Instructions to use smerchi/temp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smerchi/temp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="smerchi/temp")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("smerchi/temp") model = AutoModelForSequenceClassification.from_pretrained("smerchi/temp", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d5dd145773d4befd01141459f067a5b8b377764172b311d2477ab2566e0ab0bc
- Size of remote file:
- 5.3 kB
- SHA256:
- b0baf721d8061ef624538b801776d1d375c935da51d60df501283842e9bce012
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.