code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
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def process_mono_corpus(self,
corpus_paths: List[str],
out_path: str,
chunk_size: int = 1024 * 1024,
num_process: int = 8) -> int:
"""Preprocess the mono corpus
Parameters
----------
... | Preprocess the mono corpus
Parameters
----------
corpus_paths
Corpus paths
out_path
Write the results to the output path
chunk_size
Approximately split the corpus files into multiple chunks
num_process
The number of process... | process_mono_corpus | python | dmlc/gluon-nlp | scripts/processing/clean_tok_corpus.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/processing/clean_tok_corpus.py | Apache-2.0 |
def calc_approx_error(expected_tensor: np.ndarray, observed_tensor: np.ndarray) -> float:
'''
Calculating relative error for one tensor
'''
error = observed_tensor - expected_tensor
absolute_error = np.abs(error)
mean_absolute_error = absolute_error.mean()
mean_expected_value = np.abs(expect... |
Calculating relative error for one tensor
| calc_approx_error | python | dmlc/gluon-nlp | scripts/question_answering/custom_strategy.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/question_answering/custom_strategy.py | Apache-2.0 |
def get_approx_errors(expected_tensors, observed_tensors):
'''
Calculating relative error for multiple tensors: Dict[tensors_name: str, tensor: np.ndarray]
'''
errors = {}
for node_name in observed_tensors.keys():
expected_tensor = expected_tensors[node_name][node_name]
observed_tens... |
Calculating relative error for multiple tensors: Dict[tensors_name: str, tensor: np.ndarray]
| get_approx_errors | python | dmlc/gluon-nlp | scripts/question_answering/custom_strategy.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/question_answering/custom_strategy.py | Apache-2.0 |
def get_qtensors(self, quant_cfg, node_list):
'''
Generating quantized model based on configuration and capturing intermediate tensors
'''
qmodel = self.adaptor.quantize(quant_cfg, self.model, self.calib_dataloader)
tensors = self.adaptor.inspect_tensor(qmodel, self.calib_dataloa... |
Generating quantized model based on configuration and capturing intermediate tensors
| get_qtensors | python | dmlc/gluon-nlp | scripts/question_answering/custom_strategy.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/question_answering/custom_strategy.py | Apache-2.0 |
def bayesian_params_to_tune_configs(self, params):
'''
Creating configuration from params - changing configurations' indexes for real configurations
'''
node_cfgs = {}
for node_key, configs in self.opwise_quant_cfgs.items():
if node_key in params:
valu... |
Creating configuration from params - changing configurations' indexes for real configurations
| bayesian_params_to_tune_configs | python | dmlc/gluon-nlp | scripts/question_answering/custom_strategy.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/question_answering/custom_strategy.py | Apache-2.0 |
def normalize_answer(s):
"""Lower text and remove punctuation, articles and extra whitespace."""
def remove_articles(text):
regex = re.compile(r'\b(a|an|the)\b', re.UNICODE)
return re.sub(regex, ' ', text)
def white_space_fix(text):
return ' '.join(text.split())
def remove_pun... | Lower text and remove punctuation, articles and extra whitespace. | normalize_answer | python | dmlc/gluon-nlp | scripts/question_answering/eval_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/question_answering/eval_utils.py | Apache-2.0 |
def compute_f1(a_gold, a_pred):
"""
Compute the token-level f1 scores in which the common tokens are considered
as True Positives. Precision and recall are percentages of the number of
common tokens in the prediction and groud truth, respectively.
"""
gold_toks = get_tokens(a_gold)
pred_toks... |
Compute the token-level f1 scores in which the common tokens are considered
as True Positives. Precision and recall are percentages of the number of
common tokens in the prediction and groud truth, respectively.
| compute_f1 | python | dmlc/gluon-nlp | scripts/question_answering/eval_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/question_answering/eval_utils.py | Apache-2.0 |
def find_best_thresh(preds, scores, na_probs, qid_to_has_ans):
"""
Find the best threshold of the raw scores.
The initial score is set to the number of unanswerable questions,
assuming that each unanswerable question is successfully predicted.
In the following traverse, the best threshold is consta... |
Find the best threshold of the raw scores.
The initial score is set to the number of unanswerable questions,
assuming that each unanswerable question is successfully predicted.
In the following traverse, the best threshold is constantly adjusted
according to the difference from the assumption ('di... | find_best_thresh | python | dmlc/gluon-nlp | scripts/question_answering/eval_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/question_answering/eval_utils.py | Apache-2.0 |
def revise_unanswerable(preds, na_probs, na_prob_thresh):
"""
Revise the predictions results and return a null string for unanswerable question
whose unanswerable probability above the threshold.
Parameters
----------
preds: dict
A dictionary of full prediction of spans
na_probs: di... |
Revise the predictions results and return a null string for unanswerable question
whose unanswerable probability above the threshold.
Parameters
----------
preds: dict
A dictionary of full prediction of spans
na_probs: dict
A dictionary of unanswerable probabilities
na_prob... | revise_unanswerable | python | dmlc/gluon-nlp | scripts/question_answering/eval_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/question_answering/eval_utils.py | Apache-2.0 |
def squad_eval(data_file, preds, na_probs, na_prob_thresh=0.0, revise=False):
"""
Parameters
----------
data_file
dataset(list) or data_file(str)
preds
predictions dictionary
na_probs
probabilities dictionary of unanswerable
na_prob_thresh
threshold of unansw... |
Parameters
----------
data_file
dataset(list) or data_file(str)
preds
predictions dictionary
na_probs
probabilities dictionary of unanswerable
na_prob_thresh
threshold of unanswerable
revise
Wether to get the final predictions with impossible answers... | squad_eval | python | dmlc/gluon-nlp | scripts/question_answering/eval_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/question_answering/eval_utils.py | Apache-2.0 |
def forward(self, tokens, token_types, valid_length, p_mask):
"""
Parameters
----------
tokens
Shape (batch_size, seq_length)
The merged input tokens
token_types
Shape (batch_size, seq_length)
Token types for the sequences, used to... |
Parameters
----------
tokens
Shape (batch_size, seq_length)
The merged input tokens
token_types
Shape (batch_size, seq_length)
Token types for the sequences, used to indicate whether the word belongs to the
first sentence or t... | forward | python | dmlc/gluon-nlp | scripts/question_answering/models.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/question_answering/models.py | Apache-2.0 |
def inference(self, tokens, token_types, valid_length, p_mask,
start_top_n: int = 5, end_top_n: int = 5):
"""Get the inference result with beam search
Parameters
----------
tokens
The input tokens. Shape (batch_size, sequence_length)
token_types
... | Get the inference result with beam search
Parameters
----------
tokens
The input tokens. Shape (batch_size, sequence_length)
token_types
The input token types. Shape (batch_size, sequence_length)
valid_length
The valid length of the tokens. Sh... | inference | python | dmlc/gluon-nlp | scripts/question_answering/models.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/question_answering/models.py | Apache-2.0 |
def get_squad_features(args, tokenizer, segment):
"""
Get processed data features of SQuADExampls
Parameters
----------
args : argparse.Namespace
tokenizer:
Tokenizer instance
segment: str
train or dev
Returns
-------
data_features
The list of processed ... |
Get processed data features of SQuADExampls
Parameters
----------
args : argparse.Namespace
tokenizer:
Tokenizer instance
segment: str
train or dev
Returns
-------
data_features
The list of processed data features
| get_squad_features | python | dmlc/gluon-nlp | scripts/question_answering/run_squad.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/question_answering/run_squad.py | Apache-2.0 |
def get_network(model_name,
ctx_l,
dropout=0.1,
checkpoint_path=None,
backbone_path=None,
dtype='float32'):
"""
Get the network that fine-tune the Question Answering Task
Parameters
----------
model_name : str
T... |
Get the network that fine-tune the Question Answering Task
Parameters
----------
model_name : str
The model name of the backbone model
ctx_l :
Context list of training device like [mx.gpu(0), mx.gpu(1)]
dropout : float
Dropout probability of the task specified layer
... | get_network | python | dmlc/gluon-nlp | scripts/question_answering/run_squad.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/question_answering/run_squad.py | Apache-2.0 |
def setup_logging(args, local_rank):
"""
Setup logging configuration as well as random seed
"""
logging_config(args.output_dir,
name='finetune_squad{}'.format(args.version),# avoid race
overwrite_handler=True,
console=(local_rank == 0))
loggin... |
Setup logging configuration as well as random seed
| setup_logging | python | dmlc/gluon-nlp | scripts/question_answering/run_squad.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/question_answering/run_squad.py | Apache-2.0 |
def predict_extended(original_feature,
chunked_features,
results,
n_best_size,
max_answer_length=64,
start_top_n=5,
end_top_n=5):
"""Get prediction results for SQuAD.
