| | from datasets import concatenate_datasets, load_dataset, load_from_disk |
| | import argparse |
| | from tokenizers import Tokenizer, decoders, models, pre_tokenizers, processors, trainers |
| | from transformers import GPT2TokenizerFast, AutoTokenizer |
| | from datasets import config |
| | from datasets import DatasetDict, Dataset |
| |
|
| | code_dataset_go= load_dataset('code_x_glue_ct_code_to_text','go',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code'] |
| | code_dataset_java= load_dataset('code_x_glue_ct_code_to_text','java',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code'] |
| | code_dataset_javascript= load_dataset('code_x_glue_ct_code_to_text','javascript',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code'] |
| | code_dataset_php= load_dataset('code_x_glue_ct_code_to_text','php',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code'] |
| | code_dataset_python= load_dataset('code_x_glue_ct_code_to_text','python',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code'] |
| | code_dataset_ruby= load_dataset('code_x_glue_ct_code_to_text','ruby',split='train', cache_dir='/sml2/atul/CENTRAL_CACHE')['code'] |
| |
|
| | indic_datasets_hi= load_dataset('ai4bharat/sangraha', data_dir="verified/hin", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['type'][:1000000] |
| | indic_datasets_bn= load_dataset('ai4bharat/sangraha', data_dir="verified/ben", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['type'][:1000000] |
| | wikipedia_en = load_dataset("wikipedia", "20220301.en", cache_dir='/sml2/atul/CENTRAL_CACHE')['train']['text'][:1000000] |
| | |
| |
|
| | combined_train_set=code_dataset_go+code_dataset_java+code_dataset_javascript+code_dataset_php+code_dataset_python+code_dataset_ruby+indic_datasets_hi + indic_datasets_bn + wikipedia_en |
| |
|
| | data = { |
| | "train":{"text": combined_train_set}, |
| | "validation": {"text": []}, |
| | "test": {"text": []}, |
| | } |
| | |
| | custom_dataset = DatasetDict() |
| | for split in data: |
| | custom_dataset[split] = Dataset.from_dict(data[split]) |
| | print(custom_dataset) |