BoghdadyJR/codesearch_python · Data Studio

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def load_dataset(data_name): """Load sentiment dataset.""" if data_name == 'MR' or data_name == 'Subj': train_dataset, output_size = _load_file(data_name) vocab, max_len = _build_vocab(data_name, train_dataset, []) train_dataset, train_data_lengths = _preprocess_dataset(train_dataset, vo...
def get_home_dir(): """Get home directory for storing datasets/models/pre-trained word embeddings""" _home_dir = os.environ.get('MXNET_HOME', os.path.join('~', '.mxnet')) # expand ~ to actual path _home_dir = os.path.expanduser(_home_dir) return _home_dir
def read_dataset(args, dataset): """ Read dataset from tokenized files. """ path = os.path.join(vars(args)[dataset]) logger.info('reading data from {}'.format(path)) examples = [line.strip().split('\t') for line in open(path)] if args.max_num_examples > 0: examples = examples[:args.m...
def build_vocab(dataset): """ Build vocab given a dataset. """ counter = nlp.data.count_tokens([w for e in dataset for s in e[:2] for w in s], to_lower=True) vocab = nlp.Vocab(counter) return vocab
def prepare_data_loader(args, dataset, vocab, test=False): """ Read data and build data loader. """ # Preprocess dataset = dataset.transform(lambda s1, s2, label: (vocab(s1), vocab(s2), label), lazy=False) # Batching batchify_fn = btf.Tuple(btf.Pad(), btf.Pad...
def mxnet_prefer_gpu(): """If gpu available return gpu, else cpu Returns ------- context : Context The preferable GPU context. """ gpu = int(os.environ.get('MXNET_GPU', default=0)) if gpu in mx.test_utils.list_gpus(): return mx.gpu(gpu) return mx.cpu()
def init_logger(root_dir, name="train.log"): """Initialize a logger Parameters ---------- root_dir : str directory for saving log name : str name of logger Returns ------- logger : logging.Logger a logger """ os.makedirs(root_dir, exist_ok=True) log_...
def orthonormal_VanillaLSTMBuilder(lstm_layers, input_dims, lstm_hiddens, dropout_x=0., dropout_h=0., debug=False): """Build a standard LSTM cell, with variational dropout, with weights initialized to be orthonormal (https://arxiv.org/abs/1312.6120) Parameters ---------- lstm_layers : int C...
def biLSTM(f_lstm, b_lstm, inputs, batch_size=None, dropout_x=0., dropout_h=0.): """Feature extraction through BiLSTM Parameters ---------- f_lstm : VariationalDropoutCell Forward cell b_lstm : VariationalDropoutCell Backward cell inputs : NDArray seq_len x batch_size ...
def bilinear(x, W, y, input_size, seq_len, batch_size, num_outputs=1, bias_x=False, bias_y=False): """Do xWy Parameters ---------- x : NDArray (input_size x seq_len) x batch_size W : NDArray (num_outputs x ny) x nx y : NDArray (input_size x seq_len) x batch_size inpu...
def orthonormal_initializer(output_size, input_size, debug=False): """adopted from Timothy Dozat https://github.com/tdozat/Parser/blob/master/lib/linalg.py Parameters ---------- output_size : int input_size : int debug : bool Whether to skip this initializer Returns ------- ...
def arc_argmax(parse_probs, length, tokens_to_keep, ensure_tree=True): """MST Adopted from Timothy Dozat https://github.com/tdozat/Parser/blob/master/lib/models/nn.py Parameters ---------- parse_probs : NDArray seq_len x seq_len, the probability of arcs length : NDArray real sen...
def rel_argmax(rel_probs, length, ensure_tree=True): """Fix the relation prediction by heuristic rules Parameters ---------- rel_probs : NDArray seq_len x rel_size length : real sentence length ensure_tree : whether to apply rules Returns ------- rel_preds : ...
def reshape_fortran(tensor, shape): """The missing Fortran reshape for mx.NDArray Parameters ---------- tensor : NDArray source tensor shape : NDArray desired shape Returns ------- output : NDArray reordered result """ return tensor.T.reshape(tuple(rever...
def update(self, current, values=[], exact=[], strict=[]): """ Updates the progress bar. # Arguments current: Index of current step. values: List of tuples (name, value_for_last_step). The progress bar will display averages for these values. ex...
def get_batch(data_source, i, seq_len=None): """Get mini-batches of the dataset. Parameters ---------- data_source : NDArray The dataset is evaluated on. i : int The index of the batch, starting from 0. seq_len : int The length of each sample in the batch. Returns ...
def evaluate(data_source, batch_size, params_file_name, ctx=None): """Evaluate the model on the dataset. Parameters ---------- data_source : NDArray The dataset is evaluated on. batch_size : int The size of the mini-batch. params_file_name : str The parameter file to use...
def train(): """Training loop for awd language model. """ ntasgd = False best_val = float('Inf') start_train_time = time.time() parameters = model.collect_params() param_dict_avg = None t = 0 avg_trigger = 0 n = 5 valid_losses = [] for epoch in range(args.epochs): ...
def register(class_): """Registers a new word embedding evaluation function. Once registered, we can create an instance with :func:`~gluonnlp.embedding.evaluation.create`. Examples -------- >>> @gluonnlp.embedding.evaluation.register ... class MySimilarityFunction(gluonnlp.embedding.evalua...
