diff --git a/udpipe2/docs/.gitignore b/udpipe2/docs/.gitignore deleted file mode 100644 index 9b61c8c0423824bbec12a1b3862a4a7a8a0d79a1..0000000000000000000000000000000000000000 --- a/udpipe2/docs/.gitignore +++ /dev/null @@ -1 +0,0 @@ -/*.html diff --git a/udpipe2/docs/Makefile b/udpipe2/docs/Makefile deleted file mode 100644 index 8194de78ab3300b51ed2689c57a31db3fb7435c3..0000000000000000000000000000000000000000 --- a/udpipe2/docs/Makefile +++ /dev/null @@ -1,24 +0,0 @@ -HTMLS:=models.html udpipe_2.html - -.PHONY: all -all: $(HTMLS) - -%.md: %.md.sh $(wildcard *.md) - sh $< >$@ - -%.fragment.html: %.md - scripts/preprocess <$< | scripts/md >$@ - -%.html: %.fragment.html - scripts/html <$< >$@ - -%.noheading.html: %.html - sed '/

$@ - -.PHONY: web_install -web_install: udpipe_2.fragment.noheading.html models.fragment.html - scp $^ ufal:public_html/udpipe/2/ - -.PHONY: clean -clean: - rm -rf *.html $(patsubst %.md.sh,%.md,$(wildcard *.md.sh)) diff --git a/udpipe2/docs/models.md.sh b/udpipe2/docs/models.md.sh deleted file mode 100644 index 582a9a70b0b9769ab4e5dbc6075011fd5929e676..0000000000000000000000000000000000000000 --- a/udpipe2/docs/models.md.sh +++ /dev/null @@ -1,26 +0,0 @@ -#!/bin/sh - -models="models_ud212.md models_ud210.md models_ud26.md models_pdtc10.md models_evalatin20.md" - -cat <" $2 ""; - } - } - {print} - ' $model - echo -done diff --git a/udpipe2/docs/models_evalatin20.md b/udpipe2/docs/models_evalatin20.md deleted file mode 100644 index d9d4944659e0f3042bf1d057a6feb1755643a3c9..0000000000000000000000000000000000000000 --- a/udpipe2/docs/models_evalatin20.md +++ /dev/null @@ -1,40 +0,0 @@ -## EvaLatin 2020 Models #evalatin20_models - -EvaLatin 2020 Models are distributed under the -[CC BY-NC-SA](https://creativecommons.org/licenses/by-nc-sa/4.0/) licence. -The models are based solely on [EvaLatin 2020](https://github.com/CIRCSE/LT4HALA) -treebanks, and additionally use [multilingual BERT](https://github.com/google-research/bert/blob/master/multilingual.md). - -The models require [UDPipe 2](https://ufal.mff.cuni.cz/udpipe/2). - -### Download - -The latest version 200831 of the EvaLatin 2020 models -can be downloaded from [LINDAT/CLARIN repository](https://hdl.handle.net/11234/1-4803). - -The models are also available in the [REST service](https://lindat.mff.cuni.cz/services/udpipe/). - -### Acknowledgements #evalatin20_models_acknowledgements - -This work was supported by the grant no. GX20-16819X of the Grant Agency of the -Czech Republic, and has been using language resources stored and distributed by -the LINDAT/CLARIAH-CZ project of the Ministry of Education, Youth and Sports of -the Czech Republic (project LM2018101). - -The models were trained on [EvaLatin 2020](https://github.com/CIRCSE/LT4HALA) treebanks. - -Finally, [multilingual BERT](https://github.com/google-research/bert/blob/master/multilingual.md) -is used to provide contextualized word embeddings. - -### Publications - -- Milan Straka, Jana Straková (2020): [UDPipe at EvaLatin 2020: Contextualized Embeddings -and Treebank Embeddings](https://arxiv.org/abs/2006.03687). In: ArXiv.org Computing Research Repository, ISSN 2331-8422, 2006.03687 - -### Model Performance - -| Model | Dataset | UPOS | Lemma | -|:------|:------------------|------:|-------:| -| latin-evalatin20-200830 | test classical | 96.73 | 96.39 | -| latin-evalatin20-200830 | test cross-genre | 90.47 | 86.89 | -| latin-evalatin20-200830 | test cross-time | 87.58 | 90.59 | diff --git a/udpipe2/docs/models_pdtc10.md b/udpipe2/docs/models_pdtc10.md deleted file mode 100644 index bb2bc03c411791f408f557dd4dbc53953b1fa1a1..0000000000000000000000000000000000000000 --- a/udpipe2/docs/models_pdtc10.md +++ /dev/null @@ -1,120 +0,0 @@ -## Czech PDT-C 1.0 Model #czech_pdtc1.0_model - -PDT-C 1.0 Model is distributed under the -[CC BY-NC-SA](https://creativecommons.org/licenses/by-nc-sa/4.0/) licence. -The model is trained on [PDT-C 1.0 treebank](https://hdl.handle.net/11234/1-3185) -using [RobeCzech model](https://hdl.handle.net/11234/1-3691), and performs -morphological analysis using the [MorfFlex CZ 2.0](https://hdl.handle.net/11234/1-4794) -morphological dictionary via [MorphoDiTa](https://ufal.mff.cuni.cz/morphodita). - -The model requires [UDPipe 2.1](https://ufal.mff.cuni.cz/udpipe/2), together -with Python packages [ufal.udpipe](https://pypi.org/project/ufal.udpipe/) -version at least 1.3.1.1 and [ufal.morphodita](https://pypi.org/project/ufal.morphodita/) -version at least 1.11.2.1. - -### Download - -The latest version 231116 of the Czech PDT-C 1.0 model -can be downloaded from the [LINDAT/CLARIN repository](http://hdl.handle.net/11234/1-5293). - -The model is also available in the [REST service](https://lindat.mff.cuni.cz/services/udpipe/). - -### PDT-C 1.0 Morphological System - -PDT-C 1.0 uses the _PDT-C tag set_ from MorfFlex CZ 2.0, which is an evolution -of the original _PDT tag set_ devised by Jan Hajič -([Hajič, 2004](https://books.google.cz/books?id=sB63AAAACAAJ)). -The tags are positional with 15 positions corresponding to part of speech, -detailed part of speech, gender, number, case, etc. (e.g. `NNFS1-----A----`). -Different meanings of same lemmas are distinguished and additional comments can -be provided for every lemma meaning. The complete reference can be found in the -[Manual for Morphological Annotation, Revision for the Prague Dependency -Treebank - Consolidated 2020 release](https://ufal.mff.cuni.cz/techrep/tr64.pdf) -and quick reference is available in the [PDT-C positional morphological tags -overview](https://ufal.mff.cuni.cz/pdt-c/publications/Appendix_M_Tags_2020.pdf). - -The PDT-C 1.0 emply dependency relations from the [PDT analytical -level](https://ufal.mff.cuni.cz/pdt-c/publications/PDT20-a-man-en.pdf), with -a quick reference available in the [PDT-C analytical functions and clause -segmentation overview](http://ufal.mff.cuni.cz/pdt-c/publications/Appendix_A_Tags_2020.pdf). - -In the CoNLL-U format, the -- tags are filled in the `XPOS` column, and -- the dependency relations are filled in the `DEPREL`, even if they are - different from the universal dependency relations. - -### PDT-C 1.0 Train/Dev/Test Splits - -The PDT-C corpus consists of four datasets, but some of them do not have -an official train/dev/test split. We therefore used the following split: - -- PDT dataset is already split into train, dev (`dtest`), and test (`etest`). -- PCEDT dataset is a translated version of