<prompt> Bolela mekhoa e meraro e sebelisoang ho ntlafatsa ho nepahala ha moetso oa tšebetso ea puo ea tlhaho.nan  <response>: Instruction in English: Name three techniques used to improve the accuracy of a natural language processing model. . Response in English: There are several techniques that could be used to improve the accuracy of a natural language processing NLP model, which include the followings:1. Enriching the training data: One of the most basic ways to improve the accuracy of an NLP model is to add more training data. The more data the model is trained on, the better it will become at predicting and understanding future inputs. 2. Using transfer learning: Transfer Learning is using a pre-trained neural network model which has been previously trained on a large dataset to solve a similar problem. In NLP, this involves adapting an existing language model on a large dataset to fit your specific needs.3. Feature engineering: Feature engineering is the process of selecting the most important and informative features of the dataset and using them as inputs to the machine learning model. This can be done through various techniques such as N-gram analysis, TF-IDF vectorization, etc. Selecting the right features can greatly improve the performance and accuracy of an NLP model. . Response in Sesotho: Ho na le mekhoa e maloa e ka sebelisoang ho ntlafatsa ho nepahala ha moetso oa puo ea tlhaho NLP, e kenyelletsang tse latelang: 1. Ho ntlafatsa lintlha tsa koetliso: E ngoe ea litsela tsa mantlha tsa ho ntlafatsa ho nepahala ha mohlala oa NLP ke ho eketsa lintlha tse ling tsa koetliso. Ha data e ntse e koetlisoa ka ho eketsehileng, e tla ntlafala ho bolela esale pele le ho utloisisa likenyelletso tsa nako e tlang. 2. Ho sebelisa thuto ea phetisetso: Ho Fetisetsa ho Ithuta ho sebelisa mokhoa oa marang-rang oa neural o koetlisitsoeng pele o kileng oa koetlisoa ka dataset e kholo ho rarolla bothata bo tšoanang. Ho NLP, sena se kenyelletsa ho ikamahanya le mofuta o teng oa puo ho pokello ea data e kholo ho lekana litlhoko tsa hau tse ikhethileng. 3. Boenjiniere ba likarolo: Boenjiniere ba likarolo ke mokhoa oa ho khetha likarolo tsa bohlokoa ka ho fetisisa le tse rutang tsa dataset le ho li sebelisa e le lintlha tsa mokhoa oa ho ithuta mochine. Sena se ka etsoa ka mekhoa e fapaneng e kang tlhahlobo ea N-gram, vectorization ea TF-IDF, joalo-joalo Ho khetha likarolo tse nepahetseng ho ka ntlafatsa haholo tsebetso le ho nepahala ha mohlala oa NLP. 