FRED T5 Large Ods Ner 2023

🚀 Named Entity Recognizer for Russian Receipts

This project presents a named entity recognizer designed to extract goods and brands from Russian receipts of fiscal data operators. It offers a practical solution for receipt parsing and structurization, which is highly valuable in data - processing scenarios.

🚀 Quick Start

This named entity recognizer is developed for a special multi - staged competition focused on receipt structurization. The competition was organized by the Open Data Science community and Alpha Bank, and it consisted of [the first](https://ods.ai/competitions/nlp - receipts), [the second](https://ods.ai/competitions/alfabank - nlp - receipts - 2) and [the final](https://ods.ai/competitions/alfabank - nlp - receipts - final) stage. However, this model can be used for any receipt parsing and structurization in Russian. The repository with code for fine - tuning and inference is available on [gitflic.ru](https://gitflic.ru/project/bond005/ods - ner - 2023).

💻 Usage Examples

Basic Usage

from typing import Tuple
import torch
from transformers import T5ForConditionalGeneration, GPT2Tokenizer


MODEL_NAME = 'bond005/FRED-T5-large-ods-ner-2023'
START_TAG = '<LM>'
END_TAG = '</s>'


def initialize_recognizer(model_path: str) -> Tuple[GPT2Tokenizer, T5ForConditionalGeneration]:
    model = T5ForConditionalGeneration.from_pretrained(model_path)
    if not torch.cuda.is_available():
        raise ValueError('CUDA is not available!')
    model = model.cuda()
    model.eval()
    tokenizer = GPT2Tokenizer.from_pretrained(model_path)
    return tokenizer, model


def recognize(text: str, tokenizer: GPT2Tokenizer, model: T5ForConditionalGeneration) -> Tuple[str, str]:
    if text.startswith(START_TAG):
        x = tokenizer(text, return_tensors='pt', padding=True).to(model.device)
    else:
        x = tokenizer(START_TAG + text, return_tensors='pt', padding=True).to(model.device)
    out = model.generate(**x)
    predictions = tokenizer.decode(out[0], skip_special_tokens=True).strip()
    while predictions.endswith(END_TAG):
        predictions = predictions[:-len(END_TAG)].strip()
    prediction_pair = predictions.split(';')
    if len(prediction_pair) == 0:
        goods = ''
        brands = ''
    elif len(prediction_pair) == 1:
        goods = prediction_pair[0].strip()
        brands = ''
    else:
        goods = prediction_pair[0].strip()
        brands = prediction_pair[1].strip()
    return goods, brands


recognizer = initialize_recognizer(MODEL_NAME)

goods_and_brands = recognize(text='Водка "Русская валюта" премиум люкс 38% 0,25л, Россия',
                             tokenizer=recognizer[0], model=recognizer[1])

print(f'GOODS: {goods_and_brands[0]}')
# водка

print(f'BRANDS: {goods_and_brands[1]}')
# русская валюта

📄 License

This project is licensed under the Apache 2.0 license.