loop for awd language model.
"""
ntasgd = False
best_val = float('Inf')
start_train_time = time.time()
parameters = model.collect_params()
param_dict_avg = None
t = 0
avg_trigger = 0
n = 5
valid_losses = []
for epoch in range(args.epochs):
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dmlc/gluon-nlp | src/gluonnlp/embedding/evaluation.py | register | def register(class_):
"""Registers a new word embedding evaluation function.
Once registered, we can create an instance with
:func:`~gluonnlp.embedding.evaluation.create`.
Examples
--------
>>> @gluonnlp.embedding.evaluation.register
... class MySimilarityFunction(gluonnlp.embedding.evalua... | python | def register(class_):
"""Registers a new word embedding evaluation function.
Once registered, we can create an instance with
:func:`~gluonnlp.embedding.evaluation.create`.
Examples
--------
>>> @gluonnlp.embedding.evaluation.register
... class MySimilarityFunction(gluonnlp.embedding.evalua... | [
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dmlc/gluon-nlp | src/gluonnlp/embedding/evaluation.py | create | def create(kind, name, **kwargs):
"""Creates an instance of a registered word embedding evaluation function.
Parameters
----------
kind : ['similarity', 'analogy']
Return only valid names for similarity, analogy or both kinds of
functions.
name : str
The evaluation function ... | python | def create(kind, name, **kwargs):
"""Creates an instance of a registered word embedding evaluation function.
Parameters
----------
kind : ['similarity', 'analogy']
Return only valid names for similarity, analogy or both kinds of
functions.
name : str
The evaluation function ... | [
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dmlc/gluon-nlp | src/gluonnlp/embedding/evaluation.py | list_evaluation_functions | def list_evaluation_functions(kind=None):
"""Get valid word embedding functions names.
Parameters
----------
kind : ['similarity', 'analogy', None]
Return only valid names for similarity, analogy or both kinds of functions.
Returns
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"""Get valid word embedding functions names.
Parameters
----------
kind : ['similarity', 'analogy', None]
Return only valid names for similarity, analogy or both kinds of functions.
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dmlc/gluon-nlp | src/gluonnlp/embedding/evaluation.py | WordEmbeddingSimilarity.hybrid_forward | def hybrid_forward(self, F, words1, words2, weight): # pylint: disable=arguments-differ
"""Predict the similarity of words1 and words2.
Parameters
----------
words1 : Symbol or NDArray
The indices of the words the we wish to compare to the words in words2.
words2 : ... | python | def hybrid_forward(self, F, words1, words2, weight): # pylint: disable=arguments-differ
"""Predict the similarity of words1 and words2.
Parameters
----------
words1 : Symbol or NDArray
The indices of the words the we wish to compare to the words in words2.
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dmlc/gluon-nlp | src/gluonnlp/embedding/evaluation.py | WordEmbeddingAnalogy.hybrid_forward | def hybrid_forward(self, F, words1, words2, words3): # pylint: disable=arguments-differ, unused-argument
"""Compute analogies for given question words.
Parameters
----------
words1 : Symbol or NDArray
Word indices of first question words. Shape (batch_size, ).
words... | python | def hybrid_forward(self, F, words1, words2, words3): # pylint: disable=arguments-differ, unused-argument
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words1 : Symbol or NDArray
Word indices of first question words. Shape (batch_size, ).
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dmlc/gluon-nlp | scripts/language_model/cache_language_model.py | evaluate | def evaluate(data_source, batch_size, ctx=None):
"""Evaluate the model on the dataset with cache model.
Parameters
----------
data_source : NDArray
The dataset is evaluated on.
batch_size : int
The size of the mini-batch.
ctx : mx.cpu() or mx.gpu()
The context of the com... | python | def evaluate(data_source, batch_size, ctx=None):
"""Evaluate the model on the dataset with cache model.
Parameters
----------
data_source : NDArray
The dataset is evaluated on.
batch_size : int
The size of the mini-batch.
ctx : mx.cpu() or mx.gpu()
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dmlc/gluon-nlp | scripts/bert/staticbert/static_bert.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'.
If None, then vocab is required, for specifying embedding weight size, and is direc... | python | def get_model(name, dataset_name='wikitext-2', **kwargs):
"""Returns a pre-defined model by name.
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----------
name : str
Name of the model.
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dmlc/gluon-nlp | scripts/bert/staticbert/static_bert.py | bert_12_768_12 | def bert_12_768_12(dataset_name=None, vocab=None, pretrained=True, ctx=mx.cpu(),
root=os.path.join(get_home_dir(), 'models'), use_pooler=True,
use_decoder=True, use_classifier=True, input_size=None, seq_length=None,
**kwargs):
"""Static BERT BASE model.
... | python | def bert_12_768_12(dataset_name=None, vocab=None, pretrained=True, ctx=mx.cpu(),
root=os.path.join(get_home_dir(), 'models'), use_pooler=True,
use_decoder=True, use_classifier=True, input_size=None, seq_length=None,
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dmlc/gluon-nlp | scripts/bert/staticbert/static_bert.py | StaticBERTModel.hybrid_forward | def hybrid_forward(self, F, inputs, token_types, valid_length=None, masked_positions=None):
# pylint: disable=arguments-differ
# pylint: disable=unused-argument
"""Generate the representation given the inputs.
This is used in training or fine-tuning a static (hybridized) BERT model.
