Instructions to use DaMax96/Stick_OCR_v4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DaMax96/Stick_OCR_v4 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="DaMax96/Stick_OCR_v4")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("DaMax96/Stick_OCR_v4") model = AutoModelForMultimodalLM.from_pretrained("DaMax96/Stick_OCR_v4", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 780 Bytes
c46e307 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | from typing import Dict, List, Any
from transformers import TrOCRProcessor, VisionEncoderDecoderModel
class PreTrainedPipeline():
def __init__(self, path=""):
self.processor = TrOCRProcessor.from_pretrained(path)
self.model = VisionEncoderDecoderModel.from_pretrained(path)
def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
image = data.pop("inputs", data)
# process image
pixel_values = self.processor(images=image, return_tensors="pt").pixel_values
# run prediction
generated_ids = self.model.generate(pixel_values)
# decode output
prediction = generated_text = self.processor.batch_decode(generated_ids, skip_special_tokens=True)
return {"text":prediction[0]} |