Instructions to use cringgaard/extrapolation_year_built with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cringgaard/extrapolation_year_built with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("zero-shot-image-classification", model="cringgaard/extrapolation_year_built") pipe( "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png", candidate_labels=["animals", "humans", "landscape"], )# Load model directly from transformers import AutoProcessor, AutoModelForZeroShotImageClassification processor = AutoProcessor.from_pretrained("cringgaard/extrapolation_year_built") model = AutoModelForZeroShotImageClassification.from_pretrained("cringgaard/extrapolation_year_built") - Notebooks
- Google Colab
- Kaggle
Training in progress, epoch 3
Browse files- model.safetensors +1 -1
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 1710537716
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:18ab223841a1d1a820509d5853cb76600081e29d3fefc93b8a0911d5299d58ff
|
| 3 |
size 1710537716
|