the Wall Street Journal, so we used - the usual split into train (sections 0-18), dev (sections 19-21), and test - (sections 22-24). -- PDTSC and FAUST datasets have no split, so we split it into dev (documents - with identifiers ending with 6), test (documents with identifiers ending with 7), - and train (all the remaining documents). - -### Acknowledgements #czech_pdtc1.0_model_acknowledgements - -This work has been supported by the LINDAT/CLARIAH-CZ project funded by Ministry -of Education, Youth and Sports of the Czech Republic (project LM2023062). - -### Publications - -- Milan Straka, Jakub Náplava, Jana Straková, David Samuel (2020): [RobeCzech: Czech RoBERTa, a monolingual contextualized language representation model](https://doi.org/10.1007/978-3-030-83527-9_17). In: Text, Speech, and Dialogue. TSD 2021. Lecture Notes in Computer Science, vol 12848. Springer, Cham. -- Jan Hajič, Eduard Bejček, Jaroslava Hlavacova, Marie Mikulová, Milan Straka, Jan Štěpánek, and Barbora Štěpánková (2020). [Prague Dependency Treebank - Consolidated 1.0](https://aclanthology.org/2020.lrec-1.641.pdf). In: Proceedings of the 12th Language Resources and Evaluation Conference, pages 5208–5218, Marseille, France. European Language Resources Association. -- Milan Straka (2018): [UDPipe 2.0 Prototype at CoNLL 2018 UD Shared Task](https://www.aclweb.org/anthology/K18-2020/). In: Proceedings of CoNLL 2018: The SIGNLL Conference on Computational Natural Language Learning, pp. 197-207, Association for Computational Linguistics, Stroudsburg, PA, USA, ISBN 978-1-948087-72-8 -- Straková Jana, Straka Milan and Hajič Jan (2014): [Open-Source Tools for Morphology, Lemmatization, POS Tagging and Named Entity Recognition](https://aclanthology.org/P14-5003/). In: Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pages 13-18, Baltimore, Maryland, June 2014. Association for Computational Linguistics. - -### Model Performance - -#### Tagging and Lemmatization - -We evaluate tagging and lemmatization on the four datasets of PDT-C 1.0, -and we also compute a macro-average. For lemmatization, we use the following -metrics: -- `Lemmas`: a primary metric comparing the _lemma proper_, which is the lemma - with an optional lemma number (but we ignore the additional lemma comments - like “this is a given name”); -- `LemmasEM`: an exact match comparing also the lemma comments. This metric is - less or equal to `Lemmas`. Our model directly predicts only lemma proper (no - additional comments), and relies on the morphological dictionary to supply the - comments, so it fails to generate comments for unknown words (like an unknown - given name). - -We perform the evaluation using the -[udpipe2_eval.py](https://github.com/ufal/udpipe/blob/udpipe-2/udpipe2_eval.py), -which is a minor extension of the [CoNLL 2018 Shared -Task](https://universaldependencies.org/conll18/evaluation.html) evaluation -script. - -Because the model also include a rule-based tokenizer and sentence splitter, -we evaluate both: -- using raw input text, which must first be tokenized and split into sentences. - The resulting scores are in fact F1-scores. Note that the FAUST dataset does - not contain any discernible sentence boundaries. -- using gold tokenization. - -| Treebank | Mode | Tokens | Sents | XPOS | Lemma | LemmaEM | -|:---------|:------------------|-------:|------:|------:|------:|--------:| -| PDT | Raw text | 99.91 | 88.00 | 98.69 | 99.10 | 98.86 | -| PDT | Gold tokenization | — | — | 98.78 | 99.19 | 98.96 | -| PCEDT | Raw text | 99.97 | 94.06 | 98.77 | 99.36 | 98.75 | -| PCEDT | Gold tokenization | — | — | 98.80 | 99.40 | 98.78 | -| PDTSC | Raw text | 100.0 | 98.31 | 98.77 | 99.23 | 99.16 | -| PDTSC | Gold tokenization | — | — | 98.77 | 99.23 | 99.16 | -| FAUST | Raw text | 100.0 | 10.98 | 97.05 | 98.88 | 98.43 | -| FAUST | Gold tokenization | — | — | 97.42 | 98.78 | 98.30 | -| MacroAvg | Gold tokenization | — | — | 98.44 | 99.15 | 98.80 | - -#### Dependency Parsing - -In PDT-C 1.0, the only manually annotated dependency parsing dataset is a subset -of the PDT dataset. We perform the evaluation as in the previous section. - -| Treebank | Mode | Tokens | Sents | XPOS | Lemma | LemmaEM | UAS | LAS | -|:-----------|:------------------|-------:|------:|------:|------:|--------:|------:|------:| -| PDT subset | Raw text | 99.94 | 88.49 | 98.74 | 99.16 | 98.97 | 93.45 | 90.32 | -| PDT subset | Gold tokenization | — | — | 98.81 | 99.23 | 99.03 | 94.41 | 91.48 | diff --git a/udpipe2/docs/models_ud210.md b/udpipe2/docs/models_ud210.md deleted file mode 100644 index fa553ccf7a414aaa1967a38eb4d99e19c5faf523..0000000000000000000000000000000000000000 --- a/udpipe2/docs/models_ud210.md +++ /dev/null @@ -1,308 +0,0 @@ -## Universal Dependencies 2.10 Models #universal_dependencies_210_models - -Universal Dependencies 2.10 Models are distributed under the -[CC BY-NC-SA](https://creativecommons.org/licenses/by-nc-sa/4.0/) licence. -The models are based solely on [Universal Dependencies -2.10](https://hdl.handle.net/11234/1-4758) treebanks, and additionally -use [multilingual BERT](https://github.com/google-research/bert/blob/master/multilingual.md) -and [RobeCzech](https://huggingface.co/ufal/robeczech-base). - -The models require [UDPipe 2](https://ufal.mff.cuni.cz/udpipe/2). - -### Download - -The latest version 220711 of the Universal Dependencies 2.10 models -can be downloaded from [LINDAT/CLARIN repository](https://hdl.handle.net/11234/1-4804). - -The models are also available in the [REST service](https://lindat.mff.cuni.cz/services/udpipe/). - -### Acknowledgements #universal_dependencies_210_models_acknowledgements - -This work has been supported by the Ministry of Education, Youth and Sports of -the Czech Republic, Project No. LM2018101 LINDAT/CLARIAH-CZ. - -The models were trained on [Universal Dependencies 2.10](https://hdl.handle.net/11234/1-4758) treebanks. - -For the UD treebanks which do not contain original plain text version, -raw text is used to train the tokenizer instead. The plain texts -were taken from the [W2C -- Web to Corpus](https://hdl.handle.net/11858/00-097C-0000-0022-6133-9). - -Finally, [multilingual BERT](https://github.com/google-research/bert/blob/master/multilingual.md) -and [RobeCzech](https://huggingface.co/ufal/robeczech-base) are used to provide -contextualized word embeddings. - -### Publications - -- Milan Straka, Jakub Náplava, Jana Straková, David Samuel (2020): [RobeCzech: Czech RoBERTa, a