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train | get_model | 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 directly
returned.
vocab : gluonnlp.Vocab or None, default... | scripts/bert/staticbert/static_bert.py | 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... | 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'.
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train | bert_12_768_12 | Static BERT BASE model.
The number of layers (L) is 12, number of units (H) is 768, and the
number of self-attention heads (A) is 12.
Parameters
----------
dataset_name : str or None, default None
Options include 'book_corpus_wiki_en_cased', 'book_corpus_wiki_en_uncased',
'wiki_cn_... | scripts/bert/staticbert/static_bert.py | 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.
... | 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.
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train | StaticBERTModel.hybrid_forward | Generate the representation given the inputs.
This is used in training or fine-tuning a static (hybridized) BERT model. | scripts/bert/staticbert/static_bert.py | 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.
... | 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.
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train | CacheCell.load_parameters | Load parameters from file.
filename : str
Path to parameter file.
ctx : Context or list of Context, default cpu()
Context(s) initialize loaded parameters on. | src/gluonnlp/model/train/cache.py | 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.
"""
self... | 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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train | CacheCell.forward | Defines the forward computation for cache cell. Arguments can be either
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Parameters
----------
inputs: NDArray
The input data
target: NDArray
The label
next_word_history: NDArray
The next word ... | src/gluonnlp/model/train/cache.py | 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
... | 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
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inputs: NDArray
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train | Parallel.put | Assign input `x` to an available worker and invoke
`parallizable.forward_backward` with x. | src/gluonnlp/utils/parallel.py | def put(self, x):
"""Assign input `x` to an available worker and invoke
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if self._num_serial > 0 or len(self._threads) == 0:
self._num_serial -= 1
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"""Assign input `x` to an available worker and invoke
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train | BERTVocab.from_json | Deserialize BERTVocab object from json string.
Parameters
----------
json_str : str
Serialized json string of a BERTVocab object.
Returns
-------
BERTVocab | src/gluonnlp/vocab/bert.py | 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)
... | def from_json(cls, json_str):
"""Deserialize BERTVocab object from json string.
Parameters
----------
json_str : str
Serialized json string of a BERTVocab object.
Returns
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BERTVocab
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vocab_dict = json.loads(json_str)
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train | StandardRNN.forward | Defines the forward computation. Arguments can be either
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Parameters
-----------
inputs : NDArray
input tensor with shape `(sequence_length, batch_size)`
when `layout` is "TNC".
begin_state : list
init... | src/gluonnlp/model/train/language_model.py | def forward(self, inputs, begin_state=None): # pylint: disable=arguments-differ
"""Defines the forward computation. Arguments can be either
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Parameters
-----------
inputs : NDArray
input tensor with shape `(sequence_length, b... | def forward(self, inputs, begin_state=None): # pylint: disable=arguments-differ
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train | BigRNN.forward | Defines the forward computation.
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inputs : NDArray
input tensor with shape `(sequence_length, batch_size)`
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begin_state : list
initial recurrent state tensor with length equals to num_layers*2.
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Parameters
-----------
inputs : NDArray
input tensor with shape `(sequence_length, batch_size)`
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Parameters
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inputs : NDArray
input tensor with shape `(sequence_length, batch_size)`
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train | SG.hybrid_forward | SkipGram forward pass.
Parameters
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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 for center words are
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Parameters
----------
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Sparse CSR array of word / subword indices of shape (batch_size,
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train | evaluate | Evaluate network on the specified dataset | scripts/sentiment_analysis/finetune_lm.py | 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()
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train | train | Training process | scripts/sentiment_analysis/finetune_lm.py | def train():
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start_pipeline_time = time.time()
# Training/Testing
best_valid_acc = 0
stop_early = 0
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epoch_wc = 0... | def train():
"""Training process"""
start_pipeline_time = time.time()
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best_valid_acc = 0
stop_early = 0
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# Epoch training stats
start_epoch_time = time.time()
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epoch_sent_num = 0
epoch_wc = 0... | [
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train | AggregationLayer.hybrid_forward | Forward logic | scripts/sentiment_analysis/finetune_lm.py | 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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train | LSTMPCellWithClip.hybrid_forward | r"""Hybrid forward computation for Long-Short Term Memory Projected network cell
with cell clip and projection clip.
Parameters
----------
inputs : input tensor with shape `(batch_size, input_size)`.
states : a list of two initial recurrent state tensors, with shape
... | src/gluonnlp/model/lstmpcellwithclip.py | def hybrid_forward(self, F, inputs, states, i2h_weight,
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r"""Hybrid forward computation for Long-Short Term Memory Projected network cell
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Parameters
----------
inputs : input t... | def hybrid_forward(self, F, inputs, states, i2h_weight,
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train | clip_grad_global_norm | 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 are summed first before calculating
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.. note::
T... | src/gluonnlp/utils/parameter.py | 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 ... | 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`.
