Instructions to use vonewman/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vonewman/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="vonewman/results")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("vonewman/results") model = AutoModelForTokenClassification.from_pretrained("vonewman/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from vonewman/results: direct link, hf CLI and curl.
- Browser
- Download file 1.11 GB
-
https://huggingface.co/vonewman/results/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://vonewman/results/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/vonewman/results/resolve/main/pytorch_model.bin
1.11 GB
- Xet hash:
- 31c6c5766dad26d2914102e706ff3f622a78e32edcb92e750537057cadb4b644
- Size of remote file:
- 1.11 GB
- SHA256:
- a76a37fe9735a3085fcd85ec49334ed20e5b41807bea0233ad17ae927ffe5f25
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