Instructions to use vanshp123/ocrmnist with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vanshp123/ocrmnist with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "image-to-text" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("image-to-text", model="vanshp123/ocrmnist")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("vanshp123/ocrmnist") model = AutoModelForImageTextToText.from_pretrained("vanshp123/ocrmnist") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eb30dda05a3e7173f80837b428e1f051c7193511afc72a8cc9feac4dbdce6cac
- Size of remote file:
- 4.6 kB
- SHA256:
- 357ace32952ef34f7db430a578da2b8a15cb1387ec8acd54f74302173106c802
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.