def create(kind, name, **kwargs): """Creates an instance of a registered word embedding evaluation function. Parameters ---------- kind : ['similarity', 'analogy'] Return only valid names for similarity, analogy or both kinds of functions. name : str The evaluation function ...
def list_evaluation_functions(kind=None): """Get valid word embedding functions names. Parameters ---------- kind : ['similarity', 'analogy', None] Return only valid names for similarity, analogy or both kinds of functions. Returns ------- dict or list: A list of all the va...
def hybrid_forward(self, F, words1, words2, weight): # pylint: disable=arguments-differ """Predict the similarity of words1 and words2. Parameters ---------- words1 : Symbol or NDArray The indices of the words the we wish to compare to the words in words2. words2 : ...
def hybrid_forward(self, F, words1, words2, words3): # pylint: disable=arguments-differ, unused-argument """Compute analogies for given question words. Parameters ---------- words1 : Symbol or NDArray Word indices of first question words. Shape (batch_size, ). words...
def evaluate(data_source, batch_size, ctx=None): """Evaluate the model on the dataset with cache model. Parameters ---------- data_source : NDArray The dataset is evaluated on. batch_size : int The size of the mini-batch. ctx : mx.cpu() or mx.gpu() The context of the com...
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 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 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 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 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 put(self, x): """Assign input `x` to an available worker and invoke `parallizable.forward_backward` with x. """ if self._num_serial > 0 or len(self._threads) == 0: self._num_serial -= 1 out = self._parallizable.forward_backward(x) self._out_queue.put(out) ...
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 forward(self, inputs, begin_state=None): # pylint: disable=arguments-differ """Defines the forward computation. Arguments can be either :py:class:`NDArray` or :py:class:`Symbol`. Parameters ----------- inputs : NDArray input tensor with shape `(sequence_length, b...
def forward(self, inputs, label, begin_state, sampled_values): # pylint: disable=arguments-differ """Defines the forward computation. Parameters ----------- inputs : NDArray input tensor with shape `(sequence_length, batch_size)` when `layout` is "TNC". b...
def hybrid_forward(self, F, center, context, center_words): """SkipGram forward pass. Parameters ---------- center : mxnet.nd.NDArray or mxnet.sym.Symbol Sparse CSR array of word / subword indices of shape (batch_size, len(token_to_idx) + num_subwords). Embedding...
def evaluate(dataloader): """Evaluate network on the specified dataset""" total_L = 0.0 total_sample_num = 0 total_correct_num = 0 start_log_interval_time = time.time() print('Begin Testing...') for i, ((data, valid_length), label) in enumerate(dataloader): data = mx.nd.transpose(dat...
def train(): """Training process""" start_pipeline_time = time.time() # Training/Testing best_valid_acc = 0 stop_early = 0 for epoch in range(args.epochs): # Epoch training stats start_epoch_time = time.time() epoch_L = 0.0 epoch_sent_num = 0 epoch_wc = 0...
def hybrid_forward(self, F, data, valid_length): # pylint: disable=arguments-differ """Forward logic""" # Data will have shape (T, N, C) if self._use_mean_pool: masked_encoded = F.SequenceMask(data, sequence_length=valid_length, ...
def hybrid_forward(self, F, inputs, states, i2h_weight, h2h_weight, h2r_weight, i2h_bias, h2h_bias): r"""Hybrid forward computation for Long-Short Term Memory Projected network cell with cell clip and projection clip. Parameters ---------- inputs : input t...
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 train(data_train, model, nsp_loss, mlm_loss, vocab_size, ctx, store): """Training function.""" mlm_metric = nlp.metric.MaskedAccuracy() nsp_metric = nlp.metric.MaskedAccuracy() mlm_metric.reset() nsp_metric.reset() lr = args.lr optim_params = {'learning_rate': lr, 'epsilon': 1e-6, 'wd':...
def forward_backward(self, x): """forward backward implementation""" with mx.autograd.record(): (ls, next_sentence_label, classified, masked_id, decoded, \ masked_weight, ls1, ls2, valid_length) = forward(x, self._model, self._mlm_loss, ...
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 _add_pret_words(self, pret_embeddings): """Read pre-trained embedding file for extending vocabulary Parameters ---------- pret_embeddings : tuple (embedding_name, source), used for gluonnlp.embedding.create(embedding_name, source) """ words_in_train_data ...
def get_pret_embs(self, word_dims=None): """Read pre-trained embedding file Parameters ---------- word_dims : int or None vector size. Use `None` for auto-infer Returns ------- numpy.ndarray T x C numpy NDArray """ assert (...
def get_word_embs(self, word_dims): """Get randomly initialized embeddings when pre-trained embeddings are used, otherwise zero vectors Parameters ---------- word_dims : int word vector size Returns ------- numpy.ndarray T x C numpy NDArra...
