JorgeVB/code_unlearning_dataset · Datasets at Hugging Face

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client wrapper for kernels_pull def kernels_pull_cli(self, kernel, kernel_opt=None, path=None, metadata=False): """ client wrapper for kernels_pull """ kernel = kernel or kernel_opt effec...
retrieve output for a specified kernel Parameters ========== kernel: the kernel to output path: the path to pull files to on the filesystem force: if output already exists, force overwrite (default False) quiet: suppress verbosity (default is True...
client wrapper for kernels_output, with same arguments. Extra arguments are described below, and see kernels_output for others. Parameters ========== kernel_opt: option from client instead of kernel, if not defined def kernels_output_cli(self, ...
call to the api to get the status of a kernel. Parameters ========== kernel: the kernel to get the status for def kernels_status(self, kernel): """ call to the api to get the status of a kernel. Parameters ========== kernel: the kernel t...
client wrapper for kernel_status Parameters ========== kernel_opt: additional option from the client, if kernel not defined def kernels_status_cli(self, kernel, kernel_opt=None): """ client wrapper for kernel_status Parameters ========== ...
determine if a download is needed based on timestamp. Return True if needed (remote is newer) or False if local is newest. Parameters ========== response: the response from the API outfile: the output file to write to quiet: suppress verbose outpu...
print a table of items, for a set of fields defined Parameters ========== items: a list of items to print fields: a list of fields to select from items def print_table(self, items, fields): """ print a table of items, for a set of fields defined Par...
print a set of fields in a set of items using a csv.writer Parameters ========== items: a list of items to print fields: a list of fields to select from items def print_csv(self, items, fields): """ print a set of fields in a set of items using a csv.writer ...
process a response from the API. We check the API version against the client's to see if it's old, and give them a warning (once) Parameters ========== result: the result from the API def process_response(self, result): """ process a response from the API. We ch...
determine if a client (on the local user's machine) is up to date with the version provided on the server. Return a boolean with True or False Parameters ========== server_version: the server version string to compare to the host def is_up_to_date(self, serv...
upload files in a folder Parameters ========== request: the prepared request resources: the files to upload folder: the folder to upload from quiet: suppress verbose output (default is False) def upload_files(self, request, ...
Helper function to upload a single file Parameters ========== file_name: name of the file to upload full_path: path to the file to upload request: the prepared request resources: optional file metadata quiet: suppress verbose output ...
process a column, check for the type, and return the processed column Parameters ========== column: a list of values in a column to be processed def process_column(self, column): """ process a column, check for the type, and return the processed colu...
function to complete an upload to retrieve a path from a url Parameters ========== path: the path for the upload that is read in url: the url to send the POST to quiet: suppress verbose output (default is False) def upload_complete(self, path, url, quiet): ...
determine if a dataset string is valid, meaning it is in the format of {username}/{dataset-slug}. Parameters ========== dataset: the dataset name to validate def validate_dataset_string(self, dataset): """ determine if a dataset string is valid, meaning it is in...
determine if a kernel string is valid, meaning it is in the format of {username}/{kernel-slug}. Parameters ========== kernel: the kernel name to validate def validate_kernel_string(self, kernel): """ determine if a kernel string is valid, meaning it is in the fo...
validate resources is a wrapper to validate the existence of files and that there are no duplicates for a folder and set of resources. Parameters ========== folder: the folder to validate resources: one or more resources to validate within the folder def val...
ensure that one or more resource files exist in a folder Parameters ========== folder: the folder to validate resources: one or more resources to validate within the folder def validate_files_exist(self, folder, resources): """ ensure that one or more resource f...
ensure that the user has not provided duplicate paths in a list of resources. Parameters ========== resources: one or more resources to validate not duplicated def validate_no_duplicate_paths(self, resources): """ ensure that the user has not provided duplicate ...
convert a set of file_data to a metadata file at path Parameters ========== file_data: a dictionary of file data to write to file path: the path to write the metadata to def convert_to_dataset_file_metadata(self, file_data, path): """ convert a set of file_data ...
read the buffer, passing named and non named arguments to the io.BufferedReader function. def read(self, args, kwargs): """ read the buffer, passing named and non named arguments to the io.BufferedReader function. """ buf = io.BufferedReader.read(self, args, **kwargs...
Get parameters as list of tuples, formatting collections. :param params: Parameters as dict or list of two-tuples :param dict collection_formats: Parameter collection formats :return: Parameters as list of tuples, collections formatted def parameters_to_tuples(self, params, collection_formats)...
Builds form parameters. :param post_params: Normal form parameters. :param files: File parameters. :return: Form parameters with files. def prepare_post_parameters(self, post_params=None, files=None): """Builds form parameters. :param post_params: Normal form parameters. ...
Deserializes body to file Saves response body into a file in a temporary folder, using the filename from the `Content-Disposition` header if provided. :param response: RESTResponse. :return: file path. def __deserialize_file(self, response): """Deserializes body to file ...
Deserializes string to primitive type. :param data: str. :param klass: class literal. :return: int, long, float, str, bool. def __deserialize_primitive(self, data, klass): """Deserializes string to primitive type. :param data: str. :param klass: class literal. ...
The logger file. If the logger_file is None, then add stream handler and remove file handler. Otherwise, add file handler and remove stream handler. :param value: The logger_file path. :type: str def logger_file(self, value): """The logger file. If the logger_file is ...