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train | train | Training function. | scripts/bert/run_pretraining.py | 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()
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optim_params = {'learning_rate': lr, 'epsilon': 1e-6, 'wd':... | def train(data_train, model, nsp_loss, mlm_loss, vocab_size, ctx, store):
"""Training function."""
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nsp_metric = nlp.metric.MaskedAccuracy()
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train | ParallelBERT.forward_backward | forward backward implementation | scripts/bert/run_pretraining.py | def forward_backward(self, x):
"""forward backward implementation"""
with mx.autograd.record():
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"""forward backward implementation"""
with mx.autograd.record():
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train | ParserVocabulary.log_info | Print statistical information via the provided logger
Parameters
----------
logger : logging.Logger
logger created using logging.getLogger() | scripts/parsing/common/data.py | 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... | def log_info(self, logger):
"""Print statistical information via the provided logger
Parameters
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logger : logging.Logger
logger created using logging.getLogger()
"""
logger.info('#words in training set: %d' % self._words_in_train_data)
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train | ParserVocabulary._add_pret_words | Read pre-trained embedding file for extending vocabulary
Parameters
----------
pret_embeddings : tuple
(embedding_name, source), used for gluonnlp.embedding.create(embedding_name, source) | scripts/parsing/common/data.py | def _add_pret_words(self, pret_embeddings):
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Parameters
----------
pret_embeddings : tuple
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Parameters
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pret_embeddings : tuple
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train | ParserVocabulary.get_pret_embs | Read pre-trained embedding file
Parameters
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word_dims : int or None
vector size. Use `None` for auto-infer
Returns
-------
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Parameters
----------
word_dims : int or None
vector size. Use `None` for auto-infer
Returns
-------
numpy.ndarray
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"""Read pre-trained embedding file
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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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train | ParserVocabulary.get_word_embs | Get randomly initialized embeddings when pre-trained embeddings are used, otherwise zero vectors
Parameters
----------
word_dims : int
word vector size
Returns
-------
numpy.ndarray
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Parameters
----------
word_dims : int
word vector size
Returns
-------
numpy.ndarray
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----------
word_dims : int
word vector size
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numpy.ndarray
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train | ParserVocabulary.get_tag_embs | Randomly initialize embeddings for tag
Parameters
----------
tag_dims : int
tag vector size
Returns
-------
numpy.ndarray
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Parameters
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tag_dims : int
tag vector size
Returns
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numpy.ndarray
random embeddings
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tag_dims : int
tag vector size
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numpy.ndarray
random embeddings
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train | ParserVocabulary.word2id | 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 | scripts/parsing/common/data.py | 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
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if isinstance(xs, list):
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"""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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train | ParserVocabulary.id2word | Map id(s) to word(s)
Parameters
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xs : int
id or a list of ids
Returns
-------
str or list
word or a list of words | scripts/parsing/common/data.py | def id2word(self, xs):
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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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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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train | ParserVocabulary.rel2id | Map relation(s) to id(s)
Parameters
----------
xs : str or list
relation
Returns
-------
int or list
id(s) of relation | scripts/parsing/common/data.py | def rel2id(self, xs):
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Parameters
----------
xs : str or list
relation
Returns
-------
int or list
id(s) of relation
"""
if isinstance(xs, list):
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Parameters
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xs : str or list
relation
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int or list
id(s) of relation
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train | ParserVocabulary.id2rel | Map id(s) to relation(s)
Parameters
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xs : int
id or a list of ids
Returns
-------
str or list
relation or a list of relations | scripts/parsing/common/data.py | def id2rel(self, xs):
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Parameters
----------
xs : int
id or a list of ids
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-------
str or list
relation or a list of relations
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str or list
relation or a list of relations
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train | ParserVocabulary.tag2id | Map tag(s) to id(s)
Parameters
----------
xs : str or list
tag or tags
Returns
-------
int or list
id(s) of tag(s) | scripts/parsing/common/data.py | def tag2id(self, xs):
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Parameters
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xs : str or list
tag or tags
Returns
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int or list
id(s) of tag(s)
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xs : str or list
tag or tags
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int or list
id(s) of tag(s)
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train | DataLoader.idx_sequence | 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 | scripts/parsing/common/data.py | def idx_sequence(self):
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Useful when retrieving the correct order of sentences
Returns
-------
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list
List of ids ranging from 0 to #sent -1
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train | DataLoader.get_batches | Get batch iterator
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batch_size : int
size of one batch
shuffle : bool
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Returns
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"""Get batch iterator
Parameters
----------
batch_size : int
size of one batch
shuffle : bool
whether to shuffle batches. Don't set to True when evaluating on dev or test set.
