ailab-bio/PROTAC-Splitter-EncoderDecoder-lr_cosine_restarts-opt25
069
<!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. -->
ailab-bio/PROTAC-Splitter-EncoderDecoder-lrcosinerestarts-opt25
This model is a fine-tuned version of seyonec/ChemBERTa-zinc-base-v1 on the ailab-bio/PROTAC-Splitter-Dataset dataset. It achieves the following results on the evaluation set:
- Loss: 0.3086
- Poi Heavy Atoms Difference: 2.1208
- E3 Valid: 0.9896
- Poi Valid: 0.9272
- Poi Has Attachment Point(s): 0.9272
- All Ligands Equal: 0.5462
- Valid: 0.9157
- Reassembly: 0.5544
- Poi Tanimoto Similarity: 0.0
- Linker Tanimoto Similarity: 0.0
- Poi Graph Edit Distance: inf
- Linker Heavy Atoms Difference: 0.3144
- Linker Graph Edit Distance Norm: inf
- E3 Graph Edit Distance Norm: inf
- Num Fragments: 2.9998
- E3 Heavy Atoms Difference Norm: 0.0131
- Linker Valid: 0.9961
- E3 Heavy Atoms Difference: 0.5553
- E3 Tanimoto Similarity: 0.0
- Poi Heavy Atoms Difference Norm: 0.0719
- Reassembly Nostereo: 0.5796
- Linker Equal: 0.7666
- Linker Has Attachment Point(s): 0.9961
- Has All Attachment Points: 0.9836
- Poi Equal: 0.7680
- E3 Graph Edit Distance: inf
- Tanimoto Similarity: 0.0
- E3 Has Attachment Point(s): 0.9896
- Poi Graph Edit Distance Norm: inf
- E3 Equal: 0.8045
- Heavy Atoms Difference Norm: 0.0939
- Has Three Substructures: 0.9991
- Linker Graph Edit Distance: inf
- Heavy Atoms Difference: 7.0102
- Linker Heavy Atoms Difference Norm: 0.0033
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- trainbatchsize: 128
- evalbatchsize: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lrschedulertype: cosinewithrestarts
- lrschedulerwarmup_steps: 100
- training_steps: 10000
- mixedprecisiontraining: Native AMP
Training results
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