Sets the license_name of this DatasetNewRequest. The license that should be associated with the dataset # noqa: E501 :param license_name: The license_name of this DatasetNewRequest. # noqa: E501 :type: str def license_name(self, license_name): """Sets the license_name of this Datase...
Train textCNN model for sentiment analysis. def train(net, train_data, test_data): """Train textCNN model for sentiment analysis.""" start_pipeline_time = time.time() net, trainer = text_cnn.init(net, vocab, args.model_mode, context, args.lr) random.shuffle(train_data) sp = int(len(train_data)*0.9)...
Get tokens, tokens embedding Parameters ---------- sentences : List[str] sentences for encoding. oov_way : str, default avg. use avg, sum or last to get token embedding for those out of vocabulary words Returns ------- ...
Load, tokenize and prepare the input sentences. def data_loader(self, sentences, shuffle=False): """Load, tokenize and prepare the input sentences.""" dataset = BertEmbeddingDataset(sentences, self.transform) return DataLoader(dataset=dataset, batch_size=self.batch_size, shuffle=shuffle)
How to handle oov. Also filter out [CLS], [SEP] tokens. Parameters ---------- batches : List[(tokens_id, sequence_outputs, pooled_output]. batch token_ids (max_seq_length, ), sequence_outputs (max_seq_length, dim,...
Any BERT pretrained model. Parameters ---------- model_name : str or None, default None Options include 'bert_24_1024_16' and 'bert_12_768_12'. dataset_name : str or None, default None Options include 'book_corpus_wiki_en_cased', 'book_corpus_wiki_en_uncased' for both bert_24_10...
forward computation. def hybrid_forward(self, F, data, gamma, beta): """forward computation.""" # TODO(haibin): LayerNorm does not support fp16 safe reduction. Issue is tracked at: # https://github.com/apache/incubator-mxnet/issues/14073 if self._dtype: data = data.astype('f...
Construct a decoder for the next sentence prediction task def _get_classifier(self, prefix): """ Construct a decoder for the next sentence prediction task """ with self.name_scope(): classifier = nn.Dense(2, prefix=prefix) return classifier
Construct a decoder for the masked language model task def _get_decoder(self, units, vocab_size, embed, prefix): """ Construct a decoder for the masked language model task """ with self.name_scope(): decoder = nn.HybridSequential(prefix=prefix) decoder.add(nn.Dense(units, flatte...
Construct an embedding block. def _get_embed(self, embed, vocab_size, embed_size, initializer, dropout, prefix): """ Construct an embedding block. """ if embed is None: assert embed_size is not None, '"embed_size" cannot be None if "word_embed" or ' \ ...
Construct pooler. The pooler slices and projects the hidden output of first token in the sequence for segment level classification. def _get_pooler(self, units, prefix): """ Construct pooler. The pooler slices and projects the hidden output of first token in the sequence for s...
Generate the representation given the input sequences. This is used for pre-training or fine-tuning a BERT model. def _encode_sequence(self, inputs, token_types, valid_length=None): """Generate the representation given the input sequences. This is used for pre-training or fine-tuning a BERT m...
Generate unnormalized prediction for the masked language model task. This is only used for pre-training the BERT model. Inputs: - sequence: input tensor of sequence encodings. Shape (batch_size, seq_length, units). - masked_positions: input tensor of posit...
Extracts n-grams from an input segment. Parameters ---------- segment: list Text segment from which n-grams will be extracted. n: int Order of n-gram. Returns ------- ngram_counts: Counter Contain all the nth n-grams in segment with a count of how many times each n-...
Convert a sequence of bpe words into sentence. def _bpe_to_words(sentence, delimiter='@@'): """Convert a sequence of bpe words into sentence.""" words = [] word = '' delimiter_len = len(delimiter) for subwords in sentence: if len(subwords) >= delimiter_len and subwords[-delimiter_len:] == d...
r""" Tokenizes a string following the tokenizer in mteval-v13a.pl. See https://github.com/moses-smt/mosesdecoder/" "blob/master/scripts/generic/mteval-v14.pl#L917-L942 Parameters ---------- segment: str A string to be tokenized Returns ------- The tokenized string de...
r"""Tokenize a string following following the international tokenizer in mteval-v14a.pl. See https://github.com/moses-smt/mosesdecoder/" "blob/master/scripts/generic/mteval-v14.pl#L954-L983 Parameters ---------- segment: str A string to be tokenized Returns ------- The t...
r"""Compute bleu score of translation against references. Parameters ---------- reference_corpus_list: list of list(list(str)) or list of list(str) list of list(list(str)): tokenized references list of list(str): plain text List of references for each translation. translation_co...
Compute ngram precision. Parameters ---------- references: list(list(str)) A list of references. translation: list(str) A translation. n: int Order of n-gram. Returns ------- matches: int Number of matched nth order n-grams candidates Number ...
Calculate brevity penalty. Parameters ---------- ref_length: int Sum of all closest references'lengths for every translations in a corpus trans_length: int Sum of all translations's lengths in a corpus. Returns ------- bleu's brevity penalty: float def _brevity_penalty(ref...
Find the reference that has the closest length to the translation. Parameters ---------- references: list(list(str)) A list of references. trans_length: int Length of the translation. Returns ------- closest_ref_len: int Length of the reference that is closest to th...
Compute the smoothed precision for all the orders. Parameters ---------- precision_fractions: list(tuple) Contain a list of (precision_numerator, precision_denominator) pairs c: int, default 1 Smoothing constant to use Returns ------- ratios: list of floats Contain ...
Draw samples from log uniform distribution and returns sampled candidates, expected count for true classes and sampled classes. Parameters ---------- true_classes: NDArray The true classes. Returns ------- samples: NDArray The sampled can...
Dataset preprocessing helper. Parameters ---------- data : mx.data.Dataset Input Dataset. For example gluonnlp.data.Text8 or gluonnlp.data.Fil9 min_freq : int, default 5 Minimum token frequency for a token to be included in the vocabulary and returned DataStream. max_vocab_s...
Wikipedia dump helper. Parameters ---------- wiki_root : str Parameter for WikiDumpStream wiki_date : str Parameter for WikiDumpStream wiki_language : str Parameter for WikiDumpStream max_vocab_size : int, optional Specifies a maximum size for the vocabulary. ...