Start Logits: (B, ... | Get prediction results for SQuAD.
Start Logits: (B, N_start)
End Logits: (B, N_start, N_end)
Parameters
----------
original_feature:
The original SquadFeature before chunked
chunked_features
List of ChunkFeatures
results
List of model predictions for span start and ... | predict_extended | python | dmlc/gluon-nlp | scripts/question_answering/run_squad.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/question_answering/run_squad.py | Apache-2.0 |
def collect(self, name, op_name, arr):
"""Callback function for collecting min and max values from an NDArray."""
if name not in self.include_layers:
return
arr = arr.copyto(mx.cpu()).asnumpy()
min_range = np.min(arr)
max_range = np.max(arr)
if (name.find("sg... | Callback function for collecting min and max values from an NDArray. | collect | python | dmlc/gluon-nlp | scripts/question_answering/run_squad.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/question_answering/run_squad.py | Apache-2.0 |
def eval_validation(ckpt_name, best_eval):
"""
Model inference during validation or final evaluation.
"""
dev_dataloader = mx.gluon.data.DataLoader(
dev_all_chunk_features,
batchify_fn=dataset_processor.BatchifyFunction,
batch_size=args.eval_batch_size... |
Model inference during validation or final evaluation.
| eval_validation | python | dmlc/gluon-nlp | scripts/question_answering/run_squad.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/question_answering/run_squad.py | Apache-2.0 |
def get_squad_examples(data_dir, segment='train', version='1.1'):
"""
Parameters
----------
data_dir
The directory of the data
segment
The segment
version
Version of the SQuAD
Returns
-------
examples
A list of SquadExampls objects
"""
if ver... |
Parameters
----------
data_dir
The directory of the data
segment
The segment
version
Version of the SQuAD
Returns
-------
examples
A list of SquadExampls objects
| get_squad_examples | python | dmlc/gluon-nlp | scripts/question_answering/squad_utils.py | https://github.com/dmlc/gluon-nlp/blob/master/scripts/question_answering/squad_utils.py | Apache-2.0 |
def gen_self_attn_mask(data,
valid_length=None,
dtype: type = np.float32,
attn_type: str = 'full',
layout: str = 'NT'):
"""Generate the mask used for the encoder, i.e, self-attention.
In our implementation, 1 --> not ma... | Generate the mask used for the encoder, i.e, self-attention.
In our implementation, 1 --> not masked, 0 --> masked
Let's consider the data with two samples:
.. code-block:: none
data =
[['I', 'can', 'now', 'use', 'numpy', 'in', 'Gluon@@', 'NLP' ],
['May', 'the', 'f... | gen_self_attn_mask | python | dmlc/gluon-nlp | src/gluonnlp/attention_cell.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/attention_cell.py | Apache-2.0 |
def gen_mem_attn_mask(mem, mem_valid_length, data, data_valid_length=None,
dtype=np.float32, layout: str = 'NT'):
"""Generate the mask used for the decoder. All query slots are attended to the memory slots.
In our implementation, 1 --> not masked, 0 --> masked
Let's consider the data... | Generate the mask used for the decoder. All query slots are attended to the memory slots.
In our implementation, 1 --> not masked, 0 --> masked
Let's consider the data + mem with a batch of two samples:
.. code-block:: none
mem = [['I', 'can', 'now', 'use'],
['May', 'the', 'fo... | gen_mem_attn_mask | python | dmlc/gluon-nlp | src/gluonnlp/attention_cell.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/attention_cell.py | Apache-2.0 |
def masked_softmax(att_score, mask, axis: int = -1, temperature=None):
"""Ignore the masked elements when calculating the softmax. The mask can be broadcastable.
Parameters
----------
att_score : Symbol or NDArray
Shape (..., length, ...)
mask : Symbol or NDArray or None
Shape (...,... | Ignore the masked elements when calculating the softmax. The mask can be broadcastable.
Parameters
----------
att_score : Symbol or NDArray
Shape (..., length, ...)
mask : Symbol or NDArray or None
Shape (..., length, ...)
1 --> The element is not masked
0 --> The elemen... | masked_softmax | python | dmlc/gluon-nlp | src/gluonnlp/attention_cell.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/attention_cell.py | Apache-2.0 |
def masked_logsoftmax(att_score, mask, axis: int = -1):
"""Ignore the masked elements when calculating the softmax. The mask can be broadcastable.
Parameters
----------
att_score : Symborl or NDArray
Shape (..., length, ...)
mask : Symbol or NDArray or None
Shape (..., length, ...)
... | Ignore the masked elements when calculating the softmax. The mask can be broadcastable.
Parameters
----------
att_score : Symborl or NDArray
Shape (..., length, ...)
mask : Symbol or NDArray or None
Shape (..., length, ...)
mask = 1 --> not masked
mask = 0 --> masked
... | masked_logsoftmax | python | dmlc/gluon-nlp | src/gluonnlp/attention_cell.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/attention_cell.py | Apache-2.0 |
def multi_head_dot_attn(query, key, value,
mask=None,
edge_scores=None,
dropout: float = 0.0,
scaled: bool = True, normalized: bool = False,
eps: float = 1E-6, query_head_units: Optional[int] = None,
... | Multihead dot product attention between the query, key, value.
scaled is False, normalized is False:
D(h_q, h_k) = <h_q, h_k>
scaled is True, normalized is False:
D(h_q, h_k) = <h_q, h_k> / sqrt(dim_q)
scaled is False, normalized is True:
D(h_q, h_k) = <h_q / ||h_q||, h_k / ||h_k||>... | multi_head_dot_attn | python | dmlc/gluon-nlp | src/gluonnlp/attention_cell.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/attention_cell.py | Apache-2.0 |
def gen_rel_position(data, past_data=None, dtype=np.int32, layout='NT'):
"""Create a matrix of relative position for RelAttentionScoreCell.
The relative position is defined as the index difference: `mem_i` - `query_j`.