Returns
------... | def get_batches(self, batch_size, shuffle=True):
"""Get batch iterator
Parameters
----------
batch_size : int
size of one batch
shuffle : bool
whether to shuffle batches. Don't set to True when evaluating on dev or test set.
Returns
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train | create_ngram_set | Extract a set of n-grams from a list of integers.
>>> create_ngram_set([1, 4, 9, 4, 1, 4], ngram_value=2)
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[(1, 4, 9), (4, 9, 4), (9, 4, 1), (4, 1, 4)] | scripts/text_classification/fasttext_word_ngram.py | 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)
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>>> create_ngram_set([1, 4, 9, 4, 1, 4], ngram_value=3)
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... | 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)
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train | add_ngram | Augment the input list of list (sequences) by appending n-grams values.
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>>> sequences = [[1, 3, 4, 5], [1, 3, 7, 9, 2]]
>>> token_indice = {(1, 3): 1337, (9, 2): 42, (4, 5): 2017}
>>> add_ngram(sequences, token_indice, ngram_range=2)
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"""
Augment the input list of list (sequences) by appending n-grams values.
Example: adding bi-gram
>>> sequences = [[1, 3, 4, 5], [1, 3, 7, 9, 2]]
>>> token_indice = {(1, 3): 1337, (9, 2): 42, (4, 5): 2017}
>>> add_ngram(sequences, token_in... | def add_ngram(sequences, token_indice, ngram_range=2):
"""
Augment the input list of list (sequences) by appending n-grams values.
Example: adding bi-gram
>>> sequences = [[1, 3, 4, 5], [1, 3, 7, 9, 2]]
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train | evaluate_accuracy | This function is used for evaluating accuracy of
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"""
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loss_avg = 0.
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"""
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"""
acc = mx.metric.Accuracy()
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train | read_input_data | Helper function to get training data | scripts/text_classification/fasttext_word_ngram.py | 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())
... | 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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train | parse_args | Parse command line arguments. | scripts/text_classification/fasttext_word_ngram.py | 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... | 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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train | get_label_mapping | Create the mapping from label to numeric label | scripts/text_classification/fasttext_word_ngram.py | 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))
... | 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):
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train | convert_to_sequences | This function takes a dataset and converts
it into sequences via multiprocessing | scripts/text_classification/fasttext_word_ngram.py | def convert_to_sequences(dataset, vocab):
"""This function takes a dataset and converts
it into sequences via multiprocessing
"""
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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"""This function takes a dataset and converts
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start = time.time()
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train | preprocess_dataset | Preprocess and prepare a dataset | scripts/text_classification/fasttext_word_ngram.py | def preprocess_dataset(dataset, labels):
""" Preprocess and prepare a dataset"""
start = time.time()
with mp.Pool() as pool:
# Each sample is processed in an asynchronous manner.
dataset = gluon.data.SimpleDataset(list(zip(dataset, labels)))
lengths = gluon.data.SimpleDataset(pool.ma... | def preprocess_dataset(dataset, labels):
""" Preprocess and prepare a dataset"""
start = time.time()
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train | get_dataloader | Construct the DataLoader. Pad data, stack label and lengths | scripts/text_classification/fasttext_word_ngram.py | 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(
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""" Construct the DataLoader. Pad data, stack label and lengths"""
bucket_num, bucket_ratio = 20, 0.2
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train | train | Training function that orchestrates the Classification! | scripts/text_classification/fasttext_word_ngram.py | 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... | 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)
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inputs : NDArray, shape (batch_size, seq_length)
Input words for the sequences.
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# pylint: disable=arguments-differ
# pylint: disable=unused-argument
"""Generate the unnormalized score for the given the input sequences.
Parameters
----------
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BERTSquad forward output.
label : list, length is 2, each shape is (batch_size,1)
label[0] is the starting position of the answer,
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Parameters
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pred : NDArray, shape (batch_size, seq_length, 2)
BERTSquad forward output.
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train | NMTModel.encode | Encode the input sequence.
Parameters
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inputs : NDArray
states : list of NDArrays or None, default None
valid_length : NDArray or None, default None
Returns
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outputs : list
Outputs of the encoder. | src/gluonnlp/model/translation.py | def encode(self, inputs, states=None, valid_length=None):
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Parameters
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inputs : NDArray
states : list of NDArrays or None, default None
valid_length : NDArray or None, default None
Returns
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Parameters
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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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train | NMTModel.decode_seq | Decode given the input sequence.
