Instructions to use danielvasic/distilbert-wordnet-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use danielvasic/distilbert-wordnet-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="danielvasic/distilbert-wordnet-uncased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("danielvasic/distilbert-wordnet-uncased") model = AutoModelForMaskedLM.from_pretrained("danielvasic/distilbert-wordnet-uncased", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from danielvasic/distilbert-wordnet-uncased: direct link, hf CLI and curl.
- Browser
- Download file 268 MB
-
https://huggingface.co/danielvasic/distilbert-wordnet-uncased/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://danielvasic/distilbert-wordnet-uncased/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/danielvasic/distilbert-wordnet-uncased/resolve/main/pytorch_model.bin
268 MB
- Xet hash:
- 81ebcf2e453ae686f31ccab47ef82915b07b44c996688de635a24c1d7b305012
- Size of remote file:
- 268 MB
- SHA256:
- c9f1058c9939e7d9039ad121e8bab0c0e810195210c8a593b65e133fdb13568a
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