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input_ids
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2
4.1k
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4.14M
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154,328
92
hsapiens
389
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26,422
1,969
hsapiens
1,169
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12,471
1,875
hsapiens
1,633
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199,184
53
hsapiens
1,109
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117,813
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[ 2, 212, 3 ]
45,373
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hsapiens
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182,430
2,490
hsapiens
1,868
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3,053
4,096
hsapiens
475
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1,385,691
846
hsapiens
302
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29,315
484
hsapiens
1,193
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2,585,486
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1,183,146
4,096
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166,610
1,181
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559,461
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70,881
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71,446
2,865
hsapiens
1,585
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133,668
2,823
hsapiens
2,228
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156,303
1,110
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173,167
2,097
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1,733
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35,193
983
hsapiens
1,984
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475,814
335
hsapiens
1,259
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CellxGene + scBaseCount, human, tokenized

Geneformer-style rank-value tokenized human cells: a straightforward union of two existing public corpora, CellxGene Discover (cxg_raw_2025-11-08) and scBaseCount (2025-02-25), deduplicated (CellxGene's own is_primary_data flag, to drop repeated cells across dataset views) and tokenized. 211,221,738 cells. This is not an original data-collection effort -- all underlying cells were already public via CellxGene Discover and scBaseCount; this repo is just that union in a ready-to-train tokenized form.

2026-10-19 update: rows are now globally shuffled (previously contiguous by source study -- a streaming data loader without its own shuffle would see batches, and a leading-rows validation split, dominated by whichever study happened to be first), and 6 genes whose corpus median was exactly 0 have been dropped from every cell's token list (count / median = inf was forcing them to a spurious rank-1 position in ~1.8% of cells). Cell count and the rest of each cell's ranking are unaffected.

Contents

  • Repo root -- a HuggingFace datasets DatasetDict (single train split) saved via save_to_disk (dataset_dict.json, train/). Columns: input_ids (large_list, Geneformer-style rank-value encoding, <cls>-prefixed, truncated to 4096 tokens), cell_index, length, species (always "hsapiens" here).
  • vocab/hsapiens_token_dictionary.pkl -- the exact vocab these ids were tokenized against (23,260 genes + special tokens: <pad>=0, <mask>=1, <cls>=2, <eos>=3, genes 4.. -- 6 fewer than the original 23,266-gene vocab, the zero-median genes above). This is not the same as the default packaged token_dictionary.pkl (~20k genes) -- loading with the wrong vocab silently misaligns gene ids.
  • vocab/hsapiens_gene_medians.pkl -- non-zero median gene expression values used by the tokenizer's normalization step, computed from this same corpus/vocab.

Loading

from huggingface_hub import snapshot_download
from datasets import load_from_disk

local_dir = snapshot_download(repo_id="theislab/cellxgene-scbasecount-human-tokenized", repo_type="dataset")
ds = load_from_disk(local_dir)["train"]
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