Instructions to use sdadas/byt5-text-correction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sdadas/byt5-text-correction with Transformers:

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

pipe = pipeline("text-generation", model="sdadas/byt5-text-correction")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("sdadas/byt5-text-correction")
model = AutoModelForSeq2SeqLM.from_pretrained("sdadas/byt5-text-correction")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use sdadas/byt5-text-correction with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sdadas/byt5-text-correction"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sdadas/byt5-text-correction",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/sdadas/byt5-text-correction

SGLang

How to use sdadas/byt5-text-correction 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 "sdadas/byt5-text-correction" \
    --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": "sdadas/byt5-text-correction",
		"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 "sdadas/byt5-text-correction" \
        --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": "sdadas/byt5-text-correction",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use sdadas/byt5-text-correction with Docker Model Runner:
```
docker model run hf.co/sdadas/byt5-text-correction
```

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

ByT5-text-correction

A small multilingual utility model intended for simple text correction. It is designed to improve the quality of texts from the web, often lacking punctuation or proper word capitalization. The model was trained to perform three types of corrections:

Restoring punctuation in sentences.
Restoring word capitalization.
Restoring diacritical marks for languages that include them.

The following languages are supported: Belarusian (be), Danish (da), German (de), Greek (el), English (en), Spanish (es), French (fr), Italian (it), Dutch (nl), Polish (pl), Portuguese (pt), Romanian (ro), Russian (ru), Slovak (sk), Swedish (sv), Ukrainian (uk).

The model takes as input a sentence preceded by a language code prefix. For example:

from transformers import pipeline
generator = pipeline("text2text-generation", model="sdadas/byt5-text-correction")
sentences = [
    "<pl> ciekaw jestem na co licza onuce stawiajace na sykulskiego w nadziei na zwrot ku rosji",
    "<de> die frage die sich die europäer stellen müssen lautet ist es in unserem interesse die krise auf taiwan zu beschleunigen",
    "<ru> при своём рождении 26 августа 1910 года тереза получила имя агнес бояджиу"
]
generator(sentences, max_length=512)
# Ciekaw jestem na co liczą onuce stawiające na Sykulskiego w nadziei na zwrot ku Rosji.
# Die Frage, die sich die Europäer stellen müssen, lautet: Ist es in unserem Interesse, die Krise auf Taiwan zu beschleunigen?
# При своём рождении 26 августа 1910 года Тереза получила имя Агнес Бояджиу.

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Safetensors

Model size

0.3B params

Tensor type

F32

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