Update README.md

README.md

@@ -12,23 +12,28 @@ should probably proofread and complete it, then remove this comment. -->
 
 # Clickbait1
 
-This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) on the None dataset.
+This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) on the [Webis-Clickbait-17](https://zenodo.org/record/5530410) dataset.
 It achieves the following results on the evaluation set:
 - Loss: 0.0257
 
 ## Model description
 
-More information needed
+MiniLM is a distilled model from the paper "MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers".
+
+We fine tune this model to evaluate (regression) the clickbait level of title news.
 
 ## Intended uses & limitations
 
-More information needed
+Model looks like the model described in the paper [Predicting Clickbait Strength in Online Social Media](https://aclanthology.org/2020.coling-main.425/) by Indurthi Vijayasaradhi, Syed Bakhtiyar, Gupta Manish, Varma Vasudeva.
+
+The model was trained with english titles.
 
 ## Training and evaluation data
 
-More information needed
+We trained the model with the official training data for the chalenge (clickbait17-train-170630.zip (894 MiB, 19538 posts), plus another set that was just available after the end of the challenge (clickbait17-train-170331.zip (157 MiB, 2459 posts).
 
 ## Training procedure
+Code can be find in [Github](https://github.com/caush/Clickbait).
 
 ### Training hyperparameters