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@@ -36,10 +36,10 @@ pip install --upgrade git+https://github.com/huggingface/transformers.git
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  A small snippet of code is given here in order to retrieve both logits and embeddings from a dummy DNA sequence.
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  ```
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- ⚠️ The maximum sequence length is set by default at the training length of 30,000 nucleotides, or 5001 tokens (accounting for the CLS token). However, Segment-NT has been
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- shown to generalize up to sequences of 50,000 bp. In case you need to infer on sequences between 30kbp and 50kbp, make sure to change the `rescaling_factor` argument in
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- the config to `num_dna_tokens_inference / max_num_tokens_nt` where `num_dna_tokens_inference` is the number of tokens at inference (i.e 6669 for a sequence of 40008 base
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- pairs) and `max_num_tokens_nt` is the max number of tokens on which the backbone nucleotide-transformer was trained on, i.e `2048`.
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  ```
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  ```python
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  # Load model and tokenizer
 
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  A small snippet of code is given here in order to retrieve both logits and embeddings from a dummy DNA sequence.
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  ```
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+ ⚠️ The maximum sequence length is set by default at the training length of 30,000 nucleotides, or 5001 tokens (accounting for the CLS token). However,
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+ Segment-NT-multi-species has been shown to generalize up to sequences of 50,000 bp. In case you need to infer on sequences between 30kbp and 50kbp, make sure to change
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+ the `rescaling_factor` argument in the config to `num_dna_tokens_inference / max_num_tokens_nt` where `num_dna_tokens_inference` is the number of tokens at inference
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+ (i.e 6669 for a sequence of 40008 base pairs) and `max_num_tokens_nt` is the max number of tokens on which the backbone nucleotide-transformer was trained on, i.e `2048`.
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  ```
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  ```python
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  # Load model and tokenizer