ModelVerificadoExterno

Qwen Receipt Extractor

LoRA fine-tuned Qwen2.5-0.5B-Instruct for structured JSON extraction from noisy OCR receipts and invoices.

Fontehuggingface.co/avatar63/qwen-receipt-extractor/tree/804fa33114143abac0df50c9ec140ce5ce628318

TipoLoRAModelo baseQwen/Qwen2.5-0.5B-InstructVersãoRevision 804fa33Publicador@avatar63Cnota

Fonte do modelo

Tipo: LoRA, Text model
Tamanho do arquivo: 33,6 MB
Modelo base: Qwen/Qwen2.5-0.5B-Instruct
Versão: vRevision 804fa33
Atualizado: 3 de out. de 2026
Publicador: @avatar63
Downloads: 38
Parâmetros: 5B
Tarefas: Geração de texto
Fonte: Hugging Face

Descrição da fonte

qwen-receipt-extractor

LoRA fine-tuned Qwen2.5-0.5B-Instruct for structured JSON extraction from noisy OCR receipts and invoices.

GitHub: avatar63/llm-doc-extract

Model description

Fine-tuned on ~2040 noisy OCR receipt/invoice examples using LoRA (rank 16). Takes raw, garbled OCR text and extracts structured JSON with company name, address, date, total amount, and line items.

Trained to run entirely locally — no API calls at inference time. Documents never leave your machine.

Performance

Evaluated on 204 held-out examples vs base Qwen2.5-0.5B-Instruct:

Field	Baseline	Fine-tuned	Δ
JSON valid	80.9%	99.5%	+18.6%
Company name	38.2%	46.6%	+8.3%
Date	15.2%	83.8%	+68.6%
Total amount	0.0%	99.0%	+99.0%
Line items	58.5%	97.0%	+38.5%

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch
import json

BASE_MODEL = "Qwen/Qwen2.5-0.5B-Instruct"
ADAPTER = "avatar63/qwen-receipt-extractor"

INSTRUCTION = (
    "Extract the following fields from the OCR text as JSON: "
    "company_name, address, date, total_amount, line_items "
    "(each with item_name, quantity, price). "
    "Use null for any field that cannot be determined."
)

tokenizer = AutoTokenizer.from_pretrained(ADAPTER, trust_remote_code=True)
base = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL, dtype=torch.float16, device_map="auto", trust_remote_code=True
)
model = PeftModel.from_pretrained(base, ADAPTER)
model.eval()

noisy_text = """
RELI4NCE FR3SH
Sh0p N0 12, 5ect0r 18
D4te: O5-ll-2O24
Net P4y4ble: 34O.OO
"""

messages = [
    {"role": "system", "content": INSTRUCTION},
    {"role": "user", "content": noisy_text}
]

text = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True
)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        temperature=0.1,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )

generated = outputs[0][inputs["input_ids"].shape[1]:]
result = tokenizer.decode(generated, skip_special_tokens=True)
print(json.loads(result))

Demo Space: https://huggingface.co/spaces/avatar63/receipt-extractor-demo

Training details

Parameter	Value
Base model	Qwen/Qwen2.5-0.5B-Instruct
Method	LoRA via HuggingFace PEFT + TRL
LoRA rank	16
LoRA alpha	32
Trainable parameters	8.8M / 502M (1.75%)
Training examples	~2040
Epochs	3
Learning rate	2e-4
Hardware	RTX 3060 12GB
Training time	~28 minutes

Limitations

Partial character-level denoising — item names and company suffixes may retain some OCR noise
Address hallucination on sparse/ambiguous inputs
Net payable vs subtotal ambiguity on some receipts
Trained primarily on Malaysian and synthetic English receipts

Datasets

SROIE — ICDAR 2019
High Quality Invoice Images for OCR — Kaggle

Base model

Qwen2.5-0.5B-Instruct — Qwen Team, Alibaba Cloud

Fontes

1 fonte

Hugging FacePrimárioVersão 804fa33114143abac0df50c9ec140ce5ce628318

huggingface.co/avatar63/qwen-receipt-extractor/tree/804fa33114143abac0df50c9ec140ce5ce628318

Verificado 3 de out.

Artefatos de modelo

1 artefato

adapter_model.safetensors

safetensors · 33,6 MB · SHA-256 f2cae681c0e1…0820 · Hugging Face

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Trechos de fonte

3 trechos

Readme11 de jul.huggingface.co/avatar63/qwen-receipt-extractor

--- language: - en license: apache-2.0 base_model: - Qwen/Qwen2.5-0.5B-Instruct pipeline_tag: text-generation tags: - lora - peft - ocr - document-extraction - receipt - invoice - fine-tuned --- # qwen-receipt-extractor LoRA fine-tuned Qwen2.5-0.5B-Instruct for structured JSON extraction from noisy OCR receipts and invoices. **GitHub:** [avatar63/llm-doc-extract](https://github.com/avatar63/llm-doc-extract) --- ## Model description Fine-tuned on ~2040 noisy OCR receipt/invoice examples using LoRA (rank 16). Takes raw, garbled OCR text and extracts structured JSON with company name, address, date, total amount, and line items. Trained to run entirely locally — no API calls at inference time. Documents never leave your machine. --- ## Performance Evaluated on 204 held-out examples vs base Qwen2.5-0.5B-Instruct: | Field | Baseline | Fine-tuned | Δ | |---|---|---|---| | JSON valid | 80.9% | 99.5% | +18.6% | | Company name | 38.2% | 46.6% | +8.3% | | Date | 15.2% | 83.8% | +68.6% | | Total amount | 0.0% | 99.0% | +99.0% | | Line items | 58.5% | 97.0% | +38.5% | --- ## Usage ```python from transformers import AutoModelForCausalLM, AutoTokenizer from peft import PeftModel import torch import json BASE_MODEL = "Qwen/Qwen2.5-0.5B-Instruct" ADAPTER = "avatar63/qwen-receipt-extractor" INSTRUCTION = ( "Extract the following fields from the OCR text as JSON: " "company_name, address, date, total_amount, line_items " "(each with item_name, quantity, price). " "Use null for any field that cannot be determined." ) tokenizer = AutoTokenizer.from_pretrained(ADAPTER, trust_remote_code=True) base = AutoModelForCausalLM.from_pretrained( BASE_MODEL, dtype=torch.float16, device_map="auto", trust_remote_code=True ) model = PeftModel.from_pretrained(base, ADAPTER) model.eval() noisy_text = """ RELI4NCE FR3SH Sh0p N0 12, 5ect0r 18 D4te: O5-ll-2O24 Net P4y4ble: 34O.OO """ messages = [ {"role": "system", "content": INSTRUCTION}, {"role": "user", "content": noisy_text} ] text = tokenizer.apply_chat_template( messages, tokenize=False, add_generation_prompt=True ) inputs = tokenizer(text, return_tensors="pt").to(model.device) with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=512, temperature=0.1, do_sample=True, pad_token_id=tokenizer.eos_token_id ) generated = outputs[0][inputs["input_ids"].shape[1]:] result = tokenizer.decod...

Note3 de out.huggingface.co/avatar63/qwen-receipt-extractor

Source context: 38 downloads · 1 likes · Pipeline text-generation · Library peft · Repo avatar63/qwen-receipt-extractor

Note17 de jul.huggingface.co/avatar63/qwen-receipt-extractor

Source context: 55 downloads · 1 likes · Pipeline text-generation · Library peft · Repo avatar63/qwen-receipt-extractor