# Custom recipe: cannot run Textclassifier **URL:** \ **Category:** Uncategorized\ **Tags:** usage, ner, textcat, solved\ **Created:** [September 6, 2021, 2:13am UTC](https://support.prodi.gy/t/custom-recipe-cannot-run-textclassifier/4653 "2021-09-06T02:13:30Z")\ **Posts on this page:** 3\ **Page:** 1
**Author:** ![wannli](https://avatars.discourse-cdn.com/v4/letter/w/278dde/32.png) [@wannli](https://support.prodi.gy/u/wannli)\ **Post date:** [September 6, 2021, 2:13am UTC](https://support.prodi.gy/t/custom-recipe-cannot-run-textclassifier/4653/1 "2021-09-06T02:13:30Z")
Hi there. I'm using a variant of your cat facts custom recipe with a ner and a textcat to train an initial data set: ```python @prodigy.recipe("ares") def ares(dataset, lang="en"): blocks = [ {"view_id": "ner_manual"}, {"view_id": "choice", "text": None, "choice_style": "multiple"}, ] options = [ {"id": 5, "text": "Meeting tag"}, {"id": 4, "text": "Document tag"}, {"id": 3, "text": "Agenda tag"}, {"id": 2, "text": "OPGA tag"}, {"id": 1, "text": "Substantive tag"}, {"id": 0, "text": "No tag"}, ] def get_stream(): with Session(engine) as session: res = session.exec(select(Paragraph)).all() for fact in res: yield {"text": fact.text, "options": options} nlp = spacy.load("en_core_web_sm") # nlp = spacy.blank(lang) # blank spaCy pipeline for tokenization stream = get_stream() # set up the stream stream = add_tokens(nlp, stream) # tokenize the stream for ner_manual return { "dataset": dataset, # the dataset to save annotations to "view_id": "blocks", # set the view_id to "blocks" "stream": stream, # the stream of incoming examples "config": { "labels": [ "SESSION", "MANDATEE", ], "blocks": blocks }, } ``` The resulting data set is then passed through `prodigy train` and saved as a model. I have then modified your textcat.teach to continue training with the model 'in the loop': ```python @prodigy.recipe("ares-in-the-loop") def ares(dataset, lang="en"): blocks = [ {"view_id": "ner_manual"}, {"view_id": "choice", "text": None, "choice_style": "multiple"}, ] options = [ {"id": 5, "text": "Meeting tag"}, {"id": 4, "text": "Document tag"}, {"id": 3, "text": "Agenda tag"}, {"id": 2, "text": "OPGA tag"}, {"id": 1, "text": "Substantive tag"}, {"id": 0, "text": "No tag"}, ] def get_stream(): with Session(engine) as session: res = session.exec(select(Paragraph)).all() for fact in res: yield {"text": fact.text, "options": options} stream = get_stream() # set up the stream nlp = spacy.load("v1/model-best") label = "5", "4", "3", "2", "1", "0" model = TextClassifier(nlp, label) predict = model update = model.update stream = add_tokens(nlp, stream) # tokenize the stream for ner_manual stream = prefer_uncertain(predict(stream)) # stream = add_tokens(nlp, stream) # tokenize the stream for ner_manual return { "dataset": dataset, # the dataset to save annotations to "view_id": "classification", # set the view_id to "blocks" "stream": stream, # the stream of incoming examples "update": update # a function for updating the model } ``` Unfortunately, the above code returns an error: ```python File "/Users/lt/python/recipe-in-the-loop.py", line 42, in ares model = TextClassifier(nlp, label) File "cython_src/prodigy/models/textcat.pyx", line 87, in prodigy.models.textcat.TextClassifier. __init__ TypeError: __init__ () takes exactly 4 positional arguments (3 given) ``` Would anyone be able to tell me what is happening here? There might be other bugs in the above code - I have not been able to move beyond this above. Thanks a mil, and also for a great product! ---
**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:** [September 7, 2021, 2:27am UTC](https://support.prodi.gy/t/custom-recipe-cannot-run-textclassifier/4653/2 "2021-09-07T02:27:23Z")
Hi! The underlying problem here is that the `TextClassifier` annotation model in v1.11 takes one more argument to define the pipeline component to use for the text classifier, e.g. `textcat` or `textcat_multilabel` (depending on which one you trained and how it's called). I'll have a look for old examples that don't yet set this correctly – we should update them now that v1.11 is out! ```python model = TextClassifier(nlp, label, pipe_name="textcat") ``` That said, I'm not sure what you're trying to achieve in your recipe and whether it actually does what you want it to: it looks like you have two blocks, `ner_manual` and `choice`, so I'm guessing you want the model to pre-select the label in the multiple-choice interface? That's not currently what's happening here: the annotation model and `textcat.teach` workflow will add a single binary `"label"` to all incoming examples and select the ones with the most uncertain scores. That's not really compatible with the rest of your recipe. Maybe you're looking for something more like the `textcat.correct` recipe, maybe with the `--update` flag if you want to update in the loop? [https://prodi.gy/docs/recipes#textcat-correct](https://prodi.gy/docs/recipes#textcat-correct) You can always run `prodigy stats` to find the location of your Prodigy installation and then look at `recipes/textcat.py` to see the code. ---
**Author:** ![wannli](https://avatars.discourse-cdn.com/v4/letter/w/278dde/32.png) [@wannli](https://support.prodi.gy/u/wannli)\ **Post date:** [September 7, 2021, 8:39pm UTC](https://support.prodi.gy/t/custom-recipe-cannot-run-textclassifier/4653/3 "2021-09-07T20:39:40Z")
Dear Ines, many thanks for the quick review and advice. I will go back to the drawing board, use the `textcat-correct` and report back as needed. Keep up the amazing work that you and the entire team are doing and congratulations on the sale!