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 training_args.bin from silkski/verb-class: direct link, hf CLI and curl.
- Browser
- Download file 3.2 kB
-
https://huggingface.co/silkski/verb-class/resolve/main/training_args.bin
- Command line
-
hf download hf://silkski/verb-class/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/silkski/verb-class/resolve/main/training_args.bin
3.2 kB
- Xet hash:
- cb02ffe6fc426bcad314a8a072b3ffdf60b32282a63f82a25c4b6da8eb7d4e84
- Size of remote file:
- 3.2 kB
- SHA256:
- 4cb8e832aa9125ff9bd0da1b1126b5a5ada535552d9a8bc20ce55b56119a11a7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.