Instructions to use brao/innox-distilbert with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use brao/innox-distilbert with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="brao/innox-distilbert")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("brao/innox-distilbert") model = AutoModelForTokenClassification.from_pretrained("brao/innox-distilbert", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from brao/innox-distilbert: direct link, hf CLI and curl.
- Browser
- Download file 266 MB
-
https://huggingface.co/brao/innox-distilbert/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://brao/innox-distilbert/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/brao/innox-distilbert/resolve/main/pytorch_model.bin
266 MB
- Xet hash:
- af3344cfd5f43a92f81d033f975a52a5a9ac79b9f34d5866f0b1684d91784f2f
- Size of remote file:
- 266 MB
- SHA256:
- a74232caf791259f0447aad999342009429e7aa23ce2c03047459609c543814b
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