<prompt> Hlalosa tlatsetso ea ho ithuta ka botebo tšimong ea tsebetso ea puo ea tlhaho.nan  <response>: Instruction in English: Describe the contributions of deep learning to the field of natural language processing. . Response in English: Deep learning has made significant contributions to the field of natural language processing NLP by enabling systems to automatically learn complex patterns and relationships from large amounts of data. This has led to significant improvements in tasks such as speech recognition, machine translation, sentiment analysis, language generation, and more.One of the key advantages of deep learning is its ability to process sequences of data such as text, which made it very suitable for the NLP field. The use of Recurrent neural networks, for example, has enabled systems to understand temporal dependencies in sequences of words and sentences. Long Short-Term Memory LSTM and Gated Recurrent Unit GRU are two types of RNN used in NLP.Another significant contribution that deep learning has brought to NLP is the use of pre-trained language models. These models, such as BERT, GPT, and others, are trained on an enormous amount of text data and can be adapted with small amounts of task-specific training data for a wide range of NLP tasks. This has led to significant improvements in many areas, including text classification, information extraction, and text generation.The use of neural networks also enabled the use of word embeddings, which are vector representations of words that capture their meanings and relationships with other words. These word embeddings are learned from large amounts of text data and greatly improve the representation of text in many NLP tasks.Overall, deep learning has brought many advancements to NLP, enabling more accurate and efficient systems. It continues to be an area of active research and development, promising even more improvements in the future. . Response in Sesotho: Ho ithuta ka botebo ho entse tlatsetso e kholo tšimong ea tsebetso ea puo ea tlhaho NLP ka ho nolofalletsa litsamaiso ho ithuta mekhoa e rarahaneng le likamano ho tsoa ho data e ngata. Sena se lebisitse ntlafatsong e kholo mesebetsing e kang temoho ea puo, phetolelo ea mochini, tlhahlobo ea maikutlo, tlhahiso ea puo, le tse ling. E ngoe ea melemo ea bohlokoa ea thuto e tebileng ke bokhoni ba eona ba ho sebetsana le tatellano ea data joalo ka mongolo, e entseng hore e tšoanelehe haholo bakeng sa tšimo ea NLP. Tšebeliso ea marang-rang a Recurrent neural, mohlala, e nolofalelitse litsamaiso ho utloisisa ho itšetleha ka nakoana ka tatellano ea mantsoe le lipolelo. Memori ea Nako e Khutšoane ea Nako e Telele LSTM le Gated Recurrent Unit GRU ke mefuta e meli ea RNN e sebelisoang ho NLP. Tlatsetso e ngoe ea bohlokoa eo thuto e tebileng e tlisitseng NLP ke tšebeliso ea mehlala ea lipuo e koetlisitsoeng esale pele. Mehlala ena, joalo ka BERT, GPT, le tse ling, e koetliselitsoe ka bongata bo boholo ba data ea mongolo mme e ka fetoloa ka lintlha tse nyane tsa koetliso e ikhethileng bakeng sa mesebetsi e mengata e fapaneng ea NLP. Sena se lebisitse ntlafatsong e kholo libakeng tse ngata, ho kenyeletsoa ho hlophisoa ha mongolo, ho ntšoa ha tlhahisoleseling, le tlhahiso ea mongolo. Tšebeliso ea marang-rang a neural e boetse e nolofalitse tsebeliso ea mantsoe a kenyelletsang mantsoe, e leng liemeli tsa vector tsa mantsoe a hapang moelelo le likamano tsa ona le mantsoe a mang. Ho kenyelletsa mantsoe ana ho ithutoa ho tsoa ho data e ngata ea mongolo mme e ntlafatsa haholo boemeli ba mongolo mesebetsing e mengata ea NLP. Ka kakaretso, thuto e tebileng e tlisitse tsoelo-pele e ngata ho NLP, e nolofalletsang litsamaiso tse nepahetseng le tse sebetsang hantle. E ntse e tsoela pele ho ba sebaka sa lipatlisiso le ntsetsopele e sebetsang, e tsepisang ntlafatso le ho feta nakong e tlang. 