README: add eukaryote_generator_10B_subset, post-filter stats, fix weights/typos

README.md

@@ -27,23 +27,28 @@ configs:
     data_files:
       - split: train
         path: prokaryote_evo2/*.parquet
+  - config_name: eukaryote_generator_10B_subset
+    data_files:
+      - split: train
+        path: eukaryote_generator_10B_subset/**/*.parquet
 ---
 
 # 🧬 Carbon Pretraining Corpus
 
 ## Description
 
-> **~127M DNA & RNA sequences · 1 trillion nucleotides** — the DNA pretraining mixture used to train [Carbon](https://github.com/hf-carbon/carbon), a genomic foundation model.
+> **~173M DNA & RNA sequences · ~1.1 trillion nucleotides** — the DNA pretraining mixture used to train [Carbon](https://github.com/huggingface/carbon), a genomic foundation model.
 
 This dataset is a collection of data sources intended for training genomic foundation models, such as Carbon. It contains DNA and RNA sequences spanning eukaryote and prokaryote species.
-And has almost 1T DNA base pairs (160B tokens with Carbon's 6-mer tokenizer). 
+Across the four main configs it totals ~1.1 T DNA base pairs (~180B tokens with Carbon's 6-mer tokenizer). A pre-sampled 10B-token eukaryote subset (`eukaryote_generator_10B_subset`) is also provided for smaller / faster runs.
 
 | Subset | Domain | Source | Size | Rows | Nucleotides |
 |---|---|---|---:|---:|---:|
-| `eukaryote_generator` | Eukaryote genomes | GenerTeam / GENERATOR | 376.8 GB | 48.02 M | ~420 Gbp |
+| `eukaryote_generator` | Eukaryote genomes (≤ 100 kbp) | GenerTeam / GENERATOR | 191.9 GB | 46,323,396 | ~423.4 Gbp |
 | `mrna_evo2` | Messenger RNA | Arc Institute / OpenGenome2 | 54.8 GB | 52,702,454 | ~115.9 Gbp |
 | `mrna_splice_evo2` | mRNA + splice & promoter | Arc Institute / OpenGenome2 | 92.9 GB | 56,877,762 | ~197.4 Gbp |
 | `prokaryote_evo2` | Prokaryote genomes (GTDB + IMG/PR) | Arc Institute / OpenGenome2 | 166.0 GB | 17,408,059 | ~357.5 Gbp |
+| `eukaryote_generator_10B_subset` | Eukaryote subsample (10B tokens, natural species distribution) | derived from `eukaryote_generator` | 27.6 GB | 6,562,876 | 60.0 Gbp |
 
 Nucleotides are counted in base pairs (Gbp = billion nucleotides). 
 
@@ -63,10 +68,10 @@ DNA sequences in this corpus look like this:
 ATGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGCTAGC
 ```
 
-A 6-mer tokenizer, such as Carbon's, splits this into overlapping windows of 6 nucleotides:
+A 6-mer tokenizer, such as Carbon's, splits this into non-overlapping windows of 6 nucleotides — one token per 6 bases:
 
