Instructions to use breadlicker45/human-class-or-somthing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use breadlicker45/human-class-or-somthing with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="breadlicker45/human-class-or-somthing")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("breadlicker45/human-class-or-somthing") model = AutoModelForSequenceClassification.from_pretrained("breadlicker45/human-class-or-somthing") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 99ba00e51e9687f933611d1e83a5433166a2641eb9efc1bba209f6bee34d33ca
- Size of remote file:
- 268 MB
- SHA256:
- dca0b3dabd6973a258c87e4bdddc6bf290e85b15ee06aaad1cbd9f2374c55442
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