Note, though, that the implementation here makes sense in self-attention's settin... | Create a matrix of relative position for RelAttentionScoreCell.
The relative position is defined as the index difference: `mem_i` - `query_j`.
Note, though, that the implementation here makes sense in self-attention's setting,
but not in cross-attention's. Hence, both `mem_i` and `query_j` are time ... | gen_rel_position | python | dmlc/gluon-nlp | src/gluonnlp/attention_cell.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/attention_cell.py | Apache-2.0 |
def __init__(self, query_units,
num_heads,
pos_embed_units: Optional[int] = None,
max_distance=None,
bidirectional=False,
num_buckets=None,
method='transformer_xl',
dropout: float = 0.0,
... |
Parameters
----------
query_units
num_heads
pos_embed_units
max_distance
bidirectional
num_buckets
method
dropout
dtype
layout
use_einsum
| __init__ | python | dmlc/gluon-nlp | src/gluonnlp/attention_cell.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/attention_cell.py | Apache-2.0 |
def forward(self, rel_positions, query=None):
"""Forward function
Parameters
----------
rel_positions
The relative shifts. Shape (query_length, mem_length).
Each element represents the shift between the :math:`i-th` element of query and
the :math:`j-t... | Forward function
Parameters
----------
rel_positions
The relative shifts. Shape (query_length, mem_length).
Each element represents the shift between the :math:`i-th` element of query and
the :math:`j-th` element of memory.
query
The query... | forward | python | dmlc/gluon-nlp | src/gluonnlp/attention_cell.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/attention_cell.py | Apache-2.0 |
def get_home_dir():
"""Get home directory for storing datasets/models/pre-trained word embeddings"""
_home_dir = os.environ.get('GLUONNLP_HOME', os.path.join('~', '.gluonnlp'))
# expand ~ to actual path
_home_dir = os.path.expanduser(_home_dir)
return _home_dir | Get home directory for storing datasets/models/pre-trained word embeddings | get_home_dir | python | dmlc/gluon-nlp | src/gluonnlp/base.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/base.py | Apache-2.0 |
def get_data_home_dir():
"""Get home directory for storing the datasets"""
home_dir = get_home_dir()
return os.path.join(home_dir, 'datasets') | Get home directory for storing the datasets | get_data_home_dir | python | dmlc/gluon-nlp | src/gluonnlp/base.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/base.py | Apache-2.0 |
def get_model_zoo_home_dir():
"""Get the local directory for storing pretrained models"""
home_dir = get_home_dir()
return os.path.join(home_dir, 'models') | Get the local directory for storing pretrained models | get_model_zoo_home_dir | python | dmlc/gluon-nlp | src/gluonnlp/base.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/base.py | Apache-2.0 |
def get_model_zoo_checksum_dir():
"""Get the directory that stores the checksums of the artifacts in the model zoo """
curr_dir = os.path.realpath(os.path.dirname(os.path.realpath(__file__)))
check_sum_dir = os.path.join(curr_dir, 'models', 'model_zoo_checksums')
return check_sum_dir | Get the directory that stores the checksums of the artifacts in the model zoo | get_model_zoo_checksum_dir | python | dmlc/gluon-nlp | src/gluonnlp/base.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/base.py | Apache-2.0 |
def get_repo_url():
"""Return the base URL for Gluon dataset and model repository """
default_repo = 's3://gluonnlp-numpy-data'
repo_url = os.environ.get('GLUONNLP_REPO_URL', default_repo)
if repo_url[-1] != '/':
repo_url = repo_url + '/'
return repo_url | Return the base URL for Gluon dataset and model repository | get_repo_url | python | dmlc/gluon-nlp | src/gluonnlp/base.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/base.py | Apache-2.0 |
def get_repo_model_zoo_url():
"""Return the base URL for GluonNLP Model Zoo"""
repo_url = get_repo_url()
model_zoo_url = repo_url + 'models/'
return model_zoo_url | Return the base URL for GluonNLP Model Zoo | get_repo_model_zoo_url | python | dmlc/gluon-nlp | src/gluonnlp/base.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/base.py | Apache-2.0 |
def get_norm_layer(normalization: str = 'layer_norm',
axis: int = -1,
epsilon: float = 1e-5,
in_channels: int = 0, **kwargs):
"""
Get the normalization layer based on the type
Parameters
----------
normalization
The type of the layer ... |
Get the normalization layer based on the type
Parameters
----------
normalization
The type of the layer normalization from ['layer_norm', 'no_norm', 'batch_norm']
axis
The axis to normalize the
epsilon
The epsilon of the normalization layer
in_channels
Input... | get_norm_layer | python | dmlc/gluon-nlp | src/gluonnlp/layers.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/layers.py | Apache-2.0 |
def _fmt_and_check_cutoffs(cutoffs, vocab_size):
"""Parse and get the cutoffs used in adaptive embedding + adaptive softmax
Parameters
----------
cutoffs
The cutoffs of the
vocab_size
Size of the vocabulary
Returns
-------
cutoffs
The parsed cutoffs, will be [0,... | Parse and get the cutoffs used in adaptive embedding + adaptive softmax
Parameters
----------
cutoffs
The cutoffs of the
vocab_size
Size of the vocabulary
Returns
-------
cutoffs
The parsed cutoffs, will be [0, c0, c1, ..., c_{k-1}, V]
If the original cutoff... | _fmt_and_check_cutoffs | python | dmlc/gluon-nlp | src/gluonnlp/layers.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/layers.py | Apache-2.0 |
def get_activation(act: Optional[Union[str, HybridBlock]]) -> HybridBlock:
"""Get the activation based on the string
Parameters
----------
act
The activation
Returns
-------
ret
The activation layer
"""
if act is None:
return lambda x: x
if isinstance(a... | Get the activation based on the string
Parameters
----------
act
The activation
Returns
-------
ret
The activation layer
| get_activation | python | dmlc/gluon-nlp | src/gluonnlp/layers.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/layers.py | Apache-2.0 |
def __init__(self, units: int, dtype: Union[str, type] = 'float32'):
"""Use a geometric sequence of timescales.
Parameters
----------
units
The number of units for positional embedding
dtype
The dtype of the inner positional embeddings
"""
... | Use a geometric sequence of timescales.