Transform a DataStream of coded DataSets to a DataStream of batches. Parameters ---------- data : gluonnlp.data.DataStream DataStream where each sample is a valid input to gluonnlp.data.EmbeddingCenterContextBatchify. vocab : gluonnlp.Vocab Vocabulary containing all tokens whose...
Transform a DataStream of coded DataSets to a DataStream of batches. Parameters ---------- data : gluonnlp.data.DataStream DataStream where each sample is a valid input to gluonnlp.data.EmbeddingCenterContextBatchify. vocab : gluonnlp.Vocab Vocabulary containing all tokens whose...
Create a batch for CBOW training objective with subwords. def cbow_fasttext_batch(centers, contexts, num_tokens, subword_lookup, dtype, index_dtype): """Create a batch for CBOW training objective with subwords.""" _, contexts_row, contexts_col = contexts data, row, col = subword_loo...
Create a batch for SG training objective with subwords. def skipgram_fasttext_batch(centers, contexts, num_tokens, subword_lookup, dtype, index_dtype): """Create a batch for SG training objective with subwords.""" contexts = mx.nd.array(contexts[2], dtype=index_dtype) data, row,...
Create a batch for CBOW training objective. def cbow_batch(centers, contexts, num_tokens, dtype, index_dtype): """Create a batch for CBOW training objective.""" contexts_data, contexts_row, contexts_col = contexts centers = mx.nd.array(centers, dtype=index_dtype) contexts = mx.nd.sparse.csr_matrix( ...
Create a batch for SG training objective. def skipgram_batch(centers, contexts, num_tokens, dtype, index_dtype): """Create a batch for SG training objective.""" contexts = mx.nd.array(contexts[2], dtype=index_dtype) indptr = mx.nd.arange(len(centers) + 1) centers = mx.nd.array(centers, dtype=index_dtyp...
Get a sparse COO array of words and subwords for SkipGram. Parameters ---------- indices : numpy.ndarray Array containing numbers in [0, vocabulary_size). The element at position idx is taken to be the word that occurs at row idx in the SkipGram batch. offset : int Offse...
Get a sparse COO array of words and subwords for CBOW. Parameters ---------- context_row : numpy.ndarray of dtype int64 Array of same length as context_col containing numbers in [0, batch_size). For each idx, context_row[idx] specifies the row that context_col[idx] occurs in a spars...
Source Vocabulary of the Dataset. Returns ------- src_vocab : Vocab Source vocabulary. def src_vocab(self): """Source Vocabulary of the Dataset. Returns ------- src_vocab : Vocab Source vocabulary. """ if self._src_vocab ...
Target Vocabulary of the Dataset. Returns ------- tgt_vocab : Vocab Target vocabulary. def tgt_vocab(self): """Target Vocabulary of the Dataset. Returns ------- tgt_vocab : Vocab Target vocabulary. """ if self._tgt_vocab ...
Evaluate given the data loader Parameters ---------- data_loader : DataLoader Returns ------- avg_loss : float Average loss real_translation_out : list of list of str The translation output def evaluate(data_loader): """Evaluate given the data loader Parameters ...
Training function. def train(): """Training function.""" trainer = gluon.Trainer(model.collect_params(), args.optimizer, {'learning_rate': args.lr}) train_data_loader, val_data_loader, test_data_loader \ = dataprocessor.make_dataloader(data_train, data_val, data_test, args) best_valid_bleu = ...
r"""Returns a cache model using a pre-trained language model. We implement the neural cache language model proposed in the following work:: @article{grave2016improving, title={Improving neural language models with a continuous cache}, author={Grave, Edouard and Joulin, Armand and Usunier, ...
Training helper. def train(args): """Training helper.""" if not args.model.lower() in ['cbow', 'skipgram']: logging.error('Unsupported model %s.', args.model) sys.exit(1) if args.data.lower() == 'toy': data = mx.gluon.data.SimpleDataset(nlp.data.Text8(segment='train')[:2]) ...
Evaluation helper def evaluate(args, embedding, vocab, global_step, eval_analogy=False): """Evaluation helper""" if 'eval_tokens' not in globals(): global eval_tokens eval_tokens_set = evaluation.get_tokens_in_evaluation_datasets(args) if not args.no_eval_analogy: eval_toke...
Return the dataset corresponds to the provided key. Example:: a = np.ones((2,2)) b = np.zeros((2,2)) np.savez('data.npz', a=a, b=b) dataset = NumpyDataset('data.npz') data_a = dataset.get_field('a') data_b = dataset.get_field('b') ...
Project the tokenized prediction back to the original text. def get_final_text(pred_text, orig_text, tokenizer): """Project the tokenized prediction back to the original text.""" # When we created the data, we kept track of the alignment between original # (whitespace tokenized) tokens and our WordPiece t...
Get prediction results Parameters ---------- dev_dataset: dataset Examples of transform. all_results: dict A dictionary containing model prediction results. tokenizer: callable Tokenizer function. max_answer_length: int, default 64 Maximum length of the answer to...
Calculate the F1 and EM scores of the predicted results. Use only with the SQuAD1.1 dataset. Parameters ---------- dataset_file: string Path to the data file. predict_data: dict All final predictions. Returns ------- scores: dict F1 and EM scores. def get_F1_EM...
Data preparation function. def preprocess_data(tokenizer, task, batch_size, dev_batch_size, max_len, pad=False): """Data preparation function.""" # transformation trans = BERTDatasetTransform( tokenizer, max_len, labels=task.get_labels(), pad=pad, pair=task.is_pair, ...
Evaluate the model on validation dataset. def evaluate(dataloader_eval, metric): """Evaluate the model on validation dataset. """ metric.reset() for _, seqs in enumerate(dataloader_eval): input_ids, valid_len, type_ids, label = seqs out = model( input_ids.as_in_context(ctx),...
Generate and print out the log message for training. def log_train(batch_id, batch_num, metric, step_loss, log_interval, epoch_id, learning_rate): """Generate and print out the log message for training. """ metric_nm, metric_val = metric.get() if not isinstance(metric_nm, list): metric_nm = [me...
Generate and print out the log message for inference. def log_inference(batch_id, batch_num, metric, step_loss, log_interval): """Generate and print out the log message for inference. """ metric_nm, metric_val = metric.get() if not isinstance(metric_nm, list): metric_nm = [metric_nm] me...
Training function. def train(metric): """Training function.""" logging.info('Now we are doing BERT classification training on %s!', ctx) optimizer_params = {'learning_rate': lr, 'epsilon': epsilon, 'wd': 0.01} try: trainer = gluon.Trainer( model.collect_params(), args.o...
