Update README.md

README.md

@@ -108,11 +108,11 @@ configs:
         path: data/niah_neardup_d1_128k/test-*.parquet
 ---
 
-# Genome-NIAH
+# Genomic-NIAH
 
 **A long-context retrieval benchmark for genomic language models.**
 
-Genome-NIAH measures whether a DNA model actually *uses* its long context. Each example plants a random (KEY, VALUE) DNA pair inside a real-genome haystack and asks the model to recover VALUE given the haystack followed by KEY. Because both KEY and VALUE are uncorrelated with the surrounding sequence, the only way to succeed is to retrieve the planted pair at long range — turning the abstract question "does the model use distal context" into a concrete generative task with a per-example accuracy. This benchmark is inspired from [NIAH](https://github.com/gkamradt/LLMTest_NeedleInAHaystack) and [RULER](https://arxiv.org/abs/2404.06654), popular long context benchmarks for natural language.
+Genomic-NIAH measures whether a DNA model actually *uses* its long context. Each example plants a random (KEY, VALUE) DNA pair inside a real-genome haystack and asks the model to recover VALUE given the haystack followed by KEY. Because both KEY and VALUE are uncorrelated with the surrounding sequence, the only way to succeed is to retrieve the planted pair at long range — turning the abstract question "does the model use distal context" into a concrete generative task with a per-example accuracy. This benchmark is inspired from [NIAH](https://github.com/gkamradt/LLMTest_NeedleInAHaystack) and [RULER](https://arxiv.org/abs/2404.06654), popular long context benchmarks for natural language.
 
 The benchmark provides 4 tasks of varying difficulty, and 24 sub-configurations on a **4 × 6 grid** of task difficulty (plain retrieval and three near-duplicate variants) × context length (from 4k to 128k 6-mer tokens ≈ 24,000 DNA base pairs – 786,000 DNA base pairs). Each sub-configuration contains 500 examples, stratified across five needle depths and four eukaryotic kingdoms.
 
@@ -133,10 +133,10 @@ The benchmark provides 4 tasks of varying difficulty, and 24 sub-configurations
 from datasets import load_dataset
 
 # Plain retrieval at 16 k tokens (≈ 98 kbp)
-ds = load_dataset("hf-carbon/genome-niah", "niah_16k")
+ds = load_dataset("hf-carbon/genomic-niah", "niah_16k")
 
 # Hardest near-duplicate variant (KEY′ differs by 1 bp) at 64 k tokens (≈ 384 kbp)
-ds = load_dataset("hf-carbon/genome-niah", "niah_neardup_d1_64k")
+ds = load_dataset("hf-carbon/genomic-niah", "niah_neardup_d1_64k")
 ```
 
 For each example, the `prompt` field already contains the full input to feed the model. The expected response is in `value` (for `gen_exact_match`) or can be discriminated against `negative_value` (for `ll_correct`). See [Evaluation metrics](#evaluation-metrics).
@@ -202,7 +202,7 @@ Each row contains:
 
 The official evaluation script lives in the Carbon repository. See [`evaluation/genome_niah_eval.py`](https://github.com/huggingface/carbon/tree/main/evaluation#5-genome-niah-long-context-retrieval) for the command-line interface, supported backends, and reproducible run instructions.
 
-Genome-NIAH supports two metrics, computed per example:
+Genomic-NIAH supports two metrics, computed per example:
 
 - **`gen_exact_match` (recommended headline metric).** Greedy decoding with the next-token logit space restricted to DNA tokens. The decoded VALUE must match the ground-truth `value` exactly. This is the strict, fully-generative retrieval accuracy.
 - **`ll_correct` (complementary).** The log-likelihood of `positive_sequence` (`prompt + value`) is higher than the log-likelihood of `negative_sequence` (`prompt + negative_value`). This is a softer probe: the model only needs to prefer the correct VALUE over a fully-mutated alternative, not to generate it. Useful for diagnosing whether retrieval failures are decoding failures or attention failures.
@@ -211,7 +211,7 @@ We treat `gen_exact_match` as the task metric. `ll_correct` is informative but e
 
 ## Reference results (Carbon-3B)
 
-We report reference Genome-NIAH results on [Carbon-3B](https://huggingface.co/hf-carbon/Carbon-3B) and [Carbon-8B](https://huggingface.co/hf-carbon/Carbon-8B) to illustrate how the benchmark behaves across difficulties and context lengths. We evaluate two inference settings: Carbon until the native boundary **32k context**, and the same model under **YaRN 4×** (up to 64 k tokens, ≈ 384 kbp and 128k/131k=786kbp).
+We report reference Genomic-NIAH results on [Carbon-3B](https://huggingface.co/hf-carbon/Carbon-3B) and [Carbon-8B](https://huggingface.co/hf-carbon/Carbon-8B) to illustrate how the benchmark behaves across difficulties and context lengths. We evaluate two inference settings: Carbon until the native boundary **32k context**, and the same model under **YaRN 4×** (up to 64 k tokens, ≈ 384 kbp and 128k/131k=786kbp).
 
 | Native (32 k) | YaRN 4× (up to 64 k) |
 |:---:|:---:|