Comments (3)
- Rather than a specific number of epochs (or iterations for such a large training dataset), I recommend that you determine how long to train based on a sequence-disjoint validation set. That's also what we did when training on MassIVE-KB, and based on the validation performance we empirically noticed that the model converged already prior to having gone through a single full epoch.
- No, these are the settings that we consistently use to train the various models. No special change needed.
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Yes, that's right.
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Thanks a lot for your advice!
If I would like to fine-tune your massivekb pretrained model for my specific data, I simply need to continue train casanovo with your model and my own data (my data might be slightly different from massivekb) until it performs best on my own validation set?
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Related Issues (20)
- Exclude some PTMs for prediction? HOT 2
- Question about tracking spectra from MGF files in output HOT 1
- Using HDF5 file as train/val dataset leads to index out of bound error HOT 9
- No Models Saved, No Validation Loss Reported HOT 3
- Save final model HOT 1
- What is the criteria of saving the top k models in Casanovo version 4? HOT 1
- add additional inputs to encoder and decoder HOT 1
- Add contrastive loss term
- Implement bidirectional decoding HOT 1
- Add rotary embeddings
- mzTab validation
- Automate mzTab validation HOT 7
- More information about the train/val/test split HOT 2
- WARNING: Skipped spectra with invalid precursor info HOT 1
- Export casanovo to torchscript/onnx HOT 1
- ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() HOT 3
- Make Casanovo produce Skyline compatible output
- 9-Species Benchmark Set: Data Preprocessing Step? HOT 5
- Migrating PeptideMass, PeptideDecoder, and PeptideEncoder from depthcharge v0.2.3 to casanovo HOT 3
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