Image-to-Text
Transformers
TensorBoard
Safetensors
Sinhala
vision-encoder-decoder
image-text-to-text
Instructions to use Ransaka/TrOCR-Sinhala with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Ransaka/TrOCR-Sinhala 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="Ransaka/TrOCR-Sinhala")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("Ransaka/TrOCR-Sinhala") model = AutoModelForMultimodalLM.from_pretrained("Ransaka/TrOCR-Sinhala", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from Ransaka/TrOCR-Sinhala: direct link, hf CLI and curl.
- Browser
- Download file 988 kB
-
https://huggingface.co/Ransaka/TrOCR-Sinhala/resolve/main/tokenizer.json
- Command line
-
hf download hf://Ransaka/TrOCR-Sinhala/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/Ransaka/TrOCR-Sinhala/resolve/main/tokenizer.json
988 kB
File too large to display, you can check the raw version instead.