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--- |
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library_name: transformers |
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tags: [] |
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--- |
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# Model Card for Model ID |
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Extract POS Receipt Image Data To JSON Record |
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## Model Details |
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Finetuned Google's PaliGemma Model for Receipt Image extraction to JSON Record. |
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gradio demo app: |
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https://github.com/minyang-chen/paligemma-receipt-json-v2 |
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### Model Usage |
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Setup Environment |
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``` |
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pip install transformers==4.42.2 |
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pip install datasets |
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pip install peft accelerate bitsandbytes |
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``` |
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Specify Device |
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``` |
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import torch |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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device_map={"":0} |
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``` |
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Step-1 Load Image Processor |
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``` |
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from transformers import AutoProcessor |
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FINETUNED_MODEL_ID = "mychen76/paligemma-receipt-json-3b-mix-448-v2b" |
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processor = AutoProcessor.from_pretrained(FINETUNED_MODEL_ID) |
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``` |
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Step-2 Set Task Prompt |
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``` |
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TASK_PROMPT = "EXTRACT_JSON_RECEIPT" |
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MAX_LENGTH = 512 |
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inputs = processor(text=TASK_PROMPT, images=test_image, return_tensors="pt").to(device) |
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for k,v in inputs.items(): |
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print(k,v.shape) |
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``` |
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Step-3 load model |
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``` |
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import torch |
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from transformers import PaliGemmaForConditionalGeneration |
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from transformers import BitsAndBytesConfig |
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from transformers import BitsAndBytesConfig |
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from peft import get_peft_model, LoraConfig |
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# Load Full model |
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model = PaliGemmaForConditionalGeneration.from_pretrained(FINETUNED_MODEL_ID,device_map={"":0}) |
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``` |
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OR Load Quantized |
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``` |
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# Q-LoRa |
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bnb_config = BitsAndBytesConfig( |
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load_in_4bit=True, |
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bnb_4bit_quant_type="nf4", |
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bnb_4bit_compute_type=torch.bfloat16 |
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) |
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lora_config = LoraConfig( |
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r=8, |
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target_modules=["q_proj", "o_proj", "k_proj", "v_proj", "gate_proj", "up_proj", "down_proj"], |
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task_type="CAUSAL_LM" |
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) |
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model = PaliGemmaForConditionalGeneration.from_pretrained(FINETUNED_MODEL_ID, quantization_config=bnb_config, device_map={"":0}) |
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``` |
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Step-4 Inference |
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``` |
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# Autoregressively generate,use greedy decoding here, for more fancy methods see https://huggingface.co/blog/how-to-generate |
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generated_ids = model.generate(**inputs, max_new_tokens=MAX_LENGTH) |
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# Next turn each predicted token ID back into a string using the decode method |
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# chop of the prompt, which consists of image tokens and text prompt |
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image_token_index = model.config.image_token_index |
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num_image_tokens = len(generated_ids[generated_ids==image_token_index]) |
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num_text_tokens = len(processor.tokenizer.encode(PROMPT)) |
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num_prompt_tokens = num_image_tokens + num_text_tokens + 2 |
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generated_text = processor.batch_decode(generated_ids[:, num_prompt_tokens:], skip_special_tokens=True, clean_up_tokenization_spaces=False)[0] |
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print(generated_text) |
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``` |
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Result Tokens |
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``` |
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'<s_total></s_total><s_tips></s_tips><s_time></s_time><s_telephone>(718)308-1118</s_telephone><s_tax></s_tax><s_subtotal></s_subtotal><s_store_name></s_store_name><s_store_addr>Brooklyn,NY11211</s_store_addr><s_line_items><s_item_value>2.98</s_item_value><s_item_quantity>1</s_item_quantity><s_item_name>NORI</s_item_name><s_item_key></s_item_key><sep/><s_item_value>2.35</s_item_value><s_item_quantity>1</s_item_quantity><s_item_name>TOMATOESPLUM</s_item_name><s_item_key></s_item_key><sep/><s_item_value>0.97</s_item_value><s_item_quantity>1</s_item_quantity><s_item_name>ONIONSVIDALIA</s_item_name><s_item_key></s_item_key><sep/><s_item_value>2.48</s_item_value><s_item_quantity>1</s_item_quantity><s_item_name>HAMBURRN</s_item_name><s_item_key></s_item_key><sep/><s_item_value>0.99</s_item_value><s_item_quantity>1</s_item_quantity><s_item_name>FTRAWBERRY</s_item_name><s_item_key></s_item_key><sep/><s_item_value>0.99</s_item_value><s_item_quantity>1</s_item_quantity><s_item_name>FTRAWBERRY</s_item_name><s_item_key></s_item_key><sep/><s_item_value>0.57</s_item_value><s_item_quantity>1</s_item_quantity><s_item_name>PILSNER</' |
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``` |
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Step-5 Convert Result to Json (borrow from donut model) |
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``` |
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import re |
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# let's turn that into JSON |
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def token2json(tokens, is_inner_value=False, added_vocab=None): |
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""" |
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Convert a (generated) token sequence into an ordered JSON format. |
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""" |
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if added_vocab is None: |
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added_vocab = processor.tokenizer.get_added_vocab() |
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output = {} |
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while tokens: |
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start_token = re.search(r"<s_(.*?)>", tokens, re.IGNORECASE) |
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if start_token is None: |
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break |
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key = start_token.group(1) |
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key_escaped = re.escape(key) |
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end_token = re.search(rf"</s_{key_escaped}>", tokens, re.IGNORECASE) |
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start_token = start_token.group() |
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if end_token is None: |
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tokens = tokens.replace(start_token, "") |
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else: |
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end_token = end_token.group() |
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start_token_escaped = re.escape(start_token) |
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end_token_escaped = re.escape(end_token) |
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content = re.search( |
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f"{start_token_escaped}(.*?){end_token_escaped}", tokens, re.IGNORECASE | re.DOTALL |
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) |
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if content is not None: |
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content = content.group(1).strip() |
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if r"<s_" in content and r"</s_" in content: # non-leaf node |
