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1from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
2
3model = MBartForConditionalGeneration.from_pretrained("laihuiyuan/RPT")
4tokenizer = MBart50TokenizerFast.from_pretrained("laihuiyuan/RPT", src_lang="it_IT")
5
6source = "Provaglio d'Iseo , donna trovata morta in casa : si sospetta il compagno"
7meta_info = "Simona Simonini, Elio Cadei, partner, percosse, Provaglio d'Iseo, c." #<victim name, perpetrator name, relationship, weapon, municipality, place>
8
9inputs = meta_info + ' ' +source
10inputs = tokenizer(inputs, return_tensors="pt")
11decode_start_id =tokenizer.lang_code_to_id['it_IT']
12output = model.generate(input_ids=inputs['input_ids'], num_beams=5, max_length=80, forced_bos_token_id=decode_start_id)
13transferred_text = tokenizer.decode(output[0].tolist(), skip_special_tokens=True, clean_up_tokenization_spaces=False)1@inproceedings{minnemaa-etal-2023-responsibility,
2 title = "Responsibility Perspective Transfer for Italian Femicide News",
3 author = "Minnemaa, Gosse and Lai, Huiyuan and Muscato, Benedetta and Nissim, Malvina",
4 booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
5 month = July,
6 year = "2023",
7 address = "Toronto, Canada",
8 publisher = "Association for Computational Linguistics",
9}