API

malaya

malaya.augmentation

malaya.augmentation.synonym(string: str, threshold: float = 0.5, top_n=5, cleaning=<function augmentation_textcleaning>, **kwargs)

augmenting a string using synonym, https://github.com/huseinzol05/Malaya-Dataset#90k-synonym

Parameters

• string (str) –

• threshold (float, optional (default=0.5)) – random selection for a word.

• top_n (int, (default=5)) – number of nearest neighbors returned. Length of returned result should as top_n.

• cleaning (function, (default=malaya.text.function.augmentation_textcleaning)) – function to clean text.

Returns

result

Return type

List[str]

malaya.augmentation.wordvector(string: str, wordvector, threshold: float = 0.5, top_n: int = 5, soft: bool = False, cleaning=<function augmentation_textcleaning>)

augmenting a string using wordvector.

Parameters

• string (str) –

• wordvector (object) – wordvector interface object.

• threshold (float, optional (default=0.5)) – random selection for a word.

• soft (bool, optional (default=False)) – if True, a word not in the dictionary will be replaced with nearest jarowrinkler ratio. if False, it will throw an exception if a word not in the dictionary.

• top_n (int, (default=5)) – number of nearest neighbors returned. Length of returned result should as top_n.

• cleaning (function, (default=malaya.text.function.augmentation_textcleaning)) – function to clean text.

Returns

result

Return type

List[str]

malaya.augmentation.transformer(string: str, model, threshold: float = 0.5, top_p: float = 0.9, top_k: int = 100, temperature: float = 1.0, top_n: int = 5, cleaning=None)

augmenting a string using transformer + nucleus sampling / top-k sampling.

Parameters

• string (str) –

• model (object) – transformer interface object. Right now only supported BERT, ALBERT and ELECTRA.

• threshold (float, optional (default=0.5)) – random selection for a word.

• top_p (float, optional (default=0.8)) – cumulative sum of probabilities to sample a word. If top_n bigger than 0, the model will use nucleus sampling, else top-k sampling.

• top_k (int, optional (default=100)) – k for top-k sampling.

• temperature (float, optional (default=0.8)) – logits * temperature.

• top_n (int, (default=5)) – number of nearest neighbors returned. Length of returned result should as top_n.

• cleaning (function, (default=None)) – function to clean text.

Returns

result

Return type

List[str]

malaya.cluster

malaya.cluster.cluster_words(list_words: List[str], lowercase: bool = False)

cluster similar words based on structure, eg, [‘mahathir mohamad’, ‘mahathir’] = [‘mahathir mohamad’]. big O = n^2

Parameters

• list_words (List[str]) –

• lowercase (bool, optional (default=True)) – if True, will group using lowercase but maintain the original form.

Returns

string

Return type

List[str]

malaya.cluster.cluster_pos(result: List[Tuple[str, str]])

cluster similar POS.

Parameters

result (List[Tuple[str, str]]) –

Returns

result

Return type

Dict[str, List[str]]

malaya.cluster.cluster_entities(result: List[Tuple[str, str]])

cluster similar Entities.

Parameters

result (List[Tuple[str, str]]) –

Returns

result

Return type

Dict[str, List[str]]

malaya.cluster.cluster_tagging(result: List[Tuple[str, str]])

cluster any tagging results, as long the data passed [(string, label), (string, label)].

Parameters

result (List[Tuple[str, str]]) –

Returns

result

Return type

Dict[str, List[str]]

malaya.cluster.cluster_scatter(corpus: List[str], vectorizer, num_clusters: int = 5, titles: List[str] = None, colors: List[str] = None, stopwords=<function get_stopwords>, cleaning=<function simple_textcleaning>, clustering=<class 'sklearn.cluster._kmeans.KMeans'>, decomposition=<class 'sklearn.manifold._mds.MDS'>, ngram: Tuple[int, int] = (1, 3), figsize: Tuple[int, int] = (17, 9), batch_size: int = 20)

plot scatter plot on similar text clusters.

Parameters

• corpus (List[str]) –

• vectorizer (class) – vectorizer class.

• num_clusters (int, (default=5)) – size of unsupervised clusters.

• titles (List[str], (default=None)) – list of titles, length must same with corpus.

• colors (List[str], (default=None)) – list of colors, length must same with num_clusters.

• stopwords (List[str], (default=malaya.texts.function.get_stopwords)) – A callable that returned a List[str], or a List[str], or a Tuple[str]

• ngram (Tuple[int, int], (default=(1,3))) – n-grams size to train a corpus.

• cleaning (function, (default=malaya.texts.function.simple_textcleaning)) – function to clean the corpus.

• batch_size (int, (default=10)) – size of strings for each vectorization and attention. Only useful if use transformer vectorizer.

Returns

dictionary

Return type

{‘X’: X, ‘Y’: Y, ‘labels’: clusters, ‘vector’: transformed_text_clean, ‘titles’: titles}

malaya.cluster.cluster_dendogram(corpus: List[str], vectorizer, titles: List[str] = None, stopwords=<function get_stopwords>, cleaning=<function simple_textcleaning>, random_samples: float = 0.3, ngram: Tuple[int, int] = (1, 3), figsize: Tuple[int, int] = (17, 9), batch_size: int = 20)

plot hierarchical dendogram with similar texts.

Parameters

• corpus (List[str]) –

• vectorizer (class) – vectorizer class.

• num_clusters (int, (default=5)) – size of unsupervised clusters.

• titles (List[str], (default=None)) – list of titles, length must same with corpus.

• stopwords (List[str], (default=malaya.texts.function.get_stopwords)) – A callable that returned a List[str], or a List[str], or a Tuple[str]

• cleaning (function, (default=malaya.text.function.simple_textcleaning)) – function to clean the corpus.

• random_samples (float, (default=0.3)) – random samples from the corpus, 0.3 means 30%.

• ngram (Tuple[int, int], (default=(1,3))) – n-grams size to train a corpus.

• batch_size (int, (default=20)) – size of strings for each vectorization and attention. Only useful if use transformer vectorizer.

Returns

dictionary

Return type

{‘linkage_matrix’: linkage_matrix, ‘titles’: titles}

malaya.cluster.cluster_graph(corpus: List[str], vectorizer, threshold: float = 0.9, num_clusters: int = 5, titles: List[str] = None, colors: List[str] = None, stopwords=<function get_stopwords>, ngram: Tuple[int, int] = (1, 3), cleaning=<function simple_textcleaning>, clustering=<class 'sklearn.cluster._kmeans.KMeans'>, figsize: Tuple[int, int] = (17, 9), with_labels: bool = True, batch_size: int = 20)

plot undirected graph with similar texts.

Parameters

• corpus (List[str]) –

• vectorizer (class) – vectorizer class.

• threshold (float, (default=0.9)) – 0.9 means, 90% above absolute pearson correlation.

• num_clusters (int, (default=5)) – size of unsupervised clusters.

• titles (List[str], (default=True)) – list of titles, length must same with corpus.

• stopwords (List[str], (default=malaya.texts.function.get_stopwords)) – A callable that returned a List[str] or List[str] or Tuple[str].

• cleaning (function, (default=malaya.texts.function.simple_textcleaning)) – function to clean the corpus.

• ngram (Tuple[int, int], (default=(1,3))) – n-grams size to train a corpus.

• batch_size (int, (default=20)) – size of strings for each vectorization and attention. Only useful if use transformer vectorizer.

Returns

dictionary

Return type

{‘G’: G, ‘pos’: pos, ‘node_colors’: node_colors, ‘node_labels’: node_labels}

malaya.cluster.cluster_entity_linking(corpus: List[str], vectorizer, entity_model, topic_modeling_model, threshold: float = 0.3, topic_decomposition: int = 2, topic_length: int = 10, fuzzy_ratio: int = 70, accepted_entities: List[str] = ['law', 'location', 'organization', 'person', 'event'], cleaning=<function simple_textcleaning>, colors: List[str] = None, stopwords=<function get_stopwords>, max_df: float = 1.0, min_df: int = 1, ngram: Tuple[int, int] = (2, 3), figsize: Tuple[int, int] = (17, 9), batch_size: int = 20)

plot undirected graph for Entities and topics relationship.

Parameters

• corpus (list or str) –

• vectorizer (class) –

• titles (list) – list of titles, length must same with corpus.

• colors (list) – list of colors, length must same with num_clusters.

• threshold (float, (default=0.3)) – 0.3 means, 30% above absolute pearson correlation.

• topic_decomposition (int, (default=2)) – size of decomposition.

• topic_length (int, (default=10)) – size of topic models.

• fuzzy_ratio (int, (default=70)) – size of ratio for fuzzywuzzy.

• max_df (float, (default=0.95)) – maximum of a word selected based on document frequency.

• min_df (int, (default=2)) – minimum of a word selected on based on document frequency.

• ngram (tuple, (default=(1,3))) – n-grams size to train a corpus.

• cleaning (function, (default=simple_textcleaning)) – function to clean the corpus.

• stopwords (List[str], (default=malaya.texts.function.get_stopwords)) – A callable that returned a List[str] or List[str] or Tuple[str]

Returns

dictionary

Return type

{‘G’: G, ‘pos’: pos, ‘node_colors’: node_colors, ‘node_labels’: node_labels}

malaya.constituency

malaya.constituency.available_transformer()

List available transformer models.

malaya.constituency.transformer(model: str = 'xlnet', quantized: bool = False, **kwargs)

Load Transformer Constituency Parsing model, transfer learning Transformer + self attentive parsing.

Parameters

• model (str, optional (default='bert')) –

  Model architecture supported. Allowed values:

  • 'bert' - Google BERT BASE parameters.

  • 'tiny-bert' - Google BERT TINY parameters.

  • 'albert' - Google ALBERT BASE parameters.

  • 'tiny-albert' - Google ALBERT TINY parameters.

  • 'xlnet' - Google XLNET BASE parameters.

• quantized (bool, optional (default=False)) – if True, will load 8-bit quantized model. Quantized model not necessary faster, totally depends on the machine.

Returns

result

Return type

malaya.model.tf.Constituency class

malaya.coref

malaya.coref.parse_from_dependency(models, string: str, references: List[str] = ['dia', 'itu', 'ini', 'saya', 'awak', 'kamu', 'kita', 'kami', 'mereka'], rejected_references: List[str] = ['saya', 'awak', 'kamu', 'kita', 'kami', 'mereka', 'nya'], acceptable_subjects: List[str] = ['flat', 'subj', 'nsubj', 'csubj', 'obj'], acceptable_nested_subjects: List[str] = ['compound', 'flat'], split_nya: bool = True, aggregate: Callable = <function mean>, top_k: int = 20)

Apply Coreference Resolution using stacks of dependency models.

Parameters

• models (list) – list of dependency models, must has vectorize method.

• string (str) –

• references (List[str], optional (default=['dia', 'itu', 'ini', 'saya', 'awak', 'kamu', 'kita', 'kami', 'mereka'])) – list of references.

• rejected_references (List[str], optional (default=['saya', 'awak', 'kamu', 'kita', 'kami', 'mereka'])) – list of rejected references during populating subjects.

• acceptable_subjects (List[str], optional) – List of dependency labels for subjects.

• acceptable_nested_subjects (List[str], optional) – List of dependency labels for nested subjects, eg, syarikat (obl) facebook (compound).

• split_nya (bool, optional (default=True)) – split nya, eg, disifatkannya -> disifatkan, nya.

• aggregate (Callable, optional (default=numpy.mean)) – Aggregate function to aggregate list of vectors from model.vectorize.

• top_k (int, optional (default=20)) – only accept near top_k to assume a coherence.

Returns

result – {‘text’: [‘Husein’,’Zolkepli’,’suka’,’makan’,’ayam’,’.’,’Dia’,’pun’,’suka’,’makan’,’daging’,’.’], ‘coref’: {6: {‘index’: [0, 1], ‘text’: [‘Husein’, ‘Zolkepli’]}}}

Return type

Dict[text, coref]

malaya.dependency

malaya.dependency.describe()

Describe Dependency supported.

malaya.dependency.dependency_graph(tagging, indexing)

Return helper object for dependency parser results. Only accept tagging and indexing outputs from dependency models.

malaya.dependency.available_transformer(version: str = 'v2')

List available transformer dependency parsing models.

Parameters

version (str, optional (default='v2')) –

Version supported. Allowed values:

• 'v1' - version 1, maintain for knowledge graph.

• 'v2' - Trained on bigger dataset, better version.

malaya.dependency.transformer(version: str = 'v2', model: str = 'xlnet', quantized: bool = False, **kwargs)

Load Transformer Dependency Parsing model, transfer learning Transformer + biaffine attention.

Parameters

• version (str, optional (default='v2')) –

  Version supported. Allowed values:

  • 'v1' - version 1, maintain for knowledge graph.

  • 'v2' - Trained on bigger dataset, better version.

• model (str, optional (default='xlnet')) –

  Model architecture supported. Allowed values:

  • 'bert' - Google BERT BASE parameters.

  • 'tiny-bert' - Google BERT TINY parameters.

  • 'albert' - Google ALBERT BASE parameters.

  • 'tiny-albert' - Google ALBERT TINY parameters.

  • 'xlnet' - Google XLNET BASE parameters.

  • 'alxlnet' - Malaya ALXLNET BASE parameters.

• quantized (bool, optional (default=False)) – if True, will load 8-bit quantized model. Quantized model not necessary faster, totally depends on the machine.

Returns

result – List of model classes:

• if bert in model, will return malaya.model.bert.DependencyBERT.

• if xlnet in model, will return malaya.model.xlnet.DependencyXLNET.

Return type

model

malaya.emotion

malaya.emotion.available_transformer()

List available transformer emotion analysis models.

malaya.emotion.multinomial(**kwargs)

Load multinomial emotion model.