def get_tag_embs(self, tag_dims): """Randomly initialize embeddings for tag Parameters ---------- tag_dims : int tag vector size Returns ------- numpy.ndarray random embeddings """ return np.random.randn(self.tag_size, tag...
def word2id(self, xs): """Map word(s) to its id(s) Parameters ---------- xs : str or list word or a list of words Returns ------- int or list id or a list of ids """ if isinstance(xs, list): return [self._word2...
def id2word(self, xs): """Map id(s) to word(s) Parameters ---------- xs : int id or a list of ids Returns ------- str or list word or a list of words """ if isinstance(xs, list): return [self._id2word[x] for x ...
def rel2id(self, xs): """Map relation(s) to id(s) Parameters ---------- xs : str or list relation Returns ------- int or list id(s) of relation """ if isinstance(xs, list): return [self._rel2id[x] for x in xs] ...
def id2rel(self, xs): """Map id(s) to relation(s) Parameters ---------- xs : int id or a list of ids Returns ------- str or list relation or a list of relations """ if isinstance(xs, list): return [self._id2rel...
def tag2id(self, xs): """Map tag(s) to id(s) Parameters ---------- xs : str or list tag or tags Returns ------- int or list id(s) of tag(s) """ if isinstance(xs, list): return [self._tag2id.get(x, self.UNK) for...
def idx_sequence(self): """Indices of sentences when enumerating data set from batches. Useful when retrieving the correct order of sentences Returns ------- list List of ids ranging from 0 to #sent -1 """ return [x[1] for x in sorted(zip(self._record...
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 ------...
def create_ngram_set(input_list, ngram_value=2): """ Extract a set of n-grams from a list of integers. >>> create_ngram_set([1, 4, 9, 4, 1, 4], ngram_value=2) {(4, 9), (4, 1), (1, 4), (9, 4)} >>> create_ngram_set([1, 4, 9, 4, 1, 4], ngram_value=3) [(1, 4, 9), (4, 9, 4), (9, 4, 1), (4, 1, 4)] ...
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]] >>> token_indice = {(1, 3): 1337, (9, 2): 42, (4, 5): 2017} >>> add_ngram(sequences, token_in...
def evaluate_accuracy(data_iterator, net, ctx, loss_fun, num_classes): """ This function is used for evaluating accuracy of a given data iterator. (Either Train/Test data) It takes in the loss function used too! """ acc = mx.metric.Accuracy() loss_avg = 0. for i, ((data, length), label) ...
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 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 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 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) with mp.Pool() as pool: # Each sample is processed in an asynchronous manner. o...
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 get_dataloader(train_dataset, train_data_lengths, test_dataset, batch_size): """ Construct the DataLoader. Pad data, stack label and lengths""" bucket_num, bucket_ratio = 20, 0.2 batchify_fn = gluonnlp.data.batchify.Tuple( gluonnlp.data.batchify.Pad(axis=0, ret_length=True), ...
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 hybrid_forward(self, F, inputs, token_types, valid_length=None): # pylint: disable=arguments-differ # pylint: disable=unused-argument """Generate the unnormalized score for the given the input sequences. Parameters ---------- inputs : NDArray, shape (batch_size, seq_...
def hybrid_forward(self, F, pred, label): # pylint: disable=arguments-differ """ Parameters ---------- pred : NDArray, shape (batch_size, seq_length, 2) BERTSquad forward output. label : list, length is 2, each shape is (batch_size,1) label[0] is the star...
def encode(self, inputs, states=None, valid_length=None): """Encode the input sequence. Parameters ---------- inputs : NDArray states : list of NDArrays or None, default None valid_length : NDArray or None, default None Returns ------- outputs : ...
def decode_seq(self, inputs, states, valid_length=None): """Decode given the input sequence. Parameters ---------- inputs : NDArray states : list of NDArrays valid_length : NDArray or None, default None Returns ------- output : NDArray ...
def decode_step(self, step_input, states): """One step decoding of the translation model. Parameters ---------- step_input : NDArray Shape (batch_size,) states : list of NDArrays Returns ------- step_output : NDArray Shape (batch_...
def forward(self, src_seq, tgt_seq, src_valid_length=None, tgt_valid_length=None): #pylint: disable=arguments-differ """Generate the prediction given the src_seq and tgt_seq. This is used in training an NMT model. Parameters ---------- src_seq : NDArray tgt_seq : NDArr...
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 _index_special_tokens(self, unknown_token, special_tokens): """Indexes unknown and reserved tokens.""" self._idx_to_token = [unknown_token] if unknown_token else [] if not special_tokens: self._reserved_tokens = None else: self._reserved_tokens = special_toke...
def _index_counter_keys(self, counter, unknown_token, special_tokens, max_size, min_freq): """Indexes keys of `counter`. Indexes keys of `counter` according to frequency thresholds such as `max_size` and `min_freq`. """ unknown_and_special_tokens = ...
