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:
- 270dab988b466f54957702b459ad701dad76cca2237b17f8538e9e4acb6fe66c
- Size of remote file:
- 4.41 kB
- SHA256:
- cf039e3eb2fe435448f07b932f9a3bf865150f1258e55662f54c01136d4f7047
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.