monolingual contextualized language representation model](https://doi.org/10.1007/978-3-030-83527-9_17). In: Text, Speech, and Dialogue. TSD 2021. Lecture Notes in Computer Science, vol 12848. Springer, Cham. -- Milan Straka, Jana Straková, Jan Hajič (2019): [Evaluating Contextualized Embeddings on 54 Languages in POS Tagging, Lemmatization and Dependency Parsing](https://arxiv.org/abs/1908.07448). In: ArXiv.org Computing Research Repository, ISSN 2331-8422, 1904.02099 -- Milan Straka (2018): [UDPipe 2.0 Prototype at CoNLL 2018 UD Shared Task](https://www.aclweb.org/anthology/K18-2020/). In: Proceedings of CoNLL 2018: The SIGNLL Conference on Computational Natural Language Learning, pp. 197-207, Association for Computational Linguistics, Stroudsburg, PA, USA, ISBN 978-1-948087-72-8 - -### Model Description - -The Universal Dependencies 2.10 models contain 123 models of 69 languages, each -consisting of a tokenizer, tagger, lemmatizer and dependency parser, all trained -using the UD data. We used the original train-dev-test split, but for treebanks -with only train and no dev data we used last 10% of the train data as dev data. -We produce models only for treebanks with at least 1000 training words. - -The tokenizer is trained using the `SpaceAfter=No` features. If the features -are not present in the data, they can be filled in using raw text in the -language in question. - -The tagger, lemmatizer and parser are trained using gold UD data. - -### Model Performance - -We present the tokenizer, tagger, lemmatizer and parser performance, measured on -the testing portion of the data, evaluated both on the raw text and using the -gold tokenization. The results are F1 scores measured by the `conll18_ud_eval.py` -script. - -| Model | Mode | Words | Sents | UPOS | XPOS | UFeats | AllTags | Lemma | UAS | LAS | MLAS | BLEX | -|:------------------------------------------|:------------------|-------:|-------:|------:|------:|-------:|--------:|-------:|------:|------:|------:|------:| -| afrikaans-afribooms-ud-2.10-220711 | Raw text | 99.78 | 98.59 | 98.58 | 95.46 | 98.13 | 95.33 | 97.43 | 90.10 | 87.23 | 78.64 | 78.59 | -| afrikaans-afribooms-ud-2.10-220711 | Gold tokenization | — | — | 98.77 | 95.62 | 98.31 | 95.50 | 97.53 | 90.72 | 87.80 | 79.23 | 78.99 | -| ancient_greek-perseus-ud-2.10-220711 | Raw text | 99.97 | 98.85 | 92.83 | 85.55 | 91.45 | 84.87 | 86.68 | 80.13 | 74.36 | 54.62 | 55.72 | -| ancient_greek-perseus-ud-2.10-220711 | Gold tokenization | — | — | 92.88 | 85.60 | 91.47 | 84.90 | 86.70 | 80.32 | 74.53 | 54.73 | 55.87 | -| ancient_greek-proiel-ud-2.10-220711 | Raw text | 100.00 | 48.02 | 97.77 | 98.05 | 92.35 | 91.05 | 94.71 | 79.82 | 76.06 | 60.08 | 65.75 | -| ancient_greek-proiel-ud-2.10-220711 | Gold tokenization | — | — | 97.87 | 98.14 | 92.49 | 91.26 | 94.73 | 86.05 | 82.14 | 67.03 | 71.90 | -| ancient_hebrew-ptnk-ud-2.10-220711 | Raw text | 68.76 | 98.06 | 56.80 | 56.94 | 55.13 | 50.80 | 49.88 | 38.73 | 34.67 | 18.47 | 17.69 | -| ancient_hebrew-ptnk-ud-2.10-220711 | Gold tokenization | — | — | 68.03 | 67.97 | 66.97 | 56.15 | 53.35 | 63.31 | 51.61 | 28.08 | 24.34 | -| arabic-padt-ud-2.10-220711 | Raw text | 94.58 | 82.09 | 91.72 | 89.01 | 89.14 | 88.69 | 90.41 | 78.63 | 74.54 | 65.84 | 67.88 | -| arabic-padt-ud-2.10-220711 | Gold tokenization | — | — | 97.02 | 94.38 | 94.53 | 94.08 | 95.31 | 88.11 | 83.49 | 74.57 | 76.13 | -| armenian-armtdp-ud-2.10-220711 | Raw text | 99.28 | 95.70 | 96.07 | — | 91.39 | 90.28 | 95.04 | 86.84 | 82.22 | 69.53 | 74.39 | -| armenian-armtdp-ud-2.10-220711 | Gold tokenization | — | — | 96.63 | — | 92.03 | 90.77 | 95.70 | 88.50 | 83.81 | 70.18 | 75.42 | -| armenian-bsut-ud-2.10-220711 | Raw text | 99.79 | 98.73 | 97.31 | — | 92.01 | 91.24 | 96.62 | 90.02 | 85.75 | 71.20 | 78.86 | -| armenian-bsut-ud-2.10-220711 | Gold tokenization | — | — | 97.53 | — | 92.24 | 91.48 | 96.82 | 90.56 | 86.29 | 71.73 | 79.32 | -| basque-bdt-ud-2.10-220711 | Raw text | 99.94 | 99.83 | 96.25 | — | 92.69 | 90.69 | 96.36 | 87.40 | 84.28 | 73.94 | 79.81 | -| basque-bdt-ud-2.10-220711 | Gold tokenization | — | — | 96.30 | — | 92.73 | 90.72 | 96.39 | 87.48 | 84.36 | 73.99 | 79.86 | -| belarusian-hse-ud-2.10-220711 | Raw text | 99.47 | 83.97 | 98.30 | 96.26 | 94.38 | 92.37 | 93.35 | 86.84 | 84.85 | 76.00 | 76.01 | -| belarusian-hse-ud-2.10-220711 | Gold tokenization | — | — | 98.81 | 96.74 | 94.94 | 92.87 | 93.86 | 89.55 | 87.38 | 78.23 | 78.11 | -| bulgarian-btb-ud-2.10-220711 | Raw text | 99.91 | 94.17 | 99.19 | 97.20 | 97.97 | 96.85 | 97.99 | 94.41 | 91.67 | 85.89 | 86.31 | -| bulgarian-btb-ud-2.10-220711 | Gold tokenization | — | — | 99.29 | 97.30 | 98.07 | 96.96 | 98.09 | 95.24 | 92.44 | 86.52 | 87.03 | -| catalan-ancora-ud-2.10-220711 | Raw text | 99.95 | 99.08 | 99.07 | 97.21 | 98.70 | 96.96 | 99.40 | 94.86 | 93.14 | 87.45 | 88.92 | -| catalan-ancora-ud-2.10-220711 | Gold tokenization | — | — | 99.14 | 97.32 | 98.78 | 97.07 | 99.46 | 95.02 | 93.30 | 87.69 | 89.13 | -| chinese-gsdsimp-ud-2.10-220711 | Raw text | 90.29 | 99.10 | 87.21 | 87.16 | 89.74 | 86.42 | 90.29 | 73.11 | 70.62 | 63.58 | 67.09 | -| chinese-gsdsimp-ud-2.10-220711 | Gold tokenization | — | — | 96.14 | 96.04 | 99.45 | 95.30 | 99.99 | 87.28 | 84.07 | 78.56 | 82.64 | -| chinese-gsd-ud-2.10-220711 | Raw text | 90.27 | 99.10 | 87.15 | 87.05 | 89.71 | 86.36 | 90.27 | 72.85 | 70.29 | 63.41 | 66.89 | -| chinese-gsd-ud-2.10-220711 | Gold tokenization | — | — | 96.21 | 96.08 | 99.40 | 95.34 | 99.99 | 87.15 | 83.96 | 78.41 | 82.59 | -| classical_chinese-kyoto-ud-2.10-220711 | Raw text | 97.26 | 40.71 | 87.40 | 86.48 | 89.97 | 83.27 | 96.78 | 67.56 | 62.17 | 58.02 | 60.60 | -| classical_chinese-kyoto-ud-2.10-220711 | Gold tokenization | — | — | 92.30 | 90.87 | 93.94 | 88.19 | 99.47 | 83.16 | 77.63 | 73.15 | 76.42 | -| coptic-scriptorium-ud-2.10-220711 | Raw text | 74.49 | 33.87 | 72.43 | 72.34 | 72.53 | 71.54 | 72.91 | 51.25 | 49.43 | 36.55 | 39.14 | -| coptic-scriptorium-ud-2.10-220711 | Gold tokenization | — | — | 96.94 | 96.78 | 97.49 | 95.50 | 97.02 | 90.48 | 87.70 | 76.04 | 79.57 | -| croatian-set-ud-2.10-220711 | Raw text | 99.93 | 94.79 | 98.48 | 95.72 | 96.23 | 95.49 | 97.60 | 92.17 | 89.27 | 81.53 | 84.23 | -| croatian-set-ud-2.10-220711 | Gold tokenization | — | — | 98.54 | 95.80 | 96.30 | 95.56 | 97.68 | 92.67 | 89.75 | 81.92 | 84.69 | -| czech-pdt-ud-2.10-220711 | Raw text | 99.94 | 93.74 | 99.37 | 98.40 | 98.33 | 98.02 | 99.21 | 94.90 | 93.50 | 90.28 | 91.88 | -| czech-pdt-ud-2.10-220711 | Gold tokenization | — | — | 99.45 | 98.47 | 98.40 | 98.09 | 99.28 | 95.63 | 94.23 | 90.88 | 92.50 | -| czech-cac-ud-2.10-220711 | Raw text | 99.99 | 99.68 | 99.72 | 98.57 | 98.37 | 98.12 | 99.18 | 96.12 | 94.76 | 91.09 | 92.67 | -| czech-cac-ud-2.10-220711 | Gold tokenization | — | — | 99.73 | 98.58 | 98.38 | 98.13 | 99.19 | 96.12 | 94.76 | 91.11 | 92.69 | -| czech-cltt-ud-2.10-220711 | Raw text | 99.71 | 97.79 | 99.22 | 95.32 | 95.23 | 95.03 | 99.18 | 90.77 | 89.24 | 81.35 | 86.22 | -| czech-cltt-ud-2.10-220711 | Gold tokenization | — | — | 99.47 | 95.47 | 95.40 | 95.18 | 99.47 | 91.20 | 89.68 | 81.67 | 86.73 | -| czech-fictree-ud-2.10-220711 | Raw text | 99.99 | 98.95 | 99.17 | 97.06 | 97.83 | 96.86 | 99.35 | 96.38 | 94.91 | 89.61 | 92.81 | -| czech-fictree-ud-2.10-220711 | Gold tokenization | — | — | 99.18 | 97.08 | 97.84 | 96.88 | 99.36 | 96.46 | 94.97 | 89.71 | 92.91 | -| danish-ddt-ud-2.10-220711 | Raw text | 99.81 | 89.78 | 97.95 | — | 97.29 | 96.54 | 97.26 | 88.27 | 86.25 | 79.22 | 80.96 | -| danish-ddt-ud-2.10-220711 | Gold tokenization | — | — | 98.16 | — | 97.53 | 96.79 | 97.45 | 89.46 | 87.42 | 80.42 | 82.17 | -| dutch-alpino-ud-2.10-220711 | Raw text | 99.83 | 88.98 | 97.86 | 96.79 | 97.80 | 96.29 | 95.11 | 92.95 | 90.58 | 83.15 | 79.88 | -| dutch-alpino-ud-2.10-220711 | Gold tokenization | — | — | 97.97 | 96.87 | 97.91 | 96.41 | 95.26 | 94.00 | 91.63 | 84.17 | 80.83 | -| dutch-lassysmall-ud-2.10-220711 | Raw text | 99.80 | 74.93 | 96.98 | 95.62 | 96.63 | 94.88 | 95.70 | 90.61 | 87.94 | 79.67 | 78.25 | -| dutch-lassysmall-ud-2.10-220711 | Gold tokenization | — | — | 97.25 | 96.43 | 97.36 | 95.83 | 95.97 | 94.51 | 91.66 | 84.48 | 83.08 | -| english-ewt-ud-2.10-220711 | Raw text | 98.95 | 87.02 | 96.39 | 96.13 | 96.53 | 94.80 | 97.13 | 90.07 | 88.10 | 81.47 | 83.42 | -| english-ewt-ud-2.10-220711 | Gold tokenization | — | — | 97.35 | 97.06 | 97.52 | 95.71 | 98.07 | 92.62 | 90.56 | 84.02 | 85.98 | -| english-atis-ud-2.10-220711 | Raw text | 100.00 | 81.96 | 98.97 | — | 98.54 | 98.13 | 99.94 | 94.39 | 92.92 | 87.85 | 90.39 | -| english-atis-ud-2.10-220711 | Gold tokenization | — | — | 98.97 | — | 98.56 | 98.15 | 99.94 | 95.88 | 94.26 | 89.80 | 92.40 | -| english-gum-ud-2.10-220711 | Raw text | 99.64 | 95.36 | 97.95 | 97.91 | 97.88 | 96.91 | 98.77 | 92.30 | 90.35 | 84.63 | 86.31 | -| english-gum-ud-2.10-220711 | Gold tokenization | — | — | 98.27 | 98.26 | 98.22 | 97.24 | 99.09 | 93.17 | 91.19 | 85.42 | 87.04 | -| english-lines-ud-2.10-220711 | Raw text | 99.92 | 87.45 | 97.71 | 96.77 | 97.02 | 94.41 | 98.40 | 91.17 | 88.22 | 80.27 | 83.45 | -| english-lines-ud-2.10-220711 | Gold tokenization | — | — | 97.79 | 96.84 | 97.07 | 94.48 | 98.47 | 92.10 | 89.17 | 81.05 | 84.36 | -| english-partut-ud-2.10-220711 | Raw text | 99.72 | 100.00 | 97.23 | 97.11 | 96.35 | 95.26 | 98.14 | 94.24 | 92.21 | 83.35 | 87.34 | -| english-partut-ud-2.10-220711 | Gold tokenization | — | — | 97.48 | 97.36 | 96.60 | 95.51 | 98.42 | 94.48 | 92.46 | 83.74 | 87.62 | -| estonian-edt-ud-2.10-220711 | Raw text | 99.95 | 92.03 | 97.68 | 98.31 | 96.28 | 95.07 | 95.36 | 88.81 | 86.16 | 79.92 | 79.56 | -| estonian-edt-ud-2.10-220711 | Gold tokenization | — | — | 97.81 | 98.36 | 96.36 | 95.19 | 95.43 | 89.71 | 87.03 | 80.77 | 80.37 | -| estonian-ewt-ud-2.10-220711 | Raw text | 98.82 | 75.26 | 95.41 | 96.29 | 94.06 | 91.92 | 93.86 | 82.62 | 79.30 | 71.40 | 72.35 | -| estonian-ewt-ud-2.10-220711 | Gold tokenization | — | — | 96.65 | 97.43 | 95.15 | 93.10 | 94.97 | 86.76 | 83.25 | 74.79 | 75.57 | -| faroese-farpahc-ud-2.10-220711 | Raw text | 99.74 | 92.77 | 97.44 | 93.04 | 94.43 | 92.50 | 99.74 | 85.76 | 82.13 | 68.07 | 75.34 | -| faroese-farpahc-ud-2.10-220711 | Gold tokenization | — | — | 97.64 | 93.28 | 94.68 | 92.72 | 100.00 | 86.82 | 83.10 | 69.17 | 76.51 | -| finnish-tdt-ud-2.10-220711 | Raw text | 99.70 | 90.82 | 97.58 | 98.18 | 95.99 | 95.10 | 92.14 | 90.20 | 88.18 | 82.19 | 78.16 | -| finnish-tdt-ud-2.10-220711 | Gold tokenization | — | — | 97.92 | 98.49 | 96.29 | 95.43 | 92.46 | 91.51 | 89.46 | 83.20 | 79.17 | -| finnish-ftb-ud-2.10-220711 | Raw text | 99.91 | 86.84 | 96.69 | 95.14 | 96.83 | 94.02 | 95.57 | 89.80 | 87.18 | 80.04 | 80.49 | -| finnish-ftb-ud-2.10-220711 | Gold tokenization | — | — | 97.00 | 95.36 | 96.92 | 94.32 | 95.67 | 91.91 | 89.23 | 82.55 | 82.84 | -| french-gsd-ud-2.10-220711 | Raw text | 98.78 | 94.69 | 97.26 | — | 97.35 | 96.63 | 97.55 | 92.76 | 90.82 | 84.55 | 86.32 | -| french-gsd-ud-2.10-220711 | Gold tokenization | — | — | 98.44 | — | 98.47 | 97.71 | 98.75 | 94.55 | 92.71 | 86.34 | 87.59 | -| french-parisstories-ud-2.10-220711 | Raw text | 99.49 | 87.87 | 96.24 | — | 94.41 | 92.17 | 97.55 | 79.95 | 74.84 | 61.23 | 68.35 | -| french-parisstories-ud-2.10-220711 | Gold tokenization | — | — | 96.81 | — | 94.90 | 92.68 | 97.98 | 81.67 | 76.50 | 62.46 | 69.32 | -| french-partut-ud-2.10-220711 | Raw text | 99.48 | 100.00 | 97.26 | 96.76 | 94.72 | 93.96 | 97.33 | 94.72 | 92.81 | 81.09 | 86.22 | -| french-partut-ud-2.10-220711 | Gold tokenization | — | — | 97.89 | 97.35 | 95.27 | 94.51 | 97.89 | 95.62 | 93.85 | 82.18 | 87.24 | -| french-rhapsodie-ud-2.10-220711 | Raw text | 99.22 | 99.47 | 97.20 | 97.45 | 96.12 | 93.30 | 98.26 | 88.71 | 84.99 | 75.15 | 79.88 | -| french-rhapsodie-ud-2.10-220711 | Gold tokenization | — | — | 98.00 | 98.13 | 96.89 | 93.97 | 98.99 | 89.89 | 86.08 | 75.91 | 80.35 | -| french-sequoia-ud-2.10-220711 | Raw text | 99.15 | 84.02 | 98.32 | — | 97.15 | 96.68 | 98.33 | 93.60 | 92.22 | 86.08 | 89.00 | -| french-sequoia-ud-2.10-220711 | Gold tokenization | — | — | 99.24 | — | 97.95 | 97.54 | 99.13 | 95.43 | 94.11 | 88.00 | 90.34 | -| galician-ctg-ud-2.10-220711 | Raw text | 99.22 | 97.22 | 97.28 | 97.05 | 99.06 | 96.70 | 98.04 | 85.59 | 83.20 | 72.11 | 76.94 | -| galician-ctg-ud-2.10-220711 | Gold tokenization | — | — | 98.01 | 97.78 | 99.84 | 97.41 | 98.79 | 87.31 | 84.80 | 74.04 | 78.88 | -| galician-treegal-ud-2.10-220711 | Raw text | 98.74 | 87.99 | 96.00 | 93.69 | 94.85 | 92.82 | 96.67 | 83.44 | 79.36 | 67.82 | 71.68 | -| galician-treegal-ud-2.10-220711 | Gold tokenization | — | — | 97.19 | 94.83 | 95.94 | 93.91 | 97.86 | 86.75 | 82.40 | 71.30 | 75.54 | -| german-hdt-ud-2.10-220711 | Raw text | 99.90 | 92.34 | 98.51 | 98.45 | 94.08 | 93.70 | 97.16 | 96.94 | 96.04 | 84.79 | 90.40 | -| german-hdt-ud-2.10-220711 | Gold tokenization | — | — | 98.62 | 98.58 | 94.22 | 93.83 | 97.26 | 97.63 | 96.75 | 85.46 | 