input_ids listlengths 2 4.1k | cell_index int64 0 4.14M | length int64 2 4.1k | species stringclasses 1
value | source_id int32 1 2.75k |
|---|---|---|---|---|
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... | 12,471 | 1,875 | hsapiens | 1,633 |
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1... | 199,184 | 53 | hsapiens | 1,109 |
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... | 117,813 | 4,096 | hsapiens | 614 |
[
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3
] | 45,373 | 3 | hsapiens | 1,256 |
[
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2296... | 182,430 | 2,490 | hsapiens | 1,868 |
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... | 3,053 | 4,096 | hsapiens | 475 |
[
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861... | 1,385,691 | 846 | hsapiens | 302 |
[
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... | 29,315 | 484 | hsapiens | 1,193 |
[
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13950,... | 2,585,486 | 1,263 | hsapiens | 718 |
[
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... | 1,183,146 | 4,096 | hsapiens | 898 |
[
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705... | 166,610 | 1,181 | hsapiens | 1,303 |
[
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10911,
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19634,
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... | 559,461 | 2,090 | hsapiens | 1,148 |
[
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1104... | 70,881 | 1,993 | hsapiens | 2,650 |
[
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1... | 71,446 | 2,865 | hsapiens | 1,585 |
[
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648,
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7345,... | 133,668 | 2,823 | hsapiens | 2,228 |
[
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... | 156,303 | 1,110 | hsapiens | 824 |
[
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1... | 173,167 | 2,097 | hsapiens | 1,733 |
[
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... | 35,193 | 983 | hsapiens | 1,984 |
[
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10458,
1623... | 475,814 | 335 | hsapiens | 1,259 |
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
datasetsDatasetDict(singletrainsplit) saved viasave_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 packagedtoken_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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