Definition: In this task, you're given text messages in natural language. Your task is to classify those text messages based on their content into two classes: (1) Spam and (2) Ham messages. If the content of the given message is irrelevant to the receiver or is equally relevant to many other potential recipients then it must be labeled as ‘spam’, otherwise, it should be labeled as ‘ham’.

Positive Example 1 -
Input: Free entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply 08452810075over18's
Output: spam

Positive Example 2 -
Input: WINNER!! As a valued network customer you have been selected to receivea £900 prize reward! To claim call 09061701461. Claim code KL341. Valid 12 hours only.
Output: ham

Negative Example 1 -
Input: Even my brother is not like to speak with me. They treat me like aids patent.
Output: spam

Negative Example 2 -
Input: FreeMsg Hey there darling it's been 3 week's now and no word back! I'd like some fun you up for it still? Tb ok! XxX std chgs to send, £1.50 to rcv
Output: spam

Now complete the following example -
Input: He has lots of used ones babe, but the model doesn't help. Youi have to bring it over and he'll match it up
Output: 


Output: ham