> For the complete documentation index, see [llms.txt](https://blog.sunilgudivada.dev/notebook/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://blog.sunilgudivada.dev/notebook/transformers-and-llms/nlp-tasks.md). # NLP Tasks NLP -> Natural language Processing * Classification * Multi Classification * Generation ### **Classification** Input will be simple text, and output would be predict something **Sentiment Extraction** **Ex:** This teddy bear is so cute Datasets : amazon, IMDB, Twitter **Evaluation Metrics**: Accuracy -> % of observation that were correctly predicted Precision -> % of predicted positive that were correct Recall -> % of actually positive that were correct F1 Score -> Score that is a function of precision and recall - harmonic mean why do we need metrics -> to analyze the different datasets. Intent detection Language detection Topic Modelling ## Multi Classification Output would be multiple predictions Ex: Part of speech tagging **Named entity recognitio**n: Identify the different named entities from the input text Ex: A cute teddy bear is reading Teddy Bear -> entity **Datasets**: Annotated reuters news paper (CoNLL - 2003, CoNLL++) Dependency parsing Constituency parsing **Generation** Input as text and output also as text and dont know what is the length of the output Machine translation **Datasets** : WMT'14 English French, WMT'14 English German **Evaluation Metrics**: BLEU -> Bi lingual evaluation under study -> quality of text translated ROGUE -> Quality of text generated Perflexity -> Quantifies how 'surprised' the model is to see some words together Question Answering Summarization Text generation --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://blog.sunilgudivada.dev/notebook/transformers-and-llms/nlp-tasks.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.