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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Download README.md from jxm/u-PMLM-R: direct link, hf CLI and curl.
- Browser
- Download file 380 Bytes
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https://huggingface.co/jxm/u-PMLM-R/resolve/main/README.md
- Command line
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hf download hf://jxm/u-PMLM-R/README.md
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curl -L -o README.md https://huggingface.co/jxm/u-PMLM-R/resolve/main/README.md
380 Bytes
PMLM is the language model described in Probabilistically Masked Language Model Capable of Autoregressive Generation in Arbitrary Word Order, which is trained with probabilistic masking. This is the "PMLM-R" variant, adapted from the authors' original implementation.