Instructions to use fraternalilab/immunomatch-kappa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fraternalilab/immunomatch-kappa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fraternalilab/immunomatch-kappa")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fraternalilab/immunomatch-kappa") model = AutoModelForSequenceClassification.from_pretrained("fraternalilab/immunomatch-kappa") - Notebooks
- Google Colab
- Kaggle
ImmunoMatch-魏 is a variant of ImmunoMatch, which is a protein language model finetuned from AntiBERTa2, aiming at investigating the heavy and light chain pairing preferences in antibody. The input to the model should be a pair of sequences of VH and VL domains.
Different variants of ImmunoMatch is available according to the use of interest:
| Checkpoint name | Trained on |
|---|---|
| ImmunoMatch | A mixture of antibodies with both 魏 and 位 light chains |
| ImmunoMatch-魏 | Antibodies with 魏 light chains |
| ImmunoMatch-位 | Antibodies with 位 light chains |
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Model tree for fraternalilab/immunomatch-kappa
Base model
alchemab/antiberta2-cssp