Instructions to use laboyle1/distilbert-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use laboyle1/distilbert-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="laboyle1/distilbert-finetuned")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("laboyle1/distilbert-finetuned") model = AutoModelForMaskedLM.from_pretrained("laboyle1/distilbert-finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- c002f4803944332f6c06489f214fe01b44bc9294f4958999a0669b449b4b2002
- Size of remote file:
- 268 MB
- SHA256:
- 975fcce22b78e34876e16bf2aaeec66b7e8e9139c350cdec2031b0d10fe0969b
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