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 exact model employs seq2seq (detoxification) and contrastive 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_contrastive')
pipe('Rewrite in non-toxic way in Russian: Ненавижу блять C-GAN')
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Base model
bigscience/mt0-large