def set_embedding(self, *embeddings): """Attaches one or more embeddings to the indexed text tokens. Parameters ---------- embeddings : None or tuple of :class:`gluonnlp.embedding.TokenEmbedding` instances The embedding to be attached to the indexed tokens. If a tuple of mu...
def to_tokens(self, indices): """Converts token indices to tokens according to the vocabulary. Parameters ---------- indices : int or list of ints A source token index or token indices to be converted. Returns ------- str or list of strs ...
def to_json(self): """Serialize Vocab object to json string. This method does not serialize the underlying embedding. """ if self._embedding: warnings.warn('Serialization of attached embedding ' 'to json is not supported. ' ...
def from_json(cls, json_str): """Deserialize Vocab object from json string. Parameters ---------- json_str : str Serialized json string of a Vocab object. Returns ------- Vocab """ vocab_dict = json.loads(json_str) unknown_t...
def train(data_train, model, nsp_loss, mlm_loss, vocab_size, ctx): """Training function.""" hvd.broadcast_parameters(model.collect_params(), root_rank=0) mlm_metric = nlp.metric.MaskedAccuracy() nsp_metric = nlp.metric.MaskedAccuracy() mlm_metric.reset() nsp_metric.reset() logging.debug('C...
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 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 _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 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 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 ------- DepParser parser itself """ config = _Config.lo...
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 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 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 _get_rnn_cell(mode, num_layers, input_size, hidden_size, dropout, weight_dropout, var_drop_in, var_drop_state, var_drop_out, skip_connection, proj_size=None, cell_clip=None, proj_clip=None): """create rnn cell given specs Parameters ---------- m...
def _get_rnn_layer(mode, num_layers, input_size, hidden_size, dropout, weight_dropout): """create rnn layer given specs""" if mode == 'rnn_relu': rnn_block = functools.partial(rnn.RNN, activation='relu') elif mode == 'rnn_tanh': rnn_block = functools.partial(rnn.RNN, activation='tanh') e...
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, weight, bias): """Forward computation.""" sampled_candidates, _, _ = sampled_values # (batch_size,) label = F.reshape(label, shape=(-1,)) # (num_sampled+batch_size,) ids = F.concat(sampled_candidates, label, dim=0) ...
def forward(self, x, sampled_values, label): """Forward computation.""" sampled_candidates, _, _ = sampled_values # (batch_size,) label = label.reshape(shape=(-1,)) # (num_sampled+batch_size,) ids = nd.concat(sampled_candidates, label, dim=0) # lookup weights and ...
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 _reconstruct_flattened_structure(structure, flattened): """Reconstruct the flattened list back to (possibly) nested structure. Parameters ---------- structure : An integer or a nested container with integers. The extracted structure of the container of `data`. flattened : list or None ...
def _expand_to_beam_size(data, beam_size, batch_size, state_info=None): """Tile all the states to have batch_size * beam_size on the batch axis. Parameters ---------- data : A single NDArray/Symbol or nested container with NDArrays/Symbol Each NDArray/Symbol should have shape (N, ...) when stat...
def hybrid_forward(self, F, samples, valid_length, outputs, scores, beam_alive_mask, states): """ Parameters ---------- F samples : NDArray or Symbol The current samples generated by beam search. Shape (batch_size, beam_size, L) valid_length : NDArray or Symbo...
def hybrid_forward(self, F, inputs, states): # pylint: disable=arguments-differ """Sample by beam search. Parameters ---------- F inputs : NDArray or Symbol The initial input of the decoder. Shape is (batch_size,). states : Object that contains NDArrays or ...
def data(self, ctx=None): """Returns a copy of this parameter on one context. Must have been initialized on this context before. Parameters ---------- ctx : Context Desired context. Returns ------- NDArray on ctx """ d = self._...
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 hybrid_forward(self, F, inputs): # pylint: disable=arguments-differ """ Compute context insensitive token embeddings for ELMo representations. Parameters ---------- inputs : NDArray Shape (batch_size, sequence_length, max_character_per_token) ...
def hybrid_forward(self, F, inputs, states=None, mask=None): # pylint: disable=arguments-differ """ Parameters ---------- inputs : NDArray Shape (batch_size, sequence_length, max_character_per_token) of character ids representing the current batch. ...
def awd_lstm_lm_1150(dataset_name=None, vocab=None, pretrained=False, ctx=cpu(), root=os.path.join(get_home_dir(), 'models'), **kwargs): r"""3-layer LSTM language model with weight-drop, variational dropout, and tied weights. Embedding size is 400, and hidden layer size is 1150. Param...

