Instructions to use BMILab/TCR-BERT-MLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BMILab/TCR-BERT-MLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="BMILab/TCR-BERT-MLM")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("BMILab/TCR-BERT-MLM") model = AutoModelForMaskedLM.from_pretrained("BMILab/TCR-BERT-MLM") - Notebooks
- Google Colab
- Kaggle
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("fill-mask", model="BMILab/TCR-BERT-MLM")# Load model directly
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("BMILab/TCR-BERT-MLM")
model = AutoModelForMaskedLM.from_pretrained("BMILab/TCR-BERT-MLM")Quick Links
# Gated model: Login with a HF token with gated access permission hf auth login