Returns

result

Return type

malaya.model.ml.MulticlassBayes class

malaya.emotion.transformer(model: str = 'xlnet', quantized: bool = False, **kwargs)

Load Transformer emotion model.

Parameters

• model (str, optional (default='bert')) –

  Model architecture supported. Allowed values:

  • 'bert' - Google BERT BASE parameters.

  • 'tiny-bert' - Google BERT TINY parameters.

  • 'albert' - Google ALBERT BASE parameters.

  • 'tiny-albert' - Google ALBERT TINY parameters.

  • 'xlnet' - Google XLNET BASE parameters.

  • 'alxlnet' - Malaya ALXLNET BASE parameters.

• quantized (bool, optional (default=False)) – if True, will load 8-bit quantized model. Quantized model not necessary faster, totally depends on the machine.

Returns

result – List of model classes:

• if bert in model, will return malaya.model.bert.MulticlassBERT.

• if xlnet in model, will return malaya.model.xlnet.MulticlassXLNET.

Return type

model

malaya.entity

malaya.entity.describe()

Describe Entities supported.

malaya.entity.describe_ontonotes5()

Describe OntoNotes5 Entities supported. https://spacy.io/api/annotation#named-entities

malaya.entity.available_transformer()

List available transformer Entity Tagging models.

malaya.entity.available_transformer_ontonotes5()

List available transformer Entity Tagging models trained on Ontonotes 5 Bahasa.

malaya.entity.transformer(model: str = 'xlnet', quantized: bool = False, **kwargs)

Load Transformer Entity Tagging model, transfer learning Transformer + CRF.

Parameters

• model (str, optional (default='bert')) –

  Model architecture supported. Allowed values:

  • 'bert' - Google BERT BASE parameters.

  • 'tiny-bert' - Google BERT TINY parameters.

  • 'albert' - Google ALBERT BASE parameters.

  • 'tiny-albert' - Google ALBERT TINY parameters.

  • 'xlnet' - Google XLNET BASE parameters.

  • 'alxlnet' - Malaya ALXLNET BASE parameters.

• quantized (bool, optional (default=False)) – if True, will load 8-bit quantized model. Quantized model not necessary faster, totally depends on the machine.

Returns

result – List of model classes:

• if bert in model, will return malaya.model.bert.TaggingBERT.

• if xlnet in model, will return malaya.model.xlnet.TaggingXLNET.

Return type

model

malaya.entity.transformer_ontonotes5(model: str = 'xlnet', quantized: bool = False, **kwargs)

Load Transformer Entity Tagging model trained on Ontonotes 5 Bahasa, transfer learning Transformer + CRF.

Parameters

• model (str, optional (default='bert')) –

  Model architecture supported. Allowed values:

  • 'bert' - Google BERT BASE parameters.

  • 'tiny-bert' - Google BERT TINY parameters.

  • 'albert' - Google ALBERT BASE parameters.

  • 'tiny-albert' - Google ALBERT TINY parameters.

  • 'xlnet' - Google XLNET BASE parameters.

  • 'alxlnet' - Malaya ALXLNET BASE parameters.

• quantized (bool, optional (default=False)) – if True, will load 8-bit quantized model. Quantized model not necessary faster, totally depends on the machine.

Returns

result – List of model classes:

• if bert in model, will return malaya.model.bert.TaggingBERT.

• if xlnet in model, will return malaya.model.xlnet.TaggingXLNET.

Return type

model

malaya.entity.general_entity(model=None)

Load Regex based general entities tagging along with another supervised entity tagging model.

Parameters

model (object) – model must have predict method. Make sure the predict method returned [(string, label), (string, label)].

Returns

result

Return type

malaya.text.entity.EntityRegex class

malaya.generator

malaya.generator.ngrams(sequence, n: int, pad_left=False, pad_right=False, left_pad_symbol=None, right_pad_symbol=None)

generate ngrams.

Parameters

• sequence (List[str]) – list of tokenize words.

• n (int) – ngram size

Returns

result

Return type

List[Tuple[str, str]]

malaya.generator.pos_entities_ngram(result_pos: List[Tuple[str, str]], result_entities: List[Tuple[str, str]], ngram: Tuple[int, int] = (1, 3), accept_pos: List[str] = ['NOUN', 'PROPN', 'VERB'], accept_entities: List[str] = ['law', 'location', 'organization', 'person', 'time'])

generate ngrams.

Parameters

• result_pos (List[Tuple[str, str]]) – result from POS recognition.

• result_entities (List[Tuple[str, str]]) – result of Entities recognition.

• ngram (Tuple[int, int]) – ngram sizes.

• accept_pos (List[str]) – accepted POS elements.

• accept_entities (List[str]) – accept entities elements.

Returns

result

Return type

list

malaya.generator.sentence_ngram(sentence: str, ngram: Tuple[int, int] = (1, 3))

generate ngram for a text

Parameters

• sentence (str) –

• ngram (tuple) – ngram sizes.

Returns

result

Return type

list

malaya.generator.shortform(word: str, augment_vowel: bool = True, augment_consonant: bool = True, prob_delete_vowel: float = 0.5, **kwargs)

augmenting a formal word into socialmedia form. Purposely typo, purposely delete some vowels, purposely replaced some subwords into slang subwords.

Parameters

• word (str) –

• augment_vowel (bool, (default=True)) – if True, will augment vowels for each samples generated.

• augment_consonant (bool, (default=True)) – if True, will augment consonants for each samples generated.

• prob_delete_vowel (float, (default=0.5)) – probability to delete a vowel.

Returns

result

Return type

list

malaya.generator.babble(string: str, model, generate_length: int = 30, leed_out_len: int = 1, temperature: float = 1.0, top_k: int = 100, burnin: int = 15, batch_size: int = 5)

Use pretrained transformer models to generate a string given a prefix string. https://github.com/nyu-dl/bert-gen, https://arxiv.org/abs/1902.04094

Parameters

• string (str) –

• model (object) – transformer interface object. Right now only supported BERT, ALBERT.

• generate_length (int, optional (default=256)) – length of sentence to generate.

• leed_out_len (int, optional (default=1)) – length of extra masks for each iteration.

• temperature (float, optional (default=1.0)) – logits * temperature.

• top_k (int, optional (default=100)) – k for top-k sampling.

• burnin (int, optional (default=15)) – for the first burnin steps, sample from the entire next word distribution, instead of top_k.

• batch_size (int, optional (default=5)) – generate sentences size of batch_size.

Returns

result

Return type

List[str]

malaya.generator.available_gpt2()

List available gpt2 generator models.

malaya.generator.gpt2(model: str = '345M', generate_length: int = 256, temperature: float = 1.0, top_k: int = 40, **kwargs)

Load GPT2 model to generate a string given a prefix string.

Parameters

• model (str, optional (default='345M')) –

  Model architecture supported. Allowed values:

  • '117M' - GPT2 117M parameters.

  • '345M' - GPT2 345M parameters.

• generate_length (int, optional (default=256)) – length of sentence to generate.

• temperature (float, optional (default=1.0)) – temperature value, value should between 0 and 1.

• top_k (int, optional (default=40)) – top-k in nucleus sampling selection.

Returns

result

Return type

malaya.transformers.gpt2.Model class

malaya.generator.available_transformer()

List available transformer models.

malaya.generator.transformer(model: str = 't5', quantized: bool = False, **kwargs)

Load Transformer model to generate a string given a isu penting.

Parameters

• model (str, optional (default='base')) –

  Model architecture supported. Allowed values:

  • 't5' - T5 BASE parameters.

  • 'small-t5' - T5 SMALL parameters.

• quantized (bool, optional (default=False)) – if True, will load 8-bit quantized model. Quantized model not necessary faster, totally depends on the machine.

Returns

result – List of model classes:

• if t5 in model, will return malaya.model.t5.Generator.

Return type

model

malaya.keyword_extraction

malaya.keyword_extraction.rake(string: str, model=None, vectorizer=None, top_k: int = 5, atleast: int = 1, stopwords=<function get_stopwords>, **kwargs)

Extract keywords using Rake algorithm.

Parameters

• string (str) –

• model (Object, optional (default=None)) – Transformer model or any model has attention method.

• vectorizer (Object, optional (default=None)) – Prefer sklearn.feature_extraction.text.CountVectorizer or, malaya.text.vectorizer.SkipGramCountVectorizer. If None, will generate ngram automatically based on stopwords.

• top_k (int, optional (default=5)) – return top-k results.

• atleast (int, optional (default=1)) – at least count appeared in the string to accept as candidate.

• stopwords (List[str], (default=malaya.texts.function.get_stopwords)) – A callable that returned a List[str], or a List[str], or a Tuple[str] For automatic Ngram generator.

Returns

result

Return type

Tuple[float, str]

malaya.keyword_extraction.textrank(string: str, model=None, vectorizer=None, top_k: int = 5, atleast: int = 1, stopwords=<function get_stopwords>, **kwargs)

Extract keywords using Textrank algorithm.

Parameters

• string (str) –

• model (Object, optional (default='None')) – model has fit_transform or vectorize method.

• vectorizer (Object, optional (default=None)) – Prefer sklearn.feature_extraction.text.CountVectorizer or, malaya.text.vectorizer.SkipGramCountVectorizer. If None, will generate ngram automatically based on stopwords.

• top_k (int, optional (default=5)) – return top-k results.

• atleast (int, optional (default=1)) – at least count appeared in the string to accept as candidate.

• stopwords (List[str], (default=malaya.texts.function.get_stopwords)) – A callable that returned a List[str], or a List[str], or a Tuple[str]

Returns

result

Return type

Tuple[float, str]

malaya.keyword_extraction.attention(string: str, model, vectorizer=None, top_k: int = 5, atleast: int = 1, stopwords=<function get_stopwords>, **kwargs)

Extract keywords using Attention mechanism.

Parameters

• string (str) –

• model (Object) – Transformer model or any model has attention method.

• vectorizer (Object, optional (default=None)) – Prefer sklearn.feature_extraction.text.CountVectorizer or, malaya.text.vectorizer.SkipGramCountVectorizer. If None, will generate ngram automatically based on stopwords.

• top_k (int, optional (default=5)) – return top-k results.

• atleast (int, optional (default=1)) – at least count appeared in the string to accept as candidate.

• stopwords (List[str], (default=malaya.texts.function.get_stopwords)) – A callable that returned a List[str], or a List[str], or a Tuple[str]

Returns

result

Return type

Tuple[float, str]

malaya.keyword_extraction.similarity(string: str, model, vectorizer=None, top_k: int = 5, atleast: int = 1, stopwords=<function get_stopwords>, **kwargs)

Extract keywords using Sentence embedding VS keyword embedding similarity.

Parameters

• string (str) –

• model (Object) – Transformer model or any model has vectorize method.

• vectorizer (Object, optional (default=None)) – Prefer sklearn.feature_extraction.text.CountVectorizer or, malaya.text.vectorizer.SkipGramCountVectorizer. If None, will generate ngram automatically based on stopwords.

• top_k (int, optional (default=5)) – return top-k results.

• atleast (int, optional (default=1)) – at least count appeared in the string to accept as candidate.

• stopwords (List[str], (default=malaya.texts.function.get_stopwords)) – A callable that returned a List[str], or a List[str], or a Tuple[str]

Returns

result

Return type

Tuple[float, str]

malaya.keyword_extraction.available_transformer()

List available transformer keyword similarity model.

malaya.keyword_extraction.transformer(model: str = 'bert', quantized: bool = False, **kwargs)

Load Transformer keyword similarity model.

Parameters

• model (str, optional (default='bert')) –

  Model architecture supported. Allowed values:

  • 'bert' - Google BERT BASE parameters.

  • 'tiny-bert' - Google BERT TINY parameters.

  • 'xlnet' - Google XLNET BASE parameters.

  • 'alxlnet' - Malaya ALXLNET BASE parameters.

• quantized (bool, optional (default=False)) – if True, will load 8-bit quantized model. Quantized model not necessary faster, totally depends on the machine.

Returns

result – List of model classes:

• if bert in model, will return malaya.model.bert.KeyphraseBERT.

• if xlnet in model, will return malaya.model.xlnet.KeyphraseXLNET.

Return type

model

malaya.knowledge_graph

malaya.knowledge_graph.parse_from_dependency(tagging: List[Tuple[str, str]], indexing: List[Tuple[str, str]], subjects: List[List[str]] = [['flat', 'subj', 'nsubj', 'csubj']], relations: List[List[str]] = [['acl', 'xcomp', 'ccomp', 'obj', 'conj', 'advcl'], ['obj']], objects: List[List[str]] = [['obj', 'compound', 'flat', 'nmod', 'obl']], get_networkx: bool = True)

Generate knowledge graphs from dependency parsing, we suggest use dependency parsing v1.

Parameters

• tagging (List[Tuple(str, str)]) – tagging result from dependency model.

• indexing (List[Tuple(str, str)]) – indexing result from dependency model.

• subjects (List[List[str]], optional) – List of dependency labels for subjects.

• relations (List[List[str]], optional) – List of dependency labels for relations.

• objects (List[List[str]], optional) – List of dependency labels for objects.

• get_networkx (bool, optional (default=True)) – If True, will generate networkx.MultiDiGraph.

Returns

result

Return type

Dict[result, G]

malaya.knowledge_graph.available_transformer()

List available transformer models.

malaya.knowledge_graph.transformer(model: str = 'base', quantized: bool = False, **kwargs)

Load transformer to generate knowledge graphs in triplet format from texts, MS text -> EN triplet format.

Parameters

• model (str, optional (default='base')) –

  Model architecture supported. Allowed values:

  • 'base' - Transformer BASE parameters.