... | python | def hybrid_forward(self, F, inputs, token_types, valid_length=None, masked_positions=None):
# pylint: disable=arguments-differ
# pylint: disable=unused-argument
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dmlc/gluon-nlp | src/gluonnlp/model/train/cache.py | CacheCell.load_parameters | def load_parameters(self, filename, ctx=mx.cpu()): # pylint: disable=arguments-differ
"""Load parameters from file.
filename : str
Path to parameter file.
ctx : Context or list of Context, default cpu()
Context(s) initialize loaded parameters on.
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dmlc/gluon-nlp | src/gluonnlp/model/train/cache.py | CacheCell.forward | def forward(self, inputs, target, next_word_history, cache_history, begin_state=None): # pylint: disable=arguments-differ
"""Defines the forward computation for cache cell. Arguments can be either
:py:class:`NDArray` or :py:class:`Symbol`.
Parameters
----------
inputs: NDArray
... | python | def forward(self, inputs, target, next_word_history, cache_history, begin_state=None): # pylint: disable=arguments-differ
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dmlc/gluon-nlp | src/gluonnlp/utils/parallel.py | Parallel.put | def put(self, x):
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dmlc/gluon-nlp | src/gluonnlp/vocab/bert.py | BERTVocab.from_json | def from_json(cls, json_str):
"""Deserialize BERTVocab object from json string.
Parameters
----------
json_str : str
Serialized json string of a BERTVocab object.
Returns
-------
BERTVocab
"""
vocab_dict = json.loads(json_str)
... | python | def from_json(cls, json_str):
"""Deserialize BERTVocab object from json string.
Parameters
----------
json_str : str
Serialized json string of a BERTVocab object.
Returns
-------
BERTVocab
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vocab_dict = json.loads(json_str)
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dmlc/gluon-nlp | src/gluonnlp/model/train/language_model.py | StandardRNN.forward | def forward(self, inputs, begin_state=None): # pylint: disable=arguments-differ
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:py:class:`NDArray` or :py:class:`Symbol`.
Parameters
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inputs : NDArray
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dmlc/gluon-nlp | src/gluonnlp/model/train/language_model.py | BigRNN.forward | def forward(self, inputs, label, begin_state, sampled_values): # pylint: disable=arguments-differ
"""Defines the forward computation.
Parameters
-----------
inputs : NDArray
input tensor with shape `(sequence_length, batch_size)`
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b... | python | def forward(self, inputs, label, begin_state, sampled_values): # pylint: disable=arguments-differ
"""Defines the forward computation.
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inputs : NDArray
input tensor with shape `(sequence_length, batch_size)`
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dmlc/gluon-nlp | scripts/word_embeddings/model.py | SG.hybrid_forward | def hybrid_forward(self, F, center, context, center_words):
"""SkipGram forward pass.
Parameters
----------
center : mxnet.nd.NDArray or mxnet.sym.Symbol
Sparse CSR array of word / subword indices of shape (batch_size,
len(token_to_idx) + num_subwords). Embedding... | python | def hybrid_forward(self, F, center, context, center_words):
"""SkipGram forward pass.
Parameters
----------
center : mxnet.nd.NDArray or mxnet.sym.Symbol
Sparse CSR array of word / subword indices of shape (batch_size,
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dmlc/gluon-nlp | scripts/sentiment_analysis/finetune_lm.py | evaluate | def evaluate(dataloader):
"""Evaluate network on the specified dataset"""
total_L = 0.0
total_sample_num = 0
total_correct_num = 0
start_log_interval_time = time.time()
print('Begin Testing...')
for i, ((data, valid_length), label) in enumerate(dataloader):
data = mx.nd.transpose(dat... | python | def evaluate(dataloader):
"""Evaluate network on the specified dataset"""
total_L = 0.0
total_sample_num = 0
total_correct_num = 0
start_log_interval_time = time.time()
print('Begin Testing...')
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dmlc/gluon-nlp | scripts/sentiment_analysis/finetune_lm.py | train | def train():
"""Training process"""
start_pipeline_time = time.time()
# Training/Testing
best_valid_acc = 0
stop_early = 0
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# Epoch training stats
start_epoch_time = time.time()
epoch_L = 0.0
epoch_sent_num = 0
epoch_wc = 0... | python | def train():
"""Training process"""
start_pipeline_time = time.time()
# Training/Testing
best_valid_acc = 0
stop_early = 0
for epoch in range(args.epochs):
# Epoch training stats
start_epoch_time = time.time()
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dmlc/gluon-nlp | scripts/sentiment_analysis/finetune_lm.py | AggregationLayer.hybrid_forward | def hybrid_forward(self, F, data, valid_length): # pylint: disable=arguments-differ
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# Data will have shape (T, N, C)
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... | python | def hybrid_forward(self, F, data, valid_length): # pylint: disable=arguments-differ
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dmlc/gluon-nlp | src/gluonnlp/model/lstmpcellwithclip.py | LSTMPCellWithClip.hybrid_forward | def hybrid_forward(self, F, inputs, states, i2h_weight,
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dmlc/gluon-nlp | src/gluonnlp/utils/parameter.py | clip_grad_global_norm | def clip_grad_global_norm(parameters, max_norm, check_isfinite=True):
"""Rescales gradients of parameters so that the sum of their 2-norm is smaller than `max_norm`.
