Instructions to use plexatic/ultrawhack-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use plexatic/ultrawhack-model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="plexatic/ultrawhack-model")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("plexatic/ultrawhack-model") model = AutoModelForSequenceClassification.from_pretrained("plexatic/ultrawhack-model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d2a17dac139a840c8444a24f529e67622d1af19e33b63a998140d483ecf167a0
- Size of remote file:
- 4.48 GB
- SHA256:
- 95713ef0814771831011d52e1260b6591730482a3866ecc7521eff5af861b97b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.