  • 'large' - Transformer LARGE parameters.

• quantized (bool, optional (default=False)) – if True, will load 8-bit quantized model. Quantized model not necessary faster, totally depends on the machine.

Returns

result

Return type

malaya.model.tf.KnowledgeGraph class

malaya.language_detection

malaya.language_detection.fasttext(quantized: bool = True, **kwargs)

Load Fasttext language detection model. Original size is 353MB, Quantized size 31.1MB.

Parameters

quantized (bool, optional (default=True)) – if True, load quantized fasttext model. Else, load original fasttext model.

Returns

result

Return type

malaya.model.ml.LanguageDetection class

malaya.language_detection.deep_model(quantized: bool = False, **kwargs)

Load deep learning language detection model. Original size is 51.2MB, Quantized size 12.8MB.

quantizedbool, optional (default=False)

if True, will load 8-bit quantized model. Quantized model not necessary faster, totally depends on the machine.

Returns

result

Return type

malaya.model.tf.DeepLang class

malaya.lexicon

malaya.lexicon.random_walk(lexicon: Dict[str, List[str]], wordvector, pool_size: int = 10, top_n: int = 20, similarity_power: float = 10.0, beta: float = 0.9, arccos: bool = True, normalization: bool = True, soft: bool = False, silent: bool = False)

Induce lexicon by using random walk technique, use in paper, https://arxiv.org/pdf/1606.02820.pdf

Parameters

• lexicon (Dict[str : List[str]]) – curated lexicon from expert domain, {‘label1’: [str], ‘label2’: [str]}.

• wordvector (object) – wordvector interface object.

• pool_size (int, optional (default=10)) – pick top-pool size from each lexicons.

• top_n (int, optional (default=20)) – top_n for each vectors will multiple with similarity_power.

• similarity_power (float, optional (default=10.0)) – extra score for top_n, less will generate less bias induced but high chance unbalanced outcome.

• beta (float, optional (default=0.9)) – penalty score, towards to 1.0 means less penalty. 0 < beta < 1.

• arccos (bool, optional (default=True)) – covariance distribution for embedded.dot(embedded.T). If false, covariance + 1.

• normalization (bool, optional (default=True)) – normalize word vectors using L2 norm. L2 is good to penalize skewed vectors.

• soft (bool, optional (default=False)) – if True, a word not in the dictionary will be replaced with nearest jarowrinkler ratio. if False, it will throw an exception if a word not in the dictionary.

• silent (bool, optional (default=False)) – if True, will not print any logs.

Returns

result

Return type

tuple(labels[argmax(scores), axis = 1], scores, labels)

malaya.lexicon.propagate_probabilistic(lexicon: Dict[str, List[str]], wordvector, pool_size: int = 10, top_n: int = 20, similarity_power: float = 10.0, arccos: bool = True, normalization: bool = True, soft: bool = False, silent: bool = False)

Learns polarity scores via standard label propagation from lexicon sets.

Parameters

• lexicon (Dict[str, List[str]]) – curated lexicon from expert domain, {‘label1’: [str], ‘label2’: [str]}.

• wordvector (object) – wordvector interface object.

• pool_size (int, optional (default=10)) – pick top-pool size from each lexicons.

• top_n (int, optional (default=20)) – top_n for each vectors will multiple with similarity_power.

• similarity_power (float, optional (default=10.0)) – extra score for top_n, less will generate less bias induced but high chance unbalanced outcome.

• arccos (bool, optional (default=True)) – covariance distribution for embedded.dot(embedded.T). If false, covariance + 1.

• normalization (bool, optional (default=True)) – normalize word vectors using L2 norm. L2 is good to penalize skewed vectors.

• soft (bool, optional (default=False)) – if True, a word not in the dictionary will be replaced with nearest jarowrinkler ratio. if False, it will throw an exception if a word not in the dictionary.

• silent (bool, optional (default=False)) – if True, will not print any logs.

Returns

result

Return type

tuple(labels[argmax(scores), axis = 1], scores, labels)

malaya.lexicon.propagate_graph(lexicon: Dict[str, List[str]], wordvector, pool_size: int = 10, top_n: int = 20, similarity_power: float = 10.0, normalization: bool = True, soft: bool = False, silent: bool = False)

Graph propagation method dapted from Velikovich, Leonid, et al. “The viability of web-derived polarity lexicons.” http://www.aclweb.org/anthology/N10-1119

Parameters

• lexicon (Dict[str, List[str]]) – curated lexicon from expert domain, {‘label1’: [str], ‘label2’: [str]}.

• wordvector (object) – wordvector interface object.

• pool_size (int, optional (default=10)) – pick top-pool size from each lexicons.

• top_n (int, optional (default=20)) – top_n for each vectors will multiple with similarity_power.

• similarity_power (float, optional (default=10.0)) – extra score for top_n, less will generate less bias induced but high chance unbalanced outcome.

• normalization (bool, optional (default=True)) – normalize word vectors using L2 norm. L2 is good to penalize skewed vectors.

• soft (bool, optional (default=False)) – if True, a word not in the dictionary will be replaced with nearest jarowrinkler ratio. if False, it will throw an exception if a word not in the dictionary.

• silent (bool, optional (default=False)) – if True, will not print any logs.

Returns

result

Return type

tuple(labels[argmax(scores), axis = 1], scores, labels)

malaya.normalize

malaya.normalize.normalizer(speller=None, **kwargs)

Load a Normalizer using any spelling correction model.

Parameters

speller (spelling correction object, optional (default = None)) –

Returns

result

Return type

malaya.normalize.Normalizer class

class malaya.normalize.Normalizer

normalize(string: str, check_english: bool = True, normalize_text: bool = True, normalize_entity: bool = True, normalize_url: bool = False, normalize_email: bool = False, normalize_year: bool = True, normalize_telephone: bool = True, logging: bool = False)

Normalize a string.

Parameters

• string (str) –

• check_english (bool, (default=True)) – check a word in english dictionary.

• normalize_text (bool, (default=True)) – if True, will try to replace shortforms with internal corpus.

• normalize_entity (bool, (default=True)) – normalize entities, only effect date, datetime, time and money patterns string only.

• normalize_url (bool, (default=False)) – if True, replace :// with empty and . with dot. https://huseinhouse.com -> https huseinhouse dot com.

• normalize_email (bool, (default=False)) – if True, replace @ with di, . with dot. husein.zol05@gmail.com -> husein dot zol kosong lima di gmail dot com.

• normalize_year (bool, (default=True)) – if True, tahun 1987 -> tahun sembilan belas lapan puluh tujuh. if True, 1970-an -> sembilan belas tujuh puluh an. if False, tahun 1987 -> tahun seribu sembilan ratus lapan puluh tujuh.

• normalize_telephone (bool, (default=True)) – if True, no 012-1234567 -> no kosong satu dua, satu dua tiga empat lima enam tujuh

• logging (bool, (default=False)) – if True, will log index and token queue using logging.warn.

Returns

string

Return type

normalized string

malaya.nsfw

malaya.nsfw.lexicon(**kwargs)

Load Lexicon NSFW model.

Returns

result

Return type

malaya.text.lexicon.nsfw.Lexicon class

malaya.nsfw.multinomial(**kwargs)

Load multinomial NSFW model.

Returns

result

Return type

malaya.model.ml.BAYES class

malaya.num2word

malaya.num2word.to_cardinal(number)

Translate from number input to cardinal text representation

Parameters

number (real number) –

Returns

result – cardinal representation

Return type

str

malaya.num2word.to_ordinal(number)

Translate from number input to ordinal text representation

Parameters

number (real number) –

Returns

result – ordinal representation

Return type

str

malaya.num2word.to_ordinal_num(number)

Translate from number input to ordinal numering text representation

Parameters

number (int) –

Returns

result – ordinal numering representation

Return type

str

malaya.num2word.to_currency(value)

Translate from number input to cardinal currency text representation

Parameters

number (int) –

Returns

result – cardinal currency representation

Return type

str

malaya.num2word.to_year(value)

Translate from number input to cardinal year text representation

Parameters

number (int) –

Returns

result – cardinal year representation

Return type

str

malaya.paraphrase

malaya.paraphrase.available_transformer()

List available transformer models.

malaya.paraphrase.transformer(model: str = 't2t', quantized: bool = False, **kwargs)

Load Malaya transformer encoder-decoder model to generate a paraphrase given a string.

Parameters

• model (str, optional (default='t2t')) –

  Model architecture supported. Allowed values:

  • 't2t' - Malaya Transformer BASE parameters.

  • 'small-t2t' - Malaya Transformer SMALL parameters.

  • 't5' - T5 BASE parameters.

  • 'small-t5' - T5 SMALL parameters.

• quantized (bool, optional (default=False)) – if True, will load 8-bit quantized model. Quantized model not necessary faster, totally depends on the machine.

Returns

result – List of model classes:

• if t2t in model, will return malaya.model.tf.Paraphrase.

• if t5 in model, will return malaya.model.t5.Paraphrase.

Return type

model

malaya.pos

malaya.pos.describe()

Describe Part-Of-Speech supported.

malaya.pos.available_transformer()

List available transformer Part-Of-Speech Tagging models.

malaya.pos.naive(string: str)

Recognize POS in a string using Regex.

Parameters

string (str) –

Returns

string

Return type

List[Tuple[str, str]]

malaya.pos.transformer(model: str = 'xlnet', quantized: bool = False, **kwargs)

Load Transformer POS Tagging model, transfer learning Transformer + CRF.

Parameters

• model (str, optional (default='bert')) –

  Model architecture supported. Allowed values:

  • 'bert' - Google BERT BASE parameters.

  • 'tiny-bert' - Google BERT TINY parameters.

  • 'albert' - Google ALBERT BASE parameters.

  • 'tiny-albert' - Google ALBERT TINY parameters.

  • 'xlnet' - Google XLNET BASE parameters.

  • 'alxlnet' - Malaya ALXLNET BASE parameters.

• quantized (bool, optional (default=False)) – if True, will load 8-bit quantized model. Quantized model not necessary faster, totally depends on the machine.

Returns

result – List of model classes:

• if bert in model, will return malaya.model.bert.TaggingBERT.

• if xlnet in model, will return malaya.model.xlnet.TaggingXLNET.

Return type

model

malaya.preprocessing

malaya.preprocessing.unpack_english_contractions(text)

Replace English contractions in text str with their unshortened forms. N.B. The “‘d” and “‘s” forms are ambiguous (had/would, is/has/possessive), so are left as-is. Important Note: The function is taken from textacy (https://github.com/chartbeat-labs/textacy).

malaya.preprocessing.preprocessing(normalize: List[str] = ['url', 'email', 'percent', 'money', 'phone', 'user', 'time', 'date', 'number'], annotate: List[str] = ['allcaps', 'elongated', 'repeated', 'emphasis', 'censored', 'hashtag'], lowercase: bool = True, fix_unidecode: bool = True, expand_english_contractions: bool = True, translate_english_to_bm: bool = True, speller=None, segmenter=None, stemmer=None, **kwargs)

Load Preprocessing class.

Parameters

• normalize (list) – normalizing tokens, can check all supported normalizing at malaya.preprocessing.get_normalize().

• annotate (list) – annonate tokens <open></open>, only accept [‘hashtag’, ‘allcaps’, ‘elongated’, ‘repeated’, ‘emphasis’, ‘censored’].

• lowercase (bool) –

• fix_unidecode (bool) –

• expand_english_contractions (bool) – expand english contractions

• translate_english_to_bm (bool) – translate english words to bahasa malaysia words

• speller (object) – spelling correction object, need to have a method correct

• segmenter (object) – segmentation object, need to have a method segment. If provide, it will expand hashtags, #mondayblues == monday blues

• stemmer (object) – stemmer object, need to have a method stem. If provide, it will stem or lemmatize the string.

Returns

result

Return type

malaya.preprocessing.Preprocessing class

class malaya.preprocessing.Tokenizer

tokenize(text)

Tokenize string.

Parameters

text (str) –

Returns

result

Return type

List[str]

class malaya.preprocessing.Preprocessing

malaya.qa

malaya.qa.available_transformer_squad()

List available Transformer Span models.

malaya.qa.transformer_squad(model: str = 'xlnet', quantized: bool = False, **kwargs)

Load Transformer Span model trained on SQUAD V2 dataset.

Parameters

• model (str, optional (default='xlnet')) –

  Model architecture supported. Allowed values:

  • 'bert' - Google BERT BASE parameters.

  • 'tiny-bert' - Google BERT TINY parameters.

  • 'albert' - Google ALBERT BASE parameters.

  • 'tiny-albert' - Google ALBERT TINY parameters.

  • 'xlnet' - Google XLNET BASE parameters.

  • 'alxlnet' - Malaya ALXLNET BASE parameters.

• quantized (bool, optional (default=False)) – if True, will load 8-bit quantized model. Quantized model not necessary faster, totally depends on the machine.

Returns

result

Return type

malaya.model.tf.SQUAD class

malaya.relevancy

malaya.relevancy.available_transformer()

List available transformer relevancy analysis models.

malaya.relevancy.transformer(model: str = 'xlnet', quantized: bool = False, **kwargs)

Load Transformer relevancy model.

Parameters

• model (str, optional (default='bert')) –

  Model architecture supported. Allowed values:

  • 'bert' - Google BERT BASE parameters.

  • 'tiny-bert' - Google BERT TINY parameters.

  • 'albert' - Google ALBERT BASE parameters.

  • 'tiny-albert' - Google ALBERT TINY parameters.

  • 'xlnet' - Google XLNET BASE parameters.

  • 'alxlnet' - Malaya ALXLNET BASE parameters.

