Fill-Mask
Transformers
PyTorch
luke
named entity recognition
relation classification
question answering
Instructions to use studio-ousia/mluke-base-lite with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use studio-ousia/mluke-base-lite with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="studio-ousia/mluke-base-lite")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("studio-ousia/mluke-base-lite") model = AutoModelForMaskedLM.from_pretrained("studio-ousia/mluke-base-lite", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from studio-ousia/mluke-base-lite: direct link, hf CLI and curl.
- Browser
- Download file 1.2 GB
-
https://huggingface.co/studio-ousia/mluke-base-lite/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://studio-ousia/mluke-base-lite/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/studio-ousia/mluke-base-lite/resolve/main/pytorch_model.bin
1.2 GB
- Xet hash:
- f533dca38e27f61e90b3cfc20e851bd508a4bf47ba942dc22194cf7d6c8d1a0c
- Size of remote file:
- 1.2 GB
- SHA256:
- 5ee14925799a92d8493625ae6ff0ad4f7f61906a7ebff5ead92221afee8ca91a
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.