# How works N-gram based model

**URL:** <https://forum.rasa.com/t/how-works-n-gram-based-model/13655>\
**Category:** Rasa Open Source\
**Created:** [July 29, 2019, 5:20am UTC](https://forum.rasa.com/t/how-works-n-gram-based-model/13655 "2019-07-29T05:20:16Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![narendraprasath](https://avatars.discourse-cdn.com/v4/letter/n/cdc98d/32.png) [@narendraprasath](https://forum.rasa.com/u/narendraprasath)\
**Post date:** [July 29, 2019, 5:20am UTC](https://forum.rasa.com/t/how-works-n-gram-based-model/13655/1 "2019-07-29T05:20:16Z")

</div>

I have build RASA NLU by following intents,

## intent:greet

> - hi
> - hello
> - hey

## intent:ask\_how\_doing

> - how are you
> - are you okay
> - how are you doing

**NLU Pipeline Configuration**

```
pipeline:
- name: "WhitespaceTokenizer"
- name: "RegexFeaturizer"
- name: "CRFEntityExtractor"
- name: "EntitySynonymMapper"
- name: "CountVectorsFeaturizer"
  analyzer: "word"
  stop_words: { "english" }
  min_ngram: 1
  max_ngram: 3
- name: "EmbeddingIntentClassifier"

```

My question is, When I am testing a sentence like below

- are you how
- you doing are how

RASA NLU, still getting the intent as **ask\_how\_doing** with 91% accuracy and even when we give an proper order of sentence the accuracy is 97%. I feel that the tri-gram model is not given proper importance when sequence is missed because the sequence of words are taken from 1 to 3.

But if you try the sequence from 2 to 3. like below configuration,

> - min\_ngram: 2
> - max\_ngram: 3

The above sequence problem was solved but individual words like “hi”, “hello” and “hey” not able to get the intent because individual words are not trained in the model.

So, **How do i tackle these kind of problem in RASA NLU pipeline**?