If gradients exist for more than one context for a parameter, user needs to explicitly call
``trainer.allreduce_grads`` so that the gradients ... | python | def clip_grad_global_norm(parameters, max_norm, check_isfinite=True):
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dmlc/gluon-nlp | scripts/bert/run_pretraining.py | train | def train(data_train, model, nsp_loss, mlm_loss, vocab_size, ctx, store):
"""Training function."""
mlm_metric = nlp.metric.MaskedAccuracy()
nsp_metric = nlp.metric.MaskedAccuracy()
mlm_metric.reset()
nsp_metric.reset()
lr = args.lr
optim_params = {'learning_rate': lr, 'epsilon': 1e-6, 'wd':... | python | def train(data_train, model, nsp_loss, mlm_loss, vocab_size, ctx, store):
"""Training function."""
mlm_metric = nlp.metric.MaskedAccuracy()
nsp_metric = nlp.metric.MaskedAccuracy()
mlm_metric.reset()
nsp_metric.reset()
lr = args.lr
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dmlc/gluon-nlp | scripts/bert/run_pretraining.py | ParallelBERT.forward_backward | def forward_backward(self, x):
"""forward backward implementation"""
with mx.autograd.record():
(ls, next_sentence_label, classified, masked_id, decoded, \
masked_weight, ls1, ls2, valid_length) = forward(x, self._model, self._mlm_loss,
... | python | def forward_backward(self, x):
"""forward backward implementation"""
with mx.autograd.record():
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dmlc/gluon-nlp | scripts/parsing/common/data.py | ParserVocabulary.log_info | def log_info(self, logger):
"""Print statistical information via the provided logger
Parameters
----------
logger : logging.Logger
logger created using logging.getLogger()
"""
logger.info('#words in training set: %d' % self._words_in_train_data)
logge... | python | def log_info(self, logger):
"""Print statistical information via the provided logger
Parameters
----------
logger : logging.Logger
logger created using logging.getLogger()
"""
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dmlc/gluon-nlp | scripts/parsing/common/data.py | ParserVocabulary._add_pret_words | def _add_pret_words(self, pret_embeddings):
"""Read pre-trained embedding file for extending vocabulary
Parameters
----------
pret_embeddings : tuple
(embedding_name, source), used for gluonnlp.embedding.create(embedding_name, source)
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words_in_train_data ... | python | def _add_pret_words(self, pret_embeddings):
"""Read pre-trained embedding file for extending vocabulary
Parameters
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pret_embeddings : tuple
(embedding_name, source), used for gluonnlp.embedding.create(embedding_name, source)
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dmlc/gluon-nlp | scripts/parsing/common/data.py | ParserVocabulary.get_pret_embs | def get_pret_embs(self, word_dims=None):
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----------
word_dims : int or None
vector size. Use `None` for auto-infer
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numpy.ndarray
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"""Read pre-trained embedding file
Parameters
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word_dims : int or None
vector size. Use `None` for auto-infer
Returns
-------
numpy.ndarray
T x C numpy NDArray
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dmlc/gluon-nlp | scripts/parsing/common/data.py | ParserVocabulary.get_word_embs | def get_word_embs(self, word_dims):
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----------
word_dims : int
word vector size
Returns
-------
numpy.ndarray
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word_dims : int
word vector size
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dmlc/gluon-nlp | scripts/parsing/common/data.py | ParserVocabulary.get_tag_embs | def get_tag_embs(self, tag_dims):
"""Randomly initialize embeddings for tag
Parameters
----------
tag_dims : int
tag vector size
Returns
-------
numpy.ndarray
random embeddings
"""
return np.random.randn(self.tag_size, tag... | python | def get_tag_embs(self, tag_dims):
"""Randomly initialize embeddings for tag
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tag_dims : int
tag vector size
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numpy.ndarray
random embeddings
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dmlc/gluon-nlp | scripts/parsing/common/data.py | ParserVocabulary.word2id | def word2id(self, xs):
"""Map word(s) to its id(s)
Parameters
----------
xs : str or list
word or a list of words
Returns
-------
int or list
id or a list of ids
"""
if isinstance(xs, list):
return [self._word2... | python | def word2id(self, xs):
"""Map word(s) to its id(s)
Parameters
----------
xs : str or list
word or a list of words
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-------
int or list
id or a list of ids
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dmlc/gluon-nlp | scripts/parsing/common/data.py | ParserVocabulary.id2word | def id2word(self, xs):
"""Map id(s) to word(s)
Parameters
----------
xs : int
id or a list of ids
Returns
-------
str or list
word or a list of words
"""
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"""Map id(s) to word(s)
Parameters
----------
xs : int
id or a list of ids
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-------
str or list
word or a list of words
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dmlc/gluon-nlp | scripts/parsing/common/data.py | ParserVocabulary.rel2id | def rel2id(self, xs):
"""Map relation(s) to id(s)
Parameters
----------
xs : str or list
relation
Returns
-------
int or list
id(s) of relation
"""
if isinstance(xs, list):
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"""Map relation(s) to id(s)
Parameters
----------
xs : str or list
relation
Returns
-------
int or list
id(s) of relation
"""
if isinstance(xs, list):
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dmlc/gluon-nlp | scripts/parsing/common/data.py | ParserVocabulary.id2rel | def id2rel(self, xs):
"""Map id(s) to relation(s)
Parameters
----------
xs : int
id or a list of ids
Returns
-------
str or list
relation or a list of relations
"""
if isinstance(xs, list):
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str or list
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dmlc/gluon-nlp | scripts/parsing/common/data.py | ParserVocabulary.tag2id | def tag2id(self, xs):
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Parameters
----------
xs : str or list
tag or tags
Returns
-------
int or list
id(s) of tag(s)
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xs : str or list
tag or tags
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id(s) of tag(s)
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dmlc/gluon-nlp | scripts/parsing/common/data.py | DataLoader.idx_sequence | def idx_sequence(self):
"""Indices of sentences when enumerating data set from batches.
