Instructions to use microsoft/trocr-large-handwritten with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use microsoft/trocr-large-handwritten 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="microsoft/trocr-large-handwritten")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("microsoft/trocr-large-handwritten") model = AutoModelForMultimodalLM.from_pretrained("microsoft/trocr-large-handwritten") - Notebooks
- Google Colab
- Kaggle
warnings when loading the model
processor = TrOCRProcessor.from_pretrained('microsoft/trocr-large-handwritten')
model = VisionEncoderDecoderModel.from_pretrained('microsoft/trocr-large-handwritten')
throws these warnings:
Could not find image processor class in the image processor config or the model config. Loading based on pattern matching with the model's feature extractor configuration.
Some weights of VisionEncoderDecoderModel were not initialized from the model checkpoint at microsoft/trocr-large-handwritten and are newly initialized: ['encoder.pooler.dense.weight', 'encoder.pooler.dense.bias']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
is this the intended behavior?