<prompt> Theha mohlala oa lihlopha o hlophisang tema e fanoeng ho latela sehlooho sa eona.Ena ke pale ea ngoanana e monyenyane le lelapa labo ba lulang mahaeng.  <response>: Instruction in English: Develop a classification model that classifies the given text according to its topic. . Response in English: Im an AI language model and I am not able to create machine learning models, but I can guide you through the process of creating one. To create a classification model that classifies text according to its topic, you need to follow these steps:1. Collect and prepare the data: Collect a large dataset of text with labeled topics. You need to pre-process the text data by tokenizing, lowercasing, and removing stopwords and special characters.2. Choose a model: There are many classification algorithms that can be used, such as Naive Bayes, Logistic Regression, Support Vector Machines SVM, and Decision Trees. You should experiment with different models and choose the one that works best for your data.3. Train the model: Split the data into training and testing sets. Use the training set to train the model by feeding it the input data and the corresponding labels.4. Evaluate the model: Use the testing set to evaluate the performance of the trained model. There are several metrics that can be used to evaluate the performance, such as accuracy, precision, recall, and F1 score.5. Use the model: Once you have trained and evaluated the model, you can use it to classify new text according to its topic.For the given input This is the story of a young girl and her family who live in the countryside, a well-trained classification model should be able to classify it as belonging to the topic of family or countryside living, depending on the labels used in the training data. . Response in Sesotho: Ke mohlala oa puo ea AI me ha ke khone ho etsa mefuta ea ho ithuta ka mochini, empa nka u tataisa molemong oa ho e etsa. Ho theha mofuta oa lihlopha o hlophisang mongolo ho latela sehlooho sa ona, o hloka ho latela mehato ena: 1. Bokella le ho lokisa lintlha: Bokella pokello ea lintlha tse ngata tsa mongolo tse nang le lihlooho tse ngoliloeng. U hloka ho lokisa data ea mongolo esale pele ka ho etsa tokenizing, ho theola litlhaku le ho tlosa li-stopwords le litlhaku tse ikhethang. 2. Khetha mohlala: Ho na le li-algorithms tse ngata tsa lihlopha tse ka sebelisoang, tse kang Naive Bayes, Logistic Regression, Support Vector Machines SVM, le Decision Trees. U lokela ho leka mefuta e fapaneng ebe u khetha e sebetsang hantle bakeng sa data ea hau. 3. Koetlisa mohlala: Arola lintlha ka lihlopha tsa koetliso le liteko. Sebelisa sete ea koetliso ho koetlisa mohlala ka ho o fepa lintlha tsa ho kenya le lileibole tse tsamaellanang. 4. Lekola mohlala: Sebelisa sete ea tlhahlobo ho hlahloba tsebetso ea mohlala o koetlisitsoeng. Ho na le metrics e maloa e ka sebelisoang ho lekola tšebetso, joalo ka ho nepahala, ho nepahala, ho hopola, le lintlha tsa F1. 5. Sebelisa mohlala: Ha u se u rutile le ho hlahloba mohlala, u ka o sebelisa ho hlophisa sengoloa se secha ho latela sehlooho sa sona. Bakeng sa tlhahiso e fanoeng Ena ke pale ea ngoananyana le ba lelapa la hae ba lulang mahaeng, mohlala o koetlisitsoeng hantle oa ho hlophisa o lokela ho khona ho o arola e le oa sehlooho sa bophelo ba lelapa kapa mahaeng, ho latela mangolo. e sebelisoang ho data ea koetliso. 