source_directory string | source_manifest_sha256 string | tokens int64 | sequences int64 | context_length int64 | seed int64 | generation_batch_size int64 | sampler_version string | tokenizer_size int64 | tokenizer_name string | tokenizer_revision string | eos_token_id int64 | packing string | note string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
outputs/research/natural_language_scaling/empirical_fourgram_2b_source/fit | 1c21177eb833781d45d11acdd9ec4456b3d7b61629e5f8a891cbb871c4d6b8ae | 2,000,000,000 | 7,812,500 | 256 | 40 | 8,192 | partitioned_integer_cdf_v1 | 4,096 | eoinf/pile_tokenizer_4096 | 155b268a49b69bac2543a3eb0acb635cef3dc4b0 | 2 | Independent 257-token windows; first three tokens sampled jointly from empirical context-prefix counts | Synthetic samples from a source fitted to the pinned 2B natural token stream. This is not held-out natural-language data. No models trained. |
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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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