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import sentencepiece as spm |
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from datasets import concatenate_datasets, load_dataset, load_from_disk |
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import logging |
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from datasets import DatasetDict, Dataset |
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sp = spm.SentencePieceProcessor() |
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sp.load('ta_te_kan_ml_50kspm_tokenizer.model') |
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sentence = "The Indian cricket fans were very disappointed after India's loss against Australia in the World Cup." |
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tokens = sp.encode(sentence, out_type=str) |
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print(len(tokens) , tokens) |
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sentence = "विश्व कप में ऑस्ट्रेलिया के खिलाफ भारत की हार के बाद भारतीय क्रिकेट प्रशंसक काफी निराश थे।" |
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tokens = sp.encode(sentence, out_type=str) |
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print(len(tokens) , tokens) |
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decoded_sentence = sp.decode(tokens) |
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def initialize_logger(log_file): |
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logging.basicConfig(filename=log_file, level=logging.INFO, format='%(asctime)s: %(message)s') |
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def log_parameters(vocab_size, pretrained_model, en_fertility_score, hi_fertility_score , kn_fertility_score ,ml_fertility_score,ta_fertility_score,te_fertility_score, log_file='parameters.log'): |
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initialize_logger(log_file) |
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logging.info(f"Vocabulary Size: {vocab_size}, Tokenizer type: {pretrained_model}, English Fertility Score: {en_fertility_score} , Hindi Fertility Score: {hi_fertility_score}, Kannada Fertility Score: {kn_fertility_score}, <Malayalam Fertility Score: {ml_fertility_score}, Tamil Fertility Score: {ta_fertility_score}, Telugu Fertility Score: {te_fertility_score}") |
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dataset_hi= load_dataset('ai4bharat/samanantar', 'hi', split='train', cache_dir='/sml1/atul/CENTRAL_CACHE') |
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dataset_kn= load_dataset('ai4bharat/samanantar', 'kn', split='train', cache_dir='/sml1/atul/CENTRAL_CACHE') |
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dataset_ml= load_dataset('ai4bharat/samanantar', 'ml', split='train', cache_dir='/sml1/atul/CENTRAL_CACHE') |
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dataset_ta= load_dataset('ai4bharat/samanantar', 'ta', split='train', cache_dir='/sml1/atul/CENTRAL_CACHE') |
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dataset_te= load_dataset('ai4bharat/samanantar', 'te', split='train', cache_dir='/sml1/atul/CENTRAL_CACHE') |
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test_en = dataset_hi['src'][:10000] |
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test_hi = dataset_hi['tgt'][:10000] |
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test_kn = dataset_kn['tgt'][:10000] |
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test_ml = dataset_ml['tgt'][:10000] |
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test_ta = dataset_ta['tgt'][:10000] |
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test_te = dataset_te['tgt'][:10000] |
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en_fertility_score=0 |
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hi_fertility_score=0 |
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kn_fertility_score=0 |
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ml_fertility_score=0 |
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ta_fertility_score=0 |
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te_fertility_score=0 |
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for data in test_en: |
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tok=sp.encode(data, out_type=str) |
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en_fertility_score += (len(tok)) / len(data.split()) |
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en_fertility_score=en_fertility_score/10000 |
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for data in test_hi: |
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tok=sp.encode(data, out_type=str) |
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hi_fertility_score += (len(tok)) / len(data.split()) |
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hi_fertility_score=hi_fertility_score/10000 |
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for data in test_kn: |
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tok=sp.encode(data, out_type=str) |
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kn_fertility_score += (len(tok)) / len(data.split()) |
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kn_fertility_score=kn_fertility_score/10000 |
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for data in test_ml: |
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tok=sp.encode(data, out_type=str) |
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ml_fertility_score += (len(tok)) / len(data.split()) |
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ml_fertility_score=ml_fertility_score/10000 |
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for data in test_ta: |
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tok=sp.encode(data, out_type=str) |
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ta_fertility_score += (len(tok)) / len(data.split()) |
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ta_fertility_score=ta_fertility_score/10000 |
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for data in test_te: |
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tok=sp.encode(data, out_type=str) |
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te_fertility_score += (len(tok)) / len(data.split()) |
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te_fertility_score=te_fertility_score/10000 |
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log_parameters(64000, "kn-ml-ta-te-only-spm", en_fertility_score, hi_fertility_score , kn_fertility_score, ml_fertility_score,ta_fertility_score,te_fertility_score ) |
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