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:
- 79a5a0cd64018b2d6bf1936f0397b94921fb96f8938644c6dae60a6066fbe64a
- Size of remote file:
- 433 MB
- SHA256:
- 3b0b3ad8ea9bb2f9b498e33666587afb39ba7d774a51b4b8bd0bab94973c64b2
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