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](https://arxiv.org/abs/2004.11579), which is trained with probabilistic masking. This is the "PMLM-R" variant, adapted from [the authors' original implementation](https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/PMLM). |