Instructions to use jxm/u-PMLM-R with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jxm/u-PMLM-R with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jxm/u-PMLM-R")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("jxm/u-PMLM-R") model = AutoModel.from_pretrained("jxm/u-PMLM-R") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eff28b723418b6881fabe441e2f901eccfa03ac7b0e0ec458bc890edd8684669
- Size of remote file:
- 433 MB
- SHA256:
- e198f6bb6dc587c84ab9d5607200ba01688409467112204f33ac687ea2d782fc
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