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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 Overview
It is a SVD-compressed model of original BART-based detoxification model s-nlp/bart-base-detox.
How to use
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
model = AutoModelForSeq2SeqLM \
.from_pretrained('s-nlp/bart-base-detox-svd', trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained('facebook/bart-base')
toxics = ['that sick fuck is going to be out in 54 years.']
tokens = tokenizer(toxics)
tokens = model.generate(**tokens, num_return_sequences=1, do_sample=False,
temperature=1.0, repetition_penalty=10.0,
max_length=128, num_beams=5)
neutrals = tokenizer.decode(tokens[0, ...], skip_special_tokens=True)
print(neutrals) # stdout: She is going to be out in 54 years.
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