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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, 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 Description
This is the model presented in the paper "Exploring Methods for Cross-lingual Text Style Transfer: The Case of Text Detoxification".
The model is based on mBART-large-50 and trained on two parallel detoxification corpora: ParaDetox and RuDetox. More details about this model are in the paper.
Usage
- Model loading.
from transformers import MBartForConditionalGeneration, AutoTokenizer
model = MBartForConditionalGeneration.from_pretrained("s-nlp/mbart-detox-en-ru").cuda()
tokenizer = AutoTokenizer.from_pretrained("facebook/mbart-large-50")
- Detoxification utility.
def paraphrase(text, model, tokenizer, n=None, max_length="auto", beams=3):
texts = [text] if isinstance(text, str) else text
inputs = tokenizer(texts, return_tensors="pt", padding=True)["input_ids"].to(
model.device
)
if max_length == "auto":
max_length = inputs.shape[1] + 10
result = model.generate(
inputs,
num_return_sequences=n or 1,
do_sample=True,
temperature=1.0,
repetition_penalty=10.0,
max_length=max_length,
min_length=int(0.5 * max_length),
num_beams=beams,
forced_bos_token_id=tokenizer.lang_code_to_id[tokenizer.tgt_lang]
)
texts = [tokenizer.decode(r, skip_special_tokens=True) for r in result]
if not n and isinstance(text, str):
return texts[0]
return texts
Citation
TBD
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