Update README.md

README.md

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 We carry out byte-pair encoding (BPE) tokenization on our dataset, tailored for metagenomic sequences, and then pretrain our model. We detail the pretraining data, tokenization strategy, and model architecture, highlighting the considerations and design choices that enable the effective modeling of metagenomic data, in our technical report.
 
 ## **Benchmark Performance**
-We evaluate METAGENE-1 across three tasks: pathogen detection, zero-shot embedding benchmarks (**Gene-MTEB**), and genome understanding (**GUE**), achieving state-of-the-art performance on most benchmarks.
+We evaluate METAGENE-1 across three tasks: pathogen detection, zero-shot embedding benchmarks (**Gene-MTEB**), and genome understanding (**GUE**), achieving state-of-the-art performance on most benchmarks. For more details, check out our [paper](TODO).
 ### **Pathogen Detection**
 The pathogen detection benchmark evaluates **METAGENE-1**’s ability to classify sequencing reads as human pathogens or non-pathogens across four distinct datasets, each derived from different sequencing deliveries and designed to mimic real-world conditions with limited training data.
 |                               | **DNABERT-2** | **DNABERT-S** | **NT-2.5b-Multi** | **NT-2.5b-1000g** | **METAGENE-1** |