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README.md

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 ---
 license: apache-2.0
+pretty_name: RIBOSPAN-FM-Corpus
+
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: parquet/train.parquet
+  - split: validation
+    path: parquet/validation.parquet
+  - split: test
+    path: parquet/test.parquet
+
+tags:
+- biology
+- rna
+- genomics
+- transcriptomics
+- foundation-model
+- pretraining
+
+task_categories:
+- fill-mask
 ---
+
+# RIBOSPAN Pretraining Corpus
+
+[![arXiv](https://img.shields.io/badge/arXiv-2608.22849-b31b1b.svg)](https://arxiv.org/abs/2608.22849)
+[![GitHub](https://img.shields.io/badge/RIBOSPAN--FM-181717?logo=github)](https://github.com/GAIR-NLP/RIBOSPAN-FM)
+[![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97-RIBOSPAN--FM-yellow)](https://huggingface.co/SII-GAIR-NLP/RIBOSPAN-FM)
+
+The **RIBOSPAN Pretraining Corpus** is the RNA sequence corpus used to pretrain the [RIBOSPAN](https://huggingface.co/SII-GAIR-NLP/RIBOSPAN-FM) model family.
+
+The corpus combines diverse RNA sequences from **RNAcentral v26.0** with quality-controlled protein-coding transcripts from **Ensembl release 115** and **Ensembl Genomes release 62**.
+
+After source-specific filtering, normalization, and exact deduplication, the final training corpus contains **67.6 million RNA sequences** and **85.7 billion nucleotide tokens** in total.
+
+---
+
+## 🧬 Data Sources
+
+The RIBOSPAN pretraining corpus was constructed from:
+
+- **[RNAcentral v26.0](https://rnacentral.org/)**
+- **[Ensembl release 115](https://www.ensembl.org/)**
+- **[Ensembl Genomes release 62](https://ensemblgenomes.org/)**
+
+RNAcentral provides broad coverage across RNA classes, while Ensembl datasets contribute quality-controlled protein-coding transcripts with complete CDS and UTR annotations across vertebrates, plants, fungi, metazoans, and protists.
+
+RNAcentral v26.0, Ensembl release 115, and Ensembl Genomes release 62 were curated independently before merging.
+
+---
+
+## 🧩 Data Curation
+
+### Sequence Normalization
+
+All sequences were normalized to uppercase, with `U` mapped to `T` and unsupported symbols mapped to `N`.
+
+The resulting corpus therefore uses the normalized nucleotide alphabet:
+
+```text
+A C G T N
+```
+
+### RNAcentral
+
+For RNAcentral, the active and inactive sequence sets were combined and exact-deduplicated by sequence using **SeqKit**.
+
+### Ensembl and Ensembl Genomes
+
+Ensembl cDNA sequences from vertebrates, plants, fungi, metazoans, and protists were paired with the corresponding release-matched non-ab-initio GTF annotations and filtered to retain complete, high-quality protein-coding transcripts, followed by sequence-level exact deduplication using **SeqKit**.
+
+### Filtering Criteria for Ensembl Protein-Coding Transcripts
+
+| Criterion | Requirement |
+|---|---|
+| Annotation | Protein-coding transcript annotation and, when available, protein-coding gene annotation; neither is annotated as a pseudogene. |
+| Completeness | CDS, start codon, stop codon, 5′ UTR, and 3′ UTR annotations are present. |
+| Coding consistency | CDS length is positive and divisible by 3, with consistent start- and stop-codon annotations. |
+| Sequence quality | ≥ 20 nt, ≤ 5% N, and no homopolymer > 50 nt. |
+
+---
+
+## ✂️ Dataset Splits
+
+After source-specific filtering, normalization, and exact deduplication, the curated corpus was divided into **training**, **validation**, and **test** splits.
+
+The validation and test sets were constructed using class-specific sampling quotas to provide broad representation across RNA classes while reducing the influence of the highly imbalanced training distribution.
+
+For protein-coding transcripts, the sampling was further stratified by species.
+
+The validation and test sets each contain **90,000 held-out RNA sequences**.
+
+The released dataset consists of three sequence-level splits in compressed FASTA format:
+
+| Split | File |
+|---|---|
+| Training | `train.fasta.gz` |
+| Validation | `validation.fasta.gz` |
+| Test | `test.fasta.gz` |
+
+The validation and test sequences are held out at the sequence level and are not included in the training split.
+
+---
+
+## 📊 Dataset Statistics
+
+### Sequence-Length and RNA-Class Distributions
+
+The resulting sequence-length and RNA-class distributions are shown below.
