Instructions to use mila-intel/protst-esm1b-for-sequential-classification with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mila-intel/protst-esm1b-for-sequential-classification with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="mila-intel/protst-esm1b-for-sequential-classification", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mila-intel/protst-esm1b-for-sequential-classification", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update modeling_protst.py
Browse files- modeling_protst.py +0 -1
modeling_protst.py
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@@ -162,7 +162,6 @@ class ProtSTForProteinPropertyPrediction(ProtSTPreTrainedModel):
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self.config = config
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self.protein_model = EsmForProteinRepresentation(config.protein_config)
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self.logit_scale = nn.Parameter(torch.ones([]) * math.log(1 / 0.07))
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self.classifier = ProtSTHead(config.protein_config, out_dim=config.num_labels)
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self.post_init() # NOTE
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self.config = config
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self.protein_model = EsmForProteinRepresentation(config.protein_config)
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self.classifier = ProtSTHead(config.protein_config, out_dim=config.num_labels)
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self.post_init() # NOTE
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