Instructions to use LeCastre/DistilBert-pMHC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LeCastre/DistilBert-pMHC with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LeCastre/DistilBert-pMHC")# Load model directly from transformers import AutoTokenizer, BertRnnDist tokenizer = AutoTokenizer.from_pretrained("LeCastre/DistilBert-pMHC") model = BertRnnDist.from_pretrained("LeCastre/DistilBert-pMHC", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cac254ca96ada059784b866d92d060fbaa232c4ddb0c938549cce0eb76c863be
- Size of remote file:
- 3.32 kB
- SHA256:
- 78366fc3d9b3d417658d91bc288b3cc03fc81eb2d512042eafac7cc26a3a57e8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.