metadata
datasets:
- s-nlp/paradetox
- s-nlp/ru_paradetox
language:
- ru
- en
library_name: transformers
pipeline_tag: text2text-generation
license: openrail++
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