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valid_length : NDArray or None, default None
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output : NDArray
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states : list of NDArrays
valid_length : NDArray or None, default None
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valid_length : NDArray or None, default None
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train | NMTModel.decode_step | One step decoding of the translation model.
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step_output : NDArray
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train | NMTModel.forward | Generate the prediction given the src_seq and tgt_seq.
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src_seq : NDArray
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train | create_subword_function | Creates an instance of a subword function. | src/gluonnlp/vocab/subwords.py | 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) | def create_subword_function(subword_function_name, **kwargs):
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train | Vocab._index_special_tokens | Indexes unknown and reserved tokens. | src/gluonnlp/vocab/vocab.py | 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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self._reserved_tokens = None
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train | Vocab._index_counter_keys | Indexes keys of `counter`.
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train | Vocab.set_embedding | Attaches one or more embeddings to the indexed text tokens.
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embeddings : None or tuple of :class:`gluonnlp.embedding.TokenEmbedding` instances
The embedding to be attached to the indexed tokens. If a tuple of multiple embeddings
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train | Vocab.to_tokens | Converts token indices to tokens according to the vocabulary.
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A source token index or token indices to be converted.
Returns
-------
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----------
indices : int or list of ints
A source token index or token indices to be converted.
Returns
-------
str or list of strs
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----------
indices : int or list of ints
A source token index or token indices to be converted.
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-------
str or list of strs
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train | Vocab.to_json | Serialize Vocab object to json string.
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train | Vocab.from_json | Deserialize Vocab object from json string.
Parameters
----------
json_str : str
Serialized json string of a Vocab object.
Returns
-------
Vocab | src/gluonnlp/vocab/vocab.py | 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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"""Deserialize Vocab object from json string.
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----------
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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train | train | Training function. | scripts/bert/run_pretraining_hvd.py | def train(data_train, model, nsp_loss, mlm_loss, vocab_size, ctx):
"""Training function."""
hvd.broadcast_parameters(model.collect_params(), root_rank=0)
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nsp_metric = nlp.metric.MaskedAccuracy()
mlm_metric.reset()
nsp_metric.reset()
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"""Training function."""
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mlm_metric.reset()
nsp_metric.reset()
logging.debug('C... | [
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train | train | Training function. | scripts/bert/staticbert/static_finetune_squad.py | 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... | def train():
"""Training function."""
log.info('Loader Train data...')
if version_2:
train_data = SQuAD('train', version='2.0')
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train_data = SQuAD('train', version='1.1')
log.info('Number of records in Train data:{}'.format(len(train_data)))
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train | evaluate | Evaluate the model on validation dataset. | scripts/bert/staticbert/static_finetune_squad.py | 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 ... | 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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train | _pad_arrs_to_max_length | Inner Implementation of the Pad batchify
Parameters
----------
arrs : list
pad_axis : int
pad_val : number
use_shared_mem : bool, default False
Returns
-------
ret : NDArray
original_length : NDArray | src/gluonnlp/data/batchify/batchify.py | def _pad_arrs_to_max_length(arrs, pad_axis, pad_val, use_shared_mem, dtype):
"""Inner Implementation of the Pad batchify
Parameters
----------
arrs : list
pad_axis : int
pad_val : number
use_shared_mem : bool, default False
Returns
-------
ret : NDArray
original_length : ND... | def _pad_arrs_to_max_length(arrs, pad_axis, pad_val, use_shared_mem, dtype):
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----------
arrs : list
pad_axis : int
pad_val : number
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ret : NDArray
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train | DepParser.train | Train a deep biaffine dependency parser
Parameters
----------
train_file : str
path to training set
dev_file : str
path to dev set
test_file : str
path to test set
save_dir : str
a directory for saving model and related met... | scripts/parsing/parser/dep_parser.py | 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,
dropout_lstm_input=0.33, dropout_lstm_hidden=0.33, mlp_arc_size=500, mlp_rel_size=100,
dropo... | 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,
dropout_lstm_input=0.33, dropout_lstm_hidden=0.33, mlp_arc_size=500, mlp_rel_size=100,
dropo... | [
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train | DepParser.load | Load from disk
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path : str
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-------
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parser itself | scripts/parsing/parser/dep_parser.py | def load(self, path):
"""Load from disk
Parameters
----------
path : str
path to the directory which typically contains a config.pkl file and a model.bin file
Returns
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DepParser
parser itself
"""
config = _Config.lo... | def load(self, path):
"""Load from disk
Parameters
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path : str
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DepParser
parser itself
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train | DepParser.evaluate | Run evaluation on test set
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test_file : str
path to test set
save_dir : str
where to store intermediate results and log
logger : logging.logger
logger for printing results
num_buckets_test : int
number ... | scripts/parsing/parser/dep_parser.py | 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
l... | 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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train | DepParser.parse | Parse raw sentence into ConllSentence
Parameters
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sentence : list
a list of (word, tag) tuples
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ConllSentence object | scripts/parsing/parser/dep_parser.py | 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) ... | def parse(self, sentence):
"""Parse raw sentence into ConllSentence
Parameters
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sentence : list
a list of (word, tag) tuples
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ConllSentence
ConllSentence object
"""
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train | apply_weight_drop | Apply weight drop to the parameter of a block.
