Bengal Language Model¶
Bengali language model is build with fastai’s ULMFit and ready for prediction and classfication task.
NB:
- This tool mostly followed inltk
- We separated
Bengalipart with better evaluation results
Installation¶
pip install bnlm
Features and API¶
Download pretrained Model¶
To start, first download pretrained Language Model and Sentencepiece model
from bnlm.bnlm import download_models
download_models()
Predict N Words¶
from bnlm.bnlm import BengaliTokenizer
from bnlm.bnlm import predict_n_words
model_path = 'model'
input_sen = "আমি বাজারে"
output = predict_n_words(input_sen, 3, model_path)
print("Word Prediction: ", output)
Get Sentence Encoding¶
from bnlm.bnlm import BengaliTokenizer
from bnlm.bnlm import get_sentence_encoding
model_path = 'model'
sp_model = "model/bn_spm.model"
input_sentence = "আমি ভাত খাই।"
encoding = get_sentence_encoding(input_sentence, model_path, sp_model)
print("sentence encoding is: ", encoding)
Get Embedding Vectors¶
from bnlm.bnlm import BengaliTokenizer
from bnlm.bnlm import get_embedding_vectors
model_path = 'model'
sp_model = "model/bn_spm.model"
input_sentence = "আমি ভাত খাই।"
embed = get_embedding_vectors(input_sentence, model_path, sp_model)
print("sentence embedding is : ", embed)
Sentence Similarity¶
from bnlm.bnlm import BengaliTokenizer
from bnlm.bnlm import get_sentence_encoding
from bnlm.bnlm import get_sentence_similarity
model_path = 'model'
sp_model = "model/bn_spm.model"
sentence_1 = "আমি ভাত খাই।"
sentence_2 = "আমি ভাত খাই।"
sim = get_sentence_similarity(sentence_1, sentence_2, model_path, sp_model)
print("similarity is: ", sim)
Get Simillar Sentences¶
from bnlm.bnlm import BengaliTokenizer
from bnlm.bnlm import get_embedding_vectors
from bnlm.bnlm import get_similar_sentences
model_path = 'model'
sp_model = "model/bn_spm.model"
input_sentence = "আমি ভাত খাই।"
sen_pred = get_similar_sentences(input_sentence, 3, model_path, sp_model)
print(sen_pred)
Classification¶
upcomming