Update README.md

README.md

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 ## Abouts
 In this repository, we present GENERator, a generative genomic foundation model featuring a context length of 98k base pairs and 1.2B parameters, trained on an expansive dataset comprising 386 billion base pairs of eukaryotic DNA. Our evaluations demonstrate that the GENERator consistently achieves state-of-the-art performance across a wide spectrum of benchmarks, including [Genomic Benchmarks](https://huggingface.co/datasets/katielink/genomic-benchmarks/tree/main), [NT tasks](https://huggingface.co/datasets/InstaDeepAI/nucleotide_transformer_downstream_tasks_revised), and our newly proposed [Gener tasks](https://huggingface.co/GenerTeam).
 
-Beyond benchmark performance, the GENERator adheres to the central dogma of molecular biology, accurately generating protein-coding DNA sequences that produce proteins structurally analogous to known families. Moreover, the GENERator showcases significant promise in sequence optimization, particularly in the design of promoter sequences that regulate gene activity during various biological stages, highlighting its potential for a series of biologically significant tasks. Our findings position the GENERator as a vital resource for genomic research and biotechnological advancement.
+Beyond benchmark performance, the GENERator adheres to the central dogma of molecular biology, accurately generating protein-coding DNA sequences that produce proteins structurally analogous to known families. Moreover, the GENERator showcases significant promise in sequence optimization, particularly in the design of enhancer sequences that regulate gene expression during various biological stages, highlighting its potential for a series of biologically significant tasks. Our findings position the GENERator as a vital resource for genomic research and biotechnological advancement.
 
 For more technical details, please refer to our paper [**GENERator: A Long-Context Generative Genomic Foundation Model**](https://huggingface.co/GenerTeam).