Parameters
----------
units
The number of units for positional embedding
dtype
The dtype of the inner positional embeddings
| __init__ | python | dmlc/gluon-nlp | src/gluonnlp/layers.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/layers.py | Apache-2.0 |
def forward(self, positions):
"""
Parameters
----------
positions : NDArray
Shape (..., )
Returns
-------
ret :
Shape (..., units)
"""
emb = np.expand_dims(positions.astype(self._dtype), axis=-1) * self.base_mult.data()
... |
Parameters
----------
positions : NDArray
Shape (..., )
Returns
-------
ret :
Shape (..., units)
| forward | python | dmlc/gluon-nlp | src/gluonnlp/layers.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/layers.py | Apache-2.0 |
def __init__(self,
units: int = 512,
hidden_size: int = 2048,
use_bias=True,
activation_dropout: float = 0.0,
dropout: float = 0.1,
weight_initializer=None,
bias_initializer='zeros',
a... |
Parameters
----------
units
hidden_size
activation_dropout
dropout
weight_initializer
bias_initializer
activation
normalization
layer_norm or no_norm
layer_norm_eps
pre_norm
Pre-layer normalization ... | __init__ | python | dmlc/gluon-nlp | src/gluonnlp/layers.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/layers.py | Apache-2.0 |
def forward(self, data):
"""
Parameters
----------
F
data :
Shape (B, seq_length, C_in)
Returns
-------
out :
Shape (B, seq_length, C_out)
"""
residual = data
if self._pre_norm:
data = self.laye... |
Parameters
----------
F
data :
Shape (B, seq_length, C_in)
Returns
-------
out :
Shape (B, seq_length, C_out)
| forward | python | dmlc/gluon-nlp | src/gluonnlp/layers.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/layers.py | Apache-2.0 |
def __init__(self, vocab_size: int,
embed_size: int,
units: int,
cutoffs: Optional[Union[int, List]] = None,
div_val: float = 1.0,
dtype='float32',
scaled=True,
embedding_initializer: InitializerType =... |
Parameters
----------
vocab_size
The size of the vocabulary
embed_size
The base size of the embedding vectors. The embedding size of each cluster will be
[embed_size / div_val**0, embed_size / div_val**1, embed_size / div_val**2, ...]
units
... | __init__ | python | dmlc/gluon-nlp | src/gluonnlp/layers.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/layers.py | Apache-2.0 |
def forward(self, inp): # pylint: disable=arguments-differ
"""
Parameters
----------
inp
Shape (...,)
Returns
-------
out
Shape (..., units)
"""
if self._div_val == 1.0:
emb = np.take(getattr(self, 'embed0_wei... |
Parameters
----------
inp
Shape (...,)
Returns
-------
out
Shape (..., units)
| forward | python | dmlc/gluon-nlp | src/gluonnlp/layers.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/layers.py | Apache-2.0 |
def __init__(self, vocab_size: int, embed_size: int, in_units: int,
cutoffs: Optional[Union[int, List]] = None,
div_val: float = 1.0,
dtype='float32',
use_bias=True,
weight_initializer: InitializerType = None,
bias_ini... |
Parameters
----------
vocab_size
Size of the vocabulary
embed_size
Base embedding size. The hidden will be first projected to
embed_size and then project to vocab_size
in_units
The number of input units
cutoffs
... | __init__ | python | dmlc/gluon-nlp | src/gluonnlp/layers.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/layers.py | Apache-2.0 |
def get_logits(self, hidden):
"""Get all the logits.
Parameters
----------
hidden
The hidden representation/ Shape (..., in_units)
Returns
-------
logits
Shape (..., :math:`|V|`)
"""
if self._cutoffs is None:
... | Get all the logits.
Parameters
----------
hidden
The hidden representation/ Shape (..., in_units)
Returns
-------
logits
Shape (..., :math:`|V|`)
| get_logits | python | dmlc/gluon-nlp | src/gluonnlp/layers.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/layers.py | Apache-2.0 |
def forward(self, hidden, target):
"""
Parameters
----------
hidden
The hidden representation
Shape (..., in_units)
target
The target representation
Shape (...,)
Returns
-------
sel_logits
The l... |
Parameters
----------
hidden
The hidden representation
Shape (..., in_units)
target
The target representation
Shape (...,)
Returns
-------
sel_logits
The log probability that each hidden has when label... | forward | python | dmlc/gluon-nlp | src/gluonnlp/layers.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/layers.py | Apache-2.0 |
def forward(self, pred, label):
"""
Parameters
----------
pred :
The predictions of the network. Shape (..., V)
label :
The labels. Shape (..., )
Returns
-------
loss :
Shape (..., )
"""
if not self._fr... |
Parameters
----------
pred :
The predictions of the network. Shape (..., V)
label :
The labels. Shape (..., )
Returns
-------
loss :
Shape (..., )
| forward | python | dmlc/gluon-nlp | src/gluonnlp/loss.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/loss.py | Apache-2.0 |
def select_vectors_by_position(data, positions):
"""Select each batch with the given positions.
Once advanced indexing can be hybridized, we can revise the implementation.
out[i, j, ...] = data[i, positions[i, j], ...]
Parameters
----------
data
Input tensor of contextualized token em... | Select each batch with the given positions.
Once advanced indexing can be hybridized, we can revise the implementation.
out[i, j, ...] = data[i, positions[i, j], ...]
Parameters
----------
data
Input tensor of contextualized token embeddings
Shape (batch_size, seq_length, ...)
... | select_vectors_by_position | python | dmlc/gluon-nlp | src/gluonnlp/op.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/op.py | Apache-2.0 |
def add_vectors_by_position(data, increment, positions):
"""Scatter each batch with the given positions.
data[i, positions[i, j], ...] += increment[i, j, ...]
Parameters
----------
data
Input tensor of the array to be updated.
Shape (batch_size, seq_length, ...)
increment
... | Scatter each batch with the given positions.
data[i, positions[i, j], ...] += increment[i, j, ...]
Parameters
----------
data
Input tensor of the array to be updated.
Shape (batch_size, seq_length, ...)
increment
Input tensor of token ids
Shape (batch_size, num_disp... | add_vectors_by_position | python | dmlc/gluon-nlp | src/gluonnlp/op.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/op.py | Apache-2.0 |
def update_vectors_by_position(data, val, positions):
"""
Update each batch with the given positions. Considered as a reversed process of
"select_vectors_by_position", this is an operator similar to "add_vectors_by_position"
that updates the results instead of adding.
data[i, positions[i, j], :] = ... |
Update each batch with the given positions. Considered as a reversed process of
"select_vectors_by_position", this is an operator similar to "add_vectors_by_position"
that updates the results instead of adding.
data[i, positions[i, j], :] = val[i, j, :]
Parameters
----------
data
... | update_vectors_by_position | python | dmlc/gluon-nlp | src/gluonnlp/op.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/op.py | Apache-2.0 |
def gumbel_softmax(logits, temperature: float = 1.0, eps: float = 1E-10,
hard=True, use_np_gumbel: bool = True):
r"""Perform the gumbel-softmax trick to generate differentiable one-hot vectors from the input
logits.
Here, the gumbel distribution is
Gumbel(\alpha) = -log (-log U) + \... | Perform the gumbel-softmax trick to generate differentiable one-hot vectors from the input
logits.
Here, the gumbel distribution is
Gumbel(\alpha) = -log (-log U) + \log \alpha, in which U is the uniform(0, 1) distribution.
A nice property of Gumbel is:
\argmax({Gumbel(\alpha_i)}) \sim multinomi... | gumbel_softmax | python | dmlc/gluon-nlp | src/gluonnlp/op.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/op.py | Apache-2.0 |
def trunc_gumbel(logits, truncation):
"""Sample from the TruncGumbel distribution.
The cumulative density function (CDF) of the Truncated Gumbel distribution is defined as
TruncGumbel(\alpha, truncation) \prop max(Gumbel(\alpha), truncation)
To sample from the distribution, we can use the CDF inversi... | Sample from the TruncGumbel distribution.
The cumulative density function (CDF) of the Truncated Gumbel distribution is defined as
TruncGumbel(lpha, truncation) \prop max(Gumbel(lpha), truncation)
To sample from the distribution, we can use the CDF inversion technique.
