Instructions to use silkski/verb-class with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use silkski/verb-class with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="silkski/verb-class")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("silkski/verb-class") model = AutoModelForSequenceClassification.from_pretrained("silkski/verb-class", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from silkski/verb-class: direct link, hf CLI and curl.
- Browser
- Download file 499 MB
-
https://huggingface.co/silkski/verb-class/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://silkski/verb-class/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/silkski/verb-class/resolve/main/pytorch_model.bin
499 MB
- Xet hash:
- 7de8f89f08781379f0bcebaaede26cc4f26fa726d73e728d1087b13b3bea4089
- Size of remote file:
- 499 MB
- SHA256:
- b16ed6120e684fbd7dfca8457e694fa698200d2d79d3912f60e8d1ab755f75e6
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