def load_dataset(data_name): """Load sentiment dataset.""" if data_name == 'MR' or data_name == 'Subj': train_dataset, output_size = _load_file(data_name) vocab, max_len = _build_vocab(data_name, train_dataset, []) train_dataset, train_data_lengths = _preprocess_dataset(train_dataset, vo...

def get_home_dir(): """Get home directory for storing datasets/models/pre-trained word embeddings""" _home_dir = os.environ.get('MXNET_HOME', os.path.join('~', '.mxnet')) # expand ~ to actual path _home_dir = os.path.expanduser(_home_dir) return _home_dir

def read_dataset(args, dataset): """ Read dataset from tokenized files. """ path = os.path.join(vars(args)[dataset]) logger.info('reading data from {}'.format(path)) examples = [line.strip().split('\t') for line in open(path)] if args.max_num_examples > 0: examples = examples[:args.m...

def build_vocab(dataset): """ Build vocab given a dataset. """ counter = nlp.data.count_tokens([w for e in dataset for s in e[:2] for w in s], to_lower=True) vocab = nlp.Vocab(counter) return vocab

def prepare_data_loader(args, dataset, vocab, test=False): """ Read data and build data loader. """ # Preprocess dataset = dataset.transform(lambda s1, s2, label: (vocab(s1), vocab(s2), label), lazy=False) # Batching batchify_fn = btf.Tuple(btf.Pad(), btf.Pad...

def mxnet_prefer_gpu(): """If gpu available return gpu, else cpu Returns ------- context : Context The preferable GPU context. """ gpu = int(os.environ.get('MXNET_GPU', default=0)) if gpu in mx.test_utils.list_gpus(): return mx.gpu(gpu) return mx.cpu()

def init_logger(root_dir, name="train.log"): """Initialize a logger Parameters ---------- root_dir : str directory for saving log name : str name of logger Returns ------- logger : logging.Logger a logger """ os.makedirs(root_dir, exist_ok=True) log_...

def orthonormal_VanillaLSTMBuilder(lstm_layers, input_dims, lstm_hiddens, dropout_x=0., dropout_h=0., debug=False): """Build a standard LSTM cell, with variational dropout, with weights initialized to be orthonormal (https://arxiv.org/abs/1312.6120) Parameters ---------- lstm_layers : int C...

def biLSTM(f_lstm, b_lstm, inputs, batch_size=None, dropout_x=0., dropout_h=0.): """Feature extraction through BiLSTM Parameters ---------- f_lstm : VariationalDropoutCell Forward cell b_lstm : VariationalDropoutCell Backward cell inputs : NDArray seq_len x batch_size ...

def bilinear(x, W, y, input_size, seq_len, batch_size, num_outputs=1, bias_x=False, bias_y=False): """Do xWy Parameters ---------- x : NDArray (input_size x seq_len) x batch_size W : NDArray (num_outputs x ny) x nx y : NDArray (input_size x seq_len) x batch_size inpu...

def orthonormal_initializer(output_size, input_size, debug=False): """adopted from Timothy Dozat https://github.com/tdozat/Parser/blob/master/lib/linalg.py Parameters ---------- output_size : int input_size : int debug : bool Whether to skip this initializer Returns ------- ...

def arc_argmax(parse_probs, length, tokens_to_keep, ensure_tree=True): """MST Adopted from Timothy Dozat https://github.com/tdozat/Parser/blob/master/lib/models/nn.py Parameters ---------- parse_probs : NDArray seq_len x seq_len, the probability of arcs length : NDArray real sen...

def rel_argmax(rel_probs, length, ensure_tree=True): """Fix the relation prediction by heuristic rules Parameters ---------- rel_probs : NDArray seq_len x rel_size length : real sentence length ensure_tree : whether to apply rules Returns ------- rel_preds : ...

def reshape_fortran(tensor, shape): """The missing Fortran reshape for mx.NDArray Parameters ---------- tensor : NDArray source tensor shape : NDArray desired shape Returns ------- output : NDArray reordered result """ return tensor.T.reshape(tuple(rever...

def update(self, current, values=[], exact=[], strict=[]): """ Updates the progress bar. # Arguments current: Index of current step. values: List of tuples (name, value_for_last_step). The progress bar will display averages for these values. ex...

def get_batch(data_source, i, seq_len=None): """Get mini-batches of the dataset. Parameters ---------- data_source : NDArray The dataset is evaluated on. i : int The index of the batch, starting from 0. seq_len : int The length of each sample in the batch. Returns ...

def evaluate(data_source, batch_size, params_file_name, ctx=None): """Evaluate the model on the dataset. Parameters ---------- data_source : NDArray The dataset is evaluated on. batch_size : int The size of the mini-batch. params_file_name : str The parameter file to use...