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block : Block or HybridBlock
The block whose parameter is to be applied weight-drop.
local_param_regex : str
The regex for parameter names used in the self.params.get(), such as 'weight'.
rate : float
Fract... | src/gluonnlp/model/utils.py | 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... | def apply_weight_drop(block, local_param_regex, rate, axes=(),
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"""Apply weight drop to the parameter of a block.
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----------
block : Block or HybridBlock
The block whose parameter is to be applied weight-drop.
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train | _get_rnn_cell | create rnn cell given specs
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mode : str
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num_layers : int
The number of RNN cells in the encoder.
input_size : int
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hid... | src/gluonnlp/model/utils.py | def _get_rnn_cell(mode, num_layers, input_size, hidden_size,
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skip_connection, proj_size=None, cell_clip=None, proj_clip=None):
"""create rnn cell given specs
Parameters
----------
m... | def _get_rnn_cell(mode, num_layers, input_size, hidden_size,
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var_drop_in, var_drop_state, var_drop_out,
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"""create rnn cell given specs
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----------
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train | _get_rnn_layer | create rnn layer given specs | src/gluonnlp/model/utils.py | def _get_rnn_layer(mode, num_layers, input_size, hidden_size, dropout, weight_dropout):
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if mode == 'rnn_relu':
rnn_block = functools.partial(rnn.RNN, activation='relu')
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rnn_block = functools.partial(rnn.RNN, activation='relu')
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rnn_block = functools.partial(rnn.RNN, activation='tanh')
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train | _SampledDenseHelper.hybrid_forward | Forward computation. | src/gluonnlp/model/sampled_block.py | 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 = ... | 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
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train | _SampledDense.hybrid_forward | Forward computation. | src/gluonnlp/model/sampled_block.py | def hybrid_forward(self, F, x, sampled_values, label, weight, bias):
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sampled_candidates, _, _ = sampled_values
# (batch_size,)
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sampled_candidates, _, _ = sampled_values
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train | _SparseSampledDense.forward | Forward computation. | src/gluonnlp/model/sampled_block.py | def forward(self, x, sampled_values, label):
"""Forward computation."""
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train | _extract_and_flatten_nested_structure | 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
The container thats holds flattened result.
Returns
-------
st... | src/gluonnlp/model/sequence_sampler.py | 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
Th... | def _extract_and_flatten_nested_structure(data, flattened=None):
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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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train | _reconstruct_flattened_structure | 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
The container thats holds flattened result.
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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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----------
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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train | _expand_to_beam_size | 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
Each NDArray/Symbol should have shape (N, ...) when state_info is None,
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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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train | _SamplingStepUpdate.hybrid_forward | Parameters
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F
samples : NDArray or Symbol
The current samples generated by beam search. Shape (batch_size, beam_size, L)
valid_length : NDArray or Symbol
The current valid lengths of the samples
outputs: NDArray or Symbol
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----------
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samples : NDArray or Symbol
The current samples generated by beam search. Shape (batch_size, beam_size, L)
valid_length : NDArray or Symbo... | def hybrid_forward(self, F, samples, valid_length, outputs, scores, beam_alive_mask, states):
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samples : NDArray or Symbol
The current samples generated by beam search. Shape (batch_size, beam_size, L)
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train | HybridBeamSearchSampler.hybrid_forward | Sample by beam search.
Parameters
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inputs : NDArray or Symbol
The initial input of the decoder. Shape is (batch_size,).
states : Object that contains NDArrays or Symbols
The initial states of the decoder.
Returns
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... | src/gluonnlp/model/sequence_sampler.py | def hybrid_forward(self, F, inputs, states): # pylint: disable=arguments-differ
"""Sample by beam search.
Parameters
----------
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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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The initial input of the decoder. Shape is (batch_size,).
states : Object that contains NDArrays or ... | [
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train | WeightDropParameter.data | Returns a copy of this parameter on one context. Must have been
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ctx : Context
Desired context.
