text: Title: Request to Include Named Entity Recognition and Relation Extraction Model Finetuning Examples and Guidance Request 
  Body: ### Feature request
  
  As an NLP enthusiast working on Named Entity Recognition (NER) and Relation Extraction (RE) tasks, I would like to request the inclusion of NER and RE-related  examples, best practices, and **guidance for fine-tuning** models specifically for these tasks on your GitHub page.
  The documentation on fine-tuning models for NER and RE would help guide researchers in developing state-of-the-art models without having to reinvent the wheel.Currently Finetuning examples for NER and RE tasks are not available there.These techniques have relevance in identifying technical terms,chemical names,etc from texts and recognize the relationship between identified domain specified technical terms.
  
  ### Motivation
  
  ner models are critical in various technologies to identify particular domain related words from an input text,Especially if we are dealing with business terms,chemical names,particle names,and other domain specific technical terms.2nd importance is identify the relation between identified entities.Eg: sentence:Apple acquired Beats for $3 billion in 2014;Entities:Apple, Beats, $3 billion,2014;Relation:acquired
  
  ### Pre-requisite
  
  First there must be an appropriate dataset for finetuning, including 1)sentences,2)entities corresponding to that sentence in next column and 3)finally relation between identified entities.Dataset must include diverse domain specific datas like finance,medical,chemical,news,politics,etc then only a general kind of model can identify entities generally.If the purpose is domain specific then particulr domain specific dataset is a must.