JorgeVB/code_unlearning_dataset · Datasets at Hugging Face

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Load sentiment dataset. def load_dataset(data_name): """Load sentiment dataset.""" if data_name == 'MR' or data_name == 'Subj': train_dataset, output_size = _load_file(data_name) vocab, max_len = _build_vocab(data_name, train_dataset, []) train_dataset, train_data_lengths = _preprocess_...
Get home directory for storing datasets/models/pre-trained word embeddings 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(...
Read dataset from tokenized files. 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:...
Build vocab given a dataset. 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
Read data and build data loader. def prepare_data_loader(args, dataset, vocab, test=False): """ Read data and build data loader. """ # Preprocess dataset = dataset.transform(lambda s1, s2, label: (vocab(s1), vocab(s2), label), lazy=False) # Batching batchify...
If gpu available return gpu, else cpu Returns ------- context : Context The preferable GPU context. def mxnet_prefer_gpu(): """If gpu available return gpu, else cpu Returns ------- context : Context The preferable GPU context. """ gpu = int(os.environ.get('MXNET_GP...
Initialize a logger Parameters ---------- root_dir : str directory for saving log name : str name of logger Returns ------- logger : logging.Logger a logger def init_logger(root_dir, name="train.log"): """Initialize a logger Parameters ---------- r...
Build a standard LSTM cell, with variational dropout, with weights initialized to be orthonormal (https://arxiv.org/abs/1312.6120) Parameters ---------- lstm_layers : int Currently only support one layer input_dims : int word vector dimensions lstm_hiddens : int hidden s...
Feature extraction through BiLSTM Parameters ---------- f_lstm : VariationalDropoutCell Forward cell b_lstm : VariationalDropoutCell Backward cell inputs : NDArray seq_len x batch_size dropout_x : float Variational dropout on inputs dropout_h : Not us...
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 input_size : int input dimension seq_len : int sequence length batch_size : int ...
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 ------- Q : np.ndarray The orthonormal weight matrix of input_size x outpu...
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 sentence length tokens_to_keep : NDArray mask matrix ensure_tree ...
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 : np.ndarray prediction of relations of size (seq_len,...
The missing Fortran reshape for mx.NDArray Parameters ---------- tensor : NDArray source tensor shape : NDArray desired shape Returns ------- output : NDArray reordered result def reshape_fortran(tensor, shape): """The missing Fortran reshape for mx.NDArray ...
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. exact: List of tuples (name, value_for_last_step). The progress b...
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 ------- data: NDArray The context ...
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 to evaluate, e.g., val.params or args.save ctx : mx.cpu() or ...
Training loop for awd language model. 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 = [] ...
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.evaluation.WordEmbeddingSimilarityF...
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 name (case-insensitive). Returns ...
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 valid evaluation function names for the specified ...
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 : Symbol or NDArray The indices of the words the we wish to compare to the words in words1...
Compute analogies for given question words. Parameters ---------- words1 : Symbol or NDArray Word indices of first question words. Shape (batch_size, ). words2 : Symbol or NDArray Word indices of second question words. Shape (batch_size, ). words3 : Symbo...
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 computation. Returns ------- loss: float ...
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...
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_...
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 """Ge...
Load parameters from file. filename : str Path to parameter file. ctx : Context or list of Context, default cpu() Context(s) initialize loaded parameters on. def load_parameters(self, filename, ctx=mx.cpu()): # pylint: disable=arguments-differ """Load parameters from fi...
Defines the forward computation for cache cell. Arguments can be either :py:class:`NDArray` or :py:class:`Symbol`. Parameters ---------- inputs: NDArray The input data target: NDArray The label next_word_history: NDArray The next word ...
Assign input `x` to an available worker and invoke `parallizable.forward_backward` with x. 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...
Deserialize BERTVocab object from json string. Parameters ---------- json_str : str Serialized json string of a BERTVocab object. Returns ------- BERTVocab def from_json(cls, json_str): """Deserialize BERTVocab object from json string. Para...