Useful when retrieving the correct order of sentences
Returns
-------
list
List of ids ranging from 0 to #sent -1
"""
return [x[1] for x in sorted(zip(self._record... | python | def idx_sequence(self):
"""Indices of sentences when enumerating data set from batches.
Useful when retrieving the correct order of sentences
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-------
list
List of ids ranging from 0 to #sent -1
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return [x[1] for x in sorted(zip(self._record... | [
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dmlc/gluon-nlp | scripts/parsing/common/data.py | DataLoader.get_batches | def get_batches(self, batch_size, shuffle=True):
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batch_size : int
size of one batch
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whether to shuffle batches. Don't set to True when evaluating on dev or test set.
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size of one batch
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dmlc/gluon-nlp | scripts/text_classification/fasttext_word_ngram.py | create_ngram_set | def create_ngram_set(input_list, ngram_value=2):
"""
Extract a set of n-grams from a list of integers.
>>> create_ngram_set([1, 4, 9, 4, 1, 4], ngram_value=2)
{(4, 9), (4, 1), (1, 4), (9, 4)}
>>> create_ngram_set([1, 4, 9, 4, 1, 4], ngram_value=3)
[(1, 4, 9), (4, 9, 4), (9, 4, 1), (4, 1, 4)]
... | python | def create_ngram_set(input_list, ngram_value=2):
"""
Extract a set of n-grams from a list of integers.
>>> create_ngram_set([1, 4, 9, 4, 1, 4], ngram_value=2)
{(4, 9), (4, 1), (1, 4), (9, 4)}
>>> create_ngram_set([1, 4, 9, 4, 1, 4], ngram_value=3)
[(1, 4, 9), (4, 9, 4), (9, 4, 1), (4, 1, 4)]
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dmlc/gluon-nlp | scripts/text_classification/fasttext_word_ngram.py | add_ngram | def add_ngram(sequences, token_indice, ngram_range=2):
"""
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>>> sequences = [[1, 3, 4, 5], [1, 3, 7, 9, 2]]
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>>> add_ngram(sequences, token_in... | python | def add_ngram(sequences, token_indice, ngram_range=2):
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>>> sequences = [[1, 3, 4, 5], [1, 3, 7, 9, 2]]
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dmlc/gluon-nlp | scripts/text_classification/fasttext_word_ngram.py | evaluate_accuracy | def evaluate_accuracy(data_iterator, net, ctx, loss_fun, num_classes):
"""
This function is used for evaluating accuracy of
a given data iterator. (Either Train/Test data)
It takes in the loss function used too!
"""
acc = mx.metric.Accuracy()
loss_avg = 0.
for i, ((data, length), label) ... | python | def evaluate_accuracy(data_iterator, net, ctx, loss_fun, num_classes):
"""
This function is used for evaluating accuracy of
a given data iterator. (Either Train/Test data)
It takes in the loss function used too!
"""
acc = mx.metric.Accuracy()
loss_avg = 0.
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dmlc/gluon-nlp | scripts/text_classification/fasttext_word_ngram.py | read_input_data | def read_input_data(filename):
"""Helper function to get training data"""
logging.info('Opening file %s for reading input', filename)
input_file = open(filename, 'r')
data = []
labels = []
for line in input_file:
tokens = line.split(',', 1)
labels.append(tokens[0].strip())
... | python | def read_input_data(filename):
"""Helper function to get training data"""
logging.info('Opening file %s for reading input', filename)
input_file = open(filename, 'r')
data = []
labels = []
for line in input_file:
tokens = line.split(',', 1)
labels.append(tokens[0].strip())
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dmlc/gluon-nlp | scripts/text_classification/fasttext_word_ngram.py | parse_args | def parse_args():
"""Parse command line arguments."""
parser = argparse.ArgumentParser(
description='Text Classification with FastText',
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
# Computation options
group = parser.add_argument_group('Computation arguments')
group.add... | python | def parse_args():
"""Parse command line arguments."""
parser = argparse.ArgumentParser(
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# Computation options
group = parser.add_argument_group('Computation arguments')
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dmlc/gluon-nlp | scripts/text_classification/fasttext_word_ngram.py | get_label_mapping | def get_label_mapping(train_labels):
"""
Create the mapping from label to numeric label
"""
sorted_labels = np.sort(np.unique(train_labels))
label_mapping = {}
for i, label in enumerate(sorted_labels):
label_mapping[label] = i
logging.info('Label mapping:%s', format(label_mapping))
... | python | def get_label_mapping(train_labels):
"""
Create the mapping from label to numeric label
"""
sorted_labels = np.sort(np.unique(train_labels))
label_mapping = {}
for i, label in enumerate(sorted_labels):
label_mapping[label] = i
logging.info('Label mapping:%s', format(label_mapping))
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dmlc/gluon-nlp | scripts/text_classification/fasttext_word_ngram.py | convert_to_sequences | def convert_to_sequences(dataset, vocab):
"""This function takes a dataset and converts
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"""
start = time.time()
dataset_vocab = map(lambda x: (x, vocab), dataset)
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# Each sample is processed in an asynchronous manner.
