基础模型: Ransaka/sinhala-ocr-model 模型索引:

• 名称: sinhala-ocr-model-v2 结果: [] 管道标签: 图像转文本 语言:
• si

TrOCR-僧伽罗文版

性能详情请参见训练指标选项卡。

模型描述

该模型是微软TrOCR印刷体的微调版本。

预期用途与限制

需补充更多信息

训练与评估数据

需补充更多信息

示例

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 #岛屿面临灾害状况时萨巴拉加穆瓦省的情况

框架版本

• Transformers 4.35.2
• Pytorch 2.0.0
• Datasets 2.16.0
• Tokenizers 0.15.0