Instructions to use BMILab/TCR-BERT-Substring with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use BMILab/TCR-BERT-Substring with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="BMILab/TCR-BERT-Substring")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("BMILab/TCR-BERT-Substring") model = AutoModelForMaskedLM.from_pretrained("BMILab/TCR-BERT-Substring", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2413e483106b4e32a8df82c609986eaa9e86eb5b127295f3bd46fca0c83d62e6
- Size of remote file:
- 1.22 GB
- SHA256:
- 30dbadc81edee06d391e684779f943795b82d85269c701b2145d374ecec7e2e2
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