Returns
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Desired context.
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NDArray on ctx
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Desired context.
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train | elmo_2x1024_128_2048cnn_1xhighway | r"""ELMo 2-layer BiLSTM with 1024 hidden units, 128 projection size, 1 highway layer.
Parameters
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dataset_name : str or None, default None
The dataset name on which the pre-trained model is trained.
Options are 'gbw'.
pretrained : bool, default False
Whether to load th... | src/gluonnlp/model/elmo.py | 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... | 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.
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train | ELMoCharacterEncoder.hybrid_forward | Compute context insensitive token embeddings for ELMo representations.
Parameters
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inputs : NDArray
Shape (batch_size, sequence_length, max_character_per_token)
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Compute context insensitive token embeddings for ELMo representations.
Parameters
----------
inputs : NDArray
Shape (batch_size, sequence_length, max_character_per_token)
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Compute context insensitive token embeddings for ELMo representations.
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inputs : NDArray
Shape (batch_size, sequence_length, max_character_per_token)
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train | ELMoBiLM.hybrid_forward | Parameters
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Shape (batch_size, sequence_length, max_character_per_token)
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states : (list of list of NDArray, list of list of NDArray)
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inputs : NDArray
Shape (batch_size, sequence_length, max_character_per_token)
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train | awd_lstm_lm_1150 | r"""3-layer LSTM language model with weight-drop, variational dropout, and tied weights.
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... | src/gluonnlp/model/language_model.py | 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.
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root=os.path.join(get_home_dir(), 'models'), **kwargs):
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train | standard_lstm_lm_200 | r"""Standard 2-layer LSTM language model with tied embedding and output weights.
Both embedding and hidden dimensions are 200.
Parameters
----------
dataset_name : str or None, default None
The dataset name on which the pre-trained model is trained.
Options are 'wikitext-2'. If specifi... | src/gluonnlp/model/language_model.py | def standard_lstm_lm_200(dataset_name=None, vocab=None, pretrained=False, ctx=cpu(),
root=os.path.join(get_home_dir(), 'models'), **kwargs):
r"""Standard 2-layer LSTM language model with tied embedding and output weights.
Both embedding and hidden dimensions are 200.
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... | def standard_lstm_lm_200(dataset_name=None, vocab=None, pretrained=False, ctx=cpu(),
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r"""Standard 2-layer LSTM language model with tied embedding and output weights.
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train | big_rnn_lm_2048_512 | r"""Big 1-layer LSTMP language model.
Both embedding and projection size are 512. Hidden size is 2048.
Parameters
----------
dataset_name : str or None, default None
The dataset name on which the pre-trained model is trained.
Options are 'gbw'. If specified, then the returned vocabular... | src/gluonnlp/model/language_model.py | def big_rnn_lm_2048_512(dataset_name=None, vocab=None, pretrained=False, ctx=cpu(),
root=os.path.join(get_home_dir(), 'models'), **kwargs):
r"""Big 1-layer LSTMP language model.
Both embedding and projection size are 512. Hidden size is 2048.
Parameters
----------
dataset_n... | def big_rnn_lm_2048_512(dataset_name=None, vocab=None, pretrained=False, ctx=cpu(),
root=os.path.join(get_home_dir(), 'models'), **kwargs):
r"""Big 1-layer LSTMP language model.
Both embedding and projection size are 512. Hidden size is 2048.
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train | AWDRNN.forward | Implement forward computation.
Parameters
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inputs : NDArray
input tensor with shape `(sequence_length, batch_size)`
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begin_state : list
initial recurrent state tensor with length equals to num_layers.
the ... | src/gluonnlp/model/language_model.py | def forward(self, inputs, begin_state=None): # pylint: disable=arguments-differ
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Parameters
-----------
inputs : NDArray
input tensor with shape `(sequence_length, batch_size)`
when `layout` is "TNC".
begin_state : list
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inputs : NDArray
input tensor with shape `(sequence_length, batch_size)`
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begin_state : list
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train | BigRNN.forward | Implement forward computation.
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input tensor with shape `(sequence_length, batch_size)`
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initial recurrent state tensor with length equals to num_layers*2.
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inputs : NDArray
input tensor with shape `(sequence_length, batch_size)`
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inputs : NDArray
input tensor with shape `(sequence_length, batch_size)`
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train | _get_cell_type | Get the object type of the cell by parsing the input
Parameters
----------
cell_type : str or type
Returns
-------
cell_constructor: type
The constructor of the RNNCell | src/gluonnlp/model/seq2seq_encoder_decoder.py | def _get_cell_type(cell_type):
"""Get the object type of the cell by parsing the input
Parameters
----------
cell_type : str or type
Returns
-------
cell_constructor: type
The constructor of the RNNCell
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-------
cell_constructor: type
The constructor of the RNNCell
"""
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train | _get_context | Compute the context with respect to a center word in a sentence.