  • 'bigbird' - Google BigBird BASE parameters.

  • 'tiny-bigbird' - Malaya BigBird BASE parameters.

• quantized (bool, optional (default=False)) – if True, will load 8-bit quantized model. Quantized model not necessary faster, totally depends on the machine.

Returns

result – List of model classes:

• if bert in model, will return malaya.model.bert.MulticlassBERT.

• if xlnet in model, will return malaya.model.xlnet.MulticlassXLNET.

• if bigbird in model, will return malaya.model.xlnet.MulticlassBigBird.

Return type

model

malaya.segmentation

malaya.segmentation.viterbi(max_split_length: int = 20, **kwargs)

Load Segmenter class using viterbi algorithm.

Parameters

• max_split_length (int, (default=20)) – max length of words in a sentence to segment

• validate (bool, optional (default=True)) – if True, malaya will check model availability and download if not available.

Returns

result

Return type

malaya.segmentation.Segmenter class

malaya.segmentation.available_transformer()

List available transformer models.

malaya.segmentation.transformer(model: str = 'small', quantized: bool = False, **kwargs)

Load transformer encoder-decoder model to Segmentize.

Parameters

• model (str, optional (default='base')) –

  Model architecture supported. Allowed values:

  • 'small' - Transformer SMALL parameters.

  • 'base' - Transformer BASE parameters.

• quantized (bool, optional (default=False)) – if True, will load 8-bit quantized model. Quantized model not necessary faster, totally depends on the machine.

Returns

result

Return type

malaya.model.tf.Segmentation class

class malaya.segmentation.Segmenter

segment(strings: List[str])

Segment strings. Example, “sayasygkan negarasaya” -> “saya sygkan negara saya”

Parameters

strings (List[str]) –

Returns

result

Return type

List[str]

malaya.sentiment

malaya.sentiment.available_transformer()

List available transformer sentiment analysis models.

malaya.sentiment.multinomial(**kwargs)

Load multinomial sentiment model.

Returns

result

Return type

malaya.model.ml.Bayes class

malaya.sentiment.transformer(model: str = 'bert', quantized: bool = False, **kwargs)

Load Transformer sentiment model.

Parameters

• model (str, optional (default='bert')) –

  Model architecture supported. Allowed values:

  • 'bert' - Google BERT BASE parameters.

  • 'tiny-bert' - Google BERT TINY parameters.

  • 'albert' - Google ALBERT BASE parameters.

  • 'tiny-albert' - Google ALBERT TINY parameters.

  • 'xlnet' - Google XLNET BASE parameters.

  • 'alxlnet' - Malaya ALXLNET BASE parameters.

• quantized (bool, optional (default=False)) – if True, will load 8-bit quantized model. Quantized model not necessary faster, totally depends on the machine.

Returns

result – List of model classes:

• if bert in model, will return malaya.model.bert.BinaryBERT.

• if xlnet in model, will return malaya.model.xlnet.BinaryXLNET.

Return type

model

malaya.spell

class malaya.spell.Probability(corpus, sp_tokenizer=None)

The SpellCorrector extends the functionality of the Peter Norvig’s spell-corrector in http://norvig.com/spell-correct.html And improve it using some algorithms from Normalization of noisy texts in Malaysian online reviews, https://www.researchgate.net/publication/287050449_Normalization_of_noisy_texts_in_Malaysian_online_reviews Added custom vowels augmentation

P(word)

Probability of word.

correct(word: str, **kwargs)

Most probable spelling correction for word.

Parameters

word (str) –

Returns

result

Return type

str

correct_text(text: str)

Correct all the words within a text, returning the corrected text.

Parameters

text (str) –

Returns

result

Return type

str

correct_match(match)

Spell-correct word in match, and preserve proper upper/lower/title case.

correct_word(word: str)

Spell-correct word in match, and preserve proper upper/lower/title case.

class malaya.spell.Symspell(model, verbosity, corpus, k=10)

The SymspellCorrector extends the functionality of symspeller, https://github.com/mammothb/symspellpy And improve it using some algorithms from Normalization of noisy texts in Malaysian online reviews, https://www.researchgate.net/publication/287050449_Normalization_of_noisy_texts_in_Malaysian_online_reviews Added custom vowels augmentation

edit_step(word)

Generate candidates given a word.

Parameters

word (str) –

Returns

result

Return type

{candidate1, candidate2}

edit_candidates(word)

Generate candidates given a word.

Parameters

word (str) –

Returns

result

Return type

{candidate1, candidate2}

correct(word: str, **kwargs)

Most probable spelling correction for word.

Parameters

word (str) –

Returns

result

Return type

str

correct_text(text: str)

Correct all the words within a text, returning the corrected text.

Parameters

text (str) –

Returns

result

Return type

str

correct_match(match)

Spell-correct word in match, and preserve proper upper/lower/title case.

malaya.spell.probability(sentence_piece: bool = False, **kwargs)

Train a Probability Spell Corrector.

Parameters

sentence_piece (bool, optional (default=False)) – if True, reduce possible augmentation states using sentence piece.

Returns

result

Return type

malaya.spell.Probability class

malaya.spell.symspell(max_edit_distance_dictionary: int = 2, prefix_length: int = 7, term_index: int = 0, count_index: int = 1, top_k: int = 10, **kwargs)

Train a symspell Spell Corrector.

Returns

result

Return type

malaya.spell.Symspell class

malaya.spell.transformer(model, sentence_piece: bool = False, **kwargs)

Load a Transformer Spell Corrector. Right now only supported BERT and ALBERT.

Parameters

sentence_piece (bool, optional (default=False)) – if True, reduce possible augmentation states using sentence piece.

Returns

result

Return type

malaya.spell.Transformer class

class malaya.spell.Transformer

correct(word: str, string: str, index: int = - 1, batch_size: int = 20)

Correct a word within a text, returning the corrected word.

correct_text(text: str, batch_size: int = 20)

Correct all the words within a text, returning the corrected text.

correct_word(word: str, string: str, batch_size: int = 20)

Spell-correct word in match, and preserve proper upper/lower/title case.

class malaya.spell.Probability

The SpellCorrector extends the functionality of the Peter Norvig’s spell-corrector in http://norvig.com/spell-correct.html And improve it using some algorithms from Normalization of noisy texts in Malaysian online reviews, https://www.researchgate.net/publication/287050449_Normalization_of_noisy_texts_in_Malaysian_online_reviews Added custom vowels augmentation

P(word)

Probability of word.

correct(word: str, **kwargs)

Most probable spelling correction for word.

Parameters

word (str) –

Returns

result

Return type

str

correct_text(text: str)

Correct all the words within a text, returning the corrected text.

Parameters

text (str) –

Returns

result

Return type

str

correct_match(match)

Spell-correct word in match, and preserve proper upper/lower/title case.

correct_word(word: str)

Spell-correct word in match, and preserve proper upper/lower/title case.

class malaya.spell.Symspell

The SymspellCorrector extends the functionality of symspeller, https://github.com/mammothb/symspellpy And improve it using some algorithms from Normalization of noisy texts in Malaysian online reviews, https://www.researchgate.net/publication/287050449_Normalization_of_noisy_texts_in_Malaysian_online_reviews Added custom vowels augmentation

edit_step(word)

Generate candidates given a word.

Parameters

word (str) –

Returns

result

Return type

{candidate1, candidate2}

edit_candidates(word)

Generate candidates given a word.

Parameters

word (str) –

Returns

result

Return type

{candidate1, candidate2}

correct(word: str, **kwargs)

Most probable spelling correction for word.

Parameters

word (str) –

Returns

result

Return type

str

correct_text(text: str)

Correct all the words within a text, returning the corrected text.

Parameters

text (str) –

Returns

result

Return type

str

correct_match(match)

Spell-correct word in match, and preserve proper upper/lower/title case.

malaya.stack

malaya.stack.voting_stack(models, text: str)

Stacking for POS, Entities and Dependency models.

Parameters

• models (list) – list of models.

• text (str) – string to predict.

Returns

result

Return type

list

malaya.stack.predict_stack(models, strings: List[str], aggregate: Callable = <function gmean>, **kwargs)

Stacking for predictive models.

Parameters

• models (List[Callable]) – list of models.

• strings (List[str]) –

• aggregate (Callable, optional (default=scipy.stats.mstats.gmean)) – Aggregate function.

Returns

result

Return type

dict

malaya.stem

malaya.stem.naive()

Load stemming model using startswith and endswith naively using regex patterns.

Returns

result

Return type

malaya.stem.Naive class

malaya.stem.sastrawi()

Load stemming model using Sastrawi, this also include lemmatization.

Returns

result

Return type

malaya.stem.Sastrawi class

malaya.stem.deep_model(quantized: bool = False, **kwargs)

Load LSTM + Bahdanau Attention stemming model, this also include lemmatization. Original size 41.6MB, quantized size 10.6MB .

Parameters

quantized (bool, optional (default=False)) – if True, will load 8-bit quantized model. Quantized model not necessary faster, totally depends on the machine.

Returns

result

Return type

malaya.stem.DeepStemmer class

class malaya.stem.DeepStemmer

stem(string: str, beam_search: bool = False)

Stem a string, this also include lemmatization.

Parameters

• string (str) –

• beam_search (bool, (optional=False)) – If True, use beam search decoder, else use greedy decoder.

Returns

result

Return type

str

class malaya.stem.Sastrawi

class malaya.stem.Naive

malaya.subjectivity

malaya.subjectivity.available_transformer()

List available transformer subjective analysis models.

malaya.subjectivity.multinomial(**kwargs)

Load multinomial subjectivity model.

Parameters

validate (bool, optional (default=True)) – if True, malaya will check model availability and download if not available.

Returns

result

Return type

malaya.model.ml.Bayes class

malaya.subjectivity.transformer(model: str = 'bert', quantized: bool = False, **kwargs)

Load Transformer subjectivity model.

Parameters

• model (str, optional (default='bert')) –

  Model architecture supported. Allowed values:

  • 'bert' - Google BERT BASE parameters.

  • 'tiny-bert' - Google BERT TINY parameters.

  • 'albert' - Google ALBERT BASE parameters.

  • 'tiny-albert' - Google ALBERT TINY parameters.

  • 'xlnet' - Google XLNET BASE parameters.

  • 'alxlnet' - Malaya ALXLNET BASE parameters.

• quantized (bool, optional (default=False)) – if True, will load 8-bit quantized model. Quantized model not necessary faster, totally depends on the machine.

Returns

result – List of model classes:

• if bert in model, will return malaya.model.bert.BinaryBERT.

• if xlnet in model, will return malaya.model.xlnet.BinaryXLNET.

Return type

model

malaya.tatabahasa

malaya.tatabahasa.describe()

Describe kesalahan tatabahasa supported. Full description at https://tatabahasabm.tripod.com/tata/salahtata.htm

malaya.tatabahasa.available_transformer()

List available transformer models.

malaya.tatabahasa.transformer(model: str = 'base', quantized: bool = False, **kwargs)

Load Malaya transformer encoder-decoder + tagging model to correct a kesalahan tatabahasa text.

Parameters

• model (str, optional (default='base')) –

  Model architecture supported. Allowed values:

  • 'small' - Malaya Transformer Tag SMALL parameters.

  • 'base' - Malaya Transformer Tag BASE parameters.

• quantized (bool, optional (default=False)) – if True, will load 8-bit quantized model. Quantized model not necessary faster, totally depends on the machine.

Returns

result

Return type

malaya.model.tf.Tatabahasa class

malaya.summarization.abstractive

malaya.summarization.abstractive.available_transformer()

List available transformer models.

malaya.summarization.abstractive.transformer(model: str = 't2t', quantized: bool = False, **kwargs)

Load Malaya transformer encoder-decoder model to generate a summary given a string.

Parameters

• model (str, optional (default='t2t')) –

  Model architecture supported. Allowed values:

  • 't2t' - Malaya Transformer BASE parameters.

  • 'small-t2t' - Malaya Transformer SMALL parameters.

  • 't2t-distill' - Distilled Malaya Transformer BASE parameters.

  • 't5' - T5 BASE parameters.

  • 'small-t5' - T5 SMALL parameters.

  • 'bigbird' - BigBird + Pegasus BASE parameters.

  • 'small-bigbird' - BigBird + Pegasus SMALL parameters.

  • 'pegasus' - Pegasus BASE parameters.

  • 'small-pegasus' - Pegasus SMALL parameters.

• quantized (bool, optional (default=False)) – if True, will load 8-bit quantized model. Quantized model not necessary faster, totally depends on the machine.

Returns

result – List of model classes:

• if t2t in model, will return malaya.model.tf.Summarization.

• if t5 in model, will return malaya.model.t5.Summarization.

• if bigbird in model, will return malaya.model.bigbird.Summarization.

• if pegasus in model, will return malaya.model.pegasus.Summarization.

Return type

model

malaya.summarization.extractive

malaya.summarization.extractive.encoder(vectorizer)

Encoder interface for summarization.

Parameters

vectorizer (object) – encoder interface object, eg, BERT, XLNET, ALBERT, ALXLNET. should have vectorize method.

Returns

result

Return type

malaya.model.extractive_summarization.Encoder

malaya.summarization.extractive.doc2vec(wordvector)

Doc2Vec interface for summarization.

Parameters

wordvector (object) – malaya.wordvector.WordVector object. should have get_vector_by_name method.

Returns

result

Return type

malaya.model.extractive_summarization.Doc2Vec

malaya.summarization.extractive.sklearn(model, vectorizer)

sklearn interface for summarization.

Parameters

• model (object) –

  Should have fit_transform method. Commonly:

  • sklearn.decomposition.TruncatedSVD - LSA algorithm.

