Image-to-Text
Transformers
PyTorch
TensorBoard
Russian
English
vision-encoder-decoder
image-text-to-text
ocr
Instructions to use sherstpasha99/trocr-base-ru with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sherstpasha99/trocr-base-ru 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="sherstpasha99/trocr-base-ru")# Load model directly from transformers import AutoTokenizer, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("sherstpasha99/trocr-base-ru") model = AutoModelForImageTextToText.from_pretrained("sherstpasha99/trocr-base-ru") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 16f90f1f2825889b162b4c3a90b86c03e91cfb3681810a5a8198be8317118f0e
- Size of remote file:
- 1.34 GB
- SHA256:
- 08d66d428348fe161583c653c7e6f00215479c04a00318dc555f997a3d38149b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.