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@@ -28,7 +28,7 @@ The Gener Tasks currently includes 2 subtasks:
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  * The gene classification task assesses the model's ability to understand short to medium-length sequences. It includes six different gene types and control samples drawn from non-gene regions, with balanced sampling from six distinct eukaryotic taxonomic groups in RefSeq. The classification goal is to predict the gene type.
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  * The taxonomic classification task is designed to assess the model's comprehension of longer sequences, which include both gene and predominantly non-gene regions. Samples are similarly balanced and sourced from RefSeq across the same six taxonomic groups, with the objective being to predict the taxonomic group of each sample.
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- Note: The taxonomic classification dataset is substantial (2GB), which may result in extended training and evaluation time. To accommodate the model's maximum context length, we implement **left** truncation for sequences that exceed this limit.
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  ## How to use
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  ```python
 
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  * The gene classification task assesses the model's ability to understand short to medium-length sequences. It includes six different gene types and control samples drawn from non-gene regions, with balanced sampling from six distinct eukaryotic taxonomic groups in RefSeq. The classification goal is to predict the gene type.
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  * The taxonomic classification task is designed to assess the model's comprehension of longer sequences, which include both gene and predominantly non-gene regions. Samples are similarly balanced and sourced from RefSeq across the same six taxonomic groups, with the objective being to predict the taxonomic group of each sample.
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+ Note: The taxonomic classification dataset is substantial (2GB), which may result in extended training and evaluation time. To accommodate the model's maximum context length, we implement **right** truncation for sequences that exceed this limit.
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  ## How to use
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  ```python