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start = time.time()
dataset_vocab = map(lambda x: (x, vocab), dataset)
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dmlc/gluon-nlp | scripts/text_classification/fasttext_word_ngram.py | preprocess_dataset | def preprocess_dataset(dataset, labels):
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start = time.time()
with mp.Pool() as pool:
# Each sample is processed in an asynchronous manner.
dataset = gluon.data.SimpleDataset(list(zip(dataset, labels)))
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""" Preprocess and prepare a dataset"""
start = time.time()
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# Each sample is processed in an asynchronous manner.
dataset = gluon.data.SimpleDataset(list(zip(dataset, labels)))
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dmlc/gluon-nlp | scripts/text_classification/fasttext_word_ngram.py | get_dataloader | def get_dataloader(train_dataset, train_data_lengths,
test_dataset, batch_size):
""" Construct the DataLoader. Pad data, stack label and lengths"""
bucket_num, bucket_ratio = 20, 0.2
batchify_fn = gluonnlp.data.batchify.Tuple(
gluonnlp.data.batchify.Pad(axis=0, ret_length=True),
... | python | def get_dataloader(train_dataset, train_data_lengths,
test_dataset, batch_size):
""" Construct the DataLoader. Pad data, stack label and lengths"""
bucket_num, bucket_ratio = 20, 0.2
batchify_fn = gluonnlp.data.batchify.Tuple(
gluonnlp.data.batchify.Pad(axis=0, ret_length=True),
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dmlc/gluon-nlp | scripts/text_classification/fasttext_word_ngram.py | train | def train(args):
"""Training function that orchestrates the Classification! """
train_file = args.input
test_file = args.validation
ngram_range = args.ngrams
logging.info('Ngrams range for the training run : %s', ngram_range)
logging.info('Loading Training data')
train_labels, train_data = r... | python | def train(args):
"""Training function that orchestrates the Classification! """
train_file = args.input
test_file = args.validation
ngram_range = args.ngrams
logging.info('Ngrams range for the training run : %s', ngram_range)
logging.info('Loading Training data')
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dmlc/gluon-nlp | scripts/bert/staticbert/static_bert_qa_model.py | StaticBertForQA.hybrid_forward | def hybrid_forward(self, F, inputs, token_types, valid_length=None):
# pylint: disable=arguments-differ
# pylint: disable=unused-argument
"""Generate the unnormalized score for the given the input sequences.
Parameters
----------
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# pylint: disable=arguments-differ
# pylint: disable=unused-argument
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dmlc/gluon-nlp | scripts/bert/staticbert/static_bert_qa_model.py | BertForQALoss.hybrid_forward | def hybrid_forward(self, F, pred, label): # pylint: disable=arguments-differ
"""
Parameters
----------
pred : NDArray, shape (batch_size, seq_length, 2)
BERTSquad forward output.
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"""
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BERTSquad forward output.
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dmlc/gluon-nlp | src/gluonnlp/model/translation.py | NMTModel.encode | def encode(self, inputs, states=None, valid_length=None):
"""Encode the input sequence.
Parameters
----------
inputs : NDArray
states : list of NDArrays or None, default None
valid_length : NDArray or None, default None
Returns
-------
outputs : ... | python | def encode(self, inputs, states=None, valid_length=None):
"""Encode the input sequence.
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inputs : NDArray
states : list of NDArrays or None, default None
valid_length : NDArray or None, default None
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dmlc/gluon-nlp | src/gluonnlp/model/translation.py | NMTModel.decode_seq | def decode_seq(self, inputs, states, valid_length=None):
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Parameters
----------
inputs : NDArray
states : list of NDArrays
valid_length : NDArray or None, default None
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inputs : NDArray
states : list of NDArrays
valid_length : NDArray or None, default None
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dmlc/gluon-nlp | src/gluonnlp/model/translation.py | NMTModel.decode_step | def decode_step(self, step_input, states):
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step_input : NDArray
Shape (batch_size,)
states : list of NDArrays
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-------
step_output : NDArray
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"""One step decoding of the translation model.
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step_input : NDArray
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states : list of NDArrays
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dmlc/gluon-nlp | src/gluonnlp/model/translation.py | NMTModel.forward | def forward(self, src_seq, tgt_seq, src_valid_length=None, tgt_valid_length=None): #pylint: disable=arguments-differ
"""Generate the prediction given the src_seq and tgt_seq.
This is used in training an NMT model.
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src_seq : NDArray
tgt_seq : NDArr... | python | def forward(self, src_seq, tgt_seq, src_valid_length=None, tgt_valid_length=None): #pylint: disable=arguments-differ
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dmlc/gluon-nlp | src/gluonnlp/vocab/subwords.py | create_subword_function | def create_subword_function(subword_function_name, **kwargs):
"""Creates an instance of a subword function."""
create_ = registry.get_create_func(SubwordFunction, 'token embedding')
return create_(subword_function_name, **kwargs) | python | def create_subword_function(subword_function_name, **kwargs):
"""Creates an instance of a subword function."""
create_ = registry.get_create_func(SubwordFunction, 'token embedding')
return create_(subword_function_name, **kwargs) | [
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dmlc/gluon-nlp | src/gluonnlp/vocab/vocab.py | Vocab._index_special_tokens | def _index_special_tokens(self, unknown_token, special_tokens):
"""Indexes unknown and reserved tokens."""
self._idx_to_token = [unknown_token] if unknown_token else []
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"""Indexes unknown and reserved tokens."""
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dmlc/gluon-nlp | src/gluonnlp/vocab/vocab.py | Vocab._index_counter_keys | def _index_counter_keys(self, counter, unknown_token, special_tokens, max_size,
min_freq):
"""Indexes keys of `counter`.
