Update stanza requirement from <1.5,>=1.2 to >=1.2,<1.6

[dependabot]

Updates the requirements on stanza to permit the latest version.

Release notes

Sourced from stanza's releases.

Stanza v1.5.0

Ssurgeon interface

Headlining this release is the initial release of Ssurgeon, a rule-based dependency graph editing tool. Along with the existing Semgrex integration with CoreNLP, Ssurgeon allows for rewriting of dependencies such as in the UD datasets. More information is in the GURT 2023 paper, https://aclanthology.org/2023.tlt-1.7/

In addition to this addition, there are two other CoreNLP integrations, a long list of bugfixes, a few other minor features, and a long list of constituency parser experiments which were somewhere between "ineffective" and "small improvements" and are available for people to experiment with.

CoreNLP integration:

• Ssurgeon interface! New interface allows for editing of dependency graphs using Semgrex patterns and Ssurgeon rules. stanfordnlp/stanza#1205 https://aclanthology.org/2023.tlt-1.7/
• English Morphology class (deterministic English lemmatizer) stanfordnlp/stanza@6aed177
• English constituency -> dependency converter stanfordnlp/stanza@0987794

Bugfixes:

• Bugfix for older versions of torch: stanfordnlp/stanza@376d7ea
• Bugfix for training (integration with new scoring script) stanfordnlp/stanza#1167 stanfordnlp/stanza@9c39636
• Demo was showing constituency parser along with dependency parsing, even with conparse off: stanfordnlp/stanza@cbc13b0
• Replace absurdly long characters with UNK (thank you @​khughitt) stanfordnlp/stanza#1137 stanfordnlp/stanza#1140
• Package all relevant pretrains into default.zip - otherwise pretrains used by NER models which are not the default pretrain were being missed. stanfordnlp/stanza@435685f
• stanza-train NER training bugfix (wrong pretrain): stanfordnlp/stanza@2757cb4
• Pass around device everywhere instead of calling cuda(). this should fix models occasionally being split over multiple devices. would also allow for use of MPS, but the current torch implementation for MPS is buggy stanfordnlp/stanza#1209 stanfordnlp/stanza#1159
• Fix error in preparing tokenizer datasets (thanks @​dvzubarev): stanfordnlp/stanza#1161
• Fix unnecessary slowness in preparing tokenizer datasets (again, thanks @​dvzubarev): stanfordnlp/stanza#1162
• Fix using the correct pretrain when rebuilding POS tags for a Depparse dataset (again, thanks @​dvzubarev): stanfordnlp/stanza#1170
• When using the tregex interface to corenlp, add parse if it isn't already there (again, depparse was being confused with parse): stanfordnlp/stanza@b118473
• Update use of emoji to match latest releases: stanfordnlp/stanza#1195 stanfordnlp/stanza@ea345a8

Features:

• Mechanism for resplitting tokens into MWT stanfordnlp/stanza#95 stanfordnlp/stanza@8fac17f
• CLI for tokenizing text into one paragraph per line, whitespace separated (useful for Glove, for example) stanfordnlp/stanza@cfd44d1
• detach().cpu() speeds things up significantly in some cases stanfordnlp/stanza@ccfbc56
• Potentially use a constituency model as a classifier - WIP research project stanfordnlp/stanza#1190
• add an output format "{:C}" for document objects which prints out documents as CoNLL: stanfordnlp/stanza#1169
• If a constituency tree is available, include it when outputting conll format for documents: stanfordnlp/stanza#1171
• Same with sentiment: stanfordnlp/stanza@abb5819
• Additional language code coverage (thank you @​juanro49) stanfordnlp/stanza@5802b10 stanfordnlp/stanza@f06bf86 stanfordnlp/stanza@32f83fa stanfordnlp/stanza@3450575
• Allow loading a pipeline for new languages (useful when developing a new suite of models) stanfordnlp/stanza@e7fcd26
• Script to count the work done by annotators on aws sagemaker private workforce: stanfordnlp/stanza#1186
• Streaming interface which batch processes items in the stream: stanfordnlp/stanza@2c9fe3d stanfordnlp/stanza#550
• Can pass a defaultdict to MultilingualPipeline, useful for specifying the processors for each language at once: stanfordnlp/stanza@70fd2fd stanfordnlp/stanza#1199
• Transformer at bottom layer of POS - currently only available in English as the en_combined_bert model, others to come stanfordnlp/stanza#1132

New models:

• Armenian NER model using an NER labeling of armtdp (thanks to @​ShakeHakobyan): https://github.com/myavrum/ArmTDP-NER stanfordnlp/stanza#1206 stanfordnlp/stanza#1212
• Sindhi tokenization from ISRA stanfordnlp/stanza#1117
• Sindhi NER from SiNER: stanfordnlp/stanza@2a8ded4
• Erzya from UD 2.11 stanfordnlp/stanza@0344ac3

Conparser experiments:

• Transformer stack (initial implementation did not help) https://arxiv.org/abs/2010.10669 stanfordnlp/stanza@110031e
• TREE_LSTM constituent composition method (didn't beat MAX) stanfordnlp/stanza@2f722c8

... (truncated)

Commits

• a85cce6 Instead of dumping out a completely inscrutable semgrex result, add comments ...
• f55d635 Add flags to semgrex.py to load a conllu file and execute the semgrex on it, ...
• cc5c50a Add a note on why something does or doesn't work
• 3d58864 Add HY NER model as a default for that language
• 1a5cfd0 Added code for hy_armtdp - Armenian NER dataset
• fd6359d Notes on Indonesian transformers based on some initial experiments on POS. N...
• 00ca29c Add a sample script for ssurgeon which can be applied to the UD2.11 Pronouns ...
• b36ae8b Skip blocks of comments when reading an edit file
• 6003126 Remove TODOs which are now done from ssurgeon
• c20cd7c When rebuilding sentence text in ssurgeon, use the parent's SpaceAfter=No fie...
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