# NER for Financial Text

**URL:** <https://support.prodi.gy/t/ner-for-financial-text/6027>\
**Category:** Uncategorized\
**Tags:** ner\
**Created:** [October 13, 2022, 10:03pm UTC](https://support.prodi.gy/t/ner-for-financial-text/6027 "2022-10-13T22:03:38Z")\
**Posts on this page:** 1\
**Showing post:** 12

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**Author:** ![koaning](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/koaning/32/230_2.png) [@koaning](https://support.prodi.gy/u/koaning)\
**Post date:** [November 4, 2022, 12:59pm UTC](https://support.prodi.gy/t/ner-for-financial-text/6027/12 "2022-11-04T12:59:40Z")

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Let's mention a few things here.

> [@nlpfan](#):
>
> transformer based; with pre-training and fine tuning

You can try out a transformer, but I recommend also trying a lightweight CNN model. If the lightweight model performs nearly as well it might be a better option for production because it's able to run _much_ faster.

With regards to training with transformers, the simplest way to think about it is that you merely need to make a change to the `config.cfg` file that has all the parameters for the spaCy model that you'll train.

You might appreciate the answer I've given here:

> [@Transform annotations to match tokenization required for SpanBERT/BERT](https://support.prodi.gy/t/transform-annotations-to-match-tokenization-required-for-spanbert-bert/5923/4):
>
> Gotya. Then I think the following steps are what you need. Step 1: Annotation Label some data. In my case I've made this example dataset: {"text": "hi my name is Vincent"} {"text": "people call me Noa"} And I've annotated these two examples in Prodigy via: python -m prodigy ner.manual issue-5923 blank:en examples.jsonl --label name This saves the NER annotations in a dataset named issue-5923. Step 2: Convert Next, let's prepare the data so that spaCy can use it. python -m prodigy data-t…

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_[View the full topic](https://support.prodi.gy/t/ner-for-financial-text/6027)._