Inference function. def inference(metric): """Inference function.""" logging.info('Now we are doing BERT classification inference on %s!', ctx) model = BERTClassifier(bert, dropout=0.1, num_classes=len(task.get_labels())) model.hybridize(static_alloc=True) model.load_parameters(model_parameters, c...
Process SQuAD dataset by creating NDArray version of data :param Dataset dataset: SQuAD dataset :param int question_max_length: Maximum length of question (padded or trimmed to that size) :param int context_max_length: Maximum length of context (padded or trimmed to that size) Returns ------- ...
Find all answer spans from the context, returning start_index and end_index :param list[str] answer_list: List of all answers :param list[int] answer_start_list: List of all answers' start indices Returns ------- List[Tuple] list of Tuple(answer_start_index answer_e...
Provides word level vocabulary Returns ------- Vocab Word level vocabulary def get_word_level_vocab(self): """Provides word level vocabulary Returns ------- Vocab Word level vocabulary """ def simple_tokenize(source_str,...
Parameters ---------- states : list the stack outputs from RNN, which consists of output from each time step (TNC). Returns -------- loss : NDArray loss tensor with shape (batch_size,). Dimensions other than batch_axis are averaged out. def hybrid_forwar...
Tokenizes a piece of text. def _tokenize(self, text): """Tokenizes a piece of text.""" text = self._clean_text(text) # This was added on November 1st, 2018 for the multilingual and Chinese # models. This is also applied to the English models now, but it doesn't # matter since t...
Performs invalid character removal and whitespace cleanup on text. def _clean_text(self, text): """Performs invalid character removal and whitespace cleanup on text.""" output = [] for char in text: cp = ord(char) if cp in (0, 0xfffd) or self._is_control(char): ...
Checks whether `chars` is a control character. def _is_control(self, char): """Checks whether `chars` is a control character.""" # These are technically control characters but we count them as whitespace # characters. if char in ['\t', '\n', '\r']: return False cat =...
Splits punctuation on a piece of text. def _run_split_on_punc(self, text): """Splits punctuation on a piece of text.""" chars = list(text) i = 0 start_new_word = True output = [] while i < len(chars): char = chars[i] if self._is_punctuation(char):...
Checks whether `chars` is a punctuation character. def _is_punctuation(self, char): """Checks whether `chars` is a punctuation character.""" cp = ord(char) # We treat all non-letter/number ASCII as punctuation. # Characters such as "^", "$", and "`" are not in the Unicode # Punc...
Checks whether `chars` is a whitespace character. def _is_whitespace(self, char): """Checks whether `chars` is a whitespace character.""" # \t, \n, and \r are technically contorl characters but we treat them # as whitespace since they are generally considered as such. if char in [' ', '...
Runs basic whitespace cleaning and splitting on a piece of text. def _whitespace_tokenize(self, text): """Runs basic whitespace cleaning and splitting on a piece of text.""" text = text.strip() tokens = text.split() return tokens
Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform tokenization using the given vocabulary. For example: input = "unaffable" output = ["un", "##aff", "##able"] Args: text: A single token or whitespa...
Truncates a sequence pair in place to the maximum length. def _truncate_seq_pair(self, tokens_a, tokens_b, max_length): """Truncates a sequence pair in place to the maximum length.""" # This is a simple heuristic which will always truncate the longer sequence # one token at a time. This makes m...
Construct the argument parser. def get_args(): """Construct the argument parser.""" parser = argparse.ArgumentParser( description='Word embedding evaluation with Gluon.', formatter_class=argparse.ArgumentDefaultsHelpFormatter) # Embeddings arguments group = parser.add_argument_group('E...
Validate provided arguments and act on --help. def validate_args(args): """Validate provided arguments and act on --help.""" if args.list_embedding_sources: print('Listing all sources for {} embeddings.'.format( args.embedding_name)) print('Specify --embedding-name if you wish to ' ...
Load a TokenEmbedding. def load_embedding_from_path(args): """Load a TokenEmbedding.""" if args.embedding_path.endswith('.bin'): with utils.print_time('load fastText model.'): model = \ nlp.model.train.FasttextEmbeddingModel.load_fasttext_format( args.emb...
Calculate the 2-norm of gradients of parameters, and how much they should be scaled down such that their 2-norm does not exceed `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 c...
backward propagation with loss def backward(self, loss): """backward propagation with loss""" with mx.autograd.record(): if isinstance(loss, (tuple, list)): ls = [l * self._scaler.loss_scale for l in loss] else: ls = loss * self._scaler.loss_scale...
Makes one step of parameter update. Should be called after `fp16_optimizer.backward()`, and outside of `record()` scope. Parameters ---------- batch_size : int Batch size of data processed. Gradient will be normalized by `1/batch_size`. Set this to 1 if you norma...
detect inf and nan def has_overflow(self, params): """ detect inf and nan """ is_not_finite = 0 for param in params: if param.grad_req != 'null': grad = param.list_grad()[0] is_not_finite += mx.nd.contrib.isnan(grad).sum() is_not_finit...
dynamically update loss scale def update_scale(self, overflow): """dynamically update loss scale""" iter_since_rescale = self._num_steps - self._last_rescale_iter if overflow: self._last_overflow_iter = self._num_steps self._overflows_since_rescale += 1 perce...
Return a string representing the statistics of the bucketing sampler. Returns ------- ret : str String representing the statistics of the buckets. def stats(self): """Return a string representing the statistics of the bucketing sampler. Returns ------- ...
Training loop for language model. def train(): """Training loop for language model. """ print(model) from_epoch = 0 model.initialize(mx.init.Xavier(factor_type='out'), ctx=context) trainer_params = {'learning_rate': args.lr, 'wd': 0, 'eps': args.eps} trainer = gluon.Trainer(model.collect_pa...
Evaluate loop for the trained model def evaluate(): """ Evaluate loop for the trained model """ print(eval_model) eval_model.initialize(mx.init.Xavier(), ctx=context[0]) eval_model.hybridize(static_alloc=True, static_shape=True) epoch = args.from_epoch if args.from_epoch else 0 while epoch < ar...

