Instructions to use wukevin/tcr-bert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wukevin/tcr-bert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="wukevin/tcr-bert")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("wukevin/tcr-bert") model = AutoModelForSequenceClassification.from_pretrained("wukevin/tcr-bert") - Inference
- Notebooks
- Google Colab
- Kaggle
| # TCR transformer model | |
| See our full [codebase](https://github.com/wukevin/tcr-bert) and our [preprint](https://www.biorxiv.org/content/10.1101/2021.11.18.469186v1) for more information. | |
| This model is on: | |
| - Masked language modeling (masked amino acid or MAA modeling) | |
| - Classification across antigen labels from PIRD | |
| If you are looking for a model trained only on MAA, please see our [other model](https://huggingface.co/wukevin/tcr-bert-mlm-only). | |
| Example inputs: | |
| * `C A S S P V T G G I Y G Y T F` (binds to NLVPMVATV CMV antigen) | |
| * `C A T S G R A G V E Q F F` (binds to GILGFVFTL flu antigen) |