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--- |
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dataset_info: |
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features: |
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- name: text |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 24500165181 |
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num_examples: 80462898 |
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download_size: 14400389487 |
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dataset_size: 24500165181 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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--- |
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# Dataset Card for "bert_pretrain_datasets" |
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This dataset is essentially a concatenation of the training set of the English Wikipedia (wikipedia.20220301.en.train) and the Book Corpus (bookcorpus.train). |
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This is exactly how I get this dataset: |
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``` |
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from datasets import load_dataset, concatenate_datasets, load_from_disk |
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cache_dir = "/data/haob2/cache/" |
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# book corpus |
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bookcorpus = load_dataset("bookcorpus", split="train", cache_dir=cache_dir) |
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# english wikipedia |
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wiki = load_dataset("wikipedia", "20220301.en", split="train", cache_dir=cache_dir) |
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wiki = wiki.remove_columns([col for col in wiki.column_names if col != "text"]) |
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# # concatenation |
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concat = concatenate_datasets([bookcorpus, wiki]) |
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concat.push_to_hub("JackBAI/bert_pretrain_datasets") |
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``` |
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Note that this is a naive reproduction of the dataset that BERT is using. We believe the official BERT checkpoint is pretrained on a much more engineered dataset. |
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