bnlp-toolkit 3.0.0.dev1

BNLP is a natural language processing toolkit for Bengali Language

Bengali Natural Language Processing(BNLP)

BNLP is a natural language processing toolkit for Bengali Language. This tool will help you to tokenize Bengali text, Embedding Bengali words, Bengali POS Tagging, Bengali Name Entity Recognition, Construct Neural Model for Bengali NLP purposes.

NB: Any Researcher who refer this tool in his/her paper please let us know, we will include paper link here

Documentation

For full documentation follow bnlp documentation

Installation

PIP installer(python 3.5, 3.6, 3.7 tested okay)

pip install bnlp_toolkit

Pretrained Model

Download Link

• Bengali SentencePiece
• Bengali Word2Vec
• Bengali FastText
• Bengali GloVe Wordvectors
• Bengali POS Tag model
• Bengali NER model

Tokenization

• Bengali SentencePiece Tokenization

  • tokenization using trained model

    from bnlp import SentencepieceTokenizer
    
    bsp = SentencepieceTokenizer()
    model_path = "./model/bn_spm.model"
    input_text = "আমি ভাত খাই। সে বাজারে যায়।"
    tokens = bsp.tokenize(model_path, input_text)
    print(tokens)
    text2id = bsp.text2id(model_path, input_text)
    print(text2id)
    id2text = bsp.id2text(model_path, text2id)
    print(id2text)
    

  • Training SentencePiece

    from bnlp import SentencepieceTokenizer
    
    bsp = SentencepieceTokenizer()
    data = "test.txt"
    model_prefix = "test"
    vocab_size = 5
    bsp.train(data, model_prefix, vocab_size) 
    

• Basic Tokenizer

  from bnlp import BasicTokenizer
  basic_tokenizer = BasicTokenizer()
  raw_text = "আমি বাংলায় গান গাই।"
  tokens = basic_tokenizer.tokenize(raw_text)
  print(tokens)
  
  # output: ["আমি", "বাংলায়", "গান", "গাই", "।"]
  

• NLTK Tokenization

  from bnlp import NLTKTokenizer
  
  text = "আমি ভাত খাই। সে বাজারে যায়। তিনি কি সত্যিই ভালো মানুষ?"
  bnltk = NLTKTokenizer()
  word_tokens = bnltk.word_tokenize(text)
  sentence_tokens = bnltk.sentence_tokenize(text)
  print(word_tokens)
  print(sentence_tokens)
  
  # output
  # word_token: ["আমি", "ভাত", "খাই", "।", "সে", "বাজারে", "যায়", "।", "তিনি", "কি", "সত্যিই", "ভালো", "মানুষ", "?"]
  # sentence_token: ["আমি ভাত খাই।", "সে বাজারে যায়।", "তিনি কি সত্যিই ভালো মানুষ?"]
  

Word Embedding

• Bengali Word2Vec

  • Generate Vector using pretrain model

    from bnlp import BengaliWord2Vec
    
    bwv = BengaliWord2Vec()
    model_path = "bengali_word2vec.model"
    word = 'আমার'
    vector = bwv.generate_word_vector(model_path, word)
    print(vector.shape)
    print(vector)
    

  • Find Most Similar Word Using Pretrained Model

    from bnlp import BengaliWord2Vec
    
    bwv = BengaliWord2Vec()
    model_path = "bengali_word2vec.model"
    word = 'গ্রাম'
    similar = bwv.most_similar(model_path, word)
    print(similar)
    

  • Train Bengali Word2Vec with your own data

    from bnlp import BengaliWord2Vec
    bwv = BengaliWord2Vec()
    data_file = "sample.txt"
    model_name = "test_model.model"
    vector_name = "test_vector.vector"
    bwv.train(data_file, model_name, vector_name)
    

• Bengali FastText

  To use fasttext you need to install fasttext manually by pip install fasttext==0.9.2

  NB: it will not work in windows, it will only work in linux

  • Generate Vector Using Pretrained Model

    from bnlp.embedding.fasttext import BengaliFasttext
    
    bft = BengaliFasttext()
    word = "গ্রাম"
    model_path = "bengali_fasttext_wiki.bin"
    word_vector = bft.generate_word_vector(model_path, word)
    print(word_vector.shape)
    print(word_vector)
    

  • Train Bengali FastText Model

    from bnlp.embedding.fasttext import BengaliFasttext
    
    bft = BengaliFasttext()
    data = "sample.txt"
    model_name = "saved_model.bin"
    epoch = 50
    bft.train(data, model_name, epoch)
    

