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.

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

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

Training Details

• Sentencepiece, Word2Vec, Fasttext, GloVe model trained with Bengali Wikipedia Dump Dataset
  • Bengali Wiki Dump
• 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

  ```py
  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](https://fasttext.cc/docs/en/options.html) for details of training parameter

  ```py
  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
  ```py
  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

• Semantics Lab

Extra Contributor

• Mehadi Hasan Menon
• Kazal Chandra Barman