Instructions to use Torchborg/luke-base-classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Torchborg/luke-base-classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Torchborg/luke-base-classifier")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Torchborg/luke-base-classifier") model = AutoModelForSequenceClassification.from_pretrained("Torchborg/luke-base-classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download entity_vocab.json from Torchborg/luke-base-classifier: direct link, hf CLI and curl.
- Browser
- Download file 16.7 MB
-
https://huggingface.co/Torchborg/luke-base-classifier/resolve/main/entity_vocab.json
- Command line
-
hf download hf://Torchborg/luke-base-classifier/entity_vocab.json
-
curl -L -o entity_vocab.json https://huggingface.co/Torchborg/luke-base-classifier/resolve/main/entity_vocab.json
16.7 MB
- Xet hash:
- abf128ad95295edc76dd23a8c097436da3393747d954946b025129aa6f5ff183
- Size of remote file:
- 16.7 MB
- SHA256:
- 800c9497dd05e51fedc43d84d8faaee1280294b9384531ec6708c75a2965086d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.