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
Download tokenizer_config.json from jxm/u-PMLM-R: direct link, hf CLI and curl.
- Browser
- Download file 303 Bytes
-
https://huggingface.co/jxm/u-PMLM-R/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://jxm/u-PMLM-R/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/jxm/u-PMLM-R/resolve/main/tokenizer_config.json
303 Bytes
| {"do_lower_case": true, "do_basic_tokenize": true, "never_split": null, "unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]", "tokenize_chinese_chars": true, "strip_accents": null, "name_or_path": "vocab.txt", "tokenizer_class": "BertTokenizer"} |