Instructions to use fraternalilab/immunomatch with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fraternalilab/immunomatch with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fraternalilab/immunomatch")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fraternalilab/immunomatch") model = AutoModelForSequenceClassification.from_pretrained("fraternalilab/immunomatch", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 864 Bytes
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license: cc-by-nc-4.0
metrics:
- accuracy
tags:
- biology
base_model:
- alchemab/antiberta2-cssp
library_name: transformers
---
ImmunoMatch is a protein language model finetuned from AntiBERTa2, aiming at investigating the heavy and light chain pairing preferences in antibody. The input sequence 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](https://huggingface.co/fraternalilab/immunomatch) | A mixture of antibodies with both 魏 and 位 light chains |
| [ImmunoMatch-魏](https://huggingface.co/fraternalilab/immunomatch-kappa) | Antibodies with 魏 light chains |
| [ImmunoMatch-位](https://huggingface.co/fraternalilab/immunomatch-lambda) | Antibodies with 位 light chains | |