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train | model | Construct the model. | scripts/sentiment_analysis/text_cnn.py | def model(dropout, vocab, model_mode, output_size):
"""Construct the model."""
textCNN = SentimentNet(dropout=dropout, vocab_size=len(vocab), model_mode=model_mode,\
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textCNN.hybridize()
return textCNN | def model(dropout, vocab, model_mode, output_size):
"""Construct the model."""
textCNN = SentimentNet(dropout=dropout, vocab_size=len(vocab), model_mode=model_mode,\
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textCNN.hybridize()
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train | init | Initialize parameters. | scripts/sentiment_analysis/text_cnn.py | def init(textCNN, vocab, model_mode, context, lr):
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textCNN.initialize(mx.init.Xavier(), ctx=context, force_reinit=True)
if model_mode != 'rand':
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train | preprocess_dataset | Use multiprocessing to perform transform for dataset.
Parameters
----------
dataset: dataset-like object
Source dataset.
transform: callable
Transformer function.
num_workers: int, default 8
The number of multiprocessing workers to use for data preprocessing. | scripts/bert/bert_qa_dataset.py | def preprocess_dataset(dataset, transform, num_workers=8):
"""Use multiprocessing to perform transform for dataset.
Parameters
----------
dataset: dataset-like object
Source dataset.
transform: callable
Transformer function.
num_workers: int, default 8
The number of mult... | def preprocess_dataset(dataset, transform, num_workers=8):
"""Use multiprocessing to perform transform for dataset.
Parameters
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dataset: dataset-like object
Source dataset.
transform: callable
Transformer function.
num_workers: int, default 8
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train | get_model | Returns a pre-defined model by name.
Parameters
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name : str
Name of the model.
dataset_name : str or None, default 'wikitext-2'.
The dataset name on which the pre-trained model is trained.
For language model, options are 'wikitext-2'.
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"""Returns a pre-defined model by name.
Parameters
----------
name : str
Name of the model.
dataset_name : str or None, default 'wikitext-2'.
The dataset name on which the pre-trained model is trained.
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"""Returns a pre-defined model by name.
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----------
name : str
Name of the model.
dataset_name : str or None, default 'wikitext-2'.
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train | _masked_softmax | Ignore the masked elements when calculating the softmax
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----------
F : symbol or ndarray
att_score : Symborl or NDArray
Shape (batch_size, query_length, memory_length)
mask : Symbol or NDArray or None
Shape (batch_size, query_length, memory_length)
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Parameters
----------
F : symbol or ndarray
att_score : Symborl or NDArray
Shape (batch_size, query_length, memory_length)
mask : Symbol or NDArray or None
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att_score : Symborl or NDArray
Shape (batch_size, query_length, memory_length)
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train | AttentionCell._read_by_weight | Read from the value matrix given the attention weights.
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Attention weights.
For single-head attention,
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F : symbol or ndarray
att_weights : Symbol or NDArray
Attention weights.
For single-head attention,
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F : symbol or ndarray
att_weights : Symbol or NDArray
Attention weights.
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train | BeamSearchTranslator.translate | Get the translation result given the input sentence.
Parameters
----------
src_seq : mx.nd.NDArray
Shape (batch_size, length)
src_valid_length : mx.nd.NDArray
Shape (batch_size,)
Returns
-------
samples : NDArray
Samples draw ... | scripts/machine_translation/translation.py | def translate(self, src_seq, src_valid_length):
"""Get the translation result given the input sentence.
Parameters
----------
src_seq : mx.nd.NDArray
Shape (batch_size, length)
src_valid_length : mx.nd.NDArray
Shape (batch_size,)
Returns
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"""Get the translation result given the input sentence.