References:
1. [NIP... | trunc_gumbel | python | dmlc/gluon-nlp | src/gluonnlp/op.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/op.py | Apache-2.0 |
def relative_position_bucket(relative_position,
bidirectional: bool = True,
num_buckets: int = 32,
max_distance: int = 128):
"""Map the relative position to buckets. The implementation is consistent with that
in [mesh_tensorf... | Map the relative position to buckets. The implementation is consistent with that
in [mesh_tensorflow](https://github.com/tensorflow/mesh/blob/c59988047e49b4d2af05603e3170724cdbadc467/mesh_tensorflow/transformer/transformer_layers.py#L595-L637)
where relative position is defined as `mem_i - query_j`. Thus, a pos... | relative_position_bucket | python | dmlc/gluon-nlp | src/gluonnlp/op.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/op.py | Apache-2.0 |
def _expand_to_beam_size(data, beam_size, batch_size, state_batch_axis=None):
"""Tile all the states to have batch_size * beam_size on the batch axis.
Parameters
----------
data : A single mx.np.ndarray or nested container with mx.np.ndarray
Each mx.np.ndarray should have shape (N, ...) when st... | Tile all the states to have batch_size * beam_size on the batch axis.
Parameters
----------
data : A single mx.np.ndarray or nested container with mx.np.ndarray
Each mx.np.ndarray should have shape (N, ...) when state_info is None,
or same as the layout in state_info when it's not None.
... | _expand_to_beam_size | python | dmlc/gluon-nlp | src/gluonnlp/sequence_sampler.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/sequence_sampler.py | Apache-2.0 |
def _choose_states(states, indices, state_batch_axis=None):
"""
Parameters
----------
states : Object contains mx.np.ndarray
indices : mx.np.ndarray
Indices of the states to take. Shape (N,).
state_batch_axis
Descriptors for states, it is generated from decoder's ``state_batch_a... |
Parameters
----------
states : Object contains mx.np.ndarray
indices : mx.np.ndarray
Indices of the states to take. Shape (N,).
state_batch_axis
Descriptors for states, it is generated from decoder's ``state_batch_axis``.
When None, this method assumes that the batch axis i... | _choose_states | python | dmlc/gluon-nlp | src/gluonnlp/sequence_sampler.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/sequence_sampler.py | Apache-2.0 |
def __init__(self, beam_size, vocab_size, eos_id, scorer, state_batch_axis,
stochastic=False):
"""
Parameters
----------
beam_size : int
vocab_size : int
eos_id : int
scorer : BeamSearchScorer
state_batch_axis :
stochastic: bool
... |
Parameters
----------
beam_size : int
vocab_size : int
eos_id : int
scorer : BeamSearchScorer
state_batch_axis :
stochastic: bool
prefix : None
params : None
| __init__ | python | dmlc/gluon-nlp | src/gluonnlp/sequence_sampler.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/sequence_sampler.py | Apache-2.0 |
def gumbel_with_maximum(self, phi, T, dim=-1):
"""Calculate the Gumbel with maximum.
Parameters
----------
phi : mx.np.ndarray
Shape (batch_size, beam_size, L).
T : mx.np.ndarray
The previous scores. Shape (batch_size, beam_size)
"""
g_phi... | Calculate the Gumbel with maximum.
Parameters
----------
phi : mx.np.ndarray
Shape (batch_size, beam_size, L).
T : mx.np.ndarray
The previous scores. Shape (batch_size, beam_size)
| gumbel_with_maximum | python | dmlc/gluon-nlp | src/gluonnlp/sequence_sampler.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/sequence_sampler.py | Apache-2.0 |
def shift_gumbel_maximum(self, g_phi, T, axis=-1, Z=None):
"""
Parameters
----------
g_phi : mx.np.ndarray
Shape (batch_size, beam_size, L).
T : mx.np.ndarray
The previous scores. Shape (batch_size, beam_size)
axis
The axis
Z
... |
Parameters
----------
g_phi : mx.np.ndarray
Shape (batch_size, beam_size, L).
T : mx.np.ndarray
The previous scores. Shape (batch_size, beam_size)
axis
The axis
Z
The Z value
| shift_gumbel_maximum | python | dmlc/gluon-nlp | src/gluonnlp/sequence_sampler.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/sequence_sampler.py | Apache-2.0 |
def forward(self, samples, valid_length, outputs, scores, step, beam_alive_mask, # pylint: disable=arguments-differ
states, batch_shift):
"""
Parameters
----------
samples : mx.np.ndarray
The current samples generated by beam search.
Shape (batc... |
Parameters
----------
samples : mx.np.ndarray
The current samples generated by beam search.
Shape (batch_size, beam_size, L).
valid_length : mx.np.ndarray
The current valid lengths of the samples
outputs : mx.np.ndarray
Outputs fr... | forward | python | dmlc/gluon-nlp | src/gluonnlp/sequence_sampler.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/sequence_sampler.py | Apache-2.0 |
def forward(self, inputs, states, src_seq_lengths=None):
"""Sample by beam search.
Parameters
----------
inputs : mx.np.ndarray
The initial input of the decoder. Shape is (batch_size,).
states : Object that contains mx.np.ndarrays
The initial states of th... | Sample by beam search.
Parameters
----------
inputs : mx.np.ndarray
The initial input of the decoder. Shape is (batch_size,).
states : Object that contains mx.np.ndarrays
The initial states of the decoder.
src_seq_lengths : mx.np.ndarray
The s... | forward | python | dmlc/gluon-nlp | src/gluonnlp/sequence_sampler.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/sequence_sampler.py | Apache-2.0 |
def _pad_arrs_to_max_length(arrs, pad_axis, pad_val, use_shared_mem, dtype, round_to=None):
"""Inner Implementation of the Pad batchify
Parameters
----------
arrs : list
pad_axis : int
pad_val : number
use_shared_mem : bool, default False
dtype :
round_to : int
Returns
----... | Inner Implementation of the Pad batchify
Parameters
----------
arrs : list
pad_axis : int
pad_val : number
use_shared_mem : bool, default False
dtype :
round_to : int
Returns
-------
ret : NDArray
original_length : NDArray
| _pad_arrs_to_max_length | python | dmlc/gluon-nlp | src/gluonnlp/data/batchify.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/batchify.py | Apache-2.0 |
def __call__(self, data):
"""Batchify the input data.
The input can be list of numpy.ndarray, list of numbers or list of
mxnet.nd.NDArray. Inputting mxnet.nd.NDArray is discouraged as each
array need to be converted to numpy for efficient padding.
The arrays will be padded to t... | Batchify the input data.
The input can be list of numpy.ndarray, list of numbers or list of
mxnet.nd.NDArray. Inputting mxnet.nd.NDArray is discouraged as each
array need to be converted to numpy for efficient padding.
The arrays will be padded to the largest dimension at `axis` and th... | __call__ | python | dmlc/gluon-nlp | src/gluonnlp/data/batchify.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/batchify.py | Apache-2.0 |
def __call__(self, data):
"""Batchify the input data.
Parameters
----------
data : list
The samples to batchfy. Each sample should contain N attributes.
Returns
-------
ret : tuple
A tuple of length N. Contains the batchified result of ea... | Batchify the input data.
Parameters
----------
data : list
The samples to batchfy. Each sample should contain N attributes.