Defines the forward computation. Arguments can be either :py:class:`NDArray` or :py:class:`Symbol`. Parameters ----------- inputs : NDArray input tensor with shape `(sequence_length, batch_size)` when `layout` is "TNC". begin_state : list init...
Defines the forward computation. Parameters ----------- inputs : NDArray input tensor with shape `(sequence_length, batch_size)` when `layout` is "TNC". begin_state : list initial recurrent state tensor with length equals to num_layers*2. ...
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 for center words are computed via F.sparse.dot between the...
Evaluate network on the specified dataset 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(data...
Training process 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 ...
Forward logic 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_leng...
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 ...
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 the 2-norm. .. note:: T...
Training function. 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, 'e...
forward backward implementation 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, ...
Print statistical information via the provided logger Parameters ---------- logger : logging.Logger logger created using logging.getLogger() def log_info(self, logger): """Print statistical information via the provided logger Parameters ---------- l...
Read pre-trained embedding file for extending vocabulary Parameters ---------- pret_embeddings : tuple (embedding_name, source), used for gluonnlp.embedding.create(embedding_name, source) def _add_pret_words(self, pret_embeddings): """Read pre-trained embedding file for ext...
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 def get_pret_embs(self, word_dims=None): """Read pre-trained embedding ...
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 NDArray def get_word_embs(self, word_dims): ...
Randomly initialize embeddings for tag Parameters ---------- tag_dims : int tag vector size Returns ------- numpy.ndarray random embeddings def get_tag_embs(self, tag_dims): """Randomly initialize embeddings for tag Parameters ...
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 def word2id(self, xs): """Map word(s) to its id(s) Parameters ---------- 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 def id2word(self, xs): """Map id(s) to word(s) Parameters ---------- xs : int ...
Map relation(s) to id(s) Parameters ---------- xs : str or list relation Returns ------- int or list id(s) of relation def rel2id(self, xs): """Map relation(s) to id(s) Parameters ---------- xs : str or list ...
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 def id2rel(self, xs): """Map id(s) to relation(s) Parameters ---------- xs :...
Map tag(s) to id(s) Parameters ---------- xs : str or list tag or tags Returns ------- int or list id(s) of tag(s) def tag2id(self, xs): """Map tag(s) to id(s) Parameters ---------- xs : str or list t...
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 def idx_sequence(self): """Indices of sentences when enumerating data set from batches. ...
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 ------- tuple word_inputs, tag_inputs, arc_tar...
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 create_ngram_set(input_list, ngram_value=2): """ Ex...
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_indice, ngram_range=2) [[1, 3, 4, 5, 1337, 2017], [1, 3, 7, 9, 2,...
This function is used for evaluating accuracy of a given data iterator. (Either Train/Test data) It takes in the loss function used too! 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/T...
Helper function to get training data def read_input_data(filename): """Helper function to get training data""" logging.info('Opening file %s for reading input', filename) input_file = open(filename, 'r') data = [] labels = [] for line in input_file: tokens = line.split(',', 1) l...
Parse command line arguments. 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('Comput...
Create the mapping from label to numeric label 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.i...
This function takes a dataset and converts it into sequences via multiprocessing 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.P...
Preprocess and prepare a dataset 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 ...
Construct the DataLoader. Pad data, stack label and lengths 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( ...
Training function that orchestrates the Classification! 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(...
Generate the unnormalized score for the given the input sequences. Parameters ---------- inputs : NDArray, shape (batch_size, seq_length) Input words for the sequences. token_types : NDArray, shape (batch_size, seq_length) Token types for the sequences, used to i...
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 starting position of the answer, label[1] is the ending position of the answer. R...
Encode the input sequence. Parameters ---------- inputs : NDArray states : list of NDArrays or None, default None valid_length : NDArray or None, default None Returns ------- outputs : list Outputs of the encoder. def encode(self, inputs, st...