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unknown_and_special_tokens = ... | python | def _index_counter_keys(self, counter, unknown_token, special_tokens, max_size,
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"""Indexes keys of `counter`.
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dmlc/gluon-nlp | src/gluonnlp/vocab/vocab.py | Vocab.set_embedding | def set_embedding(self, *embeddings):
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Parameters
----------
embeddings : None or tuple of :class:`gluonnlp.embedding.TokenEmbedding` instances
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"""Attaches one or more embeddings to the indexed text tokens.
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dmlc/gluon-nlp | src/gluonnlp/vocab/vocab.py | Vocab.to_tokens | def to_tokens(self, indices):
"""Converts token indices to tokens according to the vocabulary.
Parameters
----------
indices : int or list of ints
A source token index or token indices to be converted.
Returns
-------
str or list of strs
... | python | def to_tokens(self, indices):
"""Converts token indices to tokens according to the vocabulary.
Parameters
----------
indices : int or list of ints
A source token index or token indices to be converted.
Returns
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str or list of strs
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dmlc/gluon-nlp | src/gluonnlp/vocab/vocab.py | Vocab.to_json | def to_json(self):
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"""
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... | python | def to_json(self):
"""Serialize Vocab object to json string.
This method does not serialize the underlying embedding.
"""
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dmlc/gluon-nlp | src/gluonnlp/vocab/vocab.py | Vocab.from_json | def from_json(cls, json_str):
"""Deserialize Vocab object from json string.
Parameters
----------
json_str : str
Serialized json string of a Vocab object.
Returns
-------
Vocab
"""
vocab_dict = json.loads(json_str)
unknown_t... | python | def from_json(cls, json_str):
"""Deserialize Vocab object from json string.
Parameters
----------
json_str : str
Serialized json string of a Vocab object.
Returns
-------
Vocab
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vocab_dict = json.loads(json_str)
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dmlc/gluon-nlp | scripts/bert/run_pretraining_hvd.py | train | def train(data_train, model, nsp_loss, mlm_loss, vocab_size, ctx):
"""Training function."""
hvd.broadcast_parameters(model.collect_params(), root_rank=0)
mlm_metric = nlp.metric.MaskedAccuracy()
nsp_metric = nlp.metric.MaskedAccuracy()
mlm_metric.reset()
nsp_metric.reset()
logging.debug('C... | python | def train(data_train, model, nsp_loss, mlm_loss, vocab_size, ctx):
"""Training function."""
hvd.broadcast_parameters(model.collect_params(), root_rank=0)
mlm_metric = nlp.metric.MaskedAccuracy()
nsp_metric = nlp.metric.MaskedAccuracy()
mlm_metric.reset()
nsp_metric.reset()
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dmlc/gluon-nlp | scripts/bert/staticbert/static_finetune_squad.py | train | def train():
"""Training function."""
log.info('Loader Train data...')
if version_2:
train_data = SQuAD('train', version='2.0')
else:
train_data = SQuAD('train', version='1.1')
log.info('Number of records in Train data:{}'.format(len(train_data)))
train_data_transform, _ = prepr... | python | def train():
"""Training function."""
log.info('Loader Train data...')
if version_2:
train_data = SQuAD('train', version='2.0')
else:
train_data = SQuAD('train', version='1.1')
log.info('Number of records in Train data:{}'.format(len(train_data)))
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dmlc/gluon-nlp | scripts/bert/staticbert/static_finetune_squad.py | evaluate | def evaluate():
"""Evaluate the model on validation dataset.
"""
log.info('Loader dev data...')
if version_2:
dev_data = SQuAD('dev', version='2.0')
else:
dev_data = SQuAD('dev', version='1.1')
log.info('Number of records in Train data:{}'.format(len(dev_data)))
dev_dataset ... | python | def evaluate():
"""Evaluate the model on validation dataset.
"""
log.info('Loader dev data...')
if version_2:
dev_data = SQuAD('dev', version='2.0')
else:
dev_data = SQuAD('dev', version='1.1')
log.info('Number of records in Train data:{}'.format(len(dev_data)))
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dmlc/gluon-nlp | src/gluonnlp/data/batchify/batchify.py | _pad_arrs_to_max_length | def _pad_arrs_to_max_length(arrs, pad_axis, pad_val, use_shared_mem, dtype):
"""Inner Implementation of the Pad batchify
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----------
arrs : list
pad_axis : int
pad_val : number
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-------
ret : NDArray
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"""Inner Implementation of the Pad batchify
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----------
arrs : list
pad_axis : int
pad_val : number
use_shared_mem : bool, default False
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dmlc/gluon-nlp | scripts/parsing/parser/dep_parser.py | DepParser.train | def train(self, train_file, dev_file, test_file, save_dir, pretrained_embeddings=None, min_occur_count=2,
lstm_layers=3, word_dims=100, tag_dims=100, dropout_emb=0.33, lstm_hiddens=400,
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dropo... | python | def train(self, train_file, dev_file, test_file, save_dir, pretrained_embeddings=None, min_occur_count=2,
lstm_layers=3, word_dims=100, tag_dims=100, dropout_emb=0.33, lstm_hiddens=400,
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dmlc/gluon-nlp | scripts/parsing/parser/dep_parser.py | DepParser.load | def load(self, path):
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path : str
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"""Load from disk
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path : str
path to the directory which typically contains a config.pkl file and a model.bin file
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-------
DepParser
parser itself
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dmlc/gluon-nlp | scripts/parsing/parser/dep_parser.py | DepParser.evaluate | def evaluate(self, test_file, save_dir=None, logger=None, num_buckets_test=10, test_batch_size=5000):
"""Run evaluation on test set
Parameters
----------
test_file : str
path to test set
save_dir : str
where to store intermediate results and log
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"""Run evaluation on test set
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----------
test_file : str
path to test set
save_dir : str
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dmlc/gluon-nlp | scripts/parsing/parser/dep_parser.py | DepParser.parse | def parse(self, sentence):
"""Parse raw sentence into ConllSentence
Parameters
----------
sentence : list
a list of (word, tag) tuples
Returns
-------
ConllSentence
ConllSentence object
"""
words = np.zeros((len(sentence) ... | python | def parse(self, sentence):
"""Parse raw sentence into ConllSentence
Parameters
----------
sentence : list
a list of (word, tag) tuples
Returns
-------
ConllSentence
ConllSentence object
"""
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-------
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dmlc/gluon-nlp | src/gluonnlp/model/utils.py | apply_weight_drop | def apply_weight_drop(block, local_param_regex, rate, axes=(),
weight_dropout_mode='training'):
"""Apply weight drop to the parameter of a block.