  • sklearn.decomposition.LatentDirichletAllocation - LDA algorithm.

• vectorizer (object) –

  Should have fit_transform method. Commonly:

  • sklearn.feature_extraction.text.TfidfVectorizer - TFIDF algorithm.

  • sklearn.feature_extraction.text.CountVectorizer - Bag-of-Word algorithm.

  • malaya.text.vectorizer.SkipGramCountVectorizer - Skip Gram Bag-of-Word algorithm.

  • malaya.text.vectorizer.SkipGramTfidfVectorizer - Skip Gram TFIDF algorithm.

Returns

result

Return type

malaya.model.extractive_summarization.SKLearn

malaya.similarity

malaya.similarity.doc2vec_wordvector(wordvector)

Doc2vec interface for text similarity using Word Vector.

Parameters

wordvector (object) – malaya.wordvector.WordVector object. should have get_vector_by_name method.

Returns

result

Return type

malaya.similarity.Doc2VecSimilarity

malaya.similarity.doc2vec_vectorizer(vectorizer)

Doc2vec interface for text similarity using Encoder model.

Parameters

vectorizer (object) – encoder interface object, BERT, XLNET. should have vectorize method.

Returns

result

Return type

malaya.similarity.VectorizerSimilarity

malaya.similarity.available_transformer()

List available transformer similarity models.

malaya.similarity.transformer(model: str = 'bert', quantized: bool = False, **kwargs)

Load Transformer similarity model.

Parameters

• model (str, optional (default='bert')) –

  Model architecture supported. Allowed values:

  • 'bert' - Google BERT BASE parameters.

  • 'tiny-bert' - Google BERT TINY parameters.

  • 'albert' - Google ALBERT BASE parameters.

  • 'tiny-albert' - Google ALBERT TINY parameters.

  • 'xlnet' - Google XLNET BASE parameters.

  • 'alxlnet' - Malaya ALXLNET BASE parameters.

• quantized (bool, optional (default=False)) – if True, will load 8-bit quantized model. Quantized model not necessary faster, totally depends on the machine.

Returns

result – List of model classes:

• if bert in model, will return malaya.model.bert.SiameseBERT.

• if xlnet in model, will return malaya.model.xlnet.SiameseXLNET.

Return type

model

class malaya.similarity.VectorizerSimilarity

predict_proba(left_strings: List[str], right_strings: List[str], similarity: str = 'cosine')

calculate similarity for two different batch of texts.

Parameters

• left_strings (list of str) –

• right_strings (list of str) –

• similarity (str, optional (default='mean')) –

  similarity supported. Allowed values:

  • 'cosine' - cosine similarity.

  • 'euclidean' - euclidean similarity.

  • 'manhattan' - manhattan similarity.

Returns

result

Return type

List[float]

heatmap(strings: List[str], similarity: str = 'cosine', visualize: bool = True, annotate: bool = True, figsize: Tuple[int, int] = (7, 7))

plot a heatmap based on output from bert similarity.

Parameters

• strings (list of str) – list of strings.

• similarity (str, optional (default='mean')) –

  similarity supported. Allowed values:

  • 'cosine' - cosine similarity.

  • 'euclidean' - euclidean similarity.

  • 'manhattan' - manhattan similarity.

• visualize (bool) – if True, it will render plt.show, else return data.

• figsize (tuple, (default=(7, 7))) – figure size for plot.

Returns

result – list of results

Return type

list

class malaya.similarity.Doc2VecSimilarity

predict_proba(left_strings: List[str], right_strings: List[str], aggregation: Callable = <function mean>, similarity: str = 'cosine', soft: bool = False)

calculate similarity for two different batch of texts.

Parameters

• left_strings (list of str) –

• right_strings (list of str) –

• aggregation (Callable, optional (default=numpy.mean)) –

• similarity (str, optional (default='mean')) –

  similarity supported. Allowed values:

  • 'cosine' - cosine similarity.

  • 'euclidean' - euclidean similarity.

  • 'manhattan' - manhattan similarity.

• soft (bool, optional (default=False)) – word not inside word vector will replace with nearest word if True, else, will skip.

Returns

result

Return type

List[float]

heatmap(strings: List[str], aggregation: Callable = <function mean>, similarity: str = 'cosine', soft: bool = False, visualize: bool = True, annotate: bool = True, figsize: Tuple[int, int] = (7, 7))

plot a heatmap based on output from bert similarity.

Parameters

• strings (list of str) – list of strings

• aggregation (Callable, optional (default=numpy.mean)) –

• similarity (str, optional (default='mean')) –

  similarity supported. Allowed values:

  • 'cosine' - cosine similarity.

  • 'euclidean' - euclidean similarity.

  • 'manhattan' - manhattan similarity.

• soft (bool, optional (default=True)) – word not inside word vector will replace with nearest word if True, else, will skip.

• visualize (bool) – if True, it will render plt.show, else return data.

• figsize (tuple, (default=(7, 7))) – figure size for plot.

Returns

result – list of results.

Return type

list

malaya.topic_model

malaya.topic_model.available_vectorizer()

List available vectorizer topic modeling.

malaya.topic_model.sklearn(corpus: List[str], model, vectorizer, n_topics: int, cleaning=<function simple_textcleaning>, stopwords=<function get_stopwords>, **kwargs)

Train a SKlearn model to do topic modelling based on corpus / list of strings given.

Parameters

• corpus (list) –

• model (object) –

  Should have fit_transform method. Commonly:

  • sklearn.decomposition.TruncatedSVD - LSA algorithm.

  • sklearn.decomposition.LatentDirichletAllocation - LDA algorithm.

  • sklearn.decomposition.NMF - NMF algorithm.

• vectorizer (object) –

  Should have fit_transform method. Commonly:

  • sklearn.feature_extraction.text.TfidfVectorizer - TFIDF algorithm.

  • sklearn.feature_extraction.text.CountVectorizer - Bag-of-Word algorithm.

  • malaya.text.vectorizer.SkipGramCountVectorizer - Skip Gram Bag-of-Word algorithm.

  • malaya.text.vectorizer.SkipGramTfidfVectorizer - Skip Gram TFIDF algorithm.

• n_topics (int, (default=10)) – size of decomposition column.

• cleaning (function, (default=malaya.text.function.simple_textcleaning)) – function to clean the corpus.

• stopwords (List[str], (default=malaya.texts.function.get_stopwords)) – A callable that returned a List[str], or a List[str], or a Tuple[str]

Returns

result

Return type

malaya.topic_modelling.Topic class

malaya.topic_model.lda2vec(corpus: List[str], vectorizer, n_topics: int = 10, cleaning=<function simple_textcleaning>, stopwords=<function get_stopwords>, window_size: int = 2, embedding_size: int = 128, epoch: int = 10, switch_loss: int = 1000, **kwargs)

Train a LDA2Vec model to do topic modelling based on corpus / list of strings given.

Parameters

• corpus (list) –

• vectorizer (object) –

  Should have fit_transform method. Commonly:

  • sklearn.feature_extraction.text.TfidfVectorizer - TFIDF algorithm.

  • sklearn.feature_extraction.text.CountVectorizer - Bag-of-Word algorithm.

  • malaya.text.vectorizer.SkipGramCountVectorizer - Skip Gram Bag-of-Word algorithm.

  • malaya.text.vectorizer.SkipGramTfidfVectorizer - Skip Gram TFIDF algorithm.

• n_topics (int, (default=10)) – size of decomposition column.

• cleaning (function, (default=malaya.text.function.simple_textcleaning)) – function to clean the corpus.

• stopwords (List[str], (default=malaya.texts.function.get_stopwords)) – A callable that returned a List[str], or a List[str], or a Tuple[str]

• embedding_size (int, (default=128)) – embedding size of lda2vec tensors.

• epoch (int, (default=10)) – training iteration, how many loop need to train.

• switch_loss (int, (default=3)) – baseline to switch from document based loss to document + word based loss.

Returns

result

Return type

malaya.topic_modelling.DeepTopic class

malaya.topic_model.attention(corpus: List[str], n_topics: int, vectorizer, cleaning=<function simple_textcleaning>, stopwords=<function get_stopwords>, ngram: Tuple[int, int] = (1, 3), batch_size: int = 10)

Use attention from transformer model to do topic modelling based on corpus / list of strings given.

Parameters

• corpus (list) –

• n_topics (int, (default=10)) – size of decomposition column.

• vectorizer (object) –

• cleaning (function, (default=malaya.text.function.simple_textcleaning)) – function to clean the corpus.

• stopwords (List[str], (default=malaya.texts.function.get_stopwords)) – A callable that returned a List[str], or a List[str], or a Tuple[str]

• ngram (tuple, (default=(1,3))) – n-grams size to train a corpus.

• batch_size (int, (default=10)) – size of strings for each vectorization and attention.

Returns

result

Return type

malaya.topic_modelling.AttentionTopic class

class malaya.topic_model.AttentionTopic

top_topics(len_topic: int, top_n: int = 10, return_df: bool = True)

Print important topics based on decomposition.

Parameters

• len_topic (int) – size of topics.

• top_n (int, optional (default=10)) – top n of each topic.

• return_df (bool, optional (default=True)) – return as pandas.DataFrame, else JSON.

get_topics(len_topic: int)

Return important topics based on decomposition.

Parameters

len_topic (int) – size of topics.

Returns

result

Return type

List[str]

class malaya.topic_model.DeepTopic

visualize_topics(notebook_mode: int = False, mds: str = 'pcoa')

Print important topics based on decomposition.

Parameters

mds (str, optional (default='pcoa')) –

2D Decomposition. Allowed values:

• 'pcoa' - Dimension reduction via Jensen-Shannon Divergence & Principal Coordinate Analysis (aka Classical Multidimensional Scaling)

• 'mmds' - Dimension reduction via Multidimensional scaling

• 'tsne' - Dimension reduction via t-distributed stochastic neighbor embedding

top_topics(len_topic: int, top_n: int = 10, return_df: bool = True)

Print important topics based on decomposition.

Parameters

• len_topic (int) – size of topics.

• top_n (int, optional (default=10)) – top n of each topic.

• return_df (bool, optional (default=True)) – return as pandas.DataFrame, else JSON.

get_topics(len_topic: int)

Return important topics based on decomposition.

Parameters

len_topic (int) – size of topics.

Returns

result

Return type

List[str]

get_sentences(len_sentence: int, k: int = 0)

Return important sentences related to selected column based on decomposition.

Parameters

• len_sentence (int) –

• k (int, (default=0)) – index of decomposition matrix.

Returns

result

Return type

List[str]

class malaya.topic_model.Topic

visualize_topics(notebook_mode: bool = False, mds: str = 'pcoa')

Print important topics based on decomposition.

Parameters

mds (str, optional (default='pcoa')) –

2D Decomposition. Allowed values:

• 'pcoa' - Dimension reduction via Jensen-Shannon Divergence & Principal Coordinate Analysis (aka Classical Multidimensional Scaling)

• 'mmds' - Dimension reduction via Multidimensional scaling

• 'tsne' - Dimension reduction via t-distributed stochastic neighbor embedding

top_topics(len_topic: int, top_n: int = 10, return_df: bool = True)

Print important topics based on decomposition.

Parameters

• len_topic (int) – size of topics.

• top_n (int, optional (default=10)) – top n of each topic.

• return_df (bool, optional (default=True)) – return as pandas.DataFrame, else JSON.

get_topics(len_topic: int)

Return important topics based on decomposition.

Parameters

len_topic (int) –

Returns

result

Return type

List[str]

get_sentences(len_sentence: int, k: int = 0)

Return important sentences related to selected column based on decomposition.

Parameters

• len_sentence (int) –

• k (int, (default=0)) – index of decomposition matrix.

Returns

result

Return type

List[str]

malaya.toxicity

malaya.toxicity.available_transformer()

List available transformer toxicity analysis models.

malaya.toxicity.multinomial(**kwargs)

Load multinomial toxicity model.

Returns

result

Return type

malaya.model.ml.MultilabelBayes class

malaya.toxicity.transformer(model: str = 'xlnet', quantized: bool = False, **kwargs)

Load Transformer toxicity model.

Parameters

• model (str, optional (default='bert')) –

  Model architecture supported. Allowed values:

  • 'bert' - Google BERT BASE parameters.

  • 'tiny-bert' - Google BERT TINY parameters.

  • 'albert' - Google ALBERT BASE parameters.

  • 'tiny-albert' - Google ALBERT TINY parameters.

  • 'xlnet' - Google XLNET BASE parameters.

  • 'alxlnet' - Malaya ALXLNET BASE parameters.

• quantized (bool, optional (default=False)) – if True, will load 8-bit quantized model. Quantized model not necessary faster, totally depends on the machine.

Returns

result – List of model classes:

• if bert in model, will return malaya.model.bert.SigmoidBERT.

• if xlnet in model, will return malaya.model.xlnet.SigmoidXLNET.

Return type

model

malaya.transformer

malaya.transformer.available_transformer()

List available transformer models.

malaya.transformer.load(model: str = 'electra', pool_mode: str = 'last', **kwargs)

Load transformer model.

Parameters

• model (str, optional (default='bert')) –

  Model architecture supported. Allowed values:

  • 'bert' - Google BERT BASE parameters.

  • 'tiny-bert' - Google BERT TINY parameters.

  • 'albert' - Google ALBERT BASE parameters.

  • 'tiny-albert' - Google ALBERT TINY parameters.

  • 'xlnet' - Google XLNET BASE parameters.

  • 'alxlnet' - Malaya ALXLNET BASE parameters.

  • 'electra' - Google ELECTRA BASE parameters.

  • 'small-electra' - Google ELECTRA SMALL parameters.