Returns
-------
ret : tuple
A tuple of length N. Contains the batchified result of each attribute in the input.
| __call__ | python | dmlc/gluon-nlp | src/gluonnlp/data/batchify.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/batchify.py | Apache-2.0 |
def __call__(self, data: t_List[t_Dict]) -> t_Dict:
"""
Parameters
----------
data
The samples to batchify. Each sample should be a dictionary
Returns
-------
ret
The resulting dictionary that stores the merged samples.
"""
... |
Parameters
----------
data
The samples to batchify. Each sample should be a dictionary
Returns
-------
ret
The resulting dictionary that stores the merged samples.
| __call__ | python | dmlc/gluon-nlp | src/gluonnlp/data/batchify.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/batchify.py | Apache-2.0 |
def __call__(self, data: t_List[t_NamedTuple]) -> t_NamedTuple:
"""Batchify the input data.
Parameters
----------
data
The samples to batchfy. Each sample should be a namedtuple.
Returns
-------
ret
A namedtuple of length N. Contains the ... | Batchify the input data.
Parameters
----------
data
The samples to batchfy. Each sample should be a namedtuple.
Returns
-------
ret
A namedtuple of length N. Contains the batchified result of each attribute in the input.
| __call__ | python | dmlc/gluon-nlp | src/gluonnlp/data/batchify.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/batchify.py | Apache-2.0 |
def _words_match_regex(words: List[str], ignore_case=False, replace_white_space=False) -> Pattern:
"""Obtain the regex that finds whether a given corpus contains any word in the input words
Parameters
----------
words
Returns
-------
regex
"""
words = [ele for ele in words if ele]... | Obtain the regex that finds whether a given corpus contains any word in the input words
Parameters
----------
words
Returns
-------
regex
| _words_match_regex | python | dmlc/gluon-nlp | src/gluonnlp/data/filtering.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/filtering.py | Apache-2.0 |
def __call__(self, corpus: str):
"""
Parameters
----------
corpus
Input corpus
Returns
-------
lang_label
The ISO-639 1 code of the predicted language
score
The score of the prediction
"""
if self._use_... |
Parameters
----------
corpus
Input corpus
Returns
-------
lang_label
The ISO-639 1 code of the predicted language
score
The score of the prediction
| __call__ | python | dmlc/gluon-nlp | src/gluonnlp/data/filtering.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/filtering.py | Apache-2.0 |
def _dataset_worker_fn(urls, dataset_fn, batch_sampler_fn):
"""Function to generate datasets and batch sampler for each worker."""
global _manager, _dataset
dataset = dataset_fn(urls)
batch_sampler = batch_sampler_fn(dataset)
if _manager:
dataset = _manager.list(zip(*dataset._data))
_dat... | Function to generate datasets and batch sampler for each worker. | _dataset_worker_fn | python | dmlc/gluon-nlp | src/gluonnlp/data/loading.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/loading.py | Apache-2.0 |
def _batch_worker_fn(samples, batchify_fn, dataset=None, counter=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 ov... | Function for processing data in worker process. | _batch_worker_fn | python | dmlc/gluon-nlp | src/gluonnlp/data/loading.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/loading.py | Apache-2.0 |
def _push_next(self):
"""Assign next batch workload to workers."""
if self._batch_iter is not None:
r = next(self._batch_iter, None)
else:
r = None
if r is None:
result = self._next_dataset()
if result is None:
return
... | Assign next batch workload to workers. | _push_next | python | dmlc/gluon-nlp | src/gluonnlp/data/loading.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/loading.py | Apache-2.0 |
def _push_next_dataset(self):
"""Assign next dataset workload to workers."""
current_dataset_idx = self._sent_idx * self._circle_length
if current_dataset_idx < self._num_datasets:
circle_length = min(self._circle_length,
self._num_datasets - current_d... | Assign next dataset workload to workers. | _push_next_dataset | python | dmlc/gluon-nlp | src/gluonnlp/data/loading.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/loading.py | Apache-2.0 |
def _next_dataset(self):
"""Retrieve the next dataset. Returns None if no dataset is available."""
if self._rcvd_idx == self._sent_idx:
assert not self._data_buffer, 'Data buffer should be empty at this moment'
return None
assert self._rcvd_idx < self._sent_idx, \
... | Retrieve the next dataset. Returns None if no dataset is available. | _next_dataset | python | dmlc/gluon-nlp | src/gluonnlp/data/loading.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/loading.py | Apache-2.0 |
def __call__(self, max_lengths: Union[int, Sequence[int]],
min_lengths: Union[int, Sequence[int]], num_buckets: int) -> List[int]:
"""Generate bucket keys based on the lengths of sequences and number of buckets.
Parameters
----------
max_lengths
Maximum of l... | Generate bucket keys based on the lengths of sequences and number of buckets.
Parameters
----------
max_lengths
Maximum of lengths of sequences.
min_lengths
Minimum of lengths of sequences.
num_buckets
Number of buckets
Returns
... | __call__ | python | dmlc/gluon-nlp | src/gluonnlp/data/sampler.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/sampler.py | Apache-2.0 |
def __call__(self, max_lengths: Union[int, Sequence[int]],
min_lengths: Union[int, Sequence[int]], num_buckets: int) -> List[int]:
r"""This generate bucket keys given that all the buckets have the same width.
Parameters
----------
max_lengths
Maximum of leng... | This generate bucket keys given that all the buckets have the same width.
Parameters
----------
max_lengths
Maximum of lengths of sequences.
min_lengths
Minimum of lengths of sequences.
num_buckets
Number of buckets
Returns
--... | __call__ | python | dmlc/gluon-nlp | src/gluonnlp/data/sampler.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/sampler.py | Apache-2.0 |
def __call__(self, max_lengths: Union[int, Sequence[int]],
min_lengths: Union[int, Sequence[int]], num_buckets: int) -> List[int]:
r"""This function generates bucket keys with linearly increasing bucket width:
Parameters
----------
max_lengths
Maximum of len... | This function generates bucket keys with linearly increasing bucket width:
Parameters
----------
max_lengths
Maximum of lengths of sequences.
min_lengths
Minimum of lengths of sequences.
num_buckets
Number of buckets
Returns
-... | __call__ | python | dmlc/gluon-nlp | src/gluonnlp/data/sampler.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/sampler.py | Apache-2.0 |
def __call__(self, max_lengths: Union[int, Sequence[int]],
min_lengths: Union[int, Sequence[int]], num_buckets: int) -> List[int]:
r"""This function generates bucket keys exponentially increasing bucket width.
Parameters
----------
max_lengths
Maximum of len... | This function generates bucket keys exponentially increasing bucket width.
Parameters
----------
max_lengths
Maximum of lengths of sequences.
min_lengths
Minimum of lengths of sequences.
num_buckets
Number of buckets
Returns
-... | __call__ | python | dmlc/gluon-nlp | src/gluonnlp/data/sampler.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/sampler.py | Apache-2.0 |
def __repr__(self):
"""Return a string representing the statistics of the bucketing sampler.
Returns
-------
ret : str
String representing the statistics of the buckets.
"""
ret = '{name}(\n' \
' sample_num={sample_num}, batch_num={batch_num}\n' ... | Return a string representing the statistics of the bucketing sampler.
Returns
-------
ret : str
String representing the statistics of the buckets.
| __repr__ | python | dmlc/gluon-nlp | src/gluonnlp/data/sampler.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/sampler.py | Apache-2.0 |
def _check_special_token_identifier(key):
"""Raise error if the key is not valid as a key for the special token.