Decode given the input sequence. Parameters ---------- inputs : NDArray states : list of NDArrays valid_length : NDArray or None, default None Returns ------- output : NDArray The output of the decoder. Shape is (batch_size, length, tgt_word_...
One step decoding of the translation model. Parameters ---------- step_input : NDArray Shape (batch_size,) states : list of NDArrays Returns ------- step_output : NDArray Shape (batch_size, C_out) states : list step_additi...
Generate the prediction given the src_seq and tgt_seq. This is used in training an NMT model. Parameters ---------- src_seq : NDArray tgt_seq : NDArray src_valid_length : NDArray or None tgt_valid_length : NDArray or None Returns ------- ...
Creates an instance of a subword function. def create_subword_function(subword_function_name, kwargs): """Creates an instance of a subword function.""" create_ = registry.get_create_func(SubwordFunction, 'token embedding') return create_(subword_function_name, kwargs)
Indexes unknown and reserved tokens. def _index_special_tokens(self, unknown_token, special_tokens): """Indexes unknown and reserved tokens.""" self._idx_to_token = [unknown_token] if unknown_token else [] if not special_tokens: self._reserved_tokens = None else: ...
Indexes keys of `counter`. Indexes keys of `counter` according to frequency thresholds such as `max_size` and `min_freq`. def _index_counter_keys(self, counter, unknown_token, special_tokens, max_size, min_freq): """Indexes keys of `counter`. Indexes keys...
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 multiple embeddings are provided, their...
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 A token or a list of tokens according t...
Serialize Vocab object to json string. This method does not serialize the underlying embedding. def to_json(self): """Serialize Vocab object to json string. This method does not serialize the underlying embedding. """ if self._embedding: warnings.warn('Serializatio...
Deserialize Vocab object from json string. Parameters ---------- json_str : str Serialized json string of a Vocab object. Returns ------- Vocab def from_json(cls, json_str): """Deserialize Vocab object from json string. Parameters ...
Training function. 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() ...
Training function. 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_...
Evaluate the model on validation 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:{...
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 def _pad_arrs_to_max_length(arrs, pad_axis, pad_val, use_shared_mem, dtype): ...
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...
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 def load(self, path): """Load from disk Parameters ...
Run evaluation on test set Parameters ---------- 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 ...
Parse raw sentence into ConllSentence Parameters ---------- sentence : list a list of (word, tag) tuples Returns ------- ConllSentence ConllSentence object def parse(self, sentence): """Parse raw sentence into ConllSentence Para...
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_regex : str The regex for parameter names used in the self.params.get(), such as 'weight'. rate : float Fract...
create rnn cell given specs Parameters ---------- mode : str The type of RNN cell to use. Options are 'lstmpc', 'rnn_tanh', 'rnn_relu', 'lstm', 'gru'. num_layers : int The number of RNN cells in the encoder. input_size : int The initial input size of in the RNN cell. hid...
create rnn layer given specs 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...
Forward computation. 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, No...
Forward computation. 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_candida...
Forward computation. 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) ...
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...
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 The container thats holds flattened result. Returns ...
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, or same as the layout in state_info when it's not None....
Parameters ---------- 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 Decoder outp...
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 Symbols The initial states of the decoder. Returns ------- ...
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 def data(self, ctx=None): """Returns a copy of this parame...
r"""ELMo 2-layer BiLSTM with 1024 hidden units, 128 projection size, 1 highway layer. Parameters ---------- 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...
Compute context insensitive token embeddings for ELMo representations. Parameters ---------- inputs : NDArray Shape (batch_size, sequence_length, max_character_per_token) of character ids representing the current batch. Returns ------- token_embe...
Parameters ---------- inputs : NDArray Shape (batch_size, sequence_length, max_character_per_token) of character ids representing the current batch. states : (list of list of NDArray, list of list of NDArray) The states. First tuple element is the forward laye...
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...