• pool_mode (str, optional (default='last')) –

  Model logits architecture supported. Only usable if model in [‘xlnet’, ‘alxlnet’]. Allowed values:

  • 'last' - last of the sequence.

  • 'first' - first of the sequence.

  • 'mean' - mean of the sequence.

  • 'attn' - attention of the sequence.

Returns

result – List of model classes:

• if bert in model, will return malaya.transformers.bert.Model.

• if xlnet in model, will return malaya.transformers.xlnet.Model.

• if albert in model, will return malaya.transformers.albert.Model.

• if electra in model, will return malaya.transformers.electra.Model.

Return type

model

malaya.translation.en_ms

malaya.translation.en_ms.available_transformer()

List available transformer models.

malaya.translation.en_ms.transformer(model: str = 'base', quantized: bool = False, **kwargs)

Load transformer encoder-decoder model to translate EN-to-MS.

Parameters

• model (str, optional (default='base')) –

  Model architecture supported. Allowed values:

  • 'small' - Transformer SMALL parameters.

  • 'base' - Transformer BASE parameters.

  • 'large' - Transformer LARGE parameters.

  • 'bigbird' - BigBird BASE parameters.

  • 'small-bigbird' - BigBird SMALL parameters.

• quantized (bool, optional (default=False)) – if True, will load 8-bit quantized model. Quantized model not necessary faster, totally depends on the machine.

Returns

result – List of model classes:

• if bigbird in model, return malaya.model.bigbird.Translation.

• else, return malaya.model.tf.Translation.

Return type

model

malaya.translation.ms_en

malaya.translation.ms_en.available_transformer()

List available transformer models.

malaya.translation.ms_en.transformer(model: str = 'base', quantized: bool = False, **kwargs)

Load Transformer encoder-decoder model to translate MS-to-EN.

Parameters

• model (str, optional (default='base')) –

  Model architecture supported. Allowed values:

  • 'small' - Transformer SMALL parameters.

  • 'base' - Transformer BASE parameters.

  • 'large' - Transformer LARGE parameters.

  • 'bigbird' - BigBird BASE parameters.

  • 'small-bigbird' - BigBird SMALL parameters.

• quantized (bool, optional (default=False)) – if True, will load 8-bit quantized model. Quantized model not necessary faster, totally depends on the machine.

Returns

result – List of model classes:

• if bigbird in model, return malaya.model.bigbird.Translation.

• else, return malaya.model.tf.Translation.

Return type

model

malaya.true_case

malaya.true_case.available_transformer()

List available transformer models.

malaya.true_case.transformer(model: str = 'base', quantized: bool = False, **kwargs)

Load transformer encoder-decoder model to True Case.

Parameters

• model (str, optional (default='base')) –

  Model architecture supported. Allowed values:

  • 'small' - Transformer SMALL parameters.

  • 'base' - Transformer BASE parameters.

• quantized (bool, optional (default=False)) – if True, will load 8-bit quantized model. Quantized model not necessary faster, totally depends on the machine.

Returns

result

Return type

malaya.model.tf.TrueCase class

malaya.word2num

malaya.word2num.word2num(string)

Translate from string to number, eg ‘kesepuluh’ -> 10.

Parameters

string (str) –

Returns

result

Return type

int / float

malaya.wordvector

malaya.wordvector.available_wordvector()

List available transformer models.

malaya.wordvector.load(model: str = 'wikipedia', **kwargs)

Return malaya.wordvector.WordVector object.

Parameters

model (str, optional (default='wikipedia')) –

Model architecture supported. Allowed values:

• 'wikipedia' - pretrained on Malay wikipedia word2vec size 256.

• 'socialmedia' - pretrained on cleaned Malay twitter and Malay instagram size 256.

• 'news' - pretrained on cleaned Malay news size 256.

• 'combine' - pretrained on cleaned Malay news + Malay social media + Malay wikipedia size 256.

Returns

• vocabulary (indices dictionary for vector.)

• vector (np.array, 2D.)

class malaya.wordvector.WordVector

get_vector_by_name(word: str, soft: bool = False, topn_soft: int = 5)

get vector based on string.

Parameters

• word (str) –

• soft (bool, (default=True)) – if True, a word not in the dictionary will be replaced with nearest JaroWinkler ratio. if False, it will throw an exception if a word not in the dictionary.

• topn_soft (int, (default=5)) – if word not found in dictionary, will returned topn_soft size of similar size using jarowinkler.

Returns

vector

Return type

np.array, 1D

tree_plot(labels, figsize: Tuple[int, int] = (7, 7), annotate: bool = True)

plot a tree plot based on output from calculator / n_closest / analogy.

Parameters

• labels (list) – output from calculator / n_closest / analogy.

• visualize (bool) – if True, it will render plt.show, else return data.

• figsize (tuple, (default=(7, 7))) – figure size for plot.

Returns

• embed (np.array, 2D.)

• labelled (labels for X / Y axis.)

scatter_plot(labels, centre: str = None, figsize: Tuple[int, int] = (7, 7), plus_minus: int = 25, handoff: float = 5e-05)

plot a scatter plot based on output from calculator / n_closest / analogy.

Parameters

• labels (list) – output from calculator / n_closest / analogy

• centre (str, (default=None)) – centre label, if a str, it will annotate in a red color.

• figsize (tuple, (default=(7, 7))) – figure size for plot.

Returns

tsne

Return type

np.array, 2D.

batch_calculator(equations: List[str], num_closest: int = 5, return_similarity: bool = False)

batch calculator parser for word2vec using tensorflow.

Parameters

• equations (list of str) – Eg, ‘[(mahathir + najib) - rosmah]’

• num_closest (int, (default=5)) – number of words closest to the result.

Returns

word_list

Return type

list of nearest words

calculator(equation: str, num_closest: int = 5, metric: str = 'cosine', return_similarity: bool = True)

calculator parser for word2vec.

Parameters

• equation (str) – Eg, ‘(mahathir + najib) - rosmah’

• num_closest (int, (default=5)) – number of words closest to the result.

• metric (str, (default='cosine')) – vector distance algorithm.

• return_similarity (bool, (default=True)) – if True, will return between 0-1 represents the distance.

Returns

word_list

Return type

list of nearest words

batch_n_closest(words: List[str], num_closest: int = 5, return_similarity: bool = False, soft: bool = True)

find nearest words based on a batch of words using Tensorflow.

Parameters

• words (list) – Eg, [‘najib’,’anwar’]

• num_closest (int, (default=5)) – number of words closest to the result.

• return_similarity (bool, (default=True)) – if True, will return between 0-1 represents the distance.

• soft (bool, (default=True)) – if True, a word not in the dictionary will be replaced with nearest JaroWinkler ratio. if False, it will throw an exception if a word not in the dictionary.

Returns

word_list

Return type

list of nearest words

n_closest(word: str, num_closest: int = 5, metric: str = 'cosine', return_similarity: bool = True)

find nearest words based on a word.

Parameters

• word (str) – Eg, ‘najib’

• num_closest (int, (default=5)) – number of words closest to the result.

• metric (str, (default='cosine')) – vector distance algorithm.

• return_similarity (bool, (default=True)) – if True, will return between 0-1 represents the distance.

Returns

word_list

Return type

list of nearest words

analogy(a: str, b: str, c: str, num: int = 1, metric: str = 'cosine')

analogy calculation, vb - va + vc.

Parameters

• a (str) –

• b (str) –

• c (str) –

• num (int, (default=1)) –

• metric (str, (default='cosine')) – vector distance algorithm.

Returns

word_list

Return type

list of nearest words.

project_2d(start: int, end: int)

project word2vec into 2d dimension.

Parameters

• start (int) –

• end (int) –

Returns

• embed_2d (TSNE decomposition)

• word_list (words in between start and end.)

network(word: str, num_closest: int = 8, depth: int = 4, min_distance: float = 0.5, iteration: int = 300, figsize: Tuple[int, int] = (15, 15), node_color: str = '#72bbd0', node_factor: int = 50)

plot a social network based on word given

Parameters

• word (str) – centre of social network.

• num_closest (int, (default=8)) – number of words closest to the node.

• depth (int, (default=4)) – depth of social network. More deeper more expensive to calculate, big^O(num_closest ** depth).

• min_distance (float, (default=0.5)) – minimum distance among nodes. Increase the value to increase the distance among nodes.

• iteration (int, (default=300)) – number of loops to train the social network to fit min_distace.

• figsize (tuple, (default=(15, 15))) – figure size for plot.

• node_color (str, (default='#72bbd0')) – color for nodes.

• node_factor (int, (default=10)) – size factor for depth nodes. Increase this value will increase nodes sizes based on depth.

Returns

G

Return type

networkx graph object

malaya.zero_shot.classification

malaya.zero_shot.classification.available_transformer()

List available transformer zero-shot models.

malaya.zero_shot.classification.transformer(model: str = 'bert', quantized: bool = False, **kwargs)

Load Transformer zero-shot model.

Parameters

• model (str, optional (default='bert')) –

  Model architecture supported. Allowed values:

  • 'bert' - Google BERT BASE parameters.

  • 'tiny-bert' - Google BERT TINY parameters.

  • 'albert' - Google ALBERT BASE parameters.

  • 'tiny-albert' - Google ALBERT TINY parameters.

  • 'xlnet' - Google XLNET BASE parameters.

  • 'alxlnet' - Malaya ALXLNET BASE parameters.

• quantized (bool, optional (default=False)) – if True, will load 8-bit quantized model. Quantized model not necessary faster, totally depends on the machine.

Returns

result – List of model classes:

• if bert in model, will return malaya.model.bert.ZeroshotBERT.

• if xlnet in model, will return malaya.model.xlnet.ZeroshotXLNET.

Return type

model

malaya.model.bert

class malaya.model.bert.BinaryBERT

vectorize(strings: List[str], method: str = 'first')

vectorize list of strings.

Parameters

• strings (List[str]) –

• method (str, optional (default='first')) –

  Vectorization layer supported. Allowed values:

  • 'last' - vector from last sequence.

  • 'first' - vector from first sequence.

  • 'mean' - average vectors from all sequences.

  • 'word' - average vectors based on tokens.

Returns

result

Return type

np.array

predict(strings: List[str], add_neutral: bool = True)

classify list of strings.

Parameters

• strings (List[str]) –

• add_neutral (bool, optional (default=True)) – if True, it will add neutral probability.

Returns

result

Return type

List[str]

predict_proba(strings: List[str], add_neutral: bool = True)

classify list of strings and return probability.

Parameters

• strings (List[str]) –

• add_neutral (bool, optional (default=True)) – if True, it will add neutral probability.

Returns

result

Return type

List[dict[str, float]]

predict_words(string: str, method: str = 'last', visualization: bool = True)

classify words.

Parameters

• string (str) –

• method (str, optional (default='last')) –

  Attention layer supported. Allowed values:

  • 'last' - attention from last layer.

  • 'first' - attention from first layer.

  • 'mean' - average attentions from all layers.

• visualization (bool, optional (default=True)) – If True, it will open the visualization dashboard.

Returns

result

Return type

dict

class malaya.model.bert.MulticlassBERT

vectorize(strings: List[str], method: str = 'first')

vectorize list of strings.

Parameters

• strings (List[str]) –

• method (str, optional (default='first')) –

  Vectorization layer supported. Allowed values:

  • 'last' - vector from last sequence.

  • 'first' - vector from first sequence.

  • 'mean' - average vectors from all sequences.

  • 'word' - average vectors based on tokens.

Returns

result

Return type

np.array

predict(strings: List[str])

classify list of strings.

Parameters

strings (List[str]) –

Returns

result

Return type

List[str]

predict_proba(strings: List[str])

classify list of strings and return probability.

Parameters

strings (List[str]) –

Returns

result

Return type

List[dict[str, float]]

predict_words(string: str, method: str = 'last', visualization: bool = True)

classify words.

Parameters

• string (str) –

• method (str, optional (default='last')) –

  Attention layer supported. Allowed values:

  • 'last' - attention from last layer.

  • 'first' - attention from first layer.

  • 'mean' - average attentions from all layers.

• visualization (bool, optional (default=True)) – If True, it will open the visualization dashboard.

Returns

result

Return type

dict

class malaya.model.bert.SigmoidBERT

vectorize(strings: List[str], method: str = 'first')

vectorize list of strings.

Parameters

• strings (List[str]) –

• method (str, optional (default='first')) –

  Vectorization layer supported. Allowed values:

  • 'last' - vector from last sequence.

  • 'first' - vector from first sequence.

  • 'mean' - average vectors from all sequences.

  • 'word' - average vectors based on tokens.

Returns

result

Return type

np.array

predict(strings: List[str])

classify list of strings.

Parameters

strings (List[str]) –

Returns

result

Return type

List[List[str]]

predict_proba(strings: List[str])

classify list of strings and return probability.

Parameters

strings (List[str]) –

Returns

result

Return type

List[dict[str, float]]

predict_words(string: str, method: str = 'last', visualization: bool = True)

classify words.

Parameters

• string (str) –

• method (str, optional (default='last')) –

  Attention layer supported. Allowed values:

  • 'last' - attention from last layer.

  • 'first' - attention from first layer.

  • 'mean' - average attentions from all layers.

• visualization (bool, optional (default=True)) – If True, it will open the visualization dashboard.

Returns

dictionary

Return type

results

class malaya.model.bert.SiameseBERT

vectorize(strings: List[str])

Vectorize list of strings.

Parameters

strings (List[str]) –

Returns

result

Return type

np.array

predict_proba(strings_left: List[str], strings_right: List[str])

calculate similarity for two different batch of texts.

