README.md

---
license: mit
---
### Usage
Inference Code for this model

```
import re
import transformers
from transformers import DonutProcessor, VisionEncoderDecoderModel
import torch

fine_tuned_model = VisionEncoderDecoderModel.from_pretrained("aravind-selvam/donut_finetuned_chart")
processor = DonutProcessor.from_pretrained("aravind-selvam/donut_finetuned_chart")

# Move model to GPU
device = "cuda" if torch.cuda.is_available() else "cpu"
fine_tuned_model.to(device)

# Load random document image from the test set
dataset = load_dataset("hf-internal-testing/example-documents", split="test")
sample_image = dataset[1]

def run_prediction(sample, model=fine_tuned_model, processor=processor):
    # pixel values
    pixel_values = processor(image, return_tensors="pt").pixel_values
    # prepare inputs
    task_prompt = "<s>"
    decoder_input_ids = processor.tokenizer(task_prompt, add_special_tokens=False, return_tensors="pt").input_ids

    # run inference
    outputs = model.generate(
        pixel_values.to(device),
        decoder_input_ids=decoder_input_ids.to(device),
        max_length=model.decoder.config.max_position_embeddings,
        early_stopping=True,
        pad_token_id=processor.tokenizer.pad_token_id,
        eos_token_id=processor.tokenizer.eos_token_id,
        use_cache=True,
        num_beams=2,
        # bad_words_ids=[[processor.tokenizer.unk_token_id]],
        return_dict_in_generate=True,
    )

    # process output
    prediction = processor.batch_decode(outputs.sequences)[0]
    prediction = re.sub(r"<one>", "1", prediction)
    prediction = processor.token2json(prediction)


    # load reference target
    target = processor.token2json(test_sample["target_sequence"])
    return prediction, target

prediction, target = run_prediction(sample_image)
print(f"Reference:\n {target}")
print(f"Prediction:\n {prediction}")
```