Fill-Mask
Transformers
PyTorch
luke
named entity recognition
relation classification
question answering
Instructions to use studio-ousia/mluke-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use studio-ousia/mluke-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="studio-ousia/mluke-large")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("studio-ousia/mluke-large") model = AutoModelForMaskedLM.from_pretrained("studio-ousia/mluke-large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download special_tokens_map.json from studio-ousia/mluke-large: direct link, hf CLI and curl.
- Browser
- Download file 466 Bytes
-
https://huggingface.co/studio-ousia/mluke-large/resolve/main/special_tokens_map.json
- Command line
-
hf download hf://studio-ousia/mluke-large/special_tokens_map.json
-
curl -L -o special_tokens_map.json https://huggingface.co/studio-ousia/mluke-large/resolve/main/special_tokens_map.json
466 Bytes
| {"bos_token": "<s>", "eos_token": "</s>", "unk_token": "<unk>", "sep_token": "</s>", "pad_token": "<pad>", "cls_token": "<s>", "mask_token": {"content": "<mask>", "single_word": false, "lstrip": true, "rstrip": false, "normalized": true}, "additional_special_tokens": [{"content": "<ent>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}, {"content": "<ent2>", "single_word": false, "lstrip": false, "rstrip": false, "normalized": true}]} |