<prompt> Koetlisa mohlala oa puo ea tlhaho ho bolela esale pele lentsoe le latelang le fanoeng moelelo.nan  <response>: Instruction in English: Train a natural language model to predict the next word given a context. . Response in English: To train a natural language model to predict the next word given a context, we can follow the following steps:1. Data Collection and Preparation: The first step is to gather a large dataset of text written in the language we want the model to be able to generate. This can include books, articles, and other forms of literature. The data will then be processed and cleaned to remove any irrelevant information or formatting. The data can be tokenized and split into training, validation, and testing sets.2. Model Choice and Architecture: The next step is to choose the type of natural language model that we want to use. Some common choices include Recurrent Neural Networks RNNs, Long Short-Term Memory LSTM networks, and Transformers. The architecture of the model should be designed to take in a sequence of words as input and output a probability distribution over the vocabulary.3. Training: The model is then trained on the training dataset using techniques such as backpropagation and stochastic gradient descent to optimize its parameters. During training, the model will learn to predict the next word in a sequence based on the context of the previous words.4. Validation and Hyperparameter Tuning: The performance of the model can be evaluated on the validation dataset to check for overfitting or underfitting. Hyperparameters such as the learning rate, batch size, and number of units in the hidden layers can be tuned to improve the models performance.5. Testing: Finally, the trained model can be tested on unseen data to evaluate its ability to generate coherent sequences of text.In conclusion, training a natural language model to predict the next word given a context involves data collection and preparation, choosing an appropriate model architecture, training, validation and hyperparameter tuning, and testing on unseen data. . Response in Sesotho: Ho koetlisa mohlala oa puo ea tlhaho ho bolela esale pele lentsoe le latelang ha ho fanoe ka moelelo, re ka latela mehato e latelang: 1. Pokello ea Boitsebiso le Tokisetso: Mohato oa pele ke ho bokella dataset e kholo ea mongolo o ngotsoeng ka puo eo re batlang hore mohlala o be teng. khona ho hlahisa. Sena se ka kenyelletsa libuka, lingoliloeng le mefuta e meng ea lingoliloeng. Lintlha li tla sebetsoa le ho hloekisoa ho tlosa tlhahisoleseding efe kapa efe e sa lokelang kapa fomete. Lintlha li ka etsoa tokenized me tsa aroloa ka lithupelo, netefatso le lihlopha tsa liteko. 2. Khetho ea Mohlala le Mehaho: Mohato o latelang ke ho khetha mofuta oa puo ea tlhaho eo re batlang ho e sebelisa. Likhetho tse ling tse tloaelehileng li kenyelletsa marang-rang a Recurrent Neural Networks RNNs, marang-rang a Memory ea Nako e Khutšoane LSTM, le Transformers. Sebopeho sa mohlala se lokela ho raloa ho nka tatellano ea mantsoe e le ho kenya letsoho le ho hlahisa monyetla oa ho ajoa holima pokello ea mantsoe. 3. Koetliso: Moetso o koetlisetsoa lethathamong la lithupelo ho sebelisoa mekhoa e joalo ka backpropagation le stochastic gradient descent ho ntlafatsa maemo a eona. Nakong ea koetliso, mohlala o tla ithuta ho bolela esale pele lentsoe le latelang ka tatellano ho latela moelelo oa mantsoe a fetileng. 4. Ho netefatsa le Hyperparameter Tuning: Tsebetso ea mohlala e ka hlahlojoa ho dataset ea ho netefatsa ho hlahloba ho feteletsa kapa ho fokotseha. Li-hyperparameter tse kang sekhahla sa ho ithuta, boholo ba batch, le palo ea likarolo tse ka hara likarolo tse patiloeng li ka hlophisoa ho ntlafatsa tsebetso ea mohlala. 5. Teko: Qetellong, mohlala o koetlisitsoeng o ka lekoa ho data e sa bonahaleng ho lekola bokhoni ba eona ba ho hlahisa tatellano e lumellanang ea mongolo. Qetellong, ho koetlisa mohlala oa puo ea tlhaho ho bolela esale pele lentsoe le latelang le fanoeng moelelong ho kenyelletsa ho bokella le ho hlophisa lintlha, ho khetha moralo o nepahetseng oa meralo, koetliso, netefatso le tokiso ea hyperparameter, le tlhahlobo ea data e sa bonoeng. 