client wrapper for kernels_pull def kernels_pull_cli(self, kernel, kernel_opt=None, path=None, metadata=False): """ client wrapper for kernels_pull """ kernel = kernel or kernel_opt effec...

retrieve output for a specified kernel Parameters ========== kernel: the kernel to output path: the path to pull files to on the filesystem force: if output already exists, force overwrite (default False) quiet: suppress verbosity (default is True...

client wrapper for kernels_output, with same arguments. Extra arguments are described below, and see kernels_output for others. Parameters ========== kernel_opt: option from client instead of kernel, if not defined def kernels_output_cli(self, ...

call to the api to get the status of a kernel. Parameters ========== kernel: the kernel to get the status for def kernels_status(self, kernel): """ call to the api to get the status of a kernel. Parameters ========== kernel: the kernel t...

client wrapper for kernel_status Parameters ========== kernel_opt: additional option from the client, if kernel not defined def kernels_status_cli(self, kernel, kernel_opt=None): """ client wrapper for kernel_status Parameters ========== ...

determine if a download is needed based on timestamp. Return True if needed (remote is newer) or False if local is newest. Parameters ========== response: the response from the API outfile: the output file to write to quiet: suppress verbose outpu...

print a table of items, for a set of fields defined Parameters ========== items: a list of items to print fields: a list of fields to select from items def print_table(self, items, fields): """ print a table of items, for a set of fields defined Par...

print a set of fields in a set of items using a csv.writer Parameters ========== items: a list of items to print fields: a list of fields to select from items def print_csv(self, items, fields): """ print a set of fields in a set of items using a csv.writer ...

process a response from the API. We check the API version against the client's to see if it's old, and give them a warning (once) Parameters ========== result: the result from the API def process_response(self, result): """ process a response from the API. We ch...

determine if a client (on the local user's machine) is up to date with the version provided on the server. Return a boolean with True or False Parameters ========== server_version: the server version string to compare to the host def is_up_to_date(self, serv...

upload files in a folder Parameters ========== request: the prepared request resources: the files to upload folder: the folder to upload from quiet: suppress verbose output (default is False) def upload_files(self, request, ...

Helper function to upload a single file Parameters ========== file_name: name of the file to upload full_path: path to the file to upload request: the prepared request resources: optional file metadata quiet: suppress verbose output ...

process a column, check for the type, and return the processed column Parameters ========== column: a list of values in a column to be processed def process_column(self, column): """ process a column, check for the type, and return the processed colu...

function to complete an upload to retrieve a path from a url Parameters ========== path: the path for the upload that is read in url: the url to send the POST to quiet: suppress verbose output (default is False) def upload_complete(self, path, url, quiet): ...

determine if a dataset string is valid, meaning it is in the format of {username}/{dataset-slug}. Parameters ========== dataset: the dataset name to validate def validate_dataset_string(self, dataset): """ determine if a dataset string is valid, meaning it is in...

determine if a kernel string is valid, meaning it is in the format of {username}/{kernel-slug}. Parameters ========== kernel: the kernel name to validate def validate_kernel_string(self, kernel): """ determine if a kernel string is valid, meaning it is in the fo...

validate resources is a wrapper to validate the existence of files and that there are no duplicates for a folder and set of resources. Parameters ========== folder: the folder to validate resources: one or more resources to validate within the folder def val...

ensure that one or more resource files exist in a folder Parameters ========== folder: the folder to validate resources: one or more resources to validate within the folder def validate_files_exist(self, folder, resources): """ ensure that one or more resource f...

ensure that the user has not provided duplicate paths in a list of resources. Parameters ========== resources: one or more resources to validate not duplicated def validate_no_duplicate_paths(self, resources): """ ensure that the user has not provided duplicate ...

convert a set of file_data to a metadata file at path Parameters ========== file_data: a dictionary of file data to write to file path: the path to write the metadata to def convert_to_dataset_file_metadata(self, file_data, path): """ convert a set of file_data ...

read the buffer, passing named and non named arguments to the io.BufferedReader function. def read(self, *args, **kwargs): """ read the buffer, passing named and non named arguments to the io.BufferedReader function. """ buf = io.BufferedReader.read(self, *args, **kwargs...

Get parameters as list of tuples, formatting collections. :param params: Parameters as dict or list of two-tuples :param dict collection_formats: Parameter collection formats :return: Parameters as list of tuples, collections formatted def parameters_to_tuples(self, params, collection_formats)...

Builds form parameters. :param post_params: Normal form parameters. :param files: File parameters. :return: Form parameters with files. def prepare_post_parameters(self, post_params=None, files=None): """Builds form parameters. :param post_params: Normal form parameters. ...

Deserializes body to file Saves response body into a file in a temporary folder, using the filename from the `Content-Disposition` header if provided. :param response: RESTResponse. :return: file path. def __deserialize_file(self, response): """Deserializes body to file ...

Deserializes string to primitive type. :param data: str. :param klass: class literal. :return: int, long, float, str, bool. def __deserialize_primitive(self, data, klass): """Deserializes string to primitive type. :param data: str. :param klass: class literal. ...

The logger file. If the logger_file is None, then add stream handler and remove file handler. Otherwise, add file handler and remove stream handler. :param value: The logger_file path. :type: str def logger_file(self, value): """The logger file. If the logger_file is ...

Sets the license_name of this DatasetNewRequest. The license that should be associated with the dataset # noqa: E501 :param license_name: The license_name of this DatasetNewRequest. # noqa: E501 :type: str def license_name(self, license_name): """Sets the license_name of this Datase...

Train textCNN model for sentiment analysis. def train(net, train_data, test_data): """Train textCNN model for sentiment analysis.""" start_pipeline_time = time.time() net, trainer = text_cnn.init(net, vocab, args.model_mode, context, args.lr) random.shuffle(train_data) sp = int(len(train_data)*0.9)...

