# NER - basic model doubt

**URL:** <https://support.prodi.gy/t/ner-basic-model-doubt/7050>\
**Category:** Uncategorized\
**Tags:** ner\
**Created:** [January 24, 2024, 4:09am UTC](https://support.prodi.gy/t/ner-basic-model-doubt/7050 "2024-01-24T04:09:17Z")\
**Posts on this page:** 1\
**Showing post:** 5

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**Author:** ![magdaaniol](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/magdaaniol/32/2787_2.png) [@magdaaniol](https://support.prodi.gy/u/magdaaniol)\
**Post date:** [January 30, 2024, 10:15am UTC](https://support.prodi.gy/t/ner-basic-model-doubt/7050/5 "2024-01-30T10:15:43Z")

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Hi @e101sg,

Re your first question about the metrics (no need to be sorry at all!):

ENTS\_P is the Precision of NER component, that's right.  
For the explanation of SCORE and other columns, I'll just refer you this post from Ines:

> [@Understanding outputs for new texcat model](https://support.prodi.gy/t/understanding-outputs-for-new-texcat-model/4730/2):
>
> The score is the combined weighted score and you can read more about it here: [https://spacy.io/usage/training#metrics](https://spacy.io/usage/training#metrics) If you're only training one component, this is typically identical to the main score reported by the component (e.g. F-score). If you're training multiple components, you can use the score weights to define what to prioritise (e.g. best text classifier + NER recall combination, and so on).
> 
> You can also use the `[training.score_weights]` block to define the scores shown in the table. The scores set by the individual components are available in the code via the `@Language.factory` decorator: [TextCategorizer · spaCy API Documentation](https://spacy.io/api/textcategorizer)

Re `ner.teach` command question:

The way you're planning to use it is correct. After the annotation with the model in the loop it is recommended to retrain the model on the entire dataset resulting from the `ner.teach` session. So, yes you'd be using `your_New_dataset_name` in `train`. Our docs on [active learning](https://prodi.gy/docs/named-entity-recognition#active-learning) have some more details on why this is the recommended way:

> When you annotate with a model in the loop, the model is also updated in the background. So why do you still need to train your model on the annotations afterwards, and can’t just export the model that was updated in the loop? The main reason is that the model in the loop is only updated once each new annotation. This is never going to be **as effective as batch training** a model on the whole dataset, making multiple passes over the data, shuffling on each epoch and using other deep learning tricks like dropout rates, compounding batch sizes and so on. If you batch train your model with the collected annotations _afterwards_, you should receive the same model you had in the loop, just **better**.

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