Comments (1)
Sorry for the late reply, @yangzhao1230
Regarding embedding extraction. We pulled out layers 12, 16, 21, 24, and 32. For the results shown in the figures, we used the layer that resulted in the highest performance for each score separately.
Regarding similarity calculation. We used the embeddings from the token containing the mutation.
Regarding the similarity threshold. Let me know if this is what you're referring to. But for our ROC analyses we used the scores as is. We didn't use a cutoff to classify the variants.
Hope this helps.
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Related Issues (20)
- Provide more examples and real world use cases
- Bug: jax version issues on TPU Colab
- Add Agro NT model
- NT nucleotide-level splice site prediction HOT 1
- Training process for Nucleotide Transformer Model HOT 1
- Checkpoints are not downloading. HOT 1
- v2 checkpoints for TensorFlow HOT 3
- Fix typos in colab notebook
- Extend SegmentNT notebook
- Remove unwanted prints
- Fix mismatch in sequence position for Fig 3
- Add description to SegmentNT notebook
- Cannot download the pre-training weight HOT 2
- Enable downloading model without tqdm
- Confusion about the max_positions value HOT 1
- how to deal with the seq position of reference genome? HOT 1
- where is the intermedia checkpoints HOT 1
- The length of input sequence for SegmentNT HOT 1
- error while executing the Google Colab inference_nt notebook HOT 1
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