Instructions to use anaghasavit/trocr-processor with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use anaghasavit/trocr-processor 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="anaghasavit/trocr-processor")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("anaghasavit/trocr-processor") model = AutoModelForMultimodalLM.from_pretrained("anaghasavit/trocr-processor", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 39d2dfceb970454ddc3007d8ea09e797c38d237f108f3877292594f98e8dfa28
- Size of remote file:
- 135 Bytes
- SHA256:
- bcc6345dc20003662aaa1fe2df35a20a8c36760651ec123863aa8a43f44e10cc
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