stanza
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Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages
**Describe the bug** I'm updating Stanza from 1.6.1 to 1.7.x / 1.8.x and noticed a number of breaking API changes in the Stanza Token result when handling possessives. **To Reproduce**...
Sorry for the double bug report. Can you please tell me what is the right procedure to load a model for a language that is not currently supported i..e, Albanian...
**Describe the bug** We've encountered a sentence pattern where Stanza fails to split apart two sentences. It appears when certain names are used (e.g. Max, Anna) but not with others...
**Describe the bug** I came across a set of sentences where Stanza mistakenly labels `nsubj` relationships as `compound`. This occurs with a noun in relation to the word "hops". **To...
I am working on adding a constituency model for Icelandic. I used the constituency treebank I have for training a POS tagger but how do I use it when training...
I am trying to train a pipeline for a new language (xcl). My goal is to train the full pipeline (tokenizer, tagger, parser, lemmatizer, and morphological parser) for this language,...
Latin default package (ITTB) doesn't usually lemmatize words starting with a capital letter. This seems to be the case whether the word is a proper noun, normally capitalised (eg "Iacobi"),...
**Is your feature request related to a problem? Please describe.** Missing Morphology in http://stanza.run/ **Describe the solution you'd like** Add Morphology into the pipeline in http://stanza.run/
**Is your feature request related to a problem? Please describe.** It could be fruitful to have access to the confidence of the model in its predictions. **Describe the solution you'd...
**Describe the bug** We recently trained a NER model using charlm, following the instructions [here](https://github.com/stanfordnlp/stanza-train#ner). ```bash python3 -m stanza.utils.training.run_ner bn_daffodil --charlm oscar --save_name bn_daffodil_charlm.pt ``` Then I noticed that every...