 ```
-ATGCTA | TGCTAG | GCTAGC | CTAGCT | TAGCTA | AGCTAG | ...
+ATGCTA | GCTAGC | TAGCTA | GCTAGC | TAGCTA | GCTAGC | ...
 ```
 
 ## Loading the data
@@ -77,7 +82,7 @@ from datasets import load_dataset
 
 ds = load_dataset(
     "hf-carbon/carbon-pretraining-corpus",
-    "mrna_evo2",          # or: eukaryote_generator | mrna_splice_evo2 | prokaryote_evo2
+    "mrna_evo2",          # or: eukaryote_generator | mrna_splice_evo2 | prokaryote_evo2 | eukaryote_generator_10B_subset
     split="train",
     streaming=True,
 )
@@ -93,9 +98,9 @@ for example in ds.take(3):
 
 **Source:** [GenerTeam/pretrain_data_eukaryote](https://huggingface.co/datasets/GenerTeam/pretrain_data_eukaryote)
 
-Genomic sequences from eukaryotic organisms (fungi, plants, animals, and more), packaged by the GenerTeam as part of their [GENERator](https://arxiv.org/abs/2502.07272) genomic foundation model. This is the main part is Carbon training data, which is focused on Eukaryote species. Each row carries full taxonomic metadata, gene type, strand orientation, and chromosome coordinates alongside the raw sequence. 
+Genomic sequences from eukaryotic organisms (fungi, plants, animals, and more), packaged by the GenerTeam as part of their [GENERator](https://arxiv.org/abs/2502.07272) genomic foundation model. This is the main part of Carbon's training data, which is focused on eukaryote species. Each row carries full taxonomic metadata, gene type, strand orientation, and chromosome coordinates alongside the raw sequence.
 
-Following GENERator, we filter out sequences longer than 100 kbp, as excluding them improves training performance on DNA donwstream benchmarks. For long-context training, you can concatenate genes from the same contig to build longer samples.
+Following GENERator, we filter out sequences longer than 100 kbp, as excluding them improves training performance on DNA downstream benchmarks. For long-context training, you can concatenate genes from the same contig to build longer samples.
 
 
 **Example row:**
@@ -133,6 +138,23 @@ Sequences here are systematically longer than `mrna_evo2` — the extra promoter
 
 Long chromosomal chunks from bacteria and archaea, sourced from [GTDB v220](https://gtdb.ecogenomic.org/) (a curated taxonomy of ~85 K prokaryote genomes) and [IMG/PR](https://img.jgi.doe.gov/) (a DOE database of environmental prokaryote sequences). Prokaryote genomes are compact — genes sit back-to-back with minimal intergenic space — so these sequences are biologically rich per nucleotide.
 
+### 5. `eukaryote_generator_10B_subset` — 10B-token eukaryote subsample
+
+**Source:** derived from `eukaryote_generator` (this dataset).
+
+A pre-sampled subset of `eukaryote_generator` totalling ~60 Gbp (~10 B tokens at 6-mer tokenization) for smaller / faster model trainings that don't need the full 423 Gbp eukaryote slice. Rows are uniformly sampled at the row level from the filtered eukaryote data, which keeps the species distribution proportional to the natural per-species base-pair count:
+
+| Species | Natural share | In subset |
+|---|---:|---:|
+| `vertebrate_other` | 42.03% | 42.03% |
+| `vertebrate_mammalian` | 27.20% | 27.24% |
+| `invertebrate` | 19.56% | 19.54% |
+| `plant` | 7.91% | 7.90% |
+| `fungi` | 2.69% | 2.68% |
+| `protozoa` | 0.60% | 0.60% |
+
+Same schema as `eukaryote_generator` (full structured metadata columns).
+
 
 ## ⚗️ Carbon Training Mixture
 
@@ -142,13 +164,13 @@ The proportions below reflect the target mixture for Carbon's pure-DNA pretraini
 
 | Subset | Approx. weight |
 |---|---:|
-| `prokaryote_evo2` | ~70% |
-| `mrna_splice_evo2` | ~15% |
-| `mrna_evo2` | ~10% |
-| `eukaryote_generator` | ~5% |
+| `eukaryote_generator` | ~70% |
+| `mrna_evo2` | ~16% |
+| `prokaryote_evo2` | ~10% |
+| `mrna_splice_evo2` | ~4% |
 
 
-### Metadata conditioning in Carnon training (`eukaryote_generator` only)
+### Metadata conditioning in Carbon training (`eukaryote_generator` only)
 
 In Carbon we added optional metadata tags to a fraction of eukaryote sequences, so the model learns to use biological context when available but doesn't depend on it. Tags are prepended before the sequence with random dropout at tokenization time:
 
@@ -175,6 +197,7 @@ This dataset is a mirror of two upstream sources with different permissive licen
 | Subset | License | Upstream |
 |---|---|---|
 | `eukaryote_generator` | [MIT](https://huggingface.co/datasets/GenerTeam/pretrain_data_eukaryote) | GenerTeam/pretrain_data_eukaryote |
+| `eukaryote_generator_10B_subset` | [MIT](https://huggingface.co/datasets/GenerTeam/pretrain_data_eukaryote) | derived from `eukaryote_generator` (GenerTeam/pretrain_data_eukaryote) |
 | `mrna_evo2` | [Apache-2.0](https://huggingface.co/datasets/arcinstitute/opengenome2) | arcinstitute/opengenome2 |
 | `mrna_splice_evo2` | [Apache-2.0](https://huggingface.co/datasets/arcinstitute/opengenome2) | arcinstitute/opengenome2 |
 | `prokaryote_evo2` | [Apache-2.0](https://huggingface.co/datasets/arcinstitute/opengenome2) | arcinstitute/opengenome2 |