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

Get home directory for storing datasets/models/pre-trained word embeddings 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(...

Read dataset from tokenized files. 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:...

Build vocab given a dataset. 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

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

If gpu available return gpu, else cpu Returns ------- context : Context The preferable GPU context. def mxnet_prefer_gpu(): """If gpu available return gpu, else cpu Returns ------- context : Context The preferable GPU context. """ gpu = int(os.environ.get('MXNET_GP...

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

Build a standard LSTM cell, with variational dropout, with weights initialized to be orthonormal (https://arxiv.org/abs/1312.6120) Parameters ---------- lstm_layers : int Currently only support one layer input_dims : int word vector dimensions lstm_hiddens : int hidden s...

Feature extraction through BiLSTM Parameters ---------- f_lstm : VariationalDropoutCell Forward cell b_lstm : VariationalDropoutCell Backward cell inputs : NDArray seq_len x batch_size dropout_x : float Variational dropout on inputs dropout_h : Not us...

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 input_size : int input dimension seq_len : int sequence length batch_size : int ...

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 ------- Q : np.ndarray The orthonormal weight matrix of input_size x outpu...

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 sentence length tokens_to_keep : NDArray mask matrix ensure_tree ...

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 : np.ndarray prediction of relations of size (seq_len,...

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

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. exact: List of tuples (name, value_for_last_step). The progress b...

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 ------- data: NDArray The context ...

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 to evaluate, e.g., val.params or args.save ctx : mx.cpu() or ...

Training loop for awd language model. 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 = [] ...

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.evaluation.WordEmbeddingSimilarityF...

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 name (case-insensitive). Returns ...

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 valid evaluation function names for the specified ...

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 : Symbol or NDArray The indices of the words the we wish to compare to the words in words1...

Compute analogies for given question words. Parameters ---------- words1 : Symbol or NDArray Word indices of first question words. Shape (batch_size, ). words2 : Symbol or NDArray Word indices of second question words. Shape (batch_size, ). words3 : Symbo...

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 computation. Returns ------- loss: float ...

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...

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_...

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 """Ge...

Load parameters from file. filename : str Path to parameter file. ctx : Context or list of Context, default cpu() Context(s) initialize loaded parameters on. def load_parameters(self, filename, ctx=mx.cpu()): # pylint: disable=arguments-differ """Load parameters from fi...

Defines the forward computation for cache cell. Arguments can be either :py:class:`NDArray` or :py:class:`Symbol`. Parameters ---------- inputs: NDArray The input data target: NDArray The label next_word_history: NDArray The next word ...

Assign input `x` to an available worker and invoke `parallizable.forward_backward` with x. 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...

Deserialize BERTVocab object from json string. Parameters ---------- json_str : str Serialized json string of a BERTVocab object. Returns ------- BERTVocab def from_json(cls, json_str): """Deserialize BERTVocab object from json string. Para...

Defines the forward computation. Arguments can be either :py:class:`NDArray` or :py:class:`Symbol`. Parameters ----------- inputs : NDArray input tensor with shape `(sequence_length, batch_size)` when `layout` is "TNC". begin_state : list init...

Defines the forward computation. Parameters ----------- inputs : NDArray input tensor with shape `(sequence_length, batch_size)` when `layout` is "TNC". begin_state : list initial recurrent state tensor with length equals to num_layers*2. ...

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 for center words are computed via F.sparse.dot between the...

Evaluate network on the specified dataset 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(data...

Training process 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 ...

Forward logic 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_leng...

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 ...

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 the 2-norm. .. note:: T...

Training function. 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, 'e...

forward backward implementation 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, ...

Print statistical information via the provided logger Parameters ---------- logger : logging.Logger logger created using logging.getLogger() def log_info(self, logger): """Print statistical information via the provided logger Parameters ---------- l...

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

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 def get_pret_embs(self, word_dims=None): """Read pre-trained embedding ...

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 NDArray def get_word_embs(self, word_dims): ...

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

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 def word2id(self, xs): """Map word(s) to its id(s) Parameters ---------- 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 def id2word(self, xs): """Map id(s) to word(s) Parameters ---------- xs : int ...

Map relation(s) to id(s) Parameters ---------- xs : str or list relation Returns ------- int or list id(s) of relation def rel2id(self, xs): """Map relation(s) to id(s) Parameters ---------- xs : str or list ...

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 def id2rel(self, xs): """Map id(s) to relation(s) Parameters ---------- xs :...

Map tag(s) to id(s) Parameters ---------- xs : str or list tag or tags Returns ------- int or list id(s) of tag(s) def tag2id(self, xs): """Map tag(s) to id(s) Parameters ---------- xs : str or list t...

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 def idx_sequence(self): """Indices of sentences when enumerating data set from batches. ...

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 ------- tuple word_inputs, tag_inputs, arc_tar...

