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peacock-data-public-datasets-tokenization

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peacock-data-public-datasets-tokenization
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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"
# ಮಾ', 'ಡುವುದು', '▁ಮಾಡಲು', '▁']
# 9 ['ಕುಳ', 'ಿತು', 'ಕೊಳ್ಳಿ', '▁▁', 'ಕುಳ', 'ಿತು', 'ಕೊ', 'ಳ್', 'ಲು']
# 'ഇ', 'ര', 'ിക്കും', '▁ഇരിക്ക', 'ുക', '▁'
#  ['विश्व', '▁कप', '▁में', '▁काल', '▁अंश', '▁काला', 'ंश', '▁ऑस्ट्रेलिया', '▁के', '▁खिलाफ', '▁भारत', '▁की', '▁हार', '▁के', '▁बाद', '▁भारतीय', '▁क्रिकेट', '▁प्रशंसा', '▁काफी', '▁निराश', '▁थे', '।']
# विश्व', '▁कप', '▁में', '▁ऑस्ट्रेलिया', '▁के', '▁खिलाफ', '▁भारत', '▁की', '▁हार', '▁के', '▁बाद', '▁भारतीय', '▁क्रिकेट', '▁प्रशंसा', '▁काफी', '▁निराश', '▁थे', '।'
# 19 ['विश्व', '▁कप', '▁में', '▁ऑस्ट्रेलिया', '▁के', '▁खिलाफ', '▁भारत', '▁की', '▁हार', '▁के', '▁बाद', '▁भारतीय', '▁क्रिकेट', '▁प्रश', 'ंसक', '▁काफी', '▁निराश', '▁थे', '।']
# काला', 'ंश'
# 19 ['ലോക', 'ക', 'പ്പിൽ', '▁ഓസ്', '▁ട്ര', 'േലിയ', 'യ്', '▁', 'ക്കെതിരെ', '▁ഇന്ത്യ', '▁തോറ്റ', 'തോടെ', '▁ഇന്ത്യൻ', '▁ക്രിക്കറ്റ്', '▁ആരാധകർ', '▁ഏറെ', '▁നിരാശ', 'യിലാണ്', '.']
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()
# exit()
# print("Tokens:", tokens)
# print("Decoded sentence:", decoded_sentence)
# 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 )