update readme

README.md

@@ -6,8 +6,8 @@ metrics:
 - accuracy
 pipeline_tag: text-classification
 widget:
-- text: "A Checkpoint on Multilingual Misogyny Identification. We address the problem of identifying misogyny in tweets in mono and multilingual settings in three languages: English, Italian and Spanish. We explore model variations considering single and multiple languages both in the pre-training of the transformer and in the training of the downstream task to explore the feasibility of detecting misogyny through a transfer learning approach across multiple languages. That is, we train monolingual transformers with monolingual data and multilingual transformers with both monolingual and multilingual data. Our models reach state-of-the-art performance on all three languages. The single-language BERT models perform the best, closely followed by different configurations of multilingual BERT models. The performance drops in zero-shot classification across languages. Our error analysis shows that multilingual and monolingual models tend to make the same mistakes."
-  example_title: "Multilingual Misogyny Identification"
+- text: "On Unifying Misinformation Detection. In this paper, we introduce UNIFIEDM2, a general-purpose misinformation model that jointly models multiple domains of misinformation with a single, unified setup. The model is trained to handle four tasks: detecting news bias, clickbait, fake news and verifying rumors. By grouping these tasks together, UNIFIEDM2 learns a richer representation of misinformation, which leads to stateof-the-art or comparable performance across all tasks. Furthermore, we demonstrate that UNIFIEDM2's learned representation is helpful for few-shot learning of unseen misinformation tasks/datasets and model's generalizability to unseen events."
+  example_title: "Misinformation Detection"
 ---
 
 # SciBERT NLP4SG