--- license: apache-2.0 language: - en --- # Model Card for Model ID This a finetune codellama model finetuned to convert OCR scan result (eg. PaddleOCR) text array to structure json object. the input include ocr text array and ground truth boxes. ## Model Details training dataset: [ mychen76/cord-ocr-text-v2 ] enhanced version of original: naver-clova-ix/cord-v2 Usage-1 Input OCR text array and context boxes: eval_prompt = """### Instruction: Use the Input below and Context details to create an strucuture receipt data. The output must be a well-formed JSON object: ```json ### Input: ["BAKS", "Nasgor Jawa", "32.727", "Jeruk Panas", "19.091", "1Air Mineral", "9.091", "Net Total", "60.909", "P.Resto 10", "6.091", "3 Total", "67.000", "CASH", "67.000"] ### Context: [[[[131.0, 210.0], [327.0, 210.0], [327.0, 251.0], [131.0, 251.0]], ["BAKS", 0.9765313863754272]], [[[120.0, 378.0], [273.0, 380.0], [273.0, 400.0], [120.0, 398.0]], ["Nasgor Jawa", 0.9626438021659851]], [[[340.0, 381.0], [419.0, 381.0], [419.0, 399.0], [340.0, 399.0]], ["32.727", 0.9828599095344543]], [[[106.0, 398.0], [271.0, 400.0], [271.0, 418.0], [106.0, 416.0]], ["Jeruk Panas", 0.9557318091392517]], [[[340.0, 401.0], [417.0, 401.0], [417.0, 419.0], [340.0, 419.0]], ["19.091", 0.995367705821991]], [[[98.0, 417.0], [269.0, 419.0], [269.0, 436.0], [98.0, 434.0]], ["1Air Mineral", 0.9278557300567627]], [[[348.0, 416.0], [418.0, 418.0], [418.0, 439.0], [348.0, 437.0]], ["9.091", 0.9945915937423706]], [[[97.0, 455.0], [217.0, 455.0], [217.0, 475.0], [97.0, 475.0]], ["Net Total", 0.9419357776641846]], [[[336.0, 455.0], [419.0, 457.0], [419.0, 478.0], [336.0, 476.0]], ["60.909", 0.9923689961433411]], [[[97.0, 475.0], [243.0, 474.0], [243.0, 494.0], [97.0, 495.0]], ["P.Resto 10", 0.8946446180343628]], [[[350.0, 477.0], [415.0, 477.0], [415.0, 495.0], [350.0, 495.0]], ["6.091", 0.9968243837356567]], [[[94.0, 495.0], [193.0, 497.0], [192.0, 533.0], [93.0, 531.0]], ["3 Total", 0.9634256362915039]], [[[334.0, 495.0], [420.0, 495.0], [420.0, 535.0], [334.0, 535.0]], ["67.000", 0.9943265914916992]], [[[91.0, 552.0], [154.0, 552.0], [154.0, 596.0], [91.0, 596.0]], ["CASH", 0.9981260895729065]], [[[335.0, 553.0], [419.0, 553.0], [419.0, 594.0], [335.0, 594.0]], ["67.000", 0.9952940344810486]]] ### Response: """ ## expect output ## {"menu": [{"nm": "Nasgor Jawa", "cnt": "1", "price": "32.727"}, {"nm": "Jeruk Panas", "cnt": "1", "price": "19.091"}, {"nm": "Air Mineral", "cnt": "1", "price": "9.091"}], "sub_total": {"subtotal_price": "60.909", "tax_price": "6.091"}, "total": {"total_price": "67.000", "cashprice": "67.000", "menutype_cnt": "3"}} input_ids = tokenizer(eval_prompt, return_tensors="pt", truncation=True).input_ids.cuda() outputs = model.generate(input_ids=input_ids, max_new_tokens=100, do_sample=True, top_p=0.9,temperature=0.9) print(f"Generated instruction:\n{tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0][len(eval_prompt):]}") ***Generated instruction:*** {"menu": [{"nm": "BAKS"}, {"nm": "apple"}, {"nm": "banada"}, {"nm": "Mineral Water"}], "sub_total": {"subtotal_price": "60.909"}, "total": {"total_price": "67.000", "cashprice": "67.000", "changeprice": "0"}} ### Model Description - **Developed by: mychen776** [More Information Needed] - **Shared by [optional]:** [More Information Needed] - **Model type:** [More Information Needed] - **Language(s) (NLP):** [More Information Needed] - **License:** [More Information Needed] - **Finetuned from model [codellama/CodeLlama-34b-Instruct-hf]:** [More Information Needed] ### Model Sources [optional] - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses ### Direct Use [More Information Needed] ### Downstream Use [optional] [More Information Needed] ### Out-of-Scope Use [More Information Needed] ## Bias, Risks, and Limitations [More Information Needed] ### Recommendations Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data [More Information Needed] ### Training Procedure #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] #### Speeds, Sizes, Times [optional] [More Information Needed] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data [More Information Needed] #### Factors [More Information Needed] #### Metrics [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] [More Information Needed] ## Environmental Impact Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - **Hardware Type:** [More Information Needed] - **Hours used:** [More Information Needed] - **Cloud Provider:** [More Information Needed] - **Compute Region:** [More Information Needed] - **Carbon Emitted:** [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]