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:
- 38f13552c6932cf06aa64d5ae336b70077020de8da923e969f44ce913e8f5dd5
- Size of remote file:
- 1.34 GB
- SHA256:
- 68cdd1f5833f88650f3b54347f76003a8d9eabc7eb0b0c0344ba9296ba9019a1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.