Instructions to use alexandro767/SageDetox_detox_classification_contrastive with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alexandro767/SageDetox_detox_classification_contrastive with Transformers:

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

pipe = pipeline("text-generation", model="alexandro767/SageDetox_detox_classification_contrastive")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("alexandro767/SageDetox_detox_classification_contrastive")
model = AutoModelForSeq2SeqLM.from_pretrained("alexandro767/SageDetox_detox_classification_contrastive")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use alexandro767/SageDetox_detox_classification_contrastive with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alexandro767/SageDetox_detox_classification_contrastive"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alexandro767/SageDetox_detox_classification_contrastive",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/alexandro767/SageDetox_detox_classification_contrastive

SGLang

How to use alexandro767/SageDetox_detox_classification_contrastive 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 "alexandro767/SageDetox_detox_classification_contrastive" \
    --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": "alexandro767/SageDetox_detox_classification_contrastive",
		"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 "alexandro767/SageDetox_detox_classification_contrastive" \
        --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": "alexandro767/SageDetox_detox_classification_contrastive",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use alexandro767/SageDetox_detox_classification_contrastive with Docker Model Runner:
```
docker model run hf.co/alexandro767/SageDetox_detox_classification_contrastive
```

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

Model Card for Model ID

This model (Voronin et al., 2025, TBA) is one of four model types developed during CLEF-2025 Multilingual Text Detoxification contest. The idea was to apply a Sage-T5-like approach for text detoxification tasks. The main model utilizes three loss functions:

seq2seq loss for paraphrase generations,
classification loss for token-level toxicity detection,
contrastive loss for improved semantic representation learning.

To evaluate the correctness of the approach, backbone of mT0-large was taken and four models were trained: with only seq2seq loss, seq2seq & classification losses, seq2seq & contrastive losses and all three losses. This final model employs all three described losses.

Model Details

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

Developed by: Alexandr Voronin, Nikita Sushko, Daniil Moskovsky
Model type: mT0-large
Language(s) (NLP): am, ar, de, en, es, fr, he, hi, hin, it, ja, ru, tt, uk, zh
License: MIT
Finetuned from model [optional]: mT0-large

Uses

This model is intended to be used as a text detoxification task in 15 languages: Amharic, Arabic, German, English, Spanish, French, Hebrew, Hindi, Hinglish, Italian, Japanese, Russian, Tatar, Ukranian, Chinese.

Direct Use

The model may be directly used for text detoxification tasks.

How to Get Started with the Model

import transformers

pipe = transformers.pipeline('text2text-generation', 'alexandro767/SageDetox_detox_classification_contrastive')
pipe('Rewrite in non-toxic way in Russian: Ненавижу блять C-GAN')

Downloads last month: 1

Safetensors

Model size

1B params

Tensor type

F32

Model tree for alexandro767/SageDetox_detox_classification_contrastive

Base model

bigscience/mt0-large

Finetuned

(7)

this model

alexandro767
/

SageDetox_detox_classification_contrastive