Instructions to use qwark666/exp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use qwark666/exp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="qwark666/exp", device_map="auto")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("qwark666/exp") model = AutoModelForTokenClassification.from_pretrained("qwark666/exp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 95c12c0ff61d91c10b802f28f247a94e3dc48d4872c17e5a55f6dd5810e975be
- Size of remote file:
- 5.27 kB
- SHA256:
- 51678cddfc6a431ff32e689a5c1e7b88eeac82cc2a5532b911afa5b2c5bc0089
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.