Parameters
----------
key
The identifier
"""
if not (key.endswith('_token') and key != '_token'):
raise ValueError('Each key needs to have the form "name_token".'
... | Raise error if the key is not valid as a key for the special token.
Parameters
----------
key
The identifier
| _check_special_token_identifier | python | dmlc/gluon-nlp | src/gluonnlp/data/vocab.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/vocab.py | Apache-2.0 |
def to_tokens(self, idx: Union[int, Tuple[int], List[int], np.ndarray])\
-> Union[Hashable, List[Hashable]]:
"""Get the tokens correspond to the chosen indices
Parameters
----------
idx
The index used to select the tokens.
Returns
-------
... | Get the tokens correspond to the chosen indices
Parameters
----------
idx
The index used to select the tokens.
Returns
-------
ret
The tokens of these selected indices.
| to_tokens | python | dmlc/gluon-nlp | src/gluonnlp/data/vocab.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/vocab.py | Apache-2.0 |
def __getitem__(self, tokens: Union[Hashable, List[Hashable], Tuple[Hashable]])\
-> Union[int, List[int]]:
"""Looks up indices of text tokens according to the vocabulary.
If `unknown_token` of the vocabulary is None, looking up unknown tokens results in KeyError.
Parameters
... | Looks up indices of text tokens according to the vocabulary.
If `unknown_token` of the vocabulary is None, looking up unknown tokens results in KeyError.
Parameters
----------
tokens
A source token or tokens to be converted.
Returns
-------
ret
... | __getitem__ | python | dmlc/gluon-nlp | src/gluonnlp/data/vocab.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/vocab.py | Apache-2.0 |
def __call__(self, tokens: Union[Hashable, List[Hashable], Tuple[Hashable]])\
-> Union[int, np.ndarray]:
"""Looks up indices of text tokens according to the vocabulary.
Parameters
----------
tokens
A source token or tokens to be converted.
Returns
... | Looks up indices of text tokens according to the vocabulary.
Parameters
----------
tokens
A source token or tokens to be converted.
Returns
-------
ret
A token index or a list of token indices according to the vocabulary.
| __call__ | python | dmlc/gluon-nlp | src/gluonnlp/data/vocab.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/vocab.py | Apache-2.0 |
def to_json(self) -> str:
"""Serialize Vocab object into a json string.
Returns
-------
ret
The serialized json string
"""
vocab_dict = dict()
# Perform sanity check to make sure that we are able to reconstruct the original vocab
for i, tok in... | Serialize Vocab object into a json string.
Returns
-------
ret
The serialized json string
| to_json | python | dmlc/gluon-nlp | src/gluonnlp/data/vocab.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/vocab.py | Apache-2.0 |
def from_json(cls, json_str: Union[str, bytes, bytearray]) -> 'Vocab':
"""Deserialize Vocab object from json string.
Parameters
----------
json_str
Serialized json string of a Vocab object.
Returns
-------
vocab
The constructed Vocab obje... | Deserialize Vocab object from json string.
Parameters
----------
json_str
Serialized json string of a Vocab object.
Returns
-------
vocab
The constructed Vocab object
| from_json | python | dmlc/gluon-nlp | src/gluonnlp/data/vocab.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/vocab.py | Apache-2.0 |
def load_vocab(vocab: Union[str, Vocab]) -> Vocab:
"""Quick helper function to load vocabulary from a file.
Parameters
----------
vocab
Returns
-------
"""
if isinstance(vocab, Vocab):
return vocab
elif isinstance(vocab, str):
return Vocab.load(vocab)
else:
... | Quick helper function to load vocabulary from a file.
Parameters
----------
vocab
Returns
-------
| load_vocab | python | dmlc/gluon-nlp | src/gluonnlp/data/vocab.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/vocab.py | Apache-2.0 |
def get_token_type(tokens: Union[List[str], List[int], List[List[str]],
List[List[int]]]) -> type:
"""
Parameters
----------
tokens
The input tokens.
Returns
-------
token_type
If the tokens is empty, return `str`.
Otherwise, return ... |
Parameters
----------
tokens
The input tokens.
Returns
-------
token_type
If the tokens is empty, return `str`.
Otherwise, return `str` if the input is str and `int` if the input is int.
| get_token_type | python | dmlc/gluon-nlp | src/gluonnlp/data/tokenizers/base.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/tokenizers/base.py | Apache-2.0 |
def rebuild_offset_from_tokens(sentence: str, tokens: List[str]) \
-> List[Tuple[int, int]]:
"""Recover the offset of the tokens in the original sentence.
If you are using a subword tokenizer, make sure to remove the prefix/postfix of the tokens
before using this function. Also, this does not work ... | Recover the offset of the tokens in the original sentence.
If you are using a subword tokenizer, make sure to remove the prefix/postfix of the tokens
before using this function. Also, this does not work for n-gram-based (n>1) subword
tokenization, i.e.
it works for "gluonnlp" --> ["gluon", "nlp"]
b... | rebuild_offset_from_tokens | python | dmlc/gluon-nlp | src/gluonnlp/data/tokenizers/base.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/tokenizers/base.py | Apache-2.0 |
def get_char_offset_from_byte_offset(sentence: str, byte_offsets: List[Tuple[int, int]]):
"""Get the character-level offsets based on the byte-level offsets
Parameters
----------
sentence
The input sentence
byte_offsets
The byte-level offsets
Returns
-------
char_offset... | Get the character-level offsets based on the byte-level offsets
Parameters
----------
sentence
The input sentence
byte_offsets
The byte-level offsets
Returns
-------
char_offsets
The character-level offsets
| get_char_offset_from_byte_offset | python | dmlc/gluon-nlp | src/gluonnlp/data/tokenizers/base.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/tokenizers/base.py | Apache-2.0 |
def encode(self, sentences: SentencesType,
output_type: type = str) \
-> Union[TokensType, TokenIDsType]:
"""Encode the input sentence(s) into multiple tokens.
Parameters
----------
sentences
The sentences to tokenize
output_type
... | Encode the input sentence(s) into multiple tokens.
Parameters
----------
sentences
The sentences to tokenize
output_type
The type of the output tokens.
- str means each token is represented by its original text.
- int means each token is r... | encode | python | dmlc/gluon-nlp | src/gluonnlp/data/tokenizers/base.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/tokenizers/base.py | Apache-2.0 |
def encode_with_offsets(self, sentences: SentencesType,
output_type: type = str) \
-> Tuple[Union[TokensType, TokenIDsType], TokenOffsetsType]:
"""Encode the input sentence(s) into multiple tokens. Different from encode, it
will also return the character start and... | Encode the input sentence(s) into multiple tokens. Different from encode, it
will also return the character start and end positions of each token in the original text.
The original text is assumed to be
Here, the default implementation is to use the tokenized result to recover the offsets.