Parameters
----------
block : Block or HybridBlock
The block whose parameter is to be applied weight-drop.
local_param_rege... | python | def apply_weight_drop(block, local_param_regex, rate, axes=(),
weight_dropout_mode='training'):
"""Apply weight drop to the parameter of a block.
Parameters
----------
block : Block or HybridBlock
The block whose parameter is to be applied weight-drop.
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dmlc/gluon-nlp | src/gluonnlp/model/utils.py | _get_rnn_cell | def _get_rnn_cell(mode, num_layers, input_size, hidden_size,
dropout, weight_dropout,
var_drop_in, var_drop_state, var_drop_out,
skip_connection, proj_size=None, cell_clip=None, proj_clip=None):
"""create rnn cell given specs
Parameters
----------
m... | python | def _get_rnn_cell(mode, num_layers, input_size, hidden_size,
dropout, weight_dropout,
var_drop_in, var_drop_state, var_drop_out,
skip_connection, proj_size=None, cell_clip=None, proj_clip=None):
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dmlc/gluon-nlp | src/gluonnlp/model/utils.py | _get_rnn_layer | def _get_rnn_layer(mode, num_layers, input_size, hidden_size, dropout, weight_dropout):
"""create rnn layer given specs"""
if mode == 'rnn_relu':
rnn_block = functools.partial(rnn.RNN, activation='relu')
elif mode == 'rnn_tanh':
rnn_block = functools.partial(rnn.RNN, activation='tanh')
e... | python | def _get_rnn_layer(mode, num_layers, input_size, hidden_size, dropout, weight_dropout):
"""create rnn layer given specs"""
if mode == 'rnn_relu':
rnn_block = functools.partial(rnn.RNN, activation='relu')
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rnn_block = functools.partial(rnn.RNN, activation='tanh')
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dmlc/gluon-nlp | src/gluonnlp/model/sampled_block.py | _SampledDenseHelper.hybrid_forward | def hybrid_forward(self, F, x, sampled_values, label, w_all, b_all):
"""Forward computation."""
sampled_candidates, expected_count_sampled, expected_count_true = sampled_values
# (num_sampled, in_unit)
w_sampled = w_all.slice(begin=(0, 0), end=(self._num_sampled, None))
w_true = ... | python | def hybrid_forward(self, F, x, sampled_values, label, w_all, b_all):
"""Forward computation."""
sampled_candidates, expected_count_sampled, expected_count_true = sampled_values
# (num_sampled, in_unit)
w_sampled = w_all.slice(begin=(0, 0), end=(self._num_sampled, None))
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dmlc/gluon-nlp | src/gluonnlp/model/sampled_block.py | _SampledDense.hybrid_forward | def hybrid_forward(self, F, x, sampled_values, label, weight, bias):
"""Forward computation."""
sampled_candidates, _, _ = sampled_values
# (batch_size,)
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# (num_sampled+batch_size,)
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"""Forward computation."""
sampled_candidates, _, _ = sampled_values
# (batch_size,)
label = F.reshape(label, shape=(-1,))
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dmlc/gluon-nlp | src/gluonnlp/model/sampled_block.py | _SparseSampledDense.forward | def forward(self, x, sampled_values, label):
"""Forward computation."""
sampled_candidates, _, _ = sampled_values
# (batch_size,)
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# (num_sampled+batch_size,)
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"""Forward computation."""
sampled_candidates, _, _ = sampled_values
# (batch_size,)
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dmlc/gluon-nlp | src/gluonnlp/model/sequence_sampler.py | _extract_and_flatten_nested_structure | def _extract_and_flatten_nested_structure(data, flattened=None):
"""Flatten the structure of a nested container to a list.
Parameters
----------
data : A single NDArray/Symbol or nested container with NDArrays/Symbol.
The nested container to be flattened.
flattened : list or None
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The nested container to be flattened.
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dmlc/gluon-nlp | src/gluonnlp/model/sequence_sampler.py | _reconstruct_flattened_structure | def _reconstruct_flattened_structure(structure, flattened):
"""Reconstruct the flattened list back to (possibly) nested structure.
Parameters
----------
structure : An integer or a nested container with integers.
The extracted structure of the container of `data`.
flattened : list or None
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"""Reconstruct the flattened list back to (possibly) nested structure.
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structure : An integer or a nested container with integers.
