Instructions to use bond005/FRED-T5-large-ods-ner-2023 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bond005/FRED-T5-large-ods-ner-2023 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="bond005/FRED-T5-large-ods-ner-2023")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("bond005/FRED-T5-large-ods-ner-2023")
model = AutoModelForSeq2SeqLM.from_pretrained("bond005/FRED-T5-large-ods-ner-2023", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use bond005/FRED-T5-large-ods-ner-2023 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bond005/FRED-T5-large-ods-ner-2023"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bond005/FRED-T5-large-ods-ner-2023",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/bond005/FRED-T5-large-ods-ner-2023

SGLang

How to use bond005/FRED-T5-large-ods-ner-2023 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "bond005/FRED-T5-large-ods-ner-2023" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bond005/FRED-T5-large-ods-ner-2023",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "bond005/FRED-T5-large-ods-ner-2023" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bond005/FRED-T5-large-ods-ner-2023",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use bond005/FRED-T5-large-ods-ner-2023 with Docker Model Runner:
```
docker model run hf.co/bond005/FRED-T5-large-ods-ner-2023
```

YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other

This is a named entity recognizer for goods and brands extraction from receipts of fiscal data operators in Russian.

It was developed for the special multi-staged competition devoted to receipt structurization. This competition was organized by Open Data Science community and Alpha Bank, and it was consisted of the first, the second and the final stage. But 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.

Example of using:

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

Downloads last month: 6