def train(): """Training loop for awd language model. """ ntasgd = False best_val = float('Inf') start_train_time = time.time() parameters = model.collect_params() param_dict_avg = None t = 0 avg_trigger = 0 n = 5 valid_losses = [] for epoch in range(args.epochs): ...

def register(class_): """Registers a new word embedding evaluation function. Once registered, we can create an instance with :func:`~gluonnlp.embedding.evaluation.create`. Examples -------- >>> @gluonnlp.embedding.evaluation.register ... class MySimilarityFunction(gluonnlp.embedding.evalua...

def create(kind, name, **kwargs): """Creates an instance of a registered word embedding evaluation function. Parameters ---------- kind : ['similarity', 'analogy'] Return only valid names for similarity, analogy or both kinds of functions. name : str The evaluation function ...

def list_evaluation_functions(kind=None): """Get valid word embedding functions names. Parameters ---------- kind : ['similarity', 'analogy', None] Return only valid names for similarity, analogy or both kinds of functions. Returns ------- dict or list: A list of all the va...

def hybrid_forward(self, F, words1, words2, weight): # pylint: disable=arguments-differ """Predict the similarity of words1 and words2. Parameters ---------- words1 : Symbol or NDArray The indices of the words the we wish to compare to the words in words2. words2 : ...

def hybrid_forward(self, F, words1, words2, words3): # pylint: disable=arguments-differ, unused-argument """Compute analogies for given question words. Parameters ---------- words1 : Symbol or NDArray Word indices of first question words. Shape (batch_size, ). words...

def evaluate(data_source, batch_size, ctx=None): """Evaluate the model on the dataset with cache model. Parameters ---------- data_source : NDArray The dataset is evaluated on. batch_size : int The size of the mini-batch. ctx : mx.cpu() or mx.gpu() The context of the com...

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 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 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 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 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 put(self, x): """Assign input `x` to an available worker and invoke `parallizable.forward_backward` with x. """ if self._num_serial > 0 or len(self._threads) == 0: self._num_serial -= 1 out = self._parallizable.forward_backward(x) self._out_queue.put(out) ...

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 forward(self, inputs, begin_state=None): # pylint: disable=arguments-differ """Defines the forward computation. Arguments can be either :py:class:`NDArray` or :py:class:`Symbol`. Parameters ----------- inputs : NDArray input tensor with shape `(sequence_length, b...

def forward(self, inputs, label, begin_state, sampled_values): # pylint: disable=arguments-differ """Defines the forward computation. Parameters ----------- inputs : NDArray input tensor with shape `(sequence_length, batch_size)` when `layout` is "TNC". b...

def hybrid_forward(self, F, center, context, center_words): """SkipGram forward pass. Parameters ---------- center : mxnet.nd.NDArray or mxnet.sym.Symbol Sparse CSR array of word / subword indices of shape (batch_size, len(token_to_idx) + num_subwords). Embedding...

def evaluate(dataloader): """Evaluate network on the specified dataset""" total_L = 0.0 total_sample_num = 0 total_correct_num = 0 start_log_interval_time = time.time() print('Begin Testing...') for i, ((data, valid_length), label) in enumerate(dataloader): data = mx.nd.transpose(dat...

def train(): """Training process""" start_pipeline_time = time.time() # Training/Testing best_valid_acc = 0 stop_early = 0 for epoch in range(args.epochs): # Epoch training stats start_epoch_time = time.time() epoch_L = 0.0 epoch_sent_num = 0 epoch_wc = 0...

def hybrid_forward(self, F, data, valid_length): # pylint: disable=arguments-differ """Forward logic""" # Data will have shape (T, N, C) if self._use_mean_pool: masked_encoded = F.SequenceMask(data, sequence_length=valid_length, ...

def hybrid_forward(self, F, inputs, states, i2h_weight, h2h_weight, h2r_weight, i2h_bias, h2h_bias): r"""Hybrid forward computation for Long-Short Term Memory Projected network cell with cell clip and projection clip. Parameters ---------- inputs : input t...

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 train(data_train, model, nsp_loss, mlm_loss, vocab_size, ctx, store): """Training function.""" mlm_metric = nlp.metric.MaskedAccuracy() nsp_metric = nlp.metric.MaskedAccuracy() mlm_metric.reset() nsp_metric.reset() lr = args.lr optim_params = {'learning_rate': lr, 'epsilon': 1e-6, 'wd':...

def forward_backward(self, x): """forward backward implementation""" with mx.autograd.record(): (ls, next_sentence_label, classified, masked_id, decoded, \ masked_weight, ls1, ls2, valid_length) = forward(x, self._model, self._mlm_loss, ...