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 create_ngram_set(input_list, ngram_value=2): """ Ex...

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_indice, ngram_range=2) [[1, 3, 4, 5, 1337, 2017], [1, 3, 7, 9, 2,...

This function is used for evaluating accuracy of a given data iterator. (Either Train/Test data) It takes in the loss function used too! 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/T...

Helper function to get training data def read_input_data(filename): """Helper function to get training data""" logging.info('Opening file %s for reading input', filename) input_file = open(filename, 'r') data = [] labels = [] for line in input_file: tokens = line.split(',', 1) l...

Parse command line arguments. 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('Comput...

Create the mapping from label to numeric label 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.i...

This function takes a dataset and converts it into sequences via multiprocessing 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.P...

Preprocess and prepare a dataset 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 ...

Construct the DataLoader. Pad data, stack label and lengths 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( ...

Training function that orchestrates the Classification! 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(...

Generate the unnormalized score for the given the input sequences. Parameters ---------- inputs : NDArray, shape (batch_size, seq_length) Input words for the sequences. token_types : NDArray, shape (batch_size, seq_length) Token types for the sequences, used to i...

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 starting position of the answer, label[1] is the ending position of the answer. R...

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

Decode given the input sequence. Parameters ---------- inputs : NDArray states : list of NDArrays valid_length : NDArray or None, default None Returns ------- output : NDArray The output of the decoder. Shape is (batch_size, length, tgt_word_...

One step decoding of the translation model. Parameters ---------- step_input : NDArray Shape (batch_size,) states : list of NDArrays Returns ------- step_output : NDArray Shape (batch_size, C_out) states : list step_additi...

Generate the prediction given the src_seq and tgt_seq. This is used in training an NMT model. Parameters ---------- src_seq : NDArray tgt_seq : NDArray src_valid_length : NDArray or None tgt_valid_length : NDArray or None Returns ------- ...

Creates an instance of a subword function. def create_subword_function(subword_function_name, **kwargs): """Creates an instance of a subword function.""" create_ = registry.get_create_func(SubwordFunction, 'token embedding') return create_(subword_function_name, **kwargs)

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

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

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 multiple embeddings are provided, their...

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 A token or a list of tokens according t...

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

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

Training function. 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() ...

Training function. 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_...

Evaluate the model on validation 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:{...

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 def _pad_arrs_to_max_length(arrs, pad_axis, pad_val, use_shared_mem, dtype): ...

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...

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 def load(self, path): """Load from disk Parameters ...

Run evaluation on test set Parameters ---------- 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 ...

Parse raw sentence into ConllSentence Parameters ---------- sentence : list a list of (word, tag) tuples Returns ------- ConllSentence ConllSentence object def parse(self, sentence): """Parse raw sentence into ConllSentence Para...

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_regex : str The regex for parameter names used in the self.params.get(), such as 'weight'. rate : float Fract...

create rnn cell given specs Parameters ---------- mode : str The type of RNN cell to use. Options are 'lstmpc', 'rnn_tanh', 'rnn_relu', 'lstm', 'gru'. num_layers : int The number of RNN cells in the encoder. input_size : int The initial input size of in the RNN cell. hid...

create rnn layer given specs 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...

Forward computation. 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, No...

Forward computation. 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_candida...

Forward computation. 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) ...

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...

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 The container thats holds flattened result. Returns ...

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, or same as the layout in state_info when it's not None....

Parameters ---------- 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 Decoder outp...

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 Symbols The initial states of the decoder. Returns ------- ...

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 def data(self, ctx=None): """Returns a copy of this parame...

r"""ELMo 2-layer BiLSTM with 1024 hidden units, 128 projection size, 1 highway layer. Parameters ---------- 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...

Compute context insensitive token embeddings for ELMo representations. Parameters ---------- inputs : NDArray Shape (batch_size, sequence_length, max_character_per_token) of character ids representing the current batch. Returns ------- token_embe...

Parameters ---------- inputs : NDArray Shape (batch_size, sequence_length, max_character_per_token) of character ids representing the current batch. states : (list of list of NDArray, list of list of NDArray) The states. First tuple element is the forward laye...

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...