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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4d53f4c4166bb38b271914b8c38ef6d5b943543877398058dabc3f56dc5957e9
- Size of remote file:
- 3.31 kB
- SHA256:
- 5bc0fb78c4483855b511bc759602c7069c194a002a2d6650d4437f4869d4ec60
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