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
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Download README.md from fraternalilab/immunomatch: direct link, hf CLI and curl.
- Browser
- Download file 864 Bytes
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https://huggingface.co/fraternalilab/immunomatch/resolve/main/README.md
- Command line
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hf download hf://fraternalilab/immunomatch/README.md
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curl -L -o README.md https://huggingface.co/fraternalilab/immunomatch/resolve/main/README.md
864 Bytes
| 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 | |