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 _add_pret_words(self, pret_embeddings): """Read pre-trained embedding file for extending vocabulary Parameters ---------- pret_embeddings : tuple (embedding_name, source), used for gluonnlp.embedding.create(embedding_name, source) """ words_in_train_data ...

def get_pret_embs(self, word_dims=None): """Read pre-trained embedding file Parameters ---------- word_dims : int or None vector size. Use `None` for auto-infer Returns ------- numpy.ndarray T x C numpy NDArray """ assert (...

def get_word_embs(self, word_dims): """Get randomly initialized embeddings when pre-trained embeddings are used, otherwise zero vectors Parameters ---------- word_dims : int word vector size Returns ------- numpy.ndarray T x C numpy NDArra...

def get_tag_embs(self, tag_dims): """Randomly initialize embeddings for tag Parameters ---------- tag_dims : int tag vector size Returns ------- numpy.ndarray random embeddings """ return np.random.randn(self.tag_size, tag...

def word2id(self, xs): """Map word(s) to its id(s) Parameters ---------- xs : str or list word or a list of words Returns ------- int or list id or a list of ids """ if isinstance(xs, list): return [self._word2...

def id2word(self, xs): """Map id(s) to word(s) Parameters ---------- xs : int id or a list of ids Returns ------- str or list word or a list of words """ if isinstance(xs, list): return [self._id2word[x] for x ...

def rel2id(self, xs): """Map relation(s) to id(s) Parameters ---------- xs : str or list relation Returns ------- int or list id(s) of relation """ if isinstance(xs, list): return [self._rel2id[x] for x in xs] ...

def id2rel(self, xs): """Map id(s) to relation(s) Parameters ---------- xs : int id or a list of ids Returns ------- str or list relation or a list of relations """ if isinstance(xs, list): return [self._id2rel...

def tag2id(self, xs): """Map tag(s) to id(s) Parameters ---------- xs : str or list tag or tags Returns ------- int or list id(s) of tag(s) """ if isinstance(xs, list): return [self._tag2id.get(x, self.UNK) for...

def idx_sequence(self): """Indices of sentences when enumerating data set from batches. Useful when retrieving the correct order of sentences Returns ------- list List of ids ranging from 0 to #sent -1 """ return [x[1] for x in sorted(zip(self._record...

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 ------...

def create_ngram_set(input_list, ngram_value=2): """ Extract a set of n-grams from a list of integers. >>> create_ngram_set([1, 4, 9, 4, 1, 4], ngram_value=2) {(4, 9), (4, 1), (1, 4), (9, 4)} >>> create_ngram_set([1, 4, 9, 4, 1, 4], ngram_value=3) [(1, 4, 9), (4, 9, 4), (9, 4, 1), (4, 1, 4)] ...

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]] >>> token_indice = {(1, 3): 1337, (9, 2): 42, (4, 5): 2017} >>> add_ngram(sequences, token_in...

def evaluate_accuracy(data_iterator, net, ctx, loss_fun, num_classes): """ This function is used for evaluating accuracy of a given data iterator. (Either Train/Test data) It takes in the loss function used too! """ acc = mx.metric.Accuracy() loss_avg = 0. for i, ((data, length), label) ...

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 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 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 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) with mp.Pool() as pool: # Each sample is processed in an asynchronous manner. o...

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 get_dataloader(train_dataset, train_data_lengths, test_dataset, batch_size): """ Construct the DataLoader. Pad data, stack label and lengths""" bucket_num, bucket_ratio = 20, 0.2 batchify_fn = gluonnlp.data.batchify.Tuple( gluonnlp.data.batchify.Pad(axis=0, ret_length=True), ...

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 hybrid_forward(self, F, inputs, token_types, valid_length=None): # pylint: disable=arguments-differ # pylint: disable=unused-argument """Generate the unnormalized score for the given the input sequences. Parameters ---------- inputs : NDArray, shape (batch_size, seq_...

def hybrid_forward(self, F, pred, label): # pylint: disable=arguments-differ """ Parameters ---------- pred : NDArray, shape (batch_size, seq_length, 2) BERTSquad forward output. label : list, length is 2, each shape is (batch_size,1) label[0] is the star...

def encode(self, inputs, states=None, valid_length=None): """Encode the input sequence. Parameters ---------- inputs : NDArray states : list of NDArrays or None, default None valid_length : NDArray or None, default None Returns ------- outputs : ...

def decode_seq(self, inputs, states, valid_length=None): """Decode given the input sequence. Parameters ---------- inputs : NDArray states : list of NDArrays valid_length : NDArray or None, default None Returns ------- output : NDArray ...

def decode_step(self, step_input, states): """One step decoding of the translation model. Parameters ---------- step_input : NDArray Shape (batch_size,) states : list of NDArrays Returns ------- step_output : NDArray Shape (batch_...

def forward(self, src_seq, tgt_seq, src_valid_length=None, tgt_valid_length=None): #pylint: disable=arguments-differ """Generate the prediction given the src_seq and tgt_seq. This is used in training an NMT model. Parameters ---------- src_seq : NDArray tgt_seq : NDArr...

