Image-to-Text
Transformers
Safetensors
Japanese
English
sarashina2_vision
text-generation
multimodal
ocr
document-understanding
vision-language
custom_code
Instructions to use subhash4face/sarashina2.2-ocr with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use subhash4face/sarashina2.2-ocr 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="subhash4face/sarashina2.2-ocr", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("subhash4face/sarashina2.2-ocr", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 6e02577f816da79fb1dba421f5893c678ef46e0018403c418c7776313c0a1926
- Size of remote file:
- 278 kB
- SHA256:
- a1053287c92f5825007e702c868f4e66945f77b73cf0e08bc82414d89a7254e7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.