You can deploy this model and start earning money today!

caush/
Clickbait4

generated_from_trainer

mit

autotrain_compatible

endpoints_compatible

Views

No views yet

This model is a fine-tuned version of microsoft/Multilingual-MiniLM-L12-H384 on the Webis-Clickbait-17 dataset. It achieves the following results on the evaluation set:

Loss: 0.0261

The following list presents the current performances achieved by the participants. As primary evaluation measure, Mean Squared Error (MSE) with respect to the mean judgments of the annotators is used. Our result is 0,0261 for the MSE metric. We do not compute the other metrics. We try not to cheat using unknown data at the time of the challenge. We do not use k-fold cross validation techniques.

team	MSE	F1	Precision	Recall	Accuracy	Runtime
goldfish	0.024	0.741	0.739	0.742	0.876	16:20:21
caush	0.026					00:11:00
monkfish	0.026	0.694	0.785	0.622	0.870	03:41:35
dartfish	0.027	0.706	0.733	0.681	0.865	00:47:07
torpedo19	0.03	0.677	0.755	0.614	0.861	00:52:44
albacore	0.031	0.67	0.731	0.62	0.855	00:01:10
blobfish	0.032	0.646	0.738	0.574	0.85	00:03:22
zingel	0.033	0.683	0.719	0.65	0.856	00:03:27
anchovy	0.034	0.68	0.717	0.645	0.855	00:07:20
ray	0.034	0.684	0.691	0.677	0.851	00:29:28
icarfish	0.035	0.621	0.768	0.522	0.849	01:02:57
emperor	0.036	0.641	0.714	0.581	0.845	00:04:03
carpetshark	0.036	0.638	0.728	0.568	0.847	00:08:05
electriceel	0.038	0.588	0.727	0.493	0.835	01:04:54
arowana	0.039	0.656	0.659	0.654	0.837	00:35:24
pineapplefish	0.041	0.631	0.642	0.621	0.827	00:54:28
whitebait	0.043	0.565	0.7	0.474	0.826	00:04:31

caush/Clickbait4

caush/
Clickbait4