Instructions to use zenosai/MonkeyOCRv2-S with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use zenosai/MonkeyOCRv2-S with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="zenosai/MonkeyOCRv2-S", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("zenosai/MonkeyOCRv2-S", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update pipeline tag, add library name, and add citation reference
#1
by nielsr HF Staff - opened
This PR improves the model card for the MonkeyOCRv2 vision encoder:
- Updates
pipeline_tagtoimage-feature-extractionto better describe its role as a ViT feature extractor. - Adds
library_name: transformersas the model is fully compatible with Hugging Face'stransformerslibrary. - Adds the correct paper citation at the bottom of the page.
Thanks!!
zenosai changed pull request status to merged
zenosai deleted the
refs/pr/1 ref