• Bengali GloVe Word Vectors

  We trained glove model with bengali data(wiki+news articles) and published bengali glove word vectors
  You can download and use it on your different machine learning purposes.

  from bnlp import BengaliGlove
  glove_path = "bn_glove.39M.100d.txt"
  word = "গ্রাম"
  bng = BN_Glove()
  res = bng.closest_word(glove_path, word)
  print(res)
  vec = bng.word2vec(glove_path, word)
  print(vec)
  

Bengali POS Tagging

• Bengali CRF POS Tagging

  • Find Pos Tag Using Pretrained Model

    from bnlp import POS
    bn_pos = POS()
    model_path = "model/bn_pos.pkl"
    text = "আমি ভাত খাই।"
    res = bn_pos.tag(model_path, text)
    print(res)
    # [('আমি', 'PPR'), ('ভাত', 'NC'), ('খাই', 'VM'), ('।', 'PU')]
    

  • Train POS Tag Model

    from bnlp import POS
    bn_pos = POS()
    model_name = "pos_model.pkl"
    tagged_sentences = [[('রপ্তানি', 'JJ'), ('দ্রব্য', 'NC'), ('-', 'PU'), ('তাজা', 'JJ'), ('ও', 'CCD'), ('শুকনা', 'JJ'), ('ফল', 'NC'), (',', 'PU'), ('আফিম', 'NC'), (',', 'PU'), ('পশুচর্ম', 'NC'), ('ও', 'CCD'), ('পশম', 'NC'), ('এবং', 'CCD'),('কার্পেট', 'NC'), ('৷', 'PU')], [('মাটি', 'NC'), ('থেকে', 'PP'), ('বড়জোর', 'JQ'), ('চার', 'JQ'), ('পাঁচ', 'JQ'), ('ফুট', 'CCL'), ('উঁচু', 'JJ'), ('হবে', 'VM'), ('৷', 'PU')]]
    
    bn_pos.train(model_name, tagged_sentences)
    

Bengali NER

• Bengali CRF NER

  • Find NER Tag Using Pretrained Model

    from bnlp import NER
    bn_ner = NER()
    model_path = "model/bn_ner.pkl"
    text = "সে ঢাকায় থাকে।"
    result = bn_ner.tag(model_path, text)
    print(result)
    # [('সে', 'O'), ('ঢাকায়', 'S-LOC'), ('থাকে', 'O')]
    

  • Train NER Tag Model

    from bnlp import NER
    bn_ner = NER()
    model_name = "ner_model.pkl"
    tagged_sentences = [[('ত্রাণ', 'O'),('ও', 'O'),('সমাজকল্যাণ', 'O'),('সম্পাদক', 'S-PER'),('সুজিত', 'B-PER'),('রায়', 'I-PER'),('নন্দী', 'E-PER'),('প্রমুখ', 'O'),('সংবাদ', 'O'),('সম্মেলনে', 'O'),('উপস্থিত', 'O'),('ছিলেন', 'O')], [('ত্রাণ', 'O'),('ও', 'O'),('সমাজকল্যাণ', 'O'),('সম্পাদক', 'S-PER'),('সুজিত', 'B-PER'),('রায়', 'I-PER'),('নন্দী', 'E-PER'),('প্রমুখ', 'O'),('সংবাদ', 'O'),('সম্মেলনে', 'O'),('উপস্থিত', 'O'),('ছিলেন', 'O')], [('ত্রাণ', 'O'),('ও', 'O'),('সমাজকল্যাণ', 'O'),('সম্পাদক', 'S-PER'),('সুজিত', 'B-PER'),('রায়', 'I-PER'),('নন্দী', 'E-PER'),('প্রমুখ', 'O'),('সংবাদ', 'O'),('সম্মেলনে', 'O'),('উপস্থিত', 'O'),('ছিলেন', 'O')]]
    
    bn_ner.train(model_name, tagged_sentences)
    

Bengali Corpus Class

• Stopwords and Punctuations

  from bnlp.corpus import stopwords, punctuations
  
  stopwords = stopwords() 
  print(stopwords)
  print(punctuations)
  

• Remove stopwords from Text

  from bnlp.corpus import stopwords
  from bnlp.corpus.util import remove_stopwords
  
  stopwords = stopwords()
  raw_text = 'আমি ভাত খাই।' 
  result = remove_stopwords(raw_text, stopwords)
  print(result)
  # ['ভাত', 'খাই', '।']