I am trying to create a dataset for training RASA ner_crf for one type of entity. Please let me know the minimum number of sentences/variation_in_sentence_formation for good result. When I have one type of each of the possible sentence NER_CRF is not giving good result.
Convention for creating good data set for RASA NER_CRF
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Rasa entity extraction depends heavily on the pipeline you have defined. Also depends on language model and tokenizers. So make sure you use good tokenizer. If it is normal English utterances try using tokenizer_ spacy before ner_crf. Also try with ner_spacy
As per my experience, 5 to 10 variations of utterances for each case gave a decent result to start with