Parameters

• strings_left (List[str]) –

• strings_right (List[str]) –

Returns

list

Return type

list of float

heatmap(strings: List[str], visualize: bool = True, annotate: bool = True, figsize: Tuple[int, int] = (7, 7))

plot a heatmap based on output from similarity

Parameters

• strings (list of str) – list of strings.

• visualize (bool) – if True, it will render plt.show, else return data.

• figsize (tuple, (default=(7, 7))) – figure size for plot.

Returns

result – list of results

Return type

list

class malaya.model.bert.TaggingBERT

vectorize(string: str)

vectorize a string.

Parameters

string (List[str]) –

Returns

result

Return type

np.array

analyze(string: str)

Analyze a string.

Parameters

string (str) –

Returns

result

Return type

{‘words’: List[str], ‘tags’: [{‘text’: ‘text’, ‘type’: ‘location’, ‘score’: 1.0, ‘beginOffset’: 0, ‘endOffset’: 1}]}

predict(string: str)

Tag a string.

Parameters

string (str) –

Returns

result

Return type

Tuple[str, str]

class malaya.model.bert.DependencyBERT

vectorize(string: str)

vectorize a string.

Parameters

string (List[str]) –

Returns

result

Return type

np.array

predict(string: str)

Tag a string.

Parameters

string (str) –

Returns

result

Return type

Tuple

class malaya.model.bert.ZeroshotBERT

vectorize(strings: List[str], labels: List[str], method: str = 'first')

vectorize a string.

Parameters

• strings (List[str]) –

• labels (List[str]) –

• method (str, optional (default='first')) –

  Vectorization layer supported. Allowed values:

  • 'last' - vector from last sequence.

  • 'first' - vector from first sequence.

  • 'mean' - average vectors from all sequences.

  • 'word' - average vectors based on tokens.

Returns

result

Return type

np.array

predict_proba(strings: List[str], labels: List[str])

classify list of strings and return probability.

Parameters

• strings (List[str]) –

• labels (List[str]) –

Returns

list

Return type

list of float

malaya.model.bigbird

class malaya.model.bigbird.MulticlassBigBird

vectorize(strings: List[str], method: str = 'first')

vectorize list of strings.

Parameters

• strings (List[str]) –

• method (str, optional (default='first')) –

  Vectorization layer supported. Allowed values:

  • 'last' - vector from last sequence.

  • 'first' - vector from first sequence.

  • 'mean' - average vectors from all sequences.

  • 'word' - average vectors based on tokens.

Returns

result

Return type

np.array

predict(strings: List[str])

classify list of strings.

Parameters

strings (List[str]) –

Returns

result

Return type

List[str]

predict_proba(strings: List[str])

classify list of strings and return probability.

Parameters

strings (List[str]) –

Returns

result

Return type

List[dict[str, float]]

class malaya.model.bigbird.Translation

greedy_decoder(strings: List[str])

translate list of strings.

Parameters

strings (List[str]) –

Returns

result

Return type

List[str]

class malaya.model.bigbird.Summarization

greedy_decoder(strings: List[str], temperature=0.3, postprocess: bool = True, **kwargs)

Summarize strings using greedy decoder.

Parameters

• strings (List[str]) –

• temperature (float, (default=0.3)) – logits * -log(random.uniform) * temperature.

• postprocess (bool, optional (default=True)) – If True, will filter sentence generated using ROUGE score and removed international news publisher.

Returns

result

Return type

List[str]

nucleus_decoder(strings: List[str], top_p: float = 0.7, temperature: float = 0.3, postprocess: bool = True, **kwargs)

Summarize strings using nucleus decoder.

Parameters

• strings (List[str]) –

• top_p (float, (default=0.7)) – cumulative distribution and cut off as soon as the CDF exceeds top_p.

• temperature (float, (default=0.3)) – logits * -log(random.uniform) * temperature.

• postprocess (bool, optional (default=True)) – If True, will filter sentence generated using ROUGE score and removed international news publisher.

Returns

result

Return type

List[str]

malaya.model.extractive_summarization

class malaya.model.extractive_summarization.SKLearn

word_level(corpus, isi_penting: str = None, window_size: int = 10, important_words: int = 10, **kwargs)

Summarize list of strings / string on word level.

Parameters

• corpus (str / List[str]) –

• isi_penting (str, optional (default=None)) – if not None, will put priority based on isi_penting.

• window_size (int, (default=10)) – window size for each word.

• important_words (int, (default=10)) – number of important words.

Returns

dict

Return type

{‘top-words’, ‘cluster-top-words’, ‘score’}

sentence_level(corpus, isi_penting: str = None, top_k: int = 3, important_words: int = 10, **kwargs)

Summarize list of strings / string on sentence level.

Parameters

• corpus (str / List[str]) –

• isi_penting (str, optional (default=None)) – if not None, will put priority based on isi_penting.

• top_k (int, (default=3)) – number of summarized strings.

• important_words (int, (default=10)) – number of important words.

Returns

dict

Return type

{‘summary’, ‘top-words’, ‘cluster-top-words’, ‘score’}

class malaya.model.extractive_summarization.Doc2Vec

word_level(corpus, isi_penting: str = None, window_size: int = 10, aggregation=<function mean>, soft: bool = False, **kwargs)

Summarize list of strings / string on sentence level.

Parameters

• corpus (str / List[str]) –

• isi_penting (str, optional (default=None)) – if not None, will put priority based on isi_penting.

• window_size (int, (default=10)) – window size for each word.

• aggregation (Callable, optional (default=numpy.mean)) – Aggregation method for Doc2Vec.

• soft (bool, optional (default=False)) – soft: bool, (default=True) if True, a word not in the dictionary will be replaced with nearest JaroWinkler ratio. if False, it will returned embedding full with zeros.

Returns

dict

Return type

{‘score’}

sentence_level(corpus, isi_penting: str = None, top_k: int = 3, aggregation=<function mean>, soft: bool = False, **kwargs)

Summarize list of strings / string on sentence level.

Parameters

• corpus (str / List[str]) –

• isi_penting (str, optional (default=None)) – if not None, will put priority based on isi_penting.

• top_k (int, (default=3)) – number of summarized strings.

• aggregation (Callable, optional (default=numpy.mean)) – Aggregation method for Doc2Vec.

• soft (bool, optional (default=False)) – soft: bool, (default=True) if True, a word not in the dictionary will be replaced with nearest JaroWinkler ratio. if False, it will returned embedding full with zeros.

Returns

dict

Return type

{‘summary’, ‘score’}

class malaya.model.extractive_summarization.Encoder

malaya.model.ml

class malaya.model.ml.MulticlassBayes

predict(strings: List[str])

classify list of strings.

Parameters

strings (List[str]) –

Returns

result

Return type

List[str]

predict_proba(strings: List[str])

classify list of strings and return probability.

Parameters

strings (List[str]) –

Returns

result

Return type

List[dict[str, float]]

class malaya.model.ml.BinaryBayes

predict(strings: List[str], add_neutral: bool = True)

classify list of strings.

Parameters

• strings (List[str]) –

• add_neutral (bool, optional (default=True)) – if True, it will add neutral probability.

Returns

result

Return type

List[str]

predict_proba(strings: List[str], add_neutral: bool = True)

classify list of strings and return probability.

Parameters

• strings (List[str]) –

• add_neutral (bool, optional (default=True)) – if True, it will add neutral probability.

Returns

result

Return type

List[dict[str, float]]

class malaya.model.ml.MultilabelBayes

predict(strings: List[str])

classify list of strings.

Parameters

strings (List[str]) –

Returns

result

Return type

List[List[str]]

predict_proba(strings: List[str])

classify list of strings and return probability.

Parameters

strings (list) –

Returns

result

Return type

List[dict[str, float]]

malaya.model.pegasus

class malaya.model.pegasus.Summarization

greedy_decoder(strings: List[str], temperature=0.3, postprocess: bool = True, **kwargs)

Summarize strings using greedy decoder.

Parameters

• strings (List[str]) –

• temperature (float, (default=0.3)) – logits * -log(random.uniform) * temperature.

• postprocess (bool, optional (default=True)) – If True, will filter sentence generated using ROUGE score and removed international news publisher.

Returns

result

Return type

List[str]

nucleus_decoder(strings: List[str], top_p: float = 0.7, temperature: float = 0.3, postprocess: bool = True, **kwargs)

Summarize strings using nucleus decoder.

Parameters

• strings (List[str]) –

• top_p (float, (default=0.7)) – cumulative distribution and cut off as soon as the CDF exceeds top_p.

• temperature (float, (default=0.3)) – logits * -log(random.uniform) * temperature.

• postprocess (bool, optional (default=True)) – If True, will filter sentence generated using ROUGE score and removed international news publisher.

Returns

result

Return type

List[str]

malaya.model.t5

class malaya.model.t5.Summarization

greedy_decoder(strings: List[str], mode: str = 'ringkasan', postprocess: bool = True, **kwargs)

Summarize strings. Decoder is greedy decoder with beam width size 1, alpha 0.5 .

Parameters

• strings (List[str]) –

• mode (str) –

  mode for summarization. Allowed values:

  • 'ringkasan' - summarization for long sentence, eg, news summarization.

  • 'tajuk' - title summarization for long sentence, eg, news title.

• postprocess (bool, optional (default=True)) – If True, will filter sentence generated using ROUGE score and removed international news publisher.

Returns

result

Return type

List[str]

class malaya.model.t5.Generator

greedy_decoder(strings: List[str])

generate a long text given a isi penting. Decoder is greedy decoder with beam width size 1, alpha 0.5 .

Parameters

strings (List[str]) –

Returns

result

Return type

str

class malaya.model.t5.Paraphrase

greedy_decoder(strings: List[str], split_fullstop: bool = True)

paraphrase strings. Decoder is greedy decoder with beam width size 1, alpha 0.5 .

Parameters

• strings (List[str]) –

• split_fullstop (bool, (default=True)) – if True, will generate paraphrase for each strings splitted by fullstop.

Returns

result

Return type

List[str]

malaya.model.tf

class malaya.model.tf.DeepLang

predict(strings: List[str])

classify list of strings.

Parameters

strings (List[str]) –

Returns

result

Return type

List[str]

predict_proba(strings: List[str])

classify list of strings and return probability.

Parameters

strings (List[str]) –

Returns

result

Return type

List[dict[str, float]]

class malaya.model.tf.Translation

greedy_decoder(strings: List[str])

translate list of strings.

Parameters

strings (List[str]) –

Returns

result

Return type

List[str]

beam_decoder(strings: List[str])

translate list of strings using beam decoder, beam width size 3, alpha 0.5 .

Parameters

strings (List[str]) –

Returns

result

Return type

List[str]

class malaya.model.tf.Constituency

vectorize(string: str)

vectorize a string.

Parameters

string (List[str]) –

Returns

result

Return type

np.array

parse_nltk_tree(string: str)

Parse a string into NLTK Tree, to make it useful, make sure you already installed tktinker.

Parameters

string (str) –

Returns

result

Return type

nltk.Tree object

parse_tree(string)

Parse a string into string treebank format.

Parameters

string (str) –

Returns

result

Return type

malaya.text.trees.InternalTreebankNode class

class malaya.model.tf.TrueCase

greedy_decoder(strings: List[str])

True case strings using greedy decoder. Example, “saya nak makan di us makanan di sana sedap” -> “Saya nak makan di US, makanan di sana sedap.”

Parameters

strings (List[str]) –

Returns

result

Return type

List[str]

beam_decoder(strings: List[str])

True case strings using beam decoder, beam width size 3, alpha 0.5 . Example, “saya nak makan di us makanan di sana sedap” -> “Saya nak makan di US, makanan di sana sedap.”

Parameters

strings (List[str]) –

Returns

result

Return type

List[str]

class malaya.model.tf.Segmentation

greedy_decoder(strings: List[str])

Segment strings using greedy decoder. Example, “sayasygkan negarasaya” -> “saya sygkan negara saya”

Parameters

strings (List[str]) –

Returns

result

Return type

List[str]

beam_decoder(strings: List[str])

Segment strings using beam decoder, beam width size 3, alpha 0.5 . Example, “sayasygkan negarasaya” -> “saya sygkan negara saya”

Parameters

strings (List[str]) –

Returns

result

Return type

List[str]

class malaya.model.tf.Paraphrase

greedy_decoder(strings: List[str], **kwargs)

Paraphrase strings using greedy decoder.

Parameters

strings (List[str]) –

Returns

result

Return type

List[str]

beam_decoder(strings: List[str], **kwargs)

Paraphrase strings using beam decoder, beam width size 3, alpha 0.5 .

Parameters

strings (List[str]) –

Returns

result

Return type

List[str]

nucleus_decoder(strings: List[str], top_p: float = 0.7, **kwargs)

Paraphrase strings using nucleus sampling.

Parameters

• strings (List[str]) –

• top_p (float, (default=0.7)) – cumulative distribution and cut off as soon as the CDF exceeds top_p.

Returns

result

Return type

List[str]

class malaya.model.tf.Tatabahasa

greedy_decoder(strings: List[str])

Fix kesalahan tatatabahasa.

Parameters

strings (List[str]) –

Returns

result

Return type

List[str]

class malaya.model.tf.SQUAD

predict(paragraph_text: str, question_texts: List[str], doc_stride: int = 128, max_query_length: int = 64, max_answer_length: int = 64, n_best_size: int = 20)

Predict Span from questions given a paragraph.

Parameters

• paragraph_text (str) –

• question_texts (List[str]) – List of questions, results really depends on case sensitive questions.

• doc_stride (int, optional (default=128)) – striding size to split a paragraph into multiple texts.

• max_query_length (int, optional (default=64)) – Maximum length if question tokens.

• max_answer_length (int, optional (default=30)) – Maximum length if answer tokens.

