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README.md

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+---
+license: apache-2.0
+task_categories:
+  - text-generation
+tags:
+  - biology
+  - genomics
+  - dna
+  - benchmark
+configs:
+  - config_name: tata
+    data_files:
+      - split: train
+        path: tata/data.parquet
+  - config_name: synonymous_codons
+    data_files:
+      - split: train
+        path: synonymous_codons/data.parquet
+---
+
+# Carbon Perturbation Bench
+
+Two **sequence-level perturbation tasks** for evaluating DNA foundation models:
+
+- **`tata`** (5,000 rows): the TATA-box motif inside a real promoter is
+  disrupted with random nucleotide substitutions. Probes whether the model
+  has internalised eukaryotic promoter architecture (it should assign higher
+  log-likelihood to the intact promoter than the perturbed one).
+- **`synonymous_codons`** (5,000 rows): codons in a real CDS are replaced
+  with synonyms encoding the same amino acid. Probes whether the model has
+  learned codon-usage bias (it should prefer native codon usage over the
+  synonymous variant).
+
+Both subsets share the same schema:
+
+| column | description |
+|---|---|
+| `chr`, `start`, `end`, `strand` | hg38 locus |
+| `length` | sequence length in bp |
+| `original_sequence` | the real (unperturbed) sequence — positive |
+| `sequence` | the perturbed sequence — negative control |
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+tata = load_dataset("hf-carbon/carbon-perturbation-bench", "tata", split="train")
+syn = load_dataset("hf-carbon/carbon-perturbation-bench", "synonymous_codons", split="train")
+```
+
+## Eval recipe
+
+Pairwise likelihood discrimination: score `LL(original_sequence)` vs
+`LL(sequence)` under the model, and report `mean(LL(real) >= LL(perturbed))`.
+A ready-to-run scorer for Carbon, GENERator, and Evo2 lives at
+[`evaluation/perturbation_tasks.py`](https://github.com/huggingface/carbon)
+in the Carbon release repo.

synonymous_codons/data.parquet

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tata/data.parquet

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