ailab-bio/PROTAC-Splitter-EncoderDecoder-lr_cosine-opt25
0114
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ailab-bio/PROTAC-Splitter-EncoderDecoder-lr_cosine-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: 0.3124
- E3 Graph Edit Distance Norm: inf
- Poi Has Attachment Point(s): 0.9294
- Linker Heavy Atoms Difference: 0.3252
- Reassembly Nostereo: 0.5845
- E3 Valid: 0.9942
- Poi Heavy Atoms Difference Norm: 0.0668
- Linker Graph Edit Distance: inf
- All Ligands Equal: 0.5477
- Has All Attachment Points: 0.9857
- Linker Valid: 0.9951
- E3 Tanimoto Similarity: 0.0
- Heavy Atoms Difference: 6.4929
- Tanimoto Similarity: 0.0
- Reassembly: 0.5549
- E3 Heavy Atoms Difference: 0.3628
- Poi Graph Edit Distance Norm: inf
- Valid: 0.9232
- Linker Tanimoto Similarity: 0.0
- Linker Heavy Atoms Difference Norm: 0.0050
- Poi Valid: 0.9294
- Linker Graph Edit Distance Norm: inf
- Poi Equal: 0.7673
- Linker Equal: 0.7726
- E3 Graph Edit Distance: inf
- Poi Graph Edit Distance: inf
- Has Three Substructures: 0.9983
- Poi Heavy Atoms Difference: 2.0849
- Num Fragments: 3.0008
- Poi Tanimoto Similarity: 0.0
- E3 Heavy Atoms Difference Norm: 0.0044
- E3 Has Attachment Point(s): 0.9942
- E3 Equal: 0.8035
- Linker Has Attachment Point(s): 0.9951
- Heavy Atoms Difference Norm: 0.0854
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: cosine
- lrschedulerwarmup_steps: 699
- 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