91.08 | -| german-gsd-ud-2.10-220711 | Raw text | 99.81 | 81.12 | 95.78 | 97.68 | 90.23 | 87.27 | 96.75 | 87.32 | 83.12 | 63.79 | 75.00 | -| german-gsd-ud-2.10-220711 | Gold tokenization | — | — | 95.94 | 97.87 | 90.60 | 87.60 | 96.96 | 89.28 | 85.04 | 65.33 | 76.75 | -| gothic-proiel-ud-2.10-220711 | Raw text | 100.00 | 31.12 | 96.48 | 96.98 | 90.08 | 88.19 | 94.62 | 74.17 | 68.40 | 55.39 | 62.02 | -| gothic-proiel-ud-2.10-220711 | Gold tokenization | — | — | 96.97 | 97.42 | 90.90 | 89.33 | 94.71 | 84.11 | 78.29 | 65.73 | 71.10 | -| greek-gdt-ud-2.10-220711 | Raw text | 99.87 | 90.19 | 98.09 | 98.10 | 95.60 | 95.01 | 95.61 | 93.05 | 91.24 | 81.58 | 81.04 | -| greek-gdt-ud-2.10-220711 | Gold tokenization | — | — | 98.23 | 98.24 | 95.79 | 95.20 | 95.70 | 93.85 | 92.04 | 82.28 | 81.75 | -| hebrew-htb-ud-2.10-220711 | Raw text | 85.05 | 99.39 | 82.78 | 82.80 | 81.23 | 80.53 | 82.91 | 70.63 | 68.13 | 55.31 | 59.44 | -| hebrew-htb-ud-2.10-220711 | Gold tokenization | — | — | 97.44 | 97.42 | 95.73 | 95.05 | 97.34 | 92.71 | 90.24 | 78.77 | 81.80 | -| hebrew-iahltwiki-ud-2.10-220711 | Raw text | 88.54 | 97.16 | 85.97 | 86.00 | 80.55 | 79.47 | 87.15 | 76.16 | 74.19 | 56.91 | 66.92 | -| hebrew-iahltwiki-ud-2.10-220711 | Gold tokenization | — | — | 97.09 | 97.10 | 91.59 | 90.41 | 98.24 | 93.88 | 91.45 | 74.27 | 85.44 | -| hindi-hdtb-ud-2.10-220711 | Raw text | 100.00 | 98.90 | 97.57 | 97.12 | 94.16 | 92.23 | 98.92 | 95.30 | 92.32 | 79.20 | 87.66 | -| hindi-hdtb-ud-2.10-220711 | Gold tokenization | — | — | 97.58 | 97.14 | 94.18 | 92.26 | 98.92 | 95.42 | 92.44 | 79.35 | 87.81 | -| hungarian-szeged-ud-2.10-220711 | Raw text | 99.85 | 95.89 | 96.68 | — | 94.22 | 93.53 | 94.92 | 88.81 | 85.09 | 75.22 | 78.19 | -| hungarian-szeged-ud-2.10-220711 | Gold tokenization | — | — | 96.79 | — | 94.36 | 93.64 | 95.04 | 89.31 | 85.54 | 75.51 | 78.47 | -| icelandic-icepahc-ud-2.10-220711 | Raw text | 99.82 | 92.15 | 96.90 | 93.24 | 91.32 | 86.42 | 95.99 | 87.21 | 83.36 | 65.98 | 74.25 | -| icelandic-icepahc-ud-2.10-220711 | Gold tokenization | — | — | 97.08 | 93.45 | 91.47 | 86.61 | 96.15 | 87.78 | 83.87 | 66.49 | 74.84 | -| icelandic-modern-ud-2.10-220711 | Raw text | 99.92 | 99.22 | 99.07 | 98.14 | 98.38 | 97.88 | 98.91 | 94.41 | 93.17 | 89.31 | 90.07 | -| icelandic-modern-ud-2.10-220711 | Gold tokenization | — | — | 99.14 | 98.21 | 98.45 | 97.95 | 98.98 | 94.50 | 93.26 | 89.41 | 90.16 | -| indonesian-gsd-ud-2.10-220711 | Raw text | 99.48 | 92.90 | 94.23 | 93.81 | 95.53 | 88.78 | 98.13 | 87.65 | 81.59 | 72.35 | 77.02 | -| indonesian-gsd-ud-2.10-220711 | Gold tokenization | — | — | 94.66 | 94.26 | 95.99 | 89.17 | 98.53 | 88.57 | 82.42 | 73.21 | 77.88 | -| indonesian-csui-ud-2.10-220711 | Raw text | 99.45 | 91.01 | 96.05 | 96.14 | 96.85 | 95.43 | 98.23 | 86.38 | 82.10 | 76.54 | 78.80 | -| indonesian-csui-ud-2.10-220711 | Gold tokenization | — | — | 96.56 | 96.72 | 97.37 | 95.99 | 98.87 | 87.77 | 83.28 | 77.62 | 79.92 | -| irish-idt-ud-2.10-220711 | Raw text | 99.72 | 97.25 | 95.63 | 94.76 | 90.33 | 87.14 | 95.30 | 86.74 | 81.10 | 64.20 | 71.52 | -| irish-idt-ud-2.10-220711 | Gold tokenization | — | — | 95.89 | 95.07 | 90.60 | 87.46 | 95.54 | 87.28 | 81.64 | 64.56 | 71.85 | -| italian-isdt-ud-2.10-220711 | Raw text | 99.84 | 98.76 | 98.57 | 98.50 | 98.25 | 97.67 | 98.79 | 94.66 | 93.01 | 86.61 | 88.00 | -| italian-isdt-ud-2.10-220711 | Gold tokenization | — | — | 98.72 | 98.65 | 98.41 | 97.83 | 98.95 | 94.96 | 93.34 | 86.97 | 88.40 | -| italian-markit-ud-2.10-220711 | Raw text | 99.59 | 98.24 | 96.76 | 97.00 | 93.80 | 92.08 | 88.18 | 88.36 | 84.51 | 69.95 | 77.77 | -| italian-markit-ud-2.10-220711 | Gold tokenization | — | — | 97.15 | 97.40 | 94.10 | 92.35 | 88.54 | 89.13 | 85.26 | 70.51 | 78.46 | -| italian-partut-ud-2.10-220711 | Raw text | 99.73 | 100.00 | 98.43 | 98.43 | 98.35 | 97.61 | 98.68 | 96.21 | 94.18 | 87.87 | 89.09 | -| italian-partut-ud-2.10-220711 | Gold tokenization | — | — | 98.54 | 98.57 | 98.49 | 97.69 | 98.93 | 96.26 | 94.15 | 87.68 | 89.07 | -| italian-postwita-ud-2.10-220711 | Raw text | 99.40 | 28.11 | 96.43 | 96.18 | 96.30 | 94.79 | 96.72 | 80.61 | 76.89 | 65.29 | 66.90 | -| italian-postwita-ud-2.10-220711 | Gold tokenization | — | — | 97.04 | 96.82 | 96.80 | 95.29 | 97.31 | 88.34 | 84.19 | 75.32 | 77.32 | -| italian-twittiro-ud-2.10-220711 | Raw text | 99.14 | 39.36 | 95.92 | 95.92 | 95.07 | 93.46 | 94.50 | 82.23 | 77.79 | 64.50 | 65.42 | -| italian-twittiro-ud-2.10-220711 | Gold tokenization | — | — | 96.91 | 96.61 | 96.00 | 94.15 | 95.16 | 88.07 | 83.53 | 71.89 | 72.69 | -| italian-vit-ud-2.10-220711 | Raw text | 99.76 | 96.73 | 98.14 | 97.39 | 97.64 | 96.21 | 98.89 | 92.08 | 89.16 | 80.93 | 83.70 | -| italian-vit-ud-2.10-220711 | Gold tokenization | — | — | 98.36 | 97.71 | 97.85 | 96.53 | 99.10 | 92.88 | 89.97 | 81.91 | 84.63 | -| japanese-gsd-ud-2.10-220711 | Raw text | 96.17 | 100.00 | 94.93 | 94.18 | 96.16 | 93.81 | 95.05 | 87.68 | 86.85 | 80.43 | 80.78 | -| japanese-gsd-ud-2.10-220711 | Gold tokenization | — | — | 98.55 | 97.50 | 99.99 | 97.13 | 98.47 | 94.73 | 93.75 | 88.50 | 88.34 | -| japanese-gsdluw-ud-2.10-220711 | Raw text | 95.18 | 99.72 | 93.81 | 93.54 | 95.18 | 93.46 | 93.66 | 86.22 | 85.54 | 76.27 | 76.58 | -| japanese-gsdluw-ud-2.10-220711 | Gold tokenization | — | — | 98.36 | 98.05 | 100.00 | 97.93 | 97.89 | 95.23 | 94.18 | 86.38 | 85.19 | -| korean-kaist-ud-2.10-220711 | Raw text | 100.00 | 100.00 | 95.88 | 87.74 | — | 87.56 | 94.17 | 89.33 | 87.47 | 82.15 | 80.14 | -| korean-kaist-ud-2.10-220711 | Gold tokenization | — | — | 95.88 | 87.74 | — | 87.56 | 94.17 | 89.33 | 87.47 | 82.15 | 80.14 | -| korean-gsd-ud-2.10-220711 | Raw text | 99.87 | 93.93 | 96.57 | 90.27 | 99.67 | 88.02 | 93.57 | 88.54 | 84.91 | 80.73 | 77.23 | -| korean-gsd-ud-2.10-220711 | Gold tokenization | — | — | 96.73 | 90.43 | 99.80 | 88.20 | 93.69 | 89.27 | 85.61 | 81.45 | 77.93 | -| latin-ittb-ud-2.10-220711 | Raw text | 99.99 | 91.21 | 98.91 | 96.58 | 96.75 | 95.19 | 99.18 | 90.53 | 88.53 | 82.07 | 86.07 | -| latin-ittb-ud-2.10-220711 | Gold tokenization | — | — | 98.92 | 96.57 | 96.78 | 95.20 | 99.18 | 91.50 | 89.51 | 82.63 | 86.59 | -| latin-llct-ud-2.10-220711 | Raw text | 100.00 | 99.49 | 99.68 | 97.14 | 97.26 | 96.89 | 97.78 | 95.55 | 94.56 | 89.80 | 90.95 | -| latin-llct-ud-2.10-220711 | Gold tokenization | — | — | 99.68 | 97.15 | 97.27 | 96.90 | 97.78 | 95.55 | 94.57 | 89.81 | 90.97 | -| latin-perseus-ud-2.10-220711 | Raw text | 100.00 | 98.46 | 91.83 | 80.66 | 86.12 | 78.56 | 88.13 | 77.98 | 68.59 | 52.30 | 55.51 | -| latin-perseus-ud-2.10-220711 | Gold tokenization | — | — | 91.85 | 80.66 | 86.12 | 78.55 | 88.16 | 78.14 | 68.71 | 52.39 | 55.58 | -| latin-proiel-ud-2.10-220711 | Raw text | 99.87 | 36.81 | 96.69 | 96.87 | 90.56 | 89.54 | 96.21 | 74.07 | 69.56 | 56.74 | 63.93 | -| latin-proiel-ud-2.10-220711 | Gold tokenization | — | — | 97.12 | 97.32 | 91.19 | 90.27 | 96.44 | 83.20 | 78.50 | 66.34 | 73.00 | -| latin-udante-ud-2.10-220711 | Raw text | 99.61 | 98.81 | 90.58 | 75.59 | 81.31 | 71.62 | 87.25 | 75.26 | 67.81 | 43.95 | 50.36 | -| latin-udante-ud-2.10-220711 | Gold tokenization | — | — | 90.82 | 75.70 | 81.53 | 71.70 | 87.44 | 75.50 | 67.97 | 44.08 | 50.51 | -| latvian-lvtb-ud-2.10-220711 | Raw text | 99.31 | 97.83 | 96.51 | 89.83 | 93.86 | 89.08 | 95.92 | 88.75 | 85.79 | 76.04 | 80.25 | -| latvian-lvtb-ud-2.10-220711 | Gold tokenization | — | — | 97.14 | 90.43 | 94.50 | 89.67 | 96.55 | 89.84 | 86.82 | 77.09 | 81.31 | -| lithuanian-alksnis-ud-2.10-220711 | Raw text | 99.91 | 87.87 | 95.94 | 90.44 | 91.03 | 89.52 | 93.60 | 82.45 | 78.64 | 67.97 | 71.37 | -| lithuanian-alksnis-ud-2.10-220711 | Gold tokenization | — | — | 96.04 | 90.52 | 91.16 | 89.63 | 93.69 | 83.70 | 79.88 | 68.98 | 72.36 | -| lithuanian-hse-ud-2.10-220711 | Raw text | 97.30 | 97.30 | 89.28 | 90.21 | 83.13 | 78.38 | 88.16 | 70.27 | 61.79 | 45.67 | 54.04 | -| lithuanian-hse-ud-2.10-220711 | Gold tokenization | — | — | 91.23 | 92.36 | 85.19 | 80.09 | 90.57 | 73.96 | 64.53 | 47.54 | 56.10 | -| maltese-mudt-ud-2.10-220711 | Raw text | 99.84 | 86.29 | 95.80 | 95.79 | — | 95.35 | — | 84.96 | 80.07 | 68.98 | 72.86 | -| maltese-mudt-ud-2.10-220711 | Gold tokenization | — | — | 95.95 | 95.92 | — | 95.48 | — | 85.65 | 80.70 | 69.40 | 73.33 | -| marathi-ufal-ud-2.10-220711 | Raw text | 90.25 | 92.63 | 76.50 | — | 65.25 | 60.75 | 80.75 | 60.75 | 50.75 | 28.39 | 38.00 | -| marathi-ufal-ud-2.10-220711 | Gold tokenization | — | — | 82.52 | — | 67.96 | 62.86 | 80.83 | 68.93 | 58.50 | 29.46 | 38.17 | -| naija-nsc-ud-2.10-220711 | Raw text | 99.94 | 100.00 | 98.03 | — | 98.94 | 97.53 | 99.32 | 93.65 | 90.99 | 88.13 | 89.60 | -| naija-nsc-ud-2.10-220711 | Gold tokenization | — | — | 98.08 | — | 99.00 | 97.58 | 99.38 | 93.75 | 91.08 | 88.21 | 89.68 | -| north_sami-giella-ud-2.10-220711 | Raw text | 99.87 | 98.79 | 91.77 | 93.54 | 89.30 | 85.36 | 87.01 | 75.16 | 70.43 | 59.76 | 58.27 | -| north_sami-giella-ud-2.10-220711 | Gold tokenization | — | — | 91.91 | 93.67 | 89.45 | 85.52 | 87.13 | 75.47 | 70.76 | 60.05 | 58.56 | -| norwegian-bokmaal-ud-2.10-220711 | Raw text | 99.77 | 96.05 | 98.35 | — | 97.43 | 96.82 | 98.57 | 93.62 | 92.16 | 86.91 | 88.74 | -| norwegian-bokmaal-ud-2.10-220711 | Gold tokenization | — | — | 98.61 | — | 97.68 | 97.07 | 98.82 | 94.40 | 92.91 | 87.59 | 89.43 | -| norwegian-nynorsk-ud-2.10-220711 | Raw text | 99.93 | 94.17 | 98.24 | — | 97.34 | 96.55 | 98.40 | 93.89 | 92.18 | 86.03 | 88.36 | -| norwegian-nynorsk-ud-2.10-220711 | Gold tokenization | — | — | 98.41 | — | 97.50 | 96.73 | 98.53 | 94.63 | 92.93 | 86.93 | 89.20 | -| norwegian-nynorsklia-ud-2.10-220711 | Raw text | 99.91 | 99.53 | 96.61 | — | 95.71 | 93.75 | 98.05 | 81.18 | 76.61 | 66.01 | 69.68 | -| norwegian-nynorsklia-ud-2.10-220711 | Gold tokenization | — | — | 96.72 | — | 95.80 | 93.85 | 98.14 | 81.42 | 76.84 | 66.23 | 69.90 | -| old_church_slavonic-proiel-ud-2.10-220711 | Raw text | 100.00 | 41.43 | 96.72 | 96.90 | 90.37 | 89.19 | 93.13 | 77.71 | 73.92 | 63.82 | 68.87 | -| old_church_slavonic-proiel-ud-2.10-220711 | Gold tokenization | — | — | 97.08 | 97.28 | 91.06 | 89.93 | 93.14 | 88.30 | 84.18 | 74.01 | 77.39 | -| old_french-srcmf-ud-2.10-220711 | Raw text | 99.70 | 100.00 | 96.68 | 96.50 | 97.70 | 95.72 | 99.65 | 91.17 | 87.38 | 80.76 | 84.40 | -| old_french-srcmf-ud-2.10-220711 | Gold tokenization | — | — | 96.99 | 96.82 | 98.01 | 96.03 | 99.95 | 91.58 | 87.82 | 81.20 | 84.85 | -| old_russian-torot-ud-2.10-220711 | Raw text | 100.00 | 29.60 | 94.39 | 94.70 | 87.56 | 85.23 | 85.92 | 71.00 | 65.32 | 51.64 | 53.64 | -| old_russian-torot-ud-2.10-220711 | Gold tokenization | — | — | 95.06 | 95.29 | 88.50 | 86.60 | 85.96 | 83.30 | 77.24 | 64.09 | 62.94 | -| old_russian-rnc-ud-2.10-220711 | Raw text | 97.48 | 84.03 | 90.94 | 86.55 | 76.51 | 67.15 | 75.31 | 61.28 | 55.93 | 33.24 | 34.04 | -| old_russian-rnc-ud-2.10-220711 | Gold tokenization | — | — | 93.29 | 88.93 | 78.48 | 68.86 | 76.77 | 67.13 | 61.08 | 37.15 | 37.24 | -| old_east_slavic-birchbark-ud-2.10-220711 | Raw text | 99.98 | 16.73 | 89.24 | 99.35 | 76.11 | 72.43 | 65.88 | 63.41 | 56.50 | 32.53 | 27.14 | -| old_east_slavic-birchbark-ud-2.10-220711 | Gold tokenization | — | — | 89.37 | 99.37 | 76.54 | 72.82 | 66.05 | 76.31 | 69.00 | 41.63 | 33.60 | -| persian-perdt-ud-2.10-220711 | Raw text | 99.66 | 99.83 | 97.48 | 97.36 | 97.61 | 95.60 | 98.88 | 93.63 | 91.42 | 86.18 | 88.66 | -| persian-perdt-ud-2.10-220711 | Gold tokenization | — | — | 97.78 | 97.65 | 97.90 | 95.89 | 99.19 | 94.18 | 91.95 | 86.72 | 89.23 | -| persian-seraji-ud-2.10-220711 | Raw text | 99.65 | 98.75 | 97.91 | 97.94 | 97.95 | 97.48 | 96.52 | 91.68 | 88.84 | 84.21 | 82.83 | -| persian-seraji-ud-2.10-220711 | Gold tokenization | — | — | 98.24 | 98.28 | 98.28 | 97.78 | 96.80 | 92.36 | 89.48 | 84.82 | 83.40 | -| polish-pdb-ud-2.10-220711 | Raw text | 99.85 | 97.33 | 98.89 | 95.89 | 96.11 | 95.26 | 98.10 | 94.22 | 92.19 | 85.44 | 88.36 | -| polish-pdb-ud-2.10-220711 | Gold tokenization | — | — | 99.05 | 96.03 | 96.24 | 95.40 | 98.24 | 94.72 | 92.69 | 85.83 | 88.78 | -| polish-lfg-ud-2.10-220711 | Raw text | 99.85 | 99.65 | 99.00 | 96.08 | 96.57 | 95.16 | 98.24 | 96.86 | 95.51 | 89.80 | 92.34 | -| polish-lfg-ud-2.10-220711 | Gold tokenization | — | — | 99.17 | 96.25 | 96.74 | 95.33 | 98.38 | 97.25 | 95.89 | 90.19 | 92.66 | -| pomak-philotis-ud-2.10-220711 | Raw text | 99.98 | 94.49 | 98.86 | — | 95.62 | 95.30 | 96.67 | 88.24 | 83.26 | 71.19 | 74.14 | -| pomak-philotis-ud-2.10-220711 | Gold tokenization | — | — | 98.90 | — | 95.65 | 95.33 | 96.69 | 88.68 | 83.75 | 71.48 | 74.42 | -| portuguese-gsd-ud-2.10-220711 | Raw text | 99.87 | 97.28 | 98.51 | 98.51 | 99.74 | 98.41 | 99.27 | 94.50 | 93.41 | 88.76 | 89.96 | -| portuguese-gsd-ud-2.10-220711 | Gold tokenization | — | — | 98.65 | 98.64 | 99.89 | 98.55 | 99.40 | 94.90 | 93.81 | 89.23 | 90.36 | -| portuguese-bosque-ud-2.10-220711 | Raw text | 99.68 | 89.89 | 97.87 | — | 96.95 | 96.00 | 98.35 | 92.35 | 90.07 | 81.38 | 84.69 | -| portuguese-bosque-ud-2.10-220711 | Gold tokenization | — | — | 98.22 | — | 97.23 | 96.28 | 98.66 | 93.50 | 91.16 | 82.47 | 85.87 | -| romanian-nonstandard-ud-2.10-220711 | Raw text | 98.83 | 96.77 | 96.18 | 91.87 | 90.53 | 89.18 | 94.90 | 88.85 | 84.82 | 68.21 | 76.36 | -| romanian-nonstandard-ud-2.10-220711 | Gold tokenization | — | — | 97.30 | 92.86 | 91.49 | 90.10 | 95.99 | 90.57 | 86.50 | 69.69 | 77.68 | -| romanian-rrt-ud-2.10-220711 | Raw text | 99.71 | 95.16 | 97.90 | 97.21 | 97.40 | 96.98 | 97.96 | 91.97 | 88.44 | 81.66 | 83.13 | -| romanian-rrt-ud-2.10-220711 | Gold tokenization | — | — | 98.19 | 97.45 | 97.65 | 97.22 | 98.22 | 92.72 | 89.13 | 82.15 | 83.70 | -| romanian-simonero-ud-2.10-220711 | Raw text | 99.84 | 100.00 | 98.45 | 97.97 | 97.56 | 97.25 | 98.91 | 94.08 | 92.13 | 85.52 | 88.32 | -| romanian-simonero-ud-2.10-220711 | Gold tokenization | — | — | 98.61 | 98.12 | 97.70 | 97.40 | 99.07 | 94.42 | 92.45 | 85.81 | 88.62 | -| russian-syntagrus-ud-2.10-220711 | Raw text | 99.67 | 98.31 | 98.46 | — | 93.96 | 93.71 | 98.18 | 93.84 | 91.70 | 82.72 | 88.90 | -| russian-syntagrus-ud-2.10-220711 | Gold tokenization | — | — | 98.79 | — | 94.28 | 94.03 | 98.46 | 94.56 | 92.39 | 83.28 | 89.44 | -| russian-gsd-ud-2.10-220711 | Raw text | 99.50 | 96.49 | 98.11 | 97.55 | 94.71 | 93.61 | 97.01 | 91.44 | 88.55 | 81.04 | 84.62 | -| russian-gsd-ud-2.10-220711 | Gold tokenization | — | — | 98.58 | 97.98 | 95.17 | 94.01 | 97.43 | 92.67 | 89.69 | 82.00 | 85.65 | -| russian-taiga-ud-2.10-220711 | Raw text | 98.12 | 86.33 | 95.65 | — | 93.13 | 92.06 | 94.73 | 83.08 | 79.57 | 70.60 | 73.88 | -| russian-taiga-ud-2.10-220711 | Gold tokenization | — | — | 97.34 | — | 94.90 | 93.72 | 96.37 | 85.64 | 81.92 | 72.82 | 76.10 | -| sanskrit-vedic-ud-2.10-220711 | Raw text | 100.00 | 27.18 | 89.16 | — | 81.61 | 76.76 | 87.05 | 60.92 | 50.04 | 41.66 | 44.99 | -| sanskrit-vedic-ud-2.10-220711 | Gold tokenization | — | — | 89.97 | — | 83.02 | 78.34 | 87.34 | 73.74 | 62.01 | 52.00 | 55.41 | -| scottish_gaelic-arcosg-ud-2.10-220711 | Raw text | 97.47 | 60.89 | 93.78 | 89.29 | 90.91 | 88.21 | 95.08 | 81.24 | 75.60 | 62.73 | 69.22 | -| scottish_gaelic-arcosg-ud-2.10-220711 | Gold tokenization | — | — | 96.62 | 92.24 | 94.02 | 91.39 | 97.59 | 87.33 | 81.65 | 69.25 | 75.23 | -| serbian-set-ud-2.10-220711 | Raw text | 99.99 | 93.00 | 99.09 | 96.00 | 96.21 | 95.75 | 97.76 | 93.63 | 91.20 | 83.76 | 87.00 | -| serbian-set-ud-2.10-220711 | Gold tokenization | — | — | 99.13 | 96.01 | 96.20 | 95.75 | 97.78 | 94.26 | 91.80 | 84.32 | 87.60 | -| slovak-snk-ud-2.10-220711 | Raw text | 100.00 | 81.69 | 97.65 | 90.35 | 93.50 | 89.56 | 96.46 | 91.39 | 89.65 | 80.43 | 84.44 | -| slovak-snk-ud-2.10-220711 | Gold tokenization | — | — | 97.88 | 90.55 | 93.69 | 89.80 | 96.50 | 93.91 | 92.08 | 82.89 | 86.95 | -| slovenian-ssj-ud-2.10-220711 | Raw text | 99.94 | 98.95 | 98.97 | 96.97 | 97.15 | 96.63 | 98.58 | 93.99 | 92.60 | 86.83 | 88.91 | -| slovenian-ssj-ud-2.10-220711 | Gold tokenization | — | — | 99.03 | 97.02 | 97.23 | 96.69 | 98.63 | 94.15 | 92.76 | 86.99 | 89.02 | -| slovenian-sst-ud-2.10-220711 | Raw text | 99.85 | 23.14 | 94.82 | 92.71 | 92.43 | 89.84 | 97.38 | 65.69 | 60.84 | 50.88 | 54.78 | -| slovenian-sst-ud-2.10-220711 | Gold tokenization | — | — | 95.62 | 93.09 | 92.84 | 90.89 | 97.56 | 78.39 | 73.07 | 63.39 | 68.33 | -| spanish-ancora-ud-2.10-220711 | Raw text | 99.95 | 98.78 | 99.06 | 96.02 | 98.74 | 95.59 | 99.37 | 93.70 | 91.79 | 86.41 | 87.88 | -| spanish-ancora-ud-2.10-220711 | Gold tokenization | — | — | 99.11 | 96.07 | 98.79 | 95.63 | 99.42 | 93.88 | 91.97 | 86.59 | 88.04 | -| spanish-gsd-ud-2.10-220711 | Raw text | 99.75 | 95.62 | 97.15 | — | 96.94 | 95.27 | 98.72 | 91.87 | 89.57 | 78.63 | 84.25 | -| spanish-gsd-ud-2.10-220711 | Gold tokenization | — | — | 97.39 | — | 97.19 | 95.53 | 98.97 | 92.66 | 90.32 | 79.43 | 85.04 | -| swedish-talbanken-ud-2.10-220711 | Raw text | 99.84 | 96.53 | 98.44 | 97.33 | 97.32 | 96.51 | 98.15 | 92.23 | 89.85 | 83.92 | 85.97 | -| swedish-talbanken-ud-2.10-220711 | Gold tokenization | — | — | 98.61 | 97.52 | 97.51 | 96.72 | 98.32 | 92.68 | 90.30 | 84.48 | 86.54 | -| swedish-lines-ud-2.10-220711 | Raw text | 99.96 | 88.00 | 97.66 | 95.51 | 90.84 | 88.14 | 97.72 | 90.60 | 87.38 | 71.82 | 82.17 | -| swedish-lines-ud-2.10-220711 | Gold tokenization | — | — | 97.73 | 95.52 | 90.87 | 88.15 | 97.76 | 91.44 | 88.19 | 72.50 | 82.95 | -| tamil-ttb-ud-2.10-220711 | Raw text | 94.26 | 97.52 | 84.29 | 83.18 | 84.64 | 78.22 | 89.45 | 70.43 | 61.88 | 50.61 | 55.39 | -| tamil-ttb-ud-2.10-220711 | Gold tokenization | — | — | 89.29 | 87.78 | 89.99 | 82.70 | 94.42 | 78.13 | 68.78 | 56.87 | 61.48 | -| telugu-mtg-ud-2.10-220711 | Raw text | 99.58 | 96.62 | 93.63 | 93.63 | 98.61 | 93.49 | — | 90.72 | 84.63 | 77.14 | 81.14 | -| telugu-mtg-ud-2.10-220711 | Gold tokenization | — | — | 94.04 | 94.04 | 99.03 | 93.90 | — | 91.68 | 85.58 | 77.98 | 81.98 | -| turkish-boun-ud-2.10-220711 | Raw text | 98.83 | 86.93 | 91.56 | 92.51 | 91.72 | 86.56 | 93.23 | 78.48 | 72.40 | 59.77 | 65.11 | -| turkish-boun-ud-2.10-220711 | Gold tokenization | — | — | 92.53 | 93.47 | 92.67 | 87.31 | 94.26 | 81.07 | 74.73 | 61.33 | 66.92 | -| turkish-atis-ud-2.10-220711 | Raw text | 100.00 | 80.20 | 98.96 | — | 98.46 | 98.25 | 99.15 | 89.22 | 87.49 | 85.12 | 86.08 | -| turkish-atis-ud-2.10-220711 | Gold tokenization | — | — | 99.02 | — | 98.52 | 98.32 | 99.13 | 91.11 | 89.30 | 86.98 | 87.93 | -| turkish-framenet-ud-2.10-220711 | Raw text | 100.00 | 100.00 | 96.86 | — | 94.89 | 94.21 | 96.66 | 93.39 | 84.25 | 73.98 | 77.64 | -| turkish-framenet-ud-2.10-220711 | Gold tokenization | — | — | 96.86 | — | 94.89 | 94.21 | 96.66 | 93.39 | 84.25 | 73.98 | 77.64 | -| turkish-imst-ud-2.10-220711 | Raw text | 98.30 | 96.97 | 94.38 | 93.98 | 90.92 | 88.60 | 94.54 | 74.73 | 69.04 | 58.25 | 63.10 | -| turkish-imst-ud-2.10-220711 | Gold tokenization | — | — | 95.94 | 95.49 | 92.40 | 89.97 | 96.13 | 78.07 | 72.09 | 60.26 | 65.33 | -| turkish-kenet-ud-2.10-220711 | Raw text | 100.00 | 98.12 | 93.71 | — | 92.05 | 90.86 | 93.33 | 83.91 | 71.18 | 61.81 | 64.77 | -| turkish-kenet-ud-2.10-220711 | Gold tokenization | — | — | 93.72 | — | 92.06 | 90.87 | 93.33 | 84.07 | 71.29 | 61.92 | 64.89 | -| turkish-penn-ud-2.10-220711 | Raw text | 99.34 | 80.59 | 95.60 | — | 94.41 | 93.33 | 94.36 | 84.22 | 71.67 | 62.21 | 64.53 | -| turkish-penn-ud-2.10-220711 | Gold tokenization | — | — | 96.30 | — | 