Instructions to use dbourget/phil-or-not-2e with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dbourget/phil-or-not-2e with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dbourget/phil-or-not-2e")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dbourget/phil-or-not-2e") model = AutoModelForSequenceClassification.from_pretrained("dbourget/phil-or-not-2e", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 18b4b2adf0df5359d4200cc22f735b63f47cc93c930aec3a8a0734d659541e00
- Size of remote file:
- 2.68 GB
- SHA256:
- 54dbfffcec552ed33f749409d7e920925f607657e3441a1ebd5d38b2edbb6680
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