Create README.md

README.md

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+---
+language:
+- he
+base_model:
+- naver-clova-ix/donut-base
+---
+from transformers import VisionEncoderDecoderConfig
+from transformers import DonutProcessor, VisionEncoderDecoderModel
+import torch 
+import re
+import requests
+from PIL import Image
+from io import BytesIO
+
+
+url = "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRCeH216oW6FXeTpN4ijvakW8_frP3vnCBIKQ&s"
+
+response = requests.get(url)
+img = Image.open(BytesIO(response.content))
+img.show()
+
+config = VisionEncoderDecoderConfig.from_pretrained('jjjlangem/He-Donut')
+processor = DonutProcessor.from_pretrained('jjjlangem/He-Donut')
+model = VisionEncoderDecoderModel.from_pretrained('jjjlangem/He-Donut')
+
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+model.to(device)
+
+
+
+with torch.no_grad():
+    
+    pixel_values = processor(img, random_padding=False, return_tensors="pt").pixel_values
+    batch_size = pixel_values.shape[0]
+    # In PyTorch DDP setup, .model is wrapped by DistributedDataParallel,
+    # so the actual model is accessible through .model.module.
+    decoder_input_ids = torch.full((batch_size, 1), model.config.decoder_start_token_id,
+                               device=device)
+
+
+    outputs = model.generate(pixel_values.to(device),
+                                     decoder_input_ids=decoder_input_ids,
+                                     max_length= 768,
+                                     early_stopping=True,
+                                     pad_token_id=processor.tokenizer.pad_token_id,
+                                     eos_token_id=processor.tokenizer.eos_token_id,
+                                     use_cache=True,
+                                     num_beams=1,
+                                     bad_words_ids=[[processor.tokenizer.unk_token_id]],
+                                     return_dict_in_generate=True)
+
+    predictions = []
+    for seq in processor.tokenizer.batch_decode(outputs.sequences):
+        seq = seq.replace(processor.tokenizer.eos_token, "").replace(processor.tokenizer.pad_token, "").replace(processor.tokenizer.bos_token, "")
+        seq = re.sub(r"<.*?>", "", seq, count=1).strip()
+        predictions.append(seq)
+
+
+print(predictions)