Image-to-Text
Transformers
TensorBoard
Safetensors
Sinhala
vision-encoder-decoder
image-text-to-text
# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM
tokenizer = AutoTokenizer.from_pretrained("Ransaka/TrOCR-Sinhala")
model = AutoModelForMultimodalLM.from_pretrained("Ransaka/TrOCR-Sinhala", device_map="auto")Quick Links
TrOCR-Sinhala
See training metrics tab for performance details.
Model description
This model is finetuned version of Microsoft TrOCR Printed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Example
from PIL import Image
import requests
from io import BytesIO
from transformers import TrOCRProcessor, VisionEncoderDecoderModel, AutoTokenizer
image_url = "https://datasets-server.huggingface.co/assets/Ransaka/sinhala_synthetic_ocr/--/bf7c8a455b564cd73fe035031e19a5f39babb73b/--/default/train/0/image/image.jpg"
response = requests.get(image_url)
img = Image.open(BytesIO(response.content))
processor = TrOCRProcessor.from_pretrained('Ransaka/TrOCR-Sinhala')
model = VisionEncoderDecoderModel.from_pretrained('Ransaka/TrOCR-Sinhala')
model.to("cuda:0")
pixel_values = processor(img, return_tensors="pt").pixel_values.to('cuda:0')
generated_ids = model.generate(pixel_values,num_beams=2,early_stopping=True)
generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
generated_text #දිවයිනට බලයට ඇති ආපදා තත්ත්වය හමුවේ සබරගමුව පළාතේ
Framework versions
- Transformers 4.35.2
- Pytorch 2.0.0
- Datasets 2.16.0
- Tokenizers 0.15.0
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# 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="Ransaka/TrOCR-Sinhala")