tokens int64 | sequences int64 | context_length int64 | seed int64 | source_path string | source_sha256 string | tokenizer_name string | tokenizer_revision string | tokenizer_size int64 | eos_token_id null | packing string | note string |
|---|---|---|---|---|---|---|---|---|---|---|---|
199,999,488 | 781,248 | 256 | 40 | outputs/research/natural_language_scaling/bigram_top100_58pct_200m_data/empirical_bigram.npz | 73e95885ea0d9f506ff91986f71b0a99af826e6f40a55a127bd8b2c70d826b25 | empirical_frequency_allocation | 73e95885ea0d9f506ff91986f71b0a99af826e6f40a55a127bd8b2c70d826b25 | 4,096 | null | Independent Markov sequences of 257 tokens; 256 next-token targets | Sampled from empirical training transition table; held-out synthetic data, not natural-language held-out data |
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Check out the documentation for more information.
Exact experiment data archive
Archive of exact NumPy caches, split metadata and available generator/source files. No re-sampling or train/test relabeling is performed. Split metadata records seeds, tokenizer/source revisions and packing. A training-data evaluation remains a training-data evaluation; independently sampled synthetic tests are distinct from held-out natural-language documents. dataset_manifest.json records checksums; experiment_configs/ records the consuming experiments.
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