+
+![Pretraining corpus](docs/Pretraining_Corpus.png)
+
+*Sequence-length distributions and RNA-class composition of the pretraining corpus.*
+
+### RNA-Class Composition of the Pretraining Corpus and Held-Out Sets
+
+| RNA Class | Training | Validation | Test |
+|---|---:|---:|---:|
+| rRNA | 30,748,160 | 10,000 | 10,000 |
+| mRNA | 18,316,110 | 10,000 | 10,000 |
+| tRNA | 6,135,163 | 10,000 | 10,000 |
+| misc RNA | 3,966,721 | 10,000 | 10,000 |
+| lncRNA | 3,818,804 | 10,000 | 10,000 |
+| others | 940,161 | 5,000 | 5,000 |
+| pre miRNA | 804,307 | 5,000 | 5,000 |
+| snoRNA | 598,283 | 5,000 | 5,000 |
+| sRNA | 541,829 | 5,000 | 5,000 |
+| ncRNA | 510,635 | 5,000 | 5,000 |
+| snRNA | 493,584 | 5,000 | 5,000 |
+| SRP RNA | 483,594 | 5,000 | 5,000 |
+| piRNA | 209,734 | 5,000 | 5,000 |
+| **Total** | **67,567,085** | **90,000** | **90,000** |
+
+The training corpus follows the natural RNA-class distribution and is therefore highly imbalanced, whereas the held-out validation and test sets use class-specific sampling quotas to provide broad representation across RNA classes.
+
+Because RNA classes differ substantially in sequence length, sequence-count proportions do not directly correspond to nucleotide proportions. Long transcript classes such as mRNA therefore contribute disproportionately to the total number of nucleotides relative to their sequence counts.
+
+### Pretraining Corpus Statistics by Split and Source
+
+| Split | Source | Sequences | Nucleotide Tokens |
+|---|---|---:|---:|
+| Training | RNAcentral v26.0 | 49,250,975 | 34,111,085,735 |
+| Training | Ensembl 115 / Ensembl Genomes 62 | 18,316,110 | 51,603,244,706 |
+| Validation | Combined | 90,000 | 77,781,035 |
+| Test | Combined | 90,000 | 76,893,030 |
+
+The two training sources together contain **67,567,085 RNA sequences** and **85,714,330,441 nucleotide tokens**.
+
+Including the validation and test splits, the released corpus contains **67,747,085 RNA sequences** and **85,869,004,506 nucleotides** in total.
+
+---
+
+## 🧾 Sequence Format
+
+The corpus is distributed in compressed FASTA format.
+
+Each FASTA record corresponds to one independent RNA sequence.
+
+```text
+>sequence_id
+ATGCTGAC...
+```
+
+All released sequences use the normalized nucleotide alphabet:
+
+```text
+A C G T N
+```
+
+RNA uracil (`U`) is represented as thymine (`T`), consistent with the sequence normalization used during RIBOSPAN pretraining.
+
+No `[CLS]`, `[SEP]`, `[MASK]`, padding, or other model-specific tokens are stored in the released FASTA files. These tokens are introduced during model-side tokenization and training sample construction.
+
+---
+
+## 🙏 Acknowledgements
+
+We thank [RNAcentral](https://rnacentral.org/), [Ensembl](https://www.ensembl.org/), and [Ensembl Genomes](https://ensemblgenomes.org/) for providing the sequence resources used to construct the RIBOSPAN pretraining corpus.
+
+We also acknowledge the developers of [SeqKit](https://bioinf.shenwei.me/seqkit/) for providing the sequence-processing toolkit used for exact sequence deduplication during corpus construction.
+
+Users of this dataset should appropriately acknowledge and cite the original data resources in accordance with their respective terms and recommended citation practices.
+
+---
+
+## 📖 Citation
+
+If you use the **RIBOSPAN Pretraining Corpus** in your research, please cite the RIBOSPAN paper:
+
+```bibtex
+@misc{wang2026ribospan,
+  title         = {{RIBOSPAN}: A Long-Context {RNA} Foundation Model for Versatile {RNA} Modeling},
+  author        = {Wang, Ziyuan and Tang, Bohao and Zhang, Fei and Han, Shuo and Liu, Pengfei},
+  year          = {2026},
+  eprint        = {2608.22849},
+  archivePrefix = {arXiv},
+  primaryClass  = {cs.LG}
+}
+```
+
+---
+
+**RiboSpan** — Single-nucleotide Pretraining Across Native-context.

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