Fill-Mask
Transformers
PyTorch
English
luke
named entity recognition
entity typing
relation classification
question answering
Instructions to use studio-ousia/luke-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use studio-ousia/luke-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="studio-ousia/luke-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("studio-ousia/luke-base") model = AutoModelForMaskedLM.from_pretrained("studio-ousia/luke-base", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download entity_vocab.json from studio-ousia/luke-base: direct link, hf CLI and curl.
- Browser
- Download file 15.3 MB
-
https://huggingface.co/studio-ousia/luke-base/resolve/main/entity_vocab.json
- Command line
-
hf download hf://studio-ousia/luke-base/entity_vocab.json
-
curl -L -o entity_vocab.json https://huggingface.co/studio-ousia/luke-base/resolve/main/entity_vocab.json
15.3 MB
- Xet hash:
- ed97706b5ac5382cb60a03bc27946ae48a96ba65da5552d690e84b397244a972
- Size of remote file:
- 15.3 MB
- SHA256:
- 45e0508babfee0667db9a8dfa2519a6e470c769c3489b4c87c45b26f59e30947
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.