Instructions to use BMILab/TCR-BERT-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BMILab/TCR-BERT-Large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="BMILab/TCR-BERT-Large")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("BMILab/TCR-BERT-Large") model = AutoModelForMaskedLM.from_pretrained("BMILab/TCR-BERT-Large") - Notebooks
- Google Colab
- Kaggle
Commit History
Adding `safetensors` variant of this model (#1) dfe9613
Upload tokenizer c01a813
Upload BertForMaskedLM f524068
initial commit a6c873c
Sang am Lee commited on