|
|
import sentencepiece as spm |
|
|
from datasets import concatenate_datasets, load_dataset, load_from_disk |
|
|
import logging |
|
|
from datasets import DatasetDict, Dataset |
|
|
|
|
|
sp = spm.SentencePieceProcessor() |
|
|
sp.load('ta_te_kan_ml_50kspm_tokenizer.model') |
|
|
sentence = "ಮಾಡು ಮಾಡುವುದು ಮಾಡಲು विद्यालय आलय" |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
tokens = sp.encode(sentence, out_type=str) |
|
|
print(len(tokens) , tokens) |
|
|
exit() |
|
|
sentence = "विश्व कप में ऑस्ट्रेलिया के खिलाफ भारत की हार के बाद भारतीय क्रिकेट प्रशंसक काफी निराश थे।" |
|
|
|
|
|
tokens = sp.encode(sentence, out_type=str) |
|
|
print(len(tokens) , tokens) |
|
|
|
|
|
decoded_sentence = sp.decode(tokens) |
|
|
exit() |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def initialize_logger(log_file): |
|
|
logging.basicConfig(filename=log_file, level=logging.INFO, format='%(asctime)s: %(message)s') |
|
|
|
|
|
def log_parameters(vocab_size, pretrained_model, en_fertility_score, hi_fertility_score , bn_fertility_score , log_file='parameters.log'): |
|
|
initialize_logger(log_file) |
|
|
logging.info(f"Vocabulary Size: {vocab_size}, Tokenizer type: {pretrained_model}, English Fertility Score: {en_fertility_score} , Hindi Fertility Score: {hi_fertility_score}, Bengali Fertility Score: {bn_fertility_score}") |
|
|
|
|
|
dataset_hi= load_dataset('ai4bharat/samanantar', 'hi', split='train', cache_dir='/sml1/atul/CENTRAL_CACHE') |
|
|
dataset_bn= load_dataset('ai4bharat/samanantar', 'bn', split='train', cache_dir='/sml1/atul/CENTRAL_CACHE') |
|
|
|
|
|
test_en = dataset_hi['src'][:10000] |
|
|
test_hi = dataset_hi['tgt'][:10000] |
|
|
test_bn = dataset_bn['tgt'][:10000] |
|
|
|
|
|
en_fertility_score=0 |
|
|
hi_fertility_score=0 |
|
|
bn_fertility_score=0 |
|
|
|
|
|
for data in test_en: |
|
|
tok=sp.encode(data, out_type=str) |
|
|
en_fertility_score += (len(tok)) / len(data.split()) |
|
|
en_fertility_score=en_fertility_score/10000 |
|
|
|
|
|
for data in test_hi: |
|
|
tok=sp.encode(data, out_type=str) |
|
|
hi_fertility_score += (len(tok)) / len(data.split()) |
|
|
hi_fertility_score=hi_fertility_score/10000 |
|
|
|
|
|
for data in test_bn: |
|
|
tok=sp.encode(data, out_type=str) |
|
|
bn_fertility_score += (len(tok)) / len(data.split()) |
|
|
bn_fertility_score=bn_fertility_score/10000 |
|
|
log_parameters(32000, "en-only-spm", en_fertility_score, hi_fertility_score , bn_fertility_score ) |
|
|
|
|
|
|
|
|
|
|
|
|