Get tokens, tokens embedding Parameters ---------- sentences : List[str] sentences for encoding. oov_way : str, default avg. use **avg**, **sum** or **last** to get token embedding for those out of vocabulary words Returns ------- ...

Load, tokenize and prepare the input sentences. def data_loader(self, sentences, shuffle=False): """Load, tokenize and prepare the input sentences.""" dataset = BertEmbeddingDataset(sentences, self.transform) return DataLoader(dataset=dataset, batch_size=self.batch_size, shuffle=shuffle)

How to handle oov. Also filter out [CLS], [SEP] tokens. Parameters ---------- batches : List[(tokens_id, sequence_outputs, pooled_output]. batch token_ids (max_seq_length, ), sequence_outputs (max_seq_length, dim,...

Any BERT pretrained model. Parameters ---------- model_name : str or None, default None Options include 'bert_24_1024_16' and 'bert_12_768_12'. dataset_name : str or None, default None Options include 'book_corpus_wiki_en_cased', 'book_corpus_wiki_en_uncased' for both bert_24_10...

forward computation. def hybrid_forward(self, F, data, gamma, beta): """forward computation.""" # TODO(haibin): LayerNorm does not support fp16 safe reduction. Issue is tracked at: # https://github.com/apache/incubator-mxnet/issues/14073 if self._dtype: data = data.astype('f...

Construct a decoder for the next sentence prediction task def _get_classifier(self, prefix): """ Construct a decoder for the next sentence prediction task """ with self.name_scope(): classifier = nn.Dense(2, prefix=prefix) return classifier

Construct a decoder for the masked language model task def _get_decoder(self, units, vocab_size, embed, prefix): """ Construct a decoder for the masked language model task """ with self.name_scope(): decoder = nn.HybridSequential(prefix=prefix) decoder.add(nn.Dense(units, flatte...

Construct an embedding block. def _get_embed(self, embed, vocab_size, embed_size, initializer, dropout, prefix): """ Construct an embedding block. """ if embed is None: assert embed_size is not None, '"embed_size" cannot be None if "word_embed" or ' \ ...

Construct pooler. The pooler slices and projects the hidden output of first token in the sequence for segment level classification. def _get_pooler(self, units, prefix): """ Construct pooler. The pooler slices and projects the hidden output of first token in the sequence for s...

Generate the representation given the input sequences. This is used for pre-training or fine-tuning a BERT model. def _encode_sequence(self, inputs, token_types, valid_length=None): """Generate the representation given the input sequences. This is used for pre-training or fine-tuning a BERT m...

Generate unnormalized prediction for the masked language model task. This is only used for pre-training the BERT model. Inputs: - **sequence**: input tensor of sequence encodings. Shape (batch_size, seq_length, units). - **masked_positions**: input tensor of posit...

Extracts n-grams from an input segment. Parameters ---------- segment: list Text segment from which n-grams will be extracted. n: int Order of n-gram. Returns ------- ngram_counts: Counter Contain all the nth n-grams in segment with a count of how many times each n-...

Convert a sequence of bpe words into sentence. def _bpe_to_words(sentence, delimiter='@@'): """Convert a sequence of bpe words into sentence.""" words = [] word = '' delimiter_len = len(delimiter) for subwords in sentence: if len(subwords) >= delimiter_len and subwords[-delimiter_len:] == d...

r""" Tokenizes a string following the tokenizer in mteval-v13a.pl. See https://github.com/moses-smt/mosesdecoder/" "blob/master/scripts/generic/mteval-v14.pl#L917-L942 Parameters ---------- segment: str A string to be tokenized Returns ------- The tokenized string de...

r"""Tokenize a string following following the international tokenizer in mteval-v14a.pl. See https://github.com/moses-smt/mosesdecoder/" "blob/master/scripts/generic/mteval-v14.pl#L954-L983 Parameters ---------- segment: str A string to be tokenized Returns ------- The t...

r"""Compute bleu score of translation against references. Parameters ---------- reference_corpus_list: list of list(list(str)) or list of list(str) list of list(list(str)): tokenized references list of list(str): plain text List of references for each translation. translation_co...

Compute ngram precision. Parameters ---------- references: list(list(str)) A list of references. translation: list(str) A translation. n: int Order of n-gram. Returns ------- matches: int Number of matched nth order n-grams candidates Number ...

Calculate brevity penalty. Parameters ---------- ref_length: int Sum of all closest references'lengths for every translations in a corpus trans_length: int Sum of all translations's lengths in a corpus. Returns ------- bleu's brevity penalty: float def _brevity_penalty(ref...

Find the reference that has the closest length to the translation. Parameters ---------- references: list(list(str)) A list of references. trans_length: int Length of the translation. Returns ------- closest_ref_len: int Length of the reference that is closest to th...

Compute the smoothed precision for all the orders. Parameters ---------- precision_fractions: list(tuple) Contain a list of (precision_numerator, precision_denominator) pairs c: int, default 1 Smoothing constant to use Returns ------- ratios: list of floats Contain ...

Draw samples from log uniform distribution and returns sampled candidates, expected count for true classes and sampled classes. Parameters ---------- true_classes: NDArray The true classes. Returns ------- samples: NDArray The sampled can...

Dataset preprocessing helper. Parameters ---------- data : mx.data.Dataset Input Dataset. For example gluonnlp.data.Text8 or gluonnlp.data.Fil9 min_freq : int, default 5 Minimum token frequency for a token to be included in the vocabulary and returned DataStream. max_vocab_s...

Wikipedia dump helper. Parameters ---------- wiki_root : str Parameter for WikiDumpStream wiki_date : str Parameter for WikiDumpStream wiki_language : str Parameter for WikiDumpStream max_vocab_size : int, optional Specifies a maximum size for the vocabulary. ...

Transform a DataStream of coded DataSets to a DataStream of batches. Parameters ---------- data : gluonnlp.data.DataStream DataStream where each sample is a valid input to gluonnlp.data.EmbeddingCenterContextBatchify. vocab : gluonnlp.Vocab Vocabulary containing all tokens whose...

