BNLP is a natural language processing toolkit for Bengali Language
Project description
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, construct neural model for Bengali NLP purposes.
Contents
- Current Features
- Installation
- Pretrained Model
- Tokenization
- Embedding
- Issue
- Contributor Guide
- Contributor List
Current Features
- Bengali Tokenization
- SentencePiece Tokenizer
- Basic Tokenizer
- NLTK Tokenizer
- Bengali Word Embedding
- Bengali Word2Vec
- Bengali Fasttext
- Bengali GloVe
Installation
-
pypi package installer(python 3.5, 3.6, 3.7 tested okay)
pip install bnlp_toolkit
-
Local
$git clone https://github.com/sagorbrur/bnlp.git $cd bnlp $python setup.py install
Pretrained Model
Download Link
Training Details
- All three 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 = 300
- To Know Bengali GloVe Wordvector and training process follow this repository
Tokenization
-
Bengali SentencePiece Tokenization
- tokenization using trained model
from bnlp.sentencepiece_tokenizer import SP_Tokenizer bsp = SP_Tokenizer() model_path = "./model/bn_spm.model" input_text = "আমি ভাত খাই। সে বাজারে যায়।" tokens = bsp.tokenize(model_path, input_text) print(tokens)
- Training SentencePiece
from bnlp.sentencepiece_tokenizer import SP_Tokenizer bsp = SP_Tokenizer(is_train=True) data = "test.txt" model_prefix = "test" vocab_size = 5 bsp.train_bsp(data, model_prefix, vocab_size)
- tokenization using trained model
-
Basic Tokenizer
from bnlp.basic_tokenizer import BasicTokenizer basic_t = BasicTokenizer(False) raw_text = "আমি বাংলায় গান গাই।" tokens = basic_t.tokenize(raw_text) print(tokens) # output: ["আমি", "বাংলায়", "গান", "গাই", "।"]
-
NLTK Tokenization
from bnlp.nltk_tokenizer import NLTK_Tokenizer text = "আমি ভাত খাই। সে বাজারে যায়। তিনি কি সত্যিই ভালো মানুষ?" bnltk = NLTK_Tokenizer(text) word_tokens = bnltk.word_tokenize() sentence_tokens = bnltk.sentence_tokenize() 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.bengali_word2vec import Bengali_Word2Vec bwv = Bengali_Word2Vec() model_path = "model/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.bengali_word2vec import Bengali_Word2Vec bwv = Bengali_Word2Vec() model_path = "model/bengali_word2vec.model" word = 'আমার' similar = bwv.most_similar(model_path, word) print(similar)
-
Train Bengali Word2Vec with your own data
from bnlp.bengali_word2vec import Bengali_Word2Vec bwv = Bengali_Word2Vec(is_train=True) data_file = "test.txt" model_name = "test_model.model" vector_name = "test_vector.vector" bwv.train_word2vec(data_file, model_name, vector_name)
-
-
Bengali FastText
-
Generate Vector Using Pretrained Model
from bnlp.bengali_fasttext import Bengali_Fasttext bft = Bengali_Fasttext() word = "গ্রাম" model_path = "model/bengali_fasttext.bin" word_vector = bft.generate_word_vector(model_path, word) print(word_vector.shape) print(word_vector)
-
Train Bengali FastText Model
from bnlp.bengali_fasttext import Bengali_Fasttext bft = Bengali_Fasttext(is_train=True) data = "data.txt" model_name = "saved_model.bin" epoch = 50 bft.train_fasttext(data, model_name, epoch) # epoch not implement in pypi yet # bft.train_fasttext(data, model_name) in pypi now
-
-
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.glove_wordvector import BN_Glove 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)
Issue
- if
ModuleNotFoundError: No module named 'fasttext'
problem arise please do the next line
pip install fasttext
- if
nltk
issue arise please do the following line before importingbnlp
import nltk
nltk.download("punkt")
Contributor Guide
Check CONTRIBUTING.md page for details.
Thanks To
Contributor List
Project details
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