The extracted structure of the container of `data`.
flattened : list or None
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dmlc/gluon-nlp | src/gluonnlp/model/sequence_sampler.py | _expand_to_beam_size | def _expand_to_beam_size(data, beam_size, batch_size, state_info=None):
"""Tile all the states to have batch_size * beam_size on the batch axis.
Parameters
----------
data : A single NDArray/Symbol or nested container with NDArrays/Symbol
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"""Tile all the states to have batch_size * beam_size on the batch axis.
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dmlc/gluon-nlp | src/gluonnlp/model/sequence_sampler.py | _SamplingStepUpdate.hybrid_forward | def hybrid_forward(self, F, samples, valid_length, outputs, scores, beam_alive_mask, states):
"""
Parameters
----------
F
samples : NDArray or Symbol
The current samples generated by beam search. Shape (batch_size, beam_size, L)
valid_length : NDArray or Symbo... | python | def hybrid_forward(self, F, samples, valid_length, outputs, scores, beam_alive_mask, states):
"""
Parameters
----------
F
samples : NDArray or Symbol
The current samples generated by beam search. Shape (batch_size, beam_size, L)
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dmlc/gluon-nlp | src/gluonnlp/model/sequence_sampler.py | HybridBeamSearchSampler.hybrid_forward | def hybrid_forward(self, F, inputs, states): # pylint: disable=arguments-differ
"""Sample by beam search.
Parameters
----------
F
inputs : NDArray or Symbol
The initial input of the decoder. Shape is (batch_size,).
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"""Sample by beam search.
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F
inputs : NDArray or Symbol
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dmlc/gluon-nlp | src/gluonnlp/model/parameter.py | WeightDropParameter.data | def data(self, ctx=None):
"""Returns a copy of this parameter on one context. Must have been
initialized on this context before.
Parameters
----------
ctx : Context
Desired context.
Returns
-------
NDArray on ctx
"""
d = self._... | python | def data(self, ctx=None):
"""Returns a copy of this parameter on one context. Must have been
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Parameters
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ctx : Context
Desired context.
Returns
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NDArray on ctx
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dmlc/gluon-nlp | src/gluonnlp/model/elmo.py | elmo_2x1024_128_2048cnn_1xhighway | def elmo_2x1024_128_2048cnn_1xhighway(dataset_name=None, pretrained=False, ctx=mx.cpu(),
root=os.path.join(get_home_dir(), 'models'), **kwargs):
r"""ELMo 2-layer BiLSTM with 1024 hidden units, 128 projection size, 1 highway layer.
Parameters
----------
dataset_name... | python | def elmo_2x1024_128_2048cnn_1xhighway(dataset_name=None, pretrained=False, ctx=mx.cpu(),
root=os.path.join(get_home_dir(), 'models'), **kwargs):
r"""ELMo 2-layer BiLSTM with 1024 hidden units, 128 projection size, 1 highway layer.
Parameters
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dataset_name... | [
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dmlc/gluon-nlp | src/gluonnlp/model/elmo.py | ELMoCharacterEncoder.hybrid_forward | def hybrid_forward(self, F, inputs):
# pylint: disable=arguments-differ
"""
Compute context insensitive token embeddings for ELMo representations.
Parameters
----------
inputs : NDArray
Shape (batch_size, sequence_length, max_character_per_token)
... | python | def hybrid_forward(self, F, inputs):
# pylint: disable=arguments-differ
"""
Compute context insensitive token embeddings for ELMo representations.
Parameters
----------
inputs : NDArray
Shape (batch_size, sequence_length, max_character_per_token)
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"i... | Compute context insensitive token embeddings for ELMo representations.
Parameters
----------
inputs : NDArray
Shape (batch_size, sequence_length, max_character_per_token)
of character ids representing the current batch.
Returns
-------
token_embe... | [
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] | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/model/elmo.py#L103-L131 | train |
dmlc/gluon-nlp | src/gluonnlp/model/elmo.py | ELMoBiLM.hybrid_forward | def hybrid_forward(self, F, inputs, states=None, mask=None):
# pylint: disable=arguments-differ
"""
Parameters
----------
inputs : NDArray
Shape (batch_size, sequence_length, max_character_per_token)
of character ids representing the current batch.
... | python | def hybrid_forward(self, F, inputs, states=None, mask=None):
# pylint: disable=arguments-differ
"""
Parameters
----------
inputs : NDArray
Shape (batch_size, sequence_length, max_character_per_token)
of character ids representing the current batch.
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"NDArray"... | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/model/elmo.py#L243-L284 | train |
dmlc/gluon-nlp | src/gluonnlp/model/language_model.py | awd_lstm_lm_1150 | def awd_lstm_lm_1150(dataset_name=None, vocab=None, pretrained=False, ctx=cpu(),
root=os.path.join(get_home_dir(), 'models'), **kwargs):
r"""3-layer LSTM language model with weight-drop, variational dropout, and tied weights.
Embedding size is 400, and hidden layer size is 1150.
Param... | python | def awd_lstm_lm_1150(dataset_name=None, vocab=None, pretrained=False, ctx=cpu(),
root=os.path.join(get_home_dir(), 'models'), **kwargs):
r"""3-layer LSTM language model with weight-drop, variational dropout, and tied weights.
Embedding size is 400, and hidden layer size is 1150.
Param... | [
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Embedding size is 400, and hidden layer size is 1150.
Parameters
----------
dataset_name : str or None, default None
The dataset name on which the pre-trained model is trained.
Options are 'wikitex... | [
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] | 4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/model/language_model.py#L181-L226 | train |
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