Update README.md

README.md

@@ -1,3 +1,70 @@
 ---
 license: apache-2.0
+pipeline_tag: image-to-text
 ---
+
+# <font color="turquoise"> <p style="text-align:center"> Translating Math Formula Images To LaTeX Sequences </p> </font>
+
+
+Scaling Up Image-to-LaTeX Performance: Sumen An End-to-End Transformer Model With Large Dataset
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/639ca4299e1c02384ee5d753/Rh6_Pu3wE9y3cILsl5BLb.png)
+
+## Performance
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/639ca4299e1c02384ee5d753/lm56bL2NCX-ZdbmIjCzWO.png)
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/639ca4299e1c02384ee5d753/PRcJhuPmFEPbmOPSS1ZIt.png)
+
+## Uses
+
+#### Source code: https://github.com/hoang-quoc-trung/sumen
+
+#### Inference
+
+```python
+import torch
+import requests
+from PIL import Image
+from transformers import AutoProcessor, VisionEncoderDecoderModel
+
+# Load model & processor
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = VisionEncoderDecoderModel.from_pretrained('hoang-quoc-trung/sumen-base').to(device)
+processor = AutoProcessor.from_pretrained('hoang-quoc-trung/sumen-base')
+task_prompt = processor.tokenizer.bos_token
+decoder_input_ids = processor.tokenizer(
+    task_prompt,
+    add_special_tokens=False,
+    return_tensors="pt"
+).input_ids
+# Load image
+img_url = 'https://raw.githubusercontent.com/hoang-quoc-trung/sumen/main/assets/example_1.png'
+image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
+pixel_values = processor.image_processor(
+    image,
+    return_tensors="pt",
+    data_format="channels_first",
+).pixel_values
+# Generate LaTeX expression
+with torch.no_grad():
+    outputs = model.generate(
+        pixel_values.to(device),
+        decoder_input_ids=decoder_input_ids.to(device),
+        max_length=model.decoder.config.max_length,
+        pad_token_id=processor.tokenizer.pad_token_id,
+        eos_token_id=processor.tokenizer.eos_token_id,
+        use_cache=True,
+        num_beams=4,
+        bad_words_ids=[[processor.tokenizer.unk_token_id]],
+        return_dict_in_generate=True,
+    )
+sequence = processor.tokenizer.batch_decode(outputs.sequences)[0]
+sequence = sequence.replace(
+        processor.tokenizer.eos_token, ""
+    ).replace(
+        processor.tokenizer.pad_token, ""
+    ).replace(processor.tokenizer.bos_token,"")
+print(sequence)
+```