<prompt> Koetlisa mokhethoa oa polelo: Ke batla ho reka buka.Ke batla ho reka buka  <response>: Instruction in English: Train a classifier for the sentence: I want to buy a book. . Response in English: To train a classifier for the sentence I want to buy a book, the first step would be to determine the classification categories, such as intent, object, and action. In this case, the intent would be to buy, the object would be a book and the action would be want. Then, the training data should include various example sentences that express similar and different intents, objects, and actions. These examples can then be labeled with the appropriate categories, such as:Can I purchase a novel? Intent: Buy, Object: Book, Action: WantI want to get a new phone Intent: Buy, Object: Phone, Action: WantI need to sell my car Intent: Sell, Object: Car, Action: NeedI would like to read a magazine Intent: Read, Object: Magazine, Action: WantAfter labeling the data, we would feed it to a machine learning algorithm to train the model. The algorithm would analyze the patterns and features in the text to learn to recognize and classify similar sentences based on their intent, object, and action. Once the model is trained, it can be tested with new sentences to evaluate its performance.In summary, the sentence I want to buy a book could be classified as having the intent to buy a Book with the action of want. . Response in Sesotho: Ho koetlisa mohlophisi bakeng sa polelo Ke batla ho reka buka, mohato oa pele e tla ba ho fumana likarolo tsa lihlopha, joalo ka maikemisetso, ntho le ketso. Tabeng ena, morero e tla ba ho reka, ntho e tla ba buka me ketso e tla ba batla. Joale, lintlha tsa koetliso li lokela ho kenyelletsa lipolelo tse fapaneng tsa mohlala tse hlalosang sepheo, lintho le liketso tse tšoanang le tse fapaneng. Mehlala ena joale e ka ngoloa ka lihlopha tse loketseng, tse kang: Na nka reka buka? Sepheo: Reka, Ntho: Buka, Ketso: Batla Ke batla ho fumana mohala o mocha Sepheo: Reka, Ntho: Mohala, Ketso: Ke Batla Ke hloka ho rekisa koloi eaka Sepheo: Rekisa, Ntho: Koloi, Ketso: Tlhokahalo Ke kopa ho bala makasine Sepheo: Bala, Sepheo: Makasine, Ketso: Batla Ka mora ho ngola lintlha, re tla li fepa ho algorithm ea ho ithuta mochine ho koetlisa mohlala. Algorithm e ne e tla sekaseka lipaterone le likarolo tsa mongolo ho ithuta ho lemoha le ho arola lipolelo tse tšoanang ho ipapisitsoe le sepheo sa tsona, ntho le ketso. Hang ha mohlala o se o koetlisoa, o ka lekoa ka lipolelo tse ncha ho hlahloba tsebetso ea oona. Ka kakaretso, poleloana Ke batla ho reka buka e ka hlalosoa e le sepheo sa ho reka Buka ka ketso ea batla. 