Create a batch for CBOW training objective with subwords. def cbow_fasttext_batch(centers, contexts, num_tokens, subword_lookup, dtype, index_dtype): """Create a batch for CBOW training objective with subwords.""" _, contexts_row, contexts_col = contexts data, row, col = subword_loo...

Create a batch for SG training objective with subwords. def skipgram_fasttext_batch(centers, contexts, num_tokens, subword_lookup, dtype, index_dtype): """Create a batch for SG training objective with subwords.""" contexts = mx.nd.array(contexts[2], dtype=index_dtype) data, row,...

Create a batch for CBOW training objective. def cbow_batch(centers, contexts, num_tokens, dtype, index_dtype): """Create a batch for CBOW training objective.""" contexts_data, contexts_row, contexts_col = contexts centers = mx.nd.array(centers, dtype=index_dtype) contexts = mx.nd.sparse.csr_matrix( ...

Create a batch for SG training objective. def skipgram_batch(centers, contexts, num_tokens, dtype, index_dtype): """Create a batch for SG training objective.""" contexts = mx.nd.array(contexts[2], dtype=index_dtype) indptr = mx.nd.arange(len(centers) + 1) centers = mx.nd.array(centers, dtype=index_dtyp...

Get a sparse COO array of words and subwords for SkipGram. Parameters ---------- indices : numpy.ndarray Array containing numbers in [0, vocabulary_size). The element at position idx is taken to be the word that occurs at row idx in the SkipGram batch. offset : int Offse...

Get a sparse COO array of words and subwords for CBOW. Parameters ---------- context_row : numpy.ndarray of dtype int64 Array of same length as context_col containing numbers in [0, batch_size). For each idx, context_row[idx] specifies the row that context_col[idx] occurs in a spars...

Source Vocabulary of the Dataset. Returns ------- src_vocab : Vocab Source vocabulary. def src_vocab(self): """Source Vocabulary of the Dataset. Returns ------- src_vocab : Vocab Source vocabulary. """ if self._src_vocab ...

Target Vocabulary of the Dataset. Returns ------- tgt_vocab : Vocab Target vocabulary. def tgt_vocab(self): """Target Vocabulary of the Dataset. Returns ------- tgt_vocab : Vocab Target vocabulary. """ if self._tgt_vocab ...

Evaluate given the data loader Parameters ---------- data_loader : DataLoader Returns ------- avg_loss : float Average loss real_translation_out : list of list of str The translation output def evaluate(data_loader): """Evaluate given the data loader Parameters ...

Training function. def train(): """Training function.""" trainer = gluon.Trainer(model.collect_params(), args.optimizer, {'learning_rate': args.lr}) train_data_loader, val_data_loader, test_data_loader \ = dataprocessor.make_dataloader(data_train, data_val, data_test, args) best_valid_bleu = ...

r"""Returns a cache model using a pre-trained language model. We implement the neural cache language model proposed in the following work:: @article{grave2016improving, title={Improving neural language models with a continuous cache}, author={Grave, Edouard and Joulin, Armand and Usunier, ...

Training helper. def train(args): """Training helper.""" if not args.model.lower() in ['cbow', 'skipgram']: logging.error('Unsupported model %s.', args.model) sys.exit(1) if args.data.lower() == 'toy': data = mx.gluon.data.SimpleDataset(nlp.data.Text8(segment='train')[:2]) ...

Evaluation helper def evaluate(args, embedding, vocab, global_step, eval_analogy=False): """Evaluation helper""" if 'eval_tokens' not in globals(): global eval_tokens eval_tokens_set = evaluation.get_tokens_in_evaluation_datasets(args) if not args.no_eval_analogy: eval_toke...

Return the dataset corresponds to the provided key. Example:: a = np.ones((2,2)) b = np.zeros((2,2)) np.savez('data.npz', a=a, b=b) dataset = NumpyDataset('data.npz') data_a = dataset.get_field('a') data_b = dataset.get_field('b') ...

Project the tokenized prediction back to the original text. def get_final_text(pred_text, orig_text, tokenizer): """Project the tokenized prediction back to the original text.""" # When we created the data, we kept track of the alignment between original # (whitespace tokenized) tokens and our WordPiece t...

Get prediction results Parameters ---------- dev_dataset: dataset Examples of transform. all_results: dict A dictionary containing model prediction results. tokenizer: callable Tokenizer function. max_answer_length: int, default 64 Maximum length of the answer to...

Calculate the F1 and EM scores of the predicted results. Use only with the SQuAD1.1 dataset. Parameters ---------- dataset_file: string Path to the data file. predict_data: dict All final predictions. Returns ------- scores: dict F1 and EM scores. def get_F1_EM...

Data preparation function. def preprocess_data(tokenizer, task, batch_size, dev_batch_size, max_len, pad=False): """Data preparation function.""" # transformation trans = BERTDatasetTransform( tokenizer, max_len, labels=task.get_labels(), pad=pad, pair=task.is_pair, ...

Evaluate the model on validation dataset. def evaluate(dataloader_eval, metric): """Evaluate the model on validation dataset. """ metric.reset() for _, seqs in enumerate(dataloader_eval): input_ids, valid_len, type_ids, label = seqs out = model( input_ids.as_in_context(ctx),...

Generate and print out the log message for training. def log_train(batch_id, batch_num, metric, step_loss, log_interval, epoch_id, learning_rate): """Generate and print out the log message for training. """ metric_nm, metric_val = metric.get() if not isinstance(metric_nm, list): metric_nm = [me...

Generate and print out the log message for inference. def log_inference(batch_id, batch_num, metric, step_loss, log_interval): """Generate and print out the log message for inference. """ metric_nm, metric_val = metric.get() if not isinstance(metric_nm, list): metric_nm = [metric_nm] me...

Training function. def train(metric): """Training function.""" logging.info('Now we are doing BERT classification training on %s!', ctx) optimizer_params = {'learning_rate': lr, 'epsilon': epsilon, 'wd': 0.01} try: trainer = gluon.Trainer( model.collect_params(), args.o...

