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BNLP is a natural language processing toolkit for Bengali Language

Project description

bnlp

Bengali Natural Language Processing(BNLP)

Build Status PyPI version release version Support Python Version Documentation Status Gitter

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.

Installation

PIP installer(Python: 3.6, 3.7, 3.8 tested okay, OS: linux, windows tested okay )

pip install bnlp_toolkit

or Upgrade

pip install -U bnlp_toolkit

Pretrained Model

Download Link

Training Details

  • Sentencepiece, Word2Vec, Fasttext, GloVe model trained with Bengali Wikipedia Dump Dataset
  • SentencePiece Training Vocab Size=50000
  • Fasttext trained with total words = 20M, vocab size = 1171011, epoch=50, embedding dimension = 300 and the training loss = 0.318668,
  • Word2Vec word embedding dimension = 100, min_count=5, window=5, epochs=10
  • To Know Bengali GloVe Wordvector and training process follow this repository
  • Bengali CRF POS Tagging was training with nltr dataset with 80% accuracy.
  • Bengali CRF NER Tagging was train with this data with 90% accuracy.

Tokenization

  • 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
    
    bnltk = NLTKTokenizer()
    text = "আমি ভাত খাই। সে বাজারে যায়। তিনি কি সত্যিই ভালো মানুষ?"
    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?"]
    
  • 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 = "raw_text.txt"
      model_prefix = "test"
      vocab_size = 5
      bsp.train(data, model_prefix, vocab_size) 
      

Word Embedding

  • Bengali Word2Vec

    • Generate Vector using pretrain model

      from bnlp import BengaliWord2Vec
      
      bwv = BengaliWord2Vec()
      model_path = "bengali_word2vec.model"
      word = 'গ্রাম'
      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, topn=10)
      print(similar)
      
    • Train Bengali Word2Vec with your own data

      Train Bengali word2vec with your custom raw data or tokenized sentences.

      custom tokenized sentence format example:

      sentences = [['আমি', 'ভাত', 'খাই', '।'], ['সে', 'বাজারে', 'যায়', '।']]
      

      Check gensim word2vec api for details of training parameter

      from bnlp import BengaliWord2Vec
      bwv = BengaliWord2Vec()
      data_file = "raw_text.txt" # or you can pass custom sentence tokens as list of list
      model_name = "test_model.model"
      vector_name = "test_vector.vector"
      bwv.train(data_file, model_name, vector_name, epochs=5)
      
    • Pre-train or resume word2vec training with same or new corpus or tokenized sentences

      Check gensim word2vec api for details of training parameter

      from bnlp import BengaliWord2Vec
      bwv = BengaliWord2Vec()
      
      trained_model_path = "mytrained_model.model"
      data_file = "raw_text.txt"
      model_name = "test_model.model"
      vector_name = "test_vector.vector"
      bwv.pretrain(trained_model_path, data_file, model_name, vector_name, epochs=5)
      
  • Bengali FastText

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

    NB: fasttext may not be worked 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

      Check fasttext documentation for details of training parameter

      from bnlp.embedding.fasttext import BengaliFasttext
      
      bft = BengaliFasttext()
      data = "raw_text.txt"
      model_name = "saved_model.bin"
      epoch = 50
      bft.train(data, model_name, epoch)
      
    • Generate Vector File from Fasttext Binary Model

      from bnlp.embedding.fasttext import BengaliFasttext
      
      bft = BengaliFasttext()
      
      model_path = "mymodel.bin"
      out_vector_name = "myvector.txt"
      bft.bin2vec(model_path, out_vector_name)
      
  • 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 = BengaliGlove()
    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।" # or you can pass ['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"
      train_data = [[('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')]]
      
      test_data = [[('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, train_data, test_data)
      

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।" # or you can pass ['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"
      train_data = [[('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')]]
      
      test_data = [[('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, train_data, test_data)
      

Bengali Corpus Class

  • Stopwords and Punctuations

    from bnlp.corpus import stopwords, punctuations, letters, digits
    
    print(stopwords)
    print(punctuations)
    print(letters)
    print(digits)
    
  • Remove stopwords from Text

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

Contributor Guide

Check CONTRIBUTING.md page for details.

Thanks To

Extra Contributor

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