How to extract dependencies in spaCy after using prodigy rel.manual?

[ines (Ines Montani)]

Hi! I'm kinda surprised train rel worked and didn't raise an error because there's no component to train here Actually, I just remembered that you were the same person who disabled use_plac as a workaround on this thread – this is likely the problem because it skips CLI argument validation. So I'd recommend turning that back on and using the other workaround I provided in the thread.

There's currently no built-in component for relation extraction in spaCy, so you will have to use your own implementation, depending on how you want your relation extraction to work. Here are some related threads:

Relationship between named entities

I'm having a lot of fun exploring prodigy for a text mining task. Training a NER model using annotations is extremely rewarding, and works surprisingly well with only a small amount of manual annotations. So far, I've been using prodigy train ner ... for assigning entities. Let's say I have two entity types, FIRM and TECH. Some data to illustrate my task: [IBM][FIRM] has identified [hybrid cloud][TECH] as the growth area it will focus on. In early March 2017, [Snapchat][FIRM] officially went…

How to train and correct a Named Entity Recognition with relation extraction

I can not find how to train an entity and relation annotation done at the same time. And how to correct one time done? I mean if I do prodigy rel.manual ner_rels blank:en ./data.jsonl --label SUBJECT,LOCATION --span-label PERSON,GPE --wrap --add-ents and then I use prodigy train parser ner_rels en_core_web_lg --output ./model The nerd and relation model are trained at the same time? ANd how I correct the results like ner.correct but with relations too? thx

Building model from annotated directional relations and dependencies dataset

hello, I used this command to annotate directional relations and dependencies between tokens: prodigy rel.manual my_dataset my_pre_annotated_model ./file_to_data.jsonl --label label1,label1 --span-label ner_label1,ner_label2,ner_labe3 --wrap after that, how can i convert the resulted dataset to a model so i can correct it and increase its accuracy? i tried this: prodigy dep.batch-train my_dataset blank:en --output ./model_Dependency --label label1,label2 --eval-split 0.2 --n-iter 10 but i …

For spaCy v3, @SofieVL recorded this in-depth tutorial on how to implement an entity relation extraction component from scratch. The code for this is available as a spaCy project so you can experiment with it. Even if you want to do something more custom, the video has a lot of helpful pointers on how to model the problem:

SPACY v3: Custom trainable relation extraction component