# recipe proposing list of custom chosen sentences for manual new **URL:** \ **Category:** Uncategorized\ **Tags:** usage, ner, custom, solved\ **Created:** [January 20, 2018, 8:22pm UTC](https://support.prodi.gy/t/recipe-proposing-list-of-custom-chosen-sentences-for-manual-new/238 "2018-01-20T20:22:17Z")\ **Posts on this page:** 5\ **Page:** 1
**Author:** ![qubolino](https://avatars.discourse-cdn.com/v4/letter/q/c5a1d2/32.png) [@qubolino](https://support.prodi.gy/u/qubolino)\ **Post date:** [January 20, 2018, 8:22pm UTC](https://support.prodi.gy/t/recipe-proposing-list-of-custom-chosen-sentences-for-manual-new/238/1 "2018-01-20T20:22:17Z")
I have recently bought a license of Prodigy to annotate a dataset. I realised that I could speed up the process if I could chose which sentences to present with my own algorithm (somewhat close to regex but not quite). Is there a way to easily plug such a logic? I also want to use manual annotation style where I select myself the text to annotate. I understand that the recipes are the way to go but I’m not sure how to do it though. ---
**Author:** ![ines](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/ines/32/3_2.png) [@ines](https://support.prodi.gy/u/ines)\ **Post date:** [January 20, 2018, 9:10pm UTC](https://support.prodi.gy/t/recipe-proposing-list-of-custom-chosen-sentences-for-manual-new/238/2 "2018-01-20T21:10:35Z")
Sure – that should be pretty easy! All you need to do is wrap the incoming stream of examples (a generator that yields dictionaries) in a filter function that uses your algorithm to check whether the example should be presented for annotation or not. Here’s a simple, standalone example of how a custom recipe using the `ner_manual` interface could look. You can also find more details on the recipe components and decorator in the [custom recipes workflow](https://prodi.gy/docs/workflow-custom-recipes), in your `PRODIGY_README.html` or in the source of `prodigy.recipes.ner`. ```python import prodigy from prodigy.components.preprocess import split_tokens from prodigy.components.loaders import JSONL import spacy def filter_stream(stream): # iterate over the stream of examples and filter it using your # custom algorithm, and yield the example if it should be shown for eg in stream: if YOUR_CUSTOM_LOGIC(eg['text']): yield eg @prodigy.recipe('custom-ner.manual') def custom_ner_manual(dataset, spacy_model, data_path): nlp = spacy.load(spacy_model) # load the model for tokenization stream = JSONL(data_path) # or any other way of loading in your data stream = filter_stream(stream) # filter stream using your logic above # use the model to tokenize your stream's texts and add a "tokens" # key to each example, to allow token-based manual selection stream = split_tokens(nlp, stream) labels = ['CAT', 'DOG', 'RABBIT'] # your label set – load this in however you like return { 'view_id': 'ner_manual', # use the manual NER interface 'dataset': dataset, # save annotations in this dataset 'stream': stream, # load examples from this stream 'config': {'labels': labels} # make your label set available to the front-end } ``` You can then call your custom recipe like this: ```bash prodigy custom-ner.manual my_dataset en_core_web_sm data.jsonl -F recipe.py ``` Since you’re only looking to replace the stream of examples, you could also solve this even simpler and wrap the built-in `ner.manual` recipe. Since recipes are simple Python functions that return a dictionary of components, you can also call them in Python with the respective arguments and return the result (the components) by your custom recipe. The `source` argument is usually a string (e.g. a file path), but it can also be a generator. This means that you can overwrite a recipe’s `source` with an already initialised stream. ```python import prodigy from prodigy.components.loaders import JSONL from prodigy.recipes.ner import manual as ner_manual # import recipe function @prodigy.recipe('custom-ner.manual') def custom_ner_manual(dataset, spacy_model, label, data_path): stream = JSONL(data_path) # load your data however you like stream = filter_stream(stream) # filter it using your custom logic # call the ner_manual function with the same arguments as the CLI version # this will return a dictionary of components components = ner_manual(dataset, spacy_model, source=stream, label=label) return components # return the components by the custom recipe ``` ---
**Author:** ![qubolino](https://avatars.discourse-cdn.com/v4/letter/q/c5a1d2/32.png) [@qubolino](https://support.prodi.gy/u/qubolino)\ **Post date:** [January 20, 2018, 10:41pm UTC](https://support.prodi.gy/t/recipe-proposing-list-of-custom-chosen-sentences-for-manual-new/238/3 "2018-01-20T22:41:09Z")
Thank you for your answer. Incidentally the server starts on 0.0.0.0:8080. Where can I configure IP and port please? Thanks. ---
**Author:** ![ines](https://sea2.discourse-cdn.com/flex020/user_avatar/support.prodi.gy/ines/32/3_2.png) [@ines](https://support.prodi.gy/u/ines)\ **Post date:** [January 20, 2018, 11:12pm UTC](https://support.prodi.gy/t/recipe-proposing-list-of-custom-chosen-sentences-for-manual-new/238/4 "2018-01-20T23:12:14Z")
> [@qubolino](#): > > Incidentally the server starts on 0.0.0.0:8080. Where can I configure IP and port please? Thanks. You can change both of this in your `prodigy.json` – see [this page](https://prodi.gy/docs/install#config) or the `PRODIGY_README.html` you can download with the library for more details. ---
**Author:** ![qubolino](https://avatars.discourse-cdn.com/v4/letter/q/c5a1d2/32.png) [@qubolino](https://support.prodi.gy/u/qubolino)\ **Post date:** [January 21, 2018, 7:45am UTC](https://support.prodi.gy/t/recipe-proposing-list-of-custom-chosen-sentences-for-manual-new/238/5 "2018-01-21T07:45:51Z")
Thank you.