Parameters
----------
src_seq : mx.nd.NDArray
Shape (batch_size, length)
src_valid_length : mx.nd.NDArray
Shape (batch_size,)
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train | evaluate_official_script | Evaluate parser on a data set
Parameters
----------
parser : BiaffineParser
biaffine parser
vocab : ParserVocabulary
vocabulary built from data set
num_buckets_test : int
size of buckets (cluster sentences into this number of clusters)
test_batch_size : int
batch... | scripts/parsing/parser/evaluate/evaluate.py | def evaluate_official_script(parser, vocab, num_buckets_test, test_batch_size, test_file, output_file,
debug=False):
"""Evaluate parser on a data set
Parameters
----------
parser : BiaffineParser
biaffine parser
vocab : ParserVocabulary
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"""Evaluate parser on a data set
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----------
parser : BiaffineParser
biaffine parser
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train | BiaffineParser.parameter_from_numpy | Create parameter with its value initialized according to a numpy tensor
Parameters
----------
name : str
parameter name
array : np.ndarray
initiation value
Returns
-------
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""" Create parameter with its value initialized according to a numpy tensor
Parameters
----------
name : str
parameter name
array : np.ndarray
initiation value
Returns
-------
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""" Create parameter with its value initialized according to a numpy tensor
Parameters
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name : str
parameter name
array : np.ndarray
initiation value
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-------
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train | BiaffineParser.parameter_init | Create parameter given name, shape and initiator
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name : str
parameter name
shape : tuple
parameter shape
init : mxnet.initializer
an initializer
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Parameters
----------
name : str
parameter name
shape : tuple
parameter shape
init : mxnet.initializer
an initializer
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"""Create parameter given name, shape and initiator
Parameters
----------
name : str
parameter name
shape : tuple
parameter shape
init : mxnet.initializer
an initializer
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train | BiaffineParser.forward | Run decoding
Parameters
----------
word_inputs : mxnet.ndarray.NDArray
word indices of seq_len x batch_size
tag_inputs : mxnet.ndarray.NDArray
tag indices of seq_len x batch_size
arc_targets : mxnet.ndarray.NDArray
gold arc indices of seq_len ... | scripts/parsing/parser/biaffine_parser.py | def forward(self, word_inputs, tag_inputs, arc_targets=None, rel_targets=None):
"""Run decoding
Parameters
----------
word_inputs : mxnet.ndarray.NDArray
word indices of seq_len x batch_size
tag_inputs : mxnet.ndarray.NDArray
tag indices of seq_len x batc... | def forward(self, word_inputs, tag_inputs, arc_targets=None, rel_targets=None):
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word indices of seq_len x batch_size
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tag indices of seq_len x batc... | [
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train | BiaffineParser.save_parameters | Save model
Parameters
----------
filename : str
path to model file | scripts/parsing/parser/biaffine_parser.py | def save_parameters(self, filename):
"""Save model
Parameters
----------
filename : str
path to model file
"""
params = self._collect_params_with_prefix()
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filename : str
path to model file
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train | _worker_fn | Function for processing data in worker process. | src/gluonnlp/data/dataloader.py | def _worker_fn(samples, batchify_fn, dataset=None):
"""Function for processing data in worker process."""
# pylint: disable=unused-argument
# it is required that each worker process has to fork a new MXIndexedRecordIO handle
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train | _thread_worker_fn | Threadpool worker function for processing data. | src/gluonnlp/data/dataloader.py | def _thread_worker_fn(samples, batchify_fn, dataset):
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train | create | Creates an instance of token embedding.
Creates a token embedding instance by loading embedding vectors from an externally hosted
pre-trained token embedding file, such as those of GloVe and FastText. To get all the valid
`embedding_name` and `source`, use :func:`gluonnlp.embedding.list_sources`.
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Creates a token embedding instance by loading embedding vectors from an externally hosted
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train | list_sources | Get valid token embedding names and their pre-trained file names.
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train | TokenEmbedding._load_embedding | Load embedding vectors from a pre-trained token embedding file.
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] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/embedding/token_embedding.py#L221-L253 | [
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train | TokenEmbedding._load_embedding_txt | Load embedding vectors from a pre-trained token embedding file.
For every unknown token, if its representation `self.unknown_token` is encountered in the
pre-trained token embedding file, index 0 of `self.idx_to_vec` maps to the pre-trained token
embedding vector loaded from the file; otherwise... | src/gluonnlp/embedding/token_embedding.py | def _load_embedding_txt(self, pretrained_file_path, elem_delim, encoding='utf8'):
"""Load embedding vectors from a pre-trained token embedding file.
For every unknown token, if its representation `self.unknown_token` is encountered in the
pre-trained token embedding file, index 0 of `self.idx_t... | def _load_embedding_txt(self, pretrained_file_path, elem_delim, encoding='utf8'):
"""Load embedding vectors from a pre-trained token embedding file.
For every unknown token, if its representation `self.unknown_token` is encountered in the
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] | dmlc/gluon-nlp | python | https://github.com/dmlc/gluon-nlp/blob/4b83eb6bcc8881e5f1081a3675adaa19fac5c0ba/src/gluonnlp/embedding/token_embedding.py#L255-L321 | [
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