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value = token2json(content, is_inner_value=True, added_vocab=added_vocab) |
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if value: |
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if len(value) == 1: |
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value = value[0] |
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output[key] = value |
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else: # leaf nodes |
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output[key] = [] |
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for leaf in content.split(r"<sep/>"): |
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leaf = leaf.strip() |
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if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>": |
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leaf = leaf[1:-2] # for categorical special tokens |
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output[key].append(leaf) |
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if len(output[key]) == 1: |
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output[key] = output[key][0] |
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tokens = tokens[tokens.find(end_token) + len(end_token) :].strip() |
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if tokens[:6] == r"<sep/>": # non-leaf nodes |
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return [output] + token2json(tokens[6:], is_inner_value=True, added_vocab=added_vocab) |
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if len(output): |
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return [output] if is_inner_value else output |
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else: |
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return [] if is_inner_value else {"text_sequence": tokens} |
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## generated |
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generated_json = token2json(generated_text) |
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print(generated_json) |
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``` |
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Final Result in Json |
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``` |
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[{'total': '', |
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'tips': '', |
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'time': '', |
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'telephone': '(718)308-1118', |
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'tax': '', |
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'subtotal': '', |
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'store_name': '', |
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'store_addr': 'Brooklyn,NY11211', |
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'item_value': '2.98', |
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'item_quantity': '1', |
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'item_name': 'NORI', |
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'item_key': ''}, |
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{'item_value': '2.35', |
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'item_quantity': '1', |
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'item_name': 'TOMATOESPLUM', |
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'item_key': ''}, |
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{'item_value': '0.97', |
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'item_quantity': '1', |
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'item_name': 'ONIONSVIDALIA', |
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'item_key': ''}, |
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{'item_value': '2.48', |
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'item_quantity': '1', |
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'item_name': 'HAMBURRN', |
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'item_key': ''}, |
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{'item_value': '0.99', |
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'item_quantity': '1', |
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'item_name': 'FTRAWBERRY', |
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'item_key': ''}, |
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{'item_value': '0.99', |
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'item_quantity': '1', |
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'item_name': 'FTRAWBERRY', |
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'item_key': ''}, |
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{'item_value': '0.57', 'item_quantity': '1'}] |
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``` |
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<!-- Provide a longer summary of what this model is. --> |
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. |
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- **Developed by:** [email protected] |
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- **Model type:** Vision Model for Receipt Image Data Extraction |
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- **Language(s) (NLP):** [More Information Needed] |
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- **License:** [More Information Needed] |
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- **Finetuned from model [optional]:** PaliGemma-3b-pt-224 |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** [More Information Needed] |
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- **Paper [optional]:** [More Information Needed] |
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- **Demo [optional]:** [More Information Needed] |
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## Uses |
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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[More Information Needed] |
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### Downstream Use [optional] |
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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[More Information Needed] |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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[More Information Needed] |
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## Bias, Risks, and Limitations |
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[More Information Needed] |
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### Recommendations |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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[More Information Needed] |
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## Training Details |
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### Training Data |
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see here: mychen76/invoices-and-receipts_ocr_v1 |
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[More Information Needed] |
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### Training Procedure |
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#### Preprocessing [optional] |
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[More Information Needed] |
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#### Training Hyperparameters |
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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#### Speeds, Sizes, Times [optional] |
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[More Information Needed] |
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## Evaluation |
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<!-- This section describes the evaluation protocols and provides the results. --> |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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[More Information Needed] |
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#### Factors |
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[More Information Needed] |
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#### Metrics |
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[More Information Needed] |
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### Results |
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[More Information Needed] |
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#### Summary |
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## Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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[More Information Needed] |
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## Environmental Impact |
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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). |
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- **Hardware Type:** [More Information Needed] |
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- **Hours used:** [More Information Needed] |
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- **Cloud Provider:** [More Information Needed] |
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- **Compute Region:** [More Information Needed] |
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- **Carbon Emitted:** [More Information Needed] |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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[More Information Needed] |
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### Compute Infrastructure |
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[More Information Needed] |
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#### Hardware |
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[More Information Needed] |
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#### Software |
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## Citation [optional] |
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**BibTeX:** |
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[More Information Needed] |
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**APA:** |
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## Glossary [optional] |
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## More Information [optional] |
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## Model Card Authors [optional] |
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