Instructions to use wesleymorris/content_checkpoints with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use wesleymorris/content_checkpoints with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="wesleymorris/content_checkpoints")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("wesleymorris/content_checkpoints") model = AutoModelForSequenceClassification.from_pretrained("wesleymorris/content_checkpoints", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e726bb3e85f091c9f6b04459d322853fc5e19359dbcb568a608e9f1089058437
- Size of remote file:
- 499 MB
- SHA256:
- f2ed1ae25063ee6a8e1edf509fd57240404305da442e35abf3bfff1ea7681b6e
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.