Instructions to use jacquesle/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jacquesle/output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="jacquesle/output")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("jacquesle/output") model = AutoModelForSequenceClassification.from_pretrained("jacquesle/output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 60386f6379484d906ca3810c81cc3850045d640ac9e9f1f2a8948d4a32ad0cb9
- Size of remote file:
- 3.52 kB
- SHA256:
- 78bbee1f9366938b988d747fcf47d64b0726ef721ed3653c6373e69c781d08a9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.