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
Adding `safetensors` variant of this model
#3
by SFconvertbot - opened
- model.safetensors +3 -0
model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:6cb9cb30ce4cd37a7e58165359de1e4ad63bb8151ae0b605f2df772629818afc
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size 498613948
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