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 _index_special_tokens(self, unknown_token, special_tokens): """Indexes unknown and reserved tokens.""" self._idx_to_token = [unknown_token] if unknown_token else [] if not special_tokens: self._reserved_tokens = None else: self._reserved_tokens = special_toke...

def _index_counter_keys(self, counter, unknown_token, special_tokens, max_size, min_freq): """Indexes keys of `counter`. Indexes keys of `counter` according to frequency thresholds such as `max_size` and `min_freq`. """ unknown_and_special_tokens = ...

def set_embedding(self, *embeddings): """Attaches one or more embeddings to the indexed text tokens. Parameters ---------- embeddings : None or tuple of :class:`gluonnlp.embedding.TokenEmbedding` instances The embedding to be attached to the indexed tokens. If a tuple of mu...

def to_tokens(self, indices): """Converts token indices to tokens according to the vocabulary. Parameters ---------- indices : int or list of ints A source token index or token indices to be converted. Returns ------- str or list of strs ...

def to_json(self): """Serialize Vocab object to json string. This method does not serialize the underlying embedding. """ if self._embedding: warnings.warn('Serialization of attached embedding ' 'to json is not supported. ' ...

def from_json(cls, json_str): """Deserialize Vocab object from json string. Parameters ---------- json_str : str Serialized json string of a Vocab object. Returns ------- Vocab """ vocab_dict = json.loads(json_str) unknown_t...

def train(data_train, model, nsp_loss, mlm_loss, vocab_size, ctx): """Training function.""" hvd.broadcast_parameters(model.collect_params(), root_rank=0) mlm_metric = nlp.metric.MaskedAccuracy() nsp_metric = nlp.metric.MaskedAccuracy() mlm_metric.reset() nsp_metric.reset() logging.debug('C...

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 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 _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 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 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 ------- DepParser parser itself """ config = _Config.lo...

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 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 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 _get_rnn_cell(mode, num_layers, input_size, hidden_size, dropout, weight_dropout, var_drop_in, var_drop_state, var_drop_out, skip_connection, proj_size=None, cell_clip=None, proj_clip=None): """create rnn cell given specs Parameters ---------- m...

def _get_rnn_layer(mode, num_layers, input_size, hidden_size, dropout, weight_dropout): """create rnn layer given specs""" if mode == 'rnn_relu': rnn_block = functools.partial(rnn.RNN, activation='relu') elif mode == 'rnn_tanh': rnn_block = functools.partial(rnn.RNN, activation='tanh') e...

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, weight, bias): """Forward computation.""" sampled_candidates, _, _ = sampled_values # (batch_size,) label = F.reshape(label, shape=(-1,)) # (num_sampled+batch_size,) ids = F.concat(sampled_candidates, label, dim=0) ...

def forward(self, x, sampled_values, label): """Forward computation.""" sampled_candidates, _, _ = sampled_values # (batch_size,) label = label.reshape(shape=(-1,)) # (num_sampled+batch_size,) ids = nd.concat(sampled_candidates, label, dim=0) # lookup weights and ...

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 _reconstruct_flattened_structure(structure, flattened): """Reconstruct the flattened list back to (possibly) nested structure. Parameters ---------- structure : An integer or a nested container with integers. The extracted structure of the container of `data`. flattened : list or None ...

def _expand_to_beam_size(data, beam_size, batch_size, state_info=None): """Tile all the states to have batch_size * beam_size on the batch axis. Parameters ---------- data : A single NDArray/Symbol or nested container with NDArrays/Symbol Each NDArray/Symbol should have shape (N, ...) when stat...

def hybrid_forward(self, F, samples, valid_length, outputs, scores, beam_alive_mask, states): """ Parameters ---------- F samples : NDArray or Symbol The current samples generated by beam search. Shape (batch_size, beam_size, L) valid_length : NDArray or Symbo...

def hybrid_forward(self, F, inputs, states): # pylint: disable=arguments-differ """Sample by beam search. Parameters ---------- F inputs : NDArray or Symbol The initial input of the decoder. Shape is (batch_size,). states : Object that contains NDArrays or ...

def data(self, ctx=None): """Returns a copy of this parameter on one context. Must have been initialized on this context before. Parameters ---------- ctx : Context Desired context. Returns ------- NDArray on ctx """ d = self._...

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 hybrid_forward(self, F, inputs): # pylint: disable=arguments-differ """ Compute context insensitive token embeddings for ELMo representations. Parameters ---------- inputs : NDArray Shape (batch_size, sequence_length, max_character_per_token) ...

def hybrid_forward(self, F, inputs, states=None, mask=None): # pylint: disable=arguments-differ """ Parameters ---------- inputs : NDArray Shape (batch_size, sequence_length, max_character_per_token) of character ids representing the current batch. ...

def awd_lstm_lm_1150(dataset_name=None, vocab=None, pretrained=False, ctx=cpu(), root=os.path.join(get_home_dir(), 'models'), **kwargs): r"""3-layer LSTM language model with weight-drop, variational dropout, and tied weights. Embedding size is 400, and hidden layer size is 1150. Param...