Instructions to use robertsw/tmp_trainer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use robertsw/tmp_trainer with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="robertsw/tmp_trainer") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("robertsw/tmp_trainer") model = AutoModelForImageClassification.from_pretrained("robertsw/tmp_trainer") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b7614131ee81e7e8e9c3890f4d722cb33715497ea394fca1339dfc21db84029c
- Size of remote file:
- 5.05 kB
- SHA256:
- 8e7c8308c22aed37672a0cdaf57e6e5dd82acfcd69de76eaa8957ea2c325c14d
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.