... | encode_with_offsets | python | dmlc/gluon-nlp | src/gluonnlp/data/tokenizers/base.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/tokenizers/base.py | Apache-2.0 |
def is_new_version_model_file(model_file_path: str) -> bool:
"""Check whether the model file belongs to the new version of HuggingFace Tokenizers,
i.e., >= 0.8
Parameters
----------
model_file_path
Path to the model file
Returns
-------
is_new_version
Whether the model ... | Check whether the model file belongs to the new version of HuggingFace Tokenizers,
i.e., >= 0.8
Parameters
----------
model_file_path
Path to the model file
Returns
-------
is_new_version
Whether the model file is generated by the new version of huggingface tokenizer.
| is_new_version_model_file | python | dmlc/gluon-nlp | src/gluonnlp/data/tokenizers/huggingface.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/tokenizers/huggingface.py | Apache-2.0 |
def hf_encode(model, sentences, output_type: type = str):
"""
Parameters
----------
model
Model object in HuggingFace tokenizer
sentences
Input sentences
output_type
Output type
Returns
-------
ret
"""
is_multi_sentences = isinstance(sentences, list)... |
Parameters
----------
model
Model object in HuggingFace tokenizer
sentences
Input sentences
output_type
Output type
Returns
-------
ret
| hf_encode | python | dmlc/gluon-nlp | src/gluonnlp/data/tokenizers/huggingface.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/tokenizers/huggingface.py | Apache-2.0 |
def is_last_subword(self, tokens):
"""Whether the sub-token is the last sub-token in a split token list.
Only supports the case when the tokenizer is a HuggingFaceBPETokenizer
Parameters
----------
tokens
A single token or a list of tokens
Returns
-... | Whether the sub-token is the last sub-token in a split token list.
Only supports the case when the tokenizer is a HuggingFaceBPETokenizer
Parameters
----------
tokens
A single token or a list of tokens
Returns
-------
ret
The results
... | is_last_subword | python | dmlc/gluon-nlp | src/gluonnlp/data/tokenizers/huggingface.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/tokenizers/huggingface.py | Apache-2.0 |
def is_first_subword(self, tokens):
"""Whether the sub-token is the first sub-token in a token list.
Only supports the case when the tokenizer is a HuggingFaceWordPieceTokenizer
Parameters
----------
tokens
A single token or a list of tokens
Returns
... | Whether the sub-token is the first sub-token in a token list.
Only supports the case when the tokenizer is a HuggingFaceWordPieceTokenizer
Parameters
----------
tokens
A single token or a list of tokens
Returns
-------
ret
The results
... | is_first_subword | python | dmlc/gluon-nlp | src/gluonnlp/data/tokenizers/huggingface.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/tokenizers/huggingface.py | Apache-2.0 |
def __init__(self, merges_file: Optional[str] = None,
vocab_file: Optional[str] = None,
unk_token: Optional[str] = Vocab.UNK_TOKEN,
suffix: Optional[str] = '</w>',
dropout: Optional[float] = None,
lowercase: bool = False):
"""
... |
Parameters
----------
merges_file
The merges file saved by HuggingFace
vocab_file
Vocabulary file in GluonNLP
unk_token
The unknown token
suffix
The suffix for sub-tokens. For example, "Sunnyvale" will be "Sunny vale</w>"
... | __init__ | python | dmlc/gluon-nlp | src/gluonnlp/data/tokenizers/huggingface.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/tokenizers/huggingface.py | Apache-2.0 |
def is_last_subword(self, tokens: Union[str, int, List[str], List[int]]) \
-> Union[bool, List[bool]]:
"""Whether the token is the last subword token. This can be used for whole-word masking.
Parameters
----------
tokens
The input tokens
Returns
... | Whether the token is the last subword token. This can be used for whole-word masking.
Parameters
----------
tokens
The input tokens
Returns
-------
ret
Whether the token is the last subword token in the list of subwords.
| is_last_subword | python | dmlc/gluon-nlp | src/gluonnlp/data/tokenizers/huggingface.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/tokenizers/huggingface.py | Apache-2.0 |
def is_first_subword(self, tokens: Union[str, int, List[str], List[int]]) \
-> Union[bool, List[bool]]:
"""Whether the token is the first subword token in a sequence of subword tokens.
This can be used for implementing whole-word masking.
We won't care about the special tokens
... | Whether the token is the first subword token in a sequence of subword tokens.
This can be used for implementing whole-word masking.
We won't care about the special tokens
Parameters
----------
tokens
Returns
-------
ret
| is_first_subword | python | dmlc/gluon-nlp | src/gluonnlp/data/tokenizers/huggingface.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/tokenizers/huggingface.py | Apache-2.0 |
def is_first_subword(self, tokens: Union[str, int, List[str], List[int]]) \
-> Union[bool, List[bool]]:
"""Whether the token is the first subword token. This can be used to implement
whole-word masking.
Parameters
----------
tokens
The input tokens
... | Whether the token is the first subword token. This can be used to implement
whole-word masking.
Parameters
----------
tokens
The input tokens
Returns
-------
ret
Whether the token is the first subword token in the list of subwords
... | is_first_subword | python | dmlc/gluon-nlp | src/gluonnlp/data/tokenizers/sentencepiece.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/tokenizers/sentencepiece.py | Apache-2.0 |
def set_subword_regularization(self, nbest, alpha):
"""Set the subword-regularization parameters
For more details, you may refer to the official SentencePiece library:
https://github.com/google/sentencepiece
Parameters
----------
nbest
alpha
Returns
... | Set the subword-regularization parameters
For more details, you may refer to the official SentencePiece library:
https://github.com/google/sentencepiece
Parameters
----------
nbest
alpha
Returns
-------
| set_subword_regularization | python | dmlc/gluon-nlp | src/gluonnlp/data/tokenizers/sentencepiece.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/tokenizers/sentencepiece.py | Apache-2.0 |
def __getstate__(self):
"""Make the SentencepieceTokenizer pickleble.
We will remove the _spt_cls and _sp_model, which are not picklable, and try to
reconstruct the class via the saved model_path. This behavior is only acceptable for
multiprocessing and should not be used to save sent... | Make the SentencepieceTokenizer pickleble.
We will remove the _spt_cls and _sp_model, which are not picklable, and try to
reconstruct the class via the saved model_path. This behavior is only acceptable for
multiprocessing and should not be used to save sentencepiece models. | __getstate__ | python | dmlc/gluon-nlp | src/gluonnlp/data/tokenizers/sentencepiece.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/tokenizers/sentencepiece.py | Apache-2.0 |
def transform_sentence(self, sentence):
"""replace the separator in encoded result with suffix
a@@, b@@, c -> a, b, c</w>
Parameters
----------
sentence
Returns
-------
new_sentence
"""
return [word[:-2] if len(word) > 2 and word[-2:] =... | replace the separator in encoded result with suffix
a@@, b@@, c -> a, b, c</w>
Parameters
----------
sentence
Returns
-------
new_sentence
| transform_sentence | python | dmlc/gluon-nlp | src/gluonnlp/data/tokenizers/subword_nmt.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/tokenizers/subword_nmt.py | Apache-2.0 |
def is_last_subword(self, tokens: Union[str, int, List[str], List[int]]) \
-> Union[bool, List[bool]]:
"""Whether the token is the last subword token. This can be used
for whole-word masking.
Parameters
----------
tokens
The input tokens
Returns
... | Whether the token is the last subword token. This can be used
for whole-word masking.
Parameters
----------
tokens
The input tokens
Returns
-------
ret
Whether the token is the last subword token in the list of subwords
| is_last_subword | python | dmlc/gluon-nlp | src/gluonnlp/data/tokenizers/subword_nmt.py | https://github.com/dmlc/gluon-nlp/blob/master/src/gluonnlp/data/tokenizers/subword_nmt.py | Apache-2.0 |
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