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During `nlp.update`, components can be passed a boolean set_annotations to indicate whether they should assign annotations to the `Doc`. This needs to be called if downstream components expect to use the annotations during training, e.g. if we wanted to use tagger features in the parser. Components can specify their assignments and requirements, so we can figure out which components have these inter-dependencies. After figuring this out, we can guess whether to pass set_annotations=True. We could also call set_annotations=True always, or even just have this as the only behaviour. The downside of this is that it would require the `Doc` objects to be created afresh to avoid problematic modifications. One approach would be to make a fresh copy of the `Doc` objects within `nlp.update()`, so that we can write to the objects without any problems. If we do that, we can drop this logic and also drop the `set_annotations` mechanism. I would be fine with that approach, although it runs the risk of introducing some performance overhead, and we'll have to take care to copy all extension attributes etc. |
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| .. | ||
| __init__.py | ||
| test_analysis.py | ||
| test_entity_linker.py | ||
| test_entity_ruler.py | ||
| test_factories.py | ||
| test_functions.py | ||
| test_morphologizer.py | ||
| test_pipe_methods.py | ||
| test_sentencizer.py | ||
| test_senter.py | ||
| test_simple_ner.py | ||
| test_tagger.py | ||
| test_textcat.py | ||