Add comprehensive dataset card

README.md

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+---
+license: other
+tags:
+- biology
+- genomics
+- DNA
+- huggingscience
+- science
+pretty_name: "Human DNA v0"
+size_categories:
+- 100K<n<1M
+task_categories:
+- text-generation
+- fill-mask
+---
+
+# Dataset Card for Human DNA v0
+
+## Dataset Description
+
+**Repository:** [simecek/Human_DNA_v0](https://huggingface.co/datasets/simecek/Human_DNA_v0)
+
+### Dataset Summary
+
+The `Human_DNA_v0` dataset provides a large corpus of nucleotide sequences from the human genome. It is designed to serve as a foundational training resource for developing and evaluating language models in the field of genomics. The data consists of long strings representing the fundamental DNA bases: **A** (adenine), **C** (cytosine), **G** (guanine), and **T** (thymine).
+
+This dataset is crucial for pre-training models that can learn the underlying patterns, syntax, and long-range dependencies within the human genetic code. Such "Genomic Foundation Models" have applications in predicting gene locations, understanding regulatory regions, and identifying variants associated with disease. 🧬
+
+### Supported Tasks and Leaderboards
+
+This dataset is primarily intended for **self-supervised learning** tasks, similar to how models like BERT or GPT are trained on natural language.
+
+* **Masked Language Modeling (Fill-Mask):** Models can be trained to predict a masked or missing nucleotide in a sequence. This helps the model learn contextual information within the DNA strand.
+* **Next-Token Prediction (Text Generation):** Models can be trained to predict the next nucleotide in a sequence. This is the basis for generative models that can produce realistic DNA sequences.
+* **Downstream Fine-tuning:** Models pre-trained on this dataset can be fine-tuned for a variety of specific genomic tasks, such as promoter identification, splice site prediction, or classification of non-coding regions.
+
+### Data Fields
+
+The dataset is provided in Parquet format and contains a single, straightforward column:
+
+* `sequence`: (string) A long string of characters representing a segment of the human DNA sequence. Each character is one of `A`, `C`, `G`, or `T`.
+
+### Data Splits
+
+* **`train`**: Contains 292,955 rows of DNA sequences for model training.
+
+There are no pre-defined validation or test splits, so users should create their own splits from the training data as needed for their experiments.
+
+### Curation and Rationale
+
+While the original curation details are sparse, this dataset was created to provide a simple, large-scale resource for applying modern NLP techniques to genomics. By formatting genomic data in a way that is immediately accessible to machine learning libraries, it lowers the barrier for ML practitioners to contribute to computational biology. It has been successfully used as a pre-training corpus for models like [vesteinn/gpt2-dna](https://huggingface.co/vesteinn/gpt2-dna).
+
+### Considerations for Use
+
+* **Character-level Processing:** DNA sequences are inherently character-level. Tokenization strategies are a key consideration. Simple character-level tokenizers are common, but more advanced methods like BPE (Byte-Pair Encoding) or k-mer based approaches may also be effective. A **k-mer