Update README.md

README.md

@@ -23,7 +23,9 @@ tokenizer = AutoTokenizer.from_pretrained("Musixmatch/umberto-wikipedia-uncased-
 model = AutoModelForSequenceClassification.from_pretrained("FBK-MT/GeNTE-evaluator")
 
 # neutral example
-sample = "Condividiamo il parere di chi ha presentato la relazione che ha posto notevole enfasi sull'informazione in relazione ai rischi e sulla trasparenza, in particolare nel campo sanitario e della sicurezza."
+sample = "Condividiamo il parere di chi ha presentato la relazione
+          che ha posto notevole enfasi sull'informazione in relazione ai rischi e sulla trasparenza,
+          in particolare nel campo sanitario e della sicurezza."
 input = tokenizer(sample, return_tensors='pt')
 
 with torch.no_grad():
@@ -54,8 +56,7 @@ print(predicted_label)  # 0 is neutral, 1 is gendered
     url = "https://aclanthology.org/2023.emnlp-main.873",
     doi = "10.18653/v1/2023.emnlp-main.873",
     pages = "14124--14140",
-    abstract = "Gender inequality is embedded in our communication practices and perpetuated in translation technologies. This becomes particularly apparent when translating into grammatical gender languages, where machine translation (MT) often defaults to masculine and stereotypical representations by making undue binary gender assumptions. Our work addresses the rising demand for inclusive language by focusing head-on on gender-neutral translation from English to Italian. We start from the essentials: proposing a dedicated benchmark and exploring automated evaluation methods. First, we introduce GeNTE, a natural, bilingual test set for gender-neutral translation, whose creation was informed by a survey on the perception and use of neutral language. Based on GeNTE, we then overview existing reference-based evaluation approaches, highlight their limits, and propose a reference-free method more suitable to assess gender-neutral translation.",
-}
+  }
 ```
 
 ## Contributions