Returns

result

Return type

List[{‘text’: ‘text’, ‘start’: 0, ‘end’: 1}]

vectorize(strings: List[str], method: str = 'first')

vectorize list of strings.

Parameters

• strings (List[str]) –

• method (str, optional (default='first')) –

  Vectorization layer supported. Allowed values:

  • 'last' - vector from last sequence.

  • 'first' - vector from first sequence.

  • 'mean' - average vectors from all sequences.

  • 'word' - average vectors based on tokens.

Returns

result

Return type

np.array

class malaya.model.tf.KnowledgeGraph

greedy_decoder(strings: List[str], get_networkx: bool = True)

Generate triples knowledge graph using greedy decoder. Example, “Joseph Enanga juga bermain untuk Union Douala.” -> “Joseph Enanga member of sports team Union Douala”

Parameters

• strings (List[str]) –

• get_networkx (bool, optional (default=True)) – If True, will generate networkx.MultiDiGraph.

Returns

result

Return type

List[Dict]

beam_decoder(strings: List[str], get_networkx: bool = True)

Generate triples knowledge graph using beam decoder. Example, “Joseph Enanga juga bermain untuk Union Douala.” -> “Joseph Enanga member of sports team Union Douala”

Parameters

• strings (List[str]) –

• get_networkx (bool, optional (default=True)) – If True, will generate networkx.MultiDiGraph.

Returns

result

Return type

List[Dict]

malaya.model.xlnet

class malaya.model.xlnet.BinaryXLNET

vectorize(strings: List[str], method: str = 'first')

vectorize list of strings.

Parameters

• strings (List[str]) –

• method (str, optional (default='first')) –

  Vectorization layer supported. Allowed values:

  • 'last' - vector from last sequence.

  • 'first' - vector from first sequence.

  • 'mean' - average vectors from all sequences.

  • 'word' - average vectors based on tokens.

Returns

result

Return type

np.array

predict(strings: List[str], add_neutral: bool = True)

classify list of strings.

Parameters

• strings (List[str]) –

• add_neutral (bool, optional (default=True)) – if True, it will add neutral probability.

Returns

result

Return type

List[str]

predict_proba(strings: List[str], add_neutral: bool = True)

classify list of strings and return probability.

Parameters

• strings (List[str]) –

• add_neutral (bool, optional (default=True)) – if True, it will add neutral probability.

Returns

result

Return type

List[dict[str, float]]

predict_words(string: str, method: str = 'last', visualization: bool = True)

classify words.

Parameters

• string (str) –

• method (str, optional (default='last')) –

  Attention layer supported. Allowed values:

  • 'last' - attention from last layer.

  • 'first' - attention from first layer.

  • 'mean' - average attentions from all layers.

• visualization (bool, optional (default=True)) – If True, it will open the visualization dashboard.

Returns

result

Return type

dict

class malaya.model.xlnet.MulticlassXLNET

vectorize(strings: List[str], method: str = 'first')

vectorize list of strings.

Parameters

• strings (List[str]) –

• method (str, optional (default='first')) –

  Vectorization layer supported. Allowed values:

  • 'last' - vector from last sequence.

  • 'first' - vector from first sequence.

  • 'mean' - average vectors from all sequences.

  • 'word' - average vectors based on tokens.

Returns

result

Return type

np.array

predict(strings: List[str])

classify list of strings.

Parameters

strings (List[str]) –

Returns

result

Return type

List[str]

predict_proba(strings: List[str])

classify list of strings and return probability.

Parameters

strings (List[str]) –

Returns

result

Return type

List[dict[str, float]]

predict_words(string: str, method: str = 'last', visualization: bool = True)

classify words.

Parameters

• string (str) –

• method (str, optional (default='last')) –

  Attention layer supported. Allowed values:

  • 'last' - attention from last layer.

  • 'first' - attention from first layer.

  • 'mean' - average attentions from all layers.

• visualization (bool, optional (default=True)) – If True, it will open the visualization dashboard.

Returns

result

Return type

dict

class malaya.model.xlnet.SigmoidXLNET

vectorize(strings: List[str], method: str = 'first')

vectorize list of strings.

Parameters

• strings (List[str]) –

• method (str, optional (default='first')) –

  Vectorization layer supported. Allowed values:

  • 'last' - vector from last sequence.

  • 'first' - vector from first sequence.

  • 'mean' - average vectors from all sequences.

  • 'word' - average vectors based on tokens.

Returns

result

Return type

np.array

predict(strings: List[str])

classify list of strings.

Parameters

strings (List[str]) –

Returns

result

Return type

List[List[str]]

predict_proba(strings: List[str])

classify list of strings and return probability.

Parameters

strings (List[str]) –

Returns

result

Return type

List[dict[str, float]]

predict_words(string: str, method: str = 'last', visualization: bool = True)

classify words.

Parameters

• string (str) –

• method (str, optional (default='last')) –

  Attention layer supported. Allowed values:

  • 'last' - attention from last layer.

  • 'first' - attention from first layer.

  • 'mean' - average attentions from all layers.

• visualization (bool, optional (default=True)) – If True, it will open the visualization dashboard.

Returns

dictionary

Return type

results

class malaya.model.xlnet.SiameseXLNET

vectorize(strings: List[str])

Vectorize list of strings.

Parameters

strings (List[str]) –

Returns

result

Return type

np.array

predict_proba(strings_left: List[str], strings_right: List[str])

calculate similarity for two different batch of texts.

Parameters

• string_left (List[str]) –

• string_right (List[str]) –

Returns

result

Return type

List[float]

heatmap(strings: List[str], visualize: bool = True, annotate: bool = True, figsize: Tuple[int, int] = (7, 7))

plot a heatmap based on output from similarity

Parameters

• strings (list of str) – list of strings.

• visualize (bool) – if True, it will render plt.show, else return data.

• figsize (tuple, (default=(7, 7))) – figure size for plot.

Returns

result – list of results

Return type

list

class malaya.model.xlnet.TaggingXLNET

vectorize(string: str)

vectorize a string.

Parameters

string (List[str]) –

Returns

result

Return type

np.array

analyze(string: str)

Analyze a string.

Parameters

string (str) –

Returns

result

Return type

{‘words’: List[str], ‘tags’: [{‘text’: ‘text’, ‘type’: ‘location’, ‘score’: 1.0, ‘beginOffset’: 0, ‘endOffset’: 1}]}

predict(string: str)

Tag a string.

Parameters

string (str) –

Returns

result

Return type

Tuple[str, str]

class malaya.model.xlnet.DependencyXLNET

vectorize(string: str)

vectorize a string.

Parameters

string (List[str]) –

Returns

result

Return type

np.array

predict(string: str)

Tag a string.

Parameters

string (str) –

Returns

result

Return type

Tuple

class malaya.model.xlnet.ZeroshotXLNET

vectorize(strings: List[str], labels: List[str], method: str = 'first')

vectorize a string.

Parameters

• strings (List[str]) –

• labels (List[str]) –

• method (str, optional (default='first')) –

  Vectorization layer supported. Allowed values:

  • 'last' - vector from last sequence.

  • 'first' - vector from first sequence.

  • 'mean' - average vectors from all sequences.

  • 'word' - average vectors based on tokens.

Returns

result

Return type

np.array

predict_proba(strings: List[str], labels: List[str])

classify list of strings and return probability.

Parameters

• strings (List[str]) –

• labels (List[str]) –

Returns

list

Return type

list of float

malaya.transformers.albert

malaya.transformers.albert.load(model: str = 'albert', **kwargs)

Load albert model.

Parameters

model (str, optional (default='base')) –

Model architecture supported. Allowed values:

• 'albert' - base albert-bahasa released by Malaya.

• 'tiny-albert' - tiny bert-bahasa released by Malaya.

Returns

result

Return type

malaya.transformers.albert.Model class

class malaya.transformers.albert.Model

vectorize(strings: List[str])

Vectorize string inputs.

Parameters

strings (List[str]) –

Returns

result

Return type

np.array

attention(strings: List[str], method: str = 'last', **kwargs)

Get attention string inputs.

Parameters

• strings (List[str]) –

• method (str, optional (default='last')) –

  Attention layer supported. Allowed values:

  • 'last' - attention from last layer.

  • 'first' - attention from first layer.

  • 'mean' - average attentions from all layers.

Returns

result

Return type

List[List[Tuple[str, float]]]

visualize_attention(string: str)

Visualize attention.

Parameters

string (str) –

malaya.transformers.alxlnet

malaya.transformers.alxlnet.load(model: str = 'alxlnet', pool_mode: str = 'last', **kwargs)

Load alxlnet model.

Parameters

• model (str, optional (default='base')) –

  Model architecture supported. Allowed values:

  • 'alxlnet' - XLNET architecture from google + Malaya.

• pool_mode (str, optional (default='last')) –

  Model logits architecture supported. Allowed values:

  • 'last' - last of the sequence.

  • 'first' - first of the sequence.

  • 'mean' - mean of the sequence.

  • 'attn' - attention of the sequence.

Returns

result

Return type

malaya.transformers.alxlnet.Model class

class malaya.transformers.alxlnet.Model

vectorize(strings: List[str])

Vectorize string inputs.

Parameters

strings (List[str]) –

Returns

result

Return type

np.array

attention(strings: List[str], method: str = 'last', **kwargs)

Get attention string inputs.

Parameters

• strings (List[str]) –

• method (str, optional (default='last')) –

  Attention layer supported. Allowed values:

  • 'last' - attention from last layer.

  • 'first' - attention from first layer.

  • 'mean' - average attentions from all layers.

Returns

result

Return type

List[List[Tuple[str, float]]]

visualize_attention(string: str)

Visualize attention.

Parameters

string (str) –

malaya.transformers.bert

malaya.transformers.bert.load(model: str = 'base', **kwargs)

Load bert model.

Parameters

model (str, optional (default='base')) –

Model architecture supported. Allowed values:

• 'bert' - base bert-bahasa released by Malaya.

• 'tiny-bert' - tiny bert-bahasa released by Malaya.

Returns

result

Return type

malaya.transformers.bert.Model class

class malaya.transformers.bert.Model

vectorize(strings: List[str])

Vectorize string inputs.

Parameters

strings (List[str]) –

Returns

result

Return type

np.array

attention(strings: List[str], method: str = 'last', **kwargs)

Get attention string inputs.

Parameters

• strings (List[str]) –

• method (str, optional (default='last')) –

  Attention layer supported. Allowed values:

  • 'last' - attention from last layer.

  • 'first' - attention from first layer.

  • 'mean' - average attentions from all layers.

Returns

result

Return type

List[List[Tuple[str, float]]]

visualize_attention(string: str)

Visualize attention.

Parameters

string (str) –

malaya.transformers.electra

malaya.transformers.electra.load(model: str = 'electra', **kwargs)

Load electra model.

Parameters

model (str, optional (default='base')) –

Model architecture supported. Allowed values:

• 'electra' - base electra-bahasa released by Malaya.

• 'small-electra' - small electra-bahasa released by Malaya.

Returns

result

Return type

malaya.transformers.electra.Model class

class malaya.transformers.electra.Model

vectorize(strings: List[str])

Vectorize string inputs.

Parameters

strings (List[str]) –

Returns

result

Return type

np.array

attention(strings: List[str], method: str = 'last', **kwargs)

Get attention string inputs.

Parameters

• strings (List[str]) –

• method (str, optional (default='last')) –

  Attention layer supported. Allowed values:

  • 'last' - attention from last layer.

  • 'first' - attention from first layer.

  • 'mean' - average attentions from all layers.

Returns

result

Return type

List[List[Tuple[str, float]]]

visualize_attention(string: str)

Visualize attention.

Parameters

string (str) –

malaya.transformers.gpt2

malaya.transformers.gpt2.load(model='345M', generate_length=100, temperature=1.0, top_k=40, **kwargs)

Load gpt2 model.

Parameters

• model (str, optional (default='345M')) –

  Model architecture supported. Allowed values:

  • '117M' - GPT2 117M parameters.

  • '345M' - GPT2 345M parameters.

• generate_length (int, optional (default=256)) – length of sentence to generate.

• temperature (float, optional (default=1.0)) – temperature value, value should between 0 and 1.

• top_k (int, optional (default=40)) – top-k in nucleus sampling selection.

Returns

result

Return type

malaya.transformers.gpt2.Model class

class malaya.transformers.gpt2.Model

generate(string: str)

generate a text given an initial string.

Parameters

string (str) –

Returns

result

Return type

str

malaya.transformers.xlnet

malaya.transformers.xlnet.load(model: str = 'xlnet', pool_mode: str = 'last', **kwargs)

Load xlnet model.

Parameters

• model (str, optional (default='base')) –

  Model architecture supported. Allowed values:

  • 'xlnet' - XLNET architecture from google.

• pool_mode (str, optional (default='last')) –

  Model logits architecture supported. Allowed values:

  • 'last' - last of the sequence.

  • 'first' - first of the sequence.

  • 'mean' - mean of the sequence.

  • 'attn' - attention of the sequence.

Returns

result

Return type

malaya.transformers.xlnet.Model class

class malaya.transformers.xlnet.Model

vectorize(strings: List[str])

Vectorize string inputs.

Parameters

strings (List[str]) –

Returns

result

Return type

np.array

attention(strings: List[str], method: str = 'last', **kwargs)

Get attention string inputs.

Parameters

• strings (List[str]) –

• method (str, optional (default='last')) –

  Attention layer supported. Allowed values:

  • 'last' - attention from last layer.

  • 'first' - attention from first layer.

  • 'mean' - average attentions from all layers.

Returns

result

Return type

List[List[Tuple[str, float]]]

visualize_attention(string: str)

Visualize attention.

Parameters

string (str) –