Inference function. def inference(metric): """Inference function.""" logging.info('Now we are doing BERT classification inference on %s!', ctx) model = BERTClassifier(bert, dropout=0.1, num_classes=len(task.get_labels())) model.hybridize(static_alloc=True) model.load_parameters(model_parameters, c...

Process SQuAD dataset by creating NDArray version of data :param Dataset dataset: SQuAD dataset :param int question_max_length: Maximum length of question (padded or trimmed to that size) :param int context_max_length: Maximum length of context (padded or trimmed to that size) Returns ------- ...

Find all answer spans from the context, returning start_index and end_index :param list[str] answer_list: List of all answers :param list[int] answer_start_list: List of all answers' start indices Returns ------- List[Tuple] list of Tuple(answer_start_index answer_e...

Provides word level vocabulary Returns ------- Vocab Word level vocabulary def get_word_level_vocab(self): """Provides word level vocabulary Returns ------- Vocab Word level vocabulary """ def simple_tokenize(source_str,...

Parameters ---------- states : list the stack outputs from RNN, which consists of output from each time step (TNC). Returns -------- loss : NDArray loss tensor with shape (batch_size,). Dimensions other than batch_axis are averaged out. def hybrid_forwar...

Tokenizes a piece of text. def _tokenize(self, text): """Tokenizes a piece of text.""" text = self._clean_text(text) # This was added on November 1st, 2018 for the multilingual and Chinese # models. This is also applied to the English models now, but it doesn't # matter since t...

Performs invalid character removal and whitespace cleanup on text. def _clean_text(self, text): """Performs invalid character removal and whitespace cleanup on text.""" output = [] for char in text: cp = ord(char) if cp in (0, 0xfffd) or self._is_control(char): ...

Checks whether `chars` is a control character. def _is_control(self, char): """Checks whether `chars` is a control character.""" # These are technically control characters but we count them as whitespace # characters. if char in ['\t', '\n', '\r']: return False cat =...

Splits punctuation on a piece of text. def _run_split_on_punc(self, text): """Splits punctuation on a piece of text.""" chars = list(text) i = 0 start_new_word = True output = [] while i < len(chars): char = chars[i] if self._is_punctuation(char):...

Checks whether `chars` is a punctuation character. def _is_punctuation(self, char): """Checks whether `chars` is a punctuation character.""" cp = ord(char) # We treat all non-letter/number ASCII as punctuation. # Characters such as "^", "$", and "`" are not in the Unicode # Punc...

Checks whether `chars` is a whitespace character. def _is_whitespace(self, char): """Checks whether `chars` is a whitespace character.""" # \t, \n, and \r are technically contorl characters but we treat them # as whitespace since they are generally considered as such. if char in [' ', '...

Runs basic whitespace cleaning and splitting on a piece of text. def _whitespace_tokenize(self, text): """Runs basic whitespace cleaning and splitting on a piece of text.""" text = text.strip() tokens = text.split() return tokens

Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform tokenization using the given vocabulary. For example: input = "unaffable" output = ["un", "##aff", "##able"] Args: text: A single token or whitespa...

Truncates a sequence pair in place to the maximum length. def _truncate_seq_pair(self, tokens_a, tokens_b, max_length): """Truncates a sequence pair in place to the maximum length.""" # This is a simple heuristic which will always truncate the longer sequence # one token at a time. This makes m...

Construct the argument parser. def get_args(): """Construct the argument parser.""" parser = argparse.ArgumentParser( description='Word embedding evaluation with Gluon.', formatter_class=argparse.ArgumentDefaultsHelpFormatter) # Embeddings arguments group = parser.add_argument_group('E...

Validate provided arguments and act on --help. def validate_args(args): """Validate provided arguments and act on --help.""" if args.list_embedding_sources: print('Listing all sources for {} embeddings.'.format( args.embedding_name)) print('Specify --embedding-name if you wish to ' ...

Load a TokenEmbedding. def load_embedding_from_path(args): """Load a TokenEmbedding.""" if args.embedding_path.endswith('.bin'): with utils.print_time('load fastText model.'): model = \ nlp.model.train.FasttextEmbeddingModel.load_fasttext_format( args.emb...

Calculate the 2-norm of gradients of parameters, and how much they should be scaled down such that their 2-norm does not exceed `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 c...

backward propagation with loss def backward(self, loss): """backward propagation with loss""" with mx.autograd.record(): if isinstance(loss, (tuple, list)): ls = [l * self._scaler.loss_scale for l in loss] else: ls = loss * self._scaler.loss_scale...

Makes one step of parameter update. Should be called after `fp16_optimizer.backward()`, and outside of `record()` scope. Parameters ---------- batch_size : int Batch size of data processed. Gradient will be normalized by `1/batch_size`. Set this to 1 if you norma...

detect inf and nan def has_overflow(self, params): """ detect inf and nan """ is_not_finite = 0 for param in params: if param.grad_req != 'null': grad = param.list_grad()[0] is_not_finite += mx.nd.contrib.isnan(grad).sum() is_not_finit...

dynamically update loss scale def update_scale(self, overflow): """dynamically update loss scale""" iter_since_rescale = self._num_steps - self._last_rescale_iter if overflow: self._last_overflow_iter = self._num_steps self._overflows_since_rescale += 1 perce...

Return a string representing the statistics of the bucketing sampler. Returns ------- ret : str String representing the statistics of the buckets. def stats(self): """Return a string representing the statistics of the bucketing sampler. Returns ------- ...

Training loop for language model. def train(): """Training loop for language model. """ print(model) from_epoch = 0 model.initialize(mx.init.Xavier(factor_type='out'), ctx=context) trainer_params = {'learning_rate': args.lr, 'wd': 0, 'eps': args.eps} trainer = gluon.Trainer(model.collect_pa...

Evaluate loop for the trained model def evaluate(): """ Evaluate loop for the trained model """ print(eval_model) eval_model.initialize(mx.init.Xavier(), ctx=context[0]) eval_model.hybridize(static_alloc=True, static_shape=True) epoch = args.from_epoch if args.from_epoch else 0 while epoch < ar...