Image-to-Text
Transformers
Safetensors
Polish
vision-encoder-decoder
image-text-to-text
htr
trocr
handwriting-recognition
historical-documents
metrical-registers
polish
knowledge-distillation
active-learning
Instructions to use meldynamics/polish-metrical-htr-experimental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use meldynamics/polish-metrical-htr-experimental 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="meldynamics/polish-metrical-htr-experimental")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("meldynamics/polish-metrical-htr-experimental") model = AutoModelForMultimodalLM.from_pretrained("meldynamics/polish-metrical-htr-experimental", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 473 Bytes
1722469 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | {
"image_processor": {
"data_format": "channels_first",
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.5,
0.5,
0.5
],
"image_processor_type": "ViTImageProcessorFast",
"image_std": [
0.5,
0.5,
0.5
],
"resample": 2,
"rescale_factor": 0.00392156862745098,
"size": {
"height": 192,
"width": 1024
}
},
"processor_class": "TrOCRProcessor"
}
|