moha
/

arabert_c19

@@ -7,22 +7,16 @@ widget:
 **ARABERT COVID-19** is a pretrained (fine-tuned) version of the AraBERT v2 model (https://huggingface.co/aubmindlab/bert-base-arabertv02). The pretraining was done using 1.5 million multi-dialect Arabic tweets regarding the COVID-19 pandemic from the “Large Arabic Twitter Dataset on COVID-19” (https://arxiv.org/abs/2004.04315).
 The model can achieve better results for the tasks that deal with multi-dialect Arabic tweets in relation to the COVID-19 pandemic.
-# Classification results for multiple fake-news detection tasks with and without using the arabert_c19:
 For more details refer to the paper (link)
-|                           | arabert  | mbert    | distilbert multi | arabert Covid-19 | mbert Covid-19 |
-|---------------------------|----------|----------|------------------|------------------|----------------|
-| Contains hate             |   0.8346 |   0.6675 |   0.7145         |   0.8649         |   0.8492       |
-| Talk about a cure         |   0.8193 |   0.7406 |   0.7127         |   0.9055         |   0.9176       |
-| Give advice               |   0.8287 |   0.6865 |   0.6974         |   0.9035         |   0.8948       |
-| Rise moral                |   0.8398 |   0.7075 |   0.7049         |   0.8903         |   0.8838       |
-| News or opinion           |   0.8987 |   0.8332 |   0.8099         |   0.9163         |   0.9116       |
-| Dialect                   |   0.7533 |   0.558  |   0.5433         |   0.823          |   0.7682       |
-| Blame and negative speech |   0.7426 |   0.597  |   0.6221         |   0.7997         |   0.7794       |
-| Factual                   |   0.9217 |   0.8427 |   0.8383         |   0.9575         |   0.9608       |
-| Worth fact-checking       |   0.7731 |   0.5298 |   0.5413         |   0.8265         |   0.8383       |
-| Contains fake information |   0.6415 |   0.5428 |   0.4743         |   0.7739         |   0.7228       |
 # Preprocessing

 **ARABERT COVID-19** is a pretrained (fine-tuned) version of the AraBERT v2 model (https://huggingface.co/aubmindlab/bert-base-arabertv02). The pretraining was done using 1.5 million multi-dialect Arabic tweets regarding the COVID-19 pandemic from the “Large Arabic Twitter Dataset on COVID-19” (https://arxiv.org/abs/2004.04315).
 The model can achieve better results for the tasks that deal with multi-dialect Arabic tweets in relation to the COVID-19 pandemic.
+# Classification results for multiple tasks including fake-news and hate speech detection when using arabert_c19 and mbert_ar_c19:
 For more details refer to the paper (link)
+|                                    | arabert  | mbert    | distilbert multi | arabert Covid-19 | mbert Covid-19 |
+|------------------------------------|----------|----------|------------------|------------------|----------------|
+| Contains hate (Binary)             |   0.8346 |   0.6675 |   0.7145         |   0.8649         |   0.8492       |
+| Talk about a cure (Binary)         |   0.8193 |   0.7406 |   0.7127         |   0.9055         |   0.9176       |
+| News or opinion (Binary)           |   0.8987 |   0.8332 |   0.8099         |   0.9163         |   0.9116       |
+| Contains fake information (Binary) |   0.6415 |   0.5428 |   0.4743         |   0.7739         |   0.7228       |
 # Preprocessing