Instructions to use dbourget/phil-or-not-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dbourget/phil-or-not-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dbourget/phil-or-not-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dbourget/phil-or-not-v1") model = AutoModelForSequenceClassification.from_pretrained("dbourget/phil-or-not-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bbf249d3677c025650cb37e59cf54d556946c2f0d3d6e4591686c62a0da6cf64
- Size of remote file:
- 1.34 GB
- SHA256:
- 2f0de54e09db851bf7655f068d95dee9bc1365fde4e025fd8582d604721c9bc5
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