Memes typically induce humour and strive to be relatable. Many of them aim to express solidarity during certain life phases and thus, to connect with their audience. Some memes are directly humorous whereas others go for sarcastic dig at daily life events. Inspired by the various humorous effects of memes, we propose three tasks as follows: •TaskA-Sentiment Classification: Given an Internet meme, the first task is to classify it as a positive or negative meme. We presume that a meme is not neutral. •TaskB-Humor Classification: Given an Internet meme, the system has to identify the type of humour expressed. The categories are sarcastic, humorous, and offensive meme. If a meme does not fall under any of these categories, then it is marked as the other meme. A meme can have more than one category. For instance, Fig 3 is an offensive meme but sarcastic too. •TaskC-Scales of Semantic Classes: The third task is to quantify the extent to which a particular effect is being expressed. Details of such quantifications are reported in Table 1. Appropriateannotated data will be provided We have released 10K human-annotated Internet memes labelled with semantic dimensions namely sentiment, and type of humour that is, sarcastic, humorous, or offensive. The humour types are further quantified on a Like scale as in Table 1. The dataset will also contain the extracted captions/texts from the memes.