95.11 | 94.02 | 95.01 | 86.76 | 73.91 | 63.63 | 66.02 | -| turkish-tourism-ud-2.10-220711 | Raw text | 99.96 | 99.86 | 98.80 | — | 95.08 | 94.67 | 98.36 | 97.20 | 91.52 | 81.98 | 87.38 | -| turkish-tourism-ud-2.10-220711 | Gold tokenization | — | — | 98.85 | — | 95.12 | 94.73 | 98.40 | 97.25 | 91.58 | 82.04 | 87.45 | -| turkish_german-sagt-ud-2.10-220711 | Raw text | 98.91 | 99.44 | 90.21 | — | 80.32 | 75.60 | 90.82 | 71.14 | 60.98 | 41.12 | 51.00 | -| turkish_german-sagt-ud-2.10-220711 | Gold tokenization | — | — | 91.09 | — | 80.89 | 76.08 | 91.52 | 72.69 | 62.06 | 41.64 | 51.71 | -| ukrainian-iu-ud-2.10-220711 | Raw text | 99.81 | 96.61 | 97.90 | 94.35 | 94.18 | 93.12 | 97.34 | 90.61 | 88.27 | 78.92 | 83.01 | -| ukrainian-iu-ud-2.10-220711 | Gold tokenization | — | — | 98.08 | 94.54 | 94.34 | 93.29 | 97.53 | 91.12 | 88.72 | 79.21 | 83.36 | -| urdu-udtb-ud-2.10-220711 | Raw text | 100.00 | 98.31 | 93.91 | 92.15 | 82.83 | 78.40 | 97.41 | 88.15 | 82.49 | 56.62 | 74.68 | -| urdu-udtb-ud-2.10-220711 | Gold tokenization | — | — | 93.93 | 92.17 | 82.86 | 78.43 | 97.41 | 88.23 | 82.58 | 56.67 | 74.77 | -| uyghur-udt-ud-2.10-220711 | Raw text | 99.54 | 81.81 | 89.33 | 91.75 | 88.12 | 79.98 | 94.67 | 76.66 | 64.87 | 46.84 | 55.29 | -| uyghur-udt-ud-2.10-220711 | Gold tokenization | — | — | 89.71 | 92.30 | 88.59 | 80.50 | 95.14 | 78.38 | 66.49 | 47.83 | 56.56 | -| vietnamese-vtb-ud-2.10-220711 | Raw text | 85.37 | 93.46 | 78.21 | 76.76 | 85.12 | 76.57 | 85.16 | 52.68 | 47.84 | 41.55 | 44.29 | -| vietnamese-vtb-ud-2.10-220711 | Gold tokenization | — | — | 90.36 | 88.55 | 99.72 | 88.32 | 99.59 | 72.88 | 65.41 | 58.76 | 62.51 | -| welsh-ccg-ud-2.10-220711 | Raw text | 99.42 | 97.37 | 95.33 | 94.40 | 89.82 | 87.61 | 93.93 | 86.61 | 80.67 | 63.31 | 69.02 | -| welsh-ccg-ud-2.10-220711 | Gold tokenization | — | — | 95.84 | 94.87 | 90.31 | 88.07 | 94.44 | 87.85 | 81.83 | 64.36 | 70.21 | -| western_armenian-armtdp-ud-2.10-220711 | Raw text | 99.89 | 98.68 | 96.82 | — | 92.51 | 91.83 | 97.14 | 89.39 | 84.66 | 69.84 | 76.01 | -| western_armenian-armtdp-ud-2.10-220711 | Gold tokenization | — | — | 96.90 | — | 92.60 | 91.93 | 97.22 | 89.64 | 84.89 | 70.07 | 76.23 | -| wolof-wtb-ud-2.10-220711 | Raw text | 99.23 | 91.95 | 94.20 | 94.15 | 93.50 | 91.41 | 95.20 | 84.15 | 78.69 | 66.75 | 70.23 | -| wolof-wtb-ud-2.10-220711 | Gold tokenization | — | — | 95.17 | 95.07 | 94.32 | 92.31 | 95.96 | 86.27 | 80.75 | 68.70 | 72.06 | diff --git a/udpipe2/docs/models_ud212.md b/udpipe2/docs/models_ud212.md deleted file mode 100644 index 8ffba2e9c78600b7c6079ee0100647d85548bf3b..0000000000000000000000000000000000000000 --- a/udpipe2/docs/models_ud212.md +++ /dev/null @@ -1,324 +0,0 @@ -## Universal Dependencies 2.12 Models #universal_dependencies_212_models - -Universal Dependencies 2.12 Models are distributed under the -[CC BY-NC-SA](https://creativecommons.org/licenses/by-nc-sa/4.0/) licence. -The models are based solely on [Universal Dependencies -2.12](http://hdl.handle.net/11234/1-5150) treebanks, and additionally -use [multilingual BERT](https://github.com/google-research/bert/blob/master/multilingual.md) -and [RobeCzech](https://huggingface.co/ufal/robeczech-base). - -The models require [UDPipe 2](https://ufal.mff.cuni.cz/udpipe/2). - -### Download - -The latest version 230717 of the Universal Dependencies 2.12 models -can be downloaded from [LINDAT/CLARIN repository](https://hdl.handle.net/11234/1-5200). - -The models are also available in the [REST service](https://lindat.mff.cuni.cz/services/udpipe/). - -### Acknowledgements #universal_dependencies_212_models_acknowledgements - -This work has been supported by the Ministry of Education, Youth and Sports of -the Czech Republic, Project No. LM2018101 LINDAT/CLARIAH-CZ. - -The models were trained on [Universal Dependencies 2.12](https://hdl.handle.net/11234/1-5150) treebanks. - -For the UD treebanks which do not contain original plain text version, -raw text is used to train the tokenizer instead. The plain texts -were taken from the [W2C -- Web to Corpus](https://hdl.handle.net/11858/00-097C-0000-0022-6133-9). - -Finally, [multilingual BERT](https://github.com/google-research/bert/blob/master/multilingual.md) -and [RobeCzech](https://huggingface.co/ufal/robeczech-base) are used to provide -contextualized word embeddings. - -### Publications - -- Milan Straka, Jakub Náplava, Jana Straková, David Samuel (2020): [RobeCzech: Czech RoBERTa, a monolingual contextualized language representation model](https://doi.org/10.1007/978-3-030-83527-9_17). In: Text, Speech, and Dialogue. TSD 2021. Lecture Notes in Computer Science, vol 12848. Springer, Cham. -- Milan Straka, Jana Straková, Jan Hajič (2019): [Evaluating Contextualized Embeddings on 54 Languages in POS Tagging, Lemmatization and Dependency Parsing](https://arxiv.org/abs/1908.07448). In: ArXiv.org Computing Research Repository, ISSN 2331-8422, 1904.02099 -- Milan Straka (2018): [UDPipe 2.0 Prototype at CoNLL 2018 UD Shared Task](https://www.aclweb.org/anthology/K18-2020/). In: Proceedings of CoNLL 2018: The SIGNLL Conference on Computational Natural Language Learning, pp. 197-207, Association for Computational Linguistics, Stroudsburg, PA, USA, ISBN 978-1-948087-72-8 - -### Model Description - -The Universal Dependencies 2.12 models contain 131 models of 72 languages, each -consisting of a tokenizer, tagger, lemmatizer and dependency parser, all trained -using the UD data. We used the original train-dev-test split, but for treebanks -with only train and no dev data we used last 10% of the train data as dev data. -We produce models only for treebanks with at least 1000 training words. - -The tokenizer is trained using the `SpaceAfter=No` features. If the features -are not present in the data, they can be filled in using raw text in the -language in question. - -The tagger, lemmatizer and parser are trained using gold UD data. - -### Model Performance - -We present the tokenizer, tagger, lemmatizer and parser performance, measured on -the testing portion of the data, evaluated both on the raw text and using the -gold tokenization. The results are F1 scores measured by the `conll18_ud_eval.py` -script. - -| Model | Mode | Words | Sents | UPOS | XPOS | UFeats | AllTags | Lemma | UAS | LAS | MLAS | BLEX | -|:----------------------------------------------|:------------------|-------:|-------:|------:|------:|-------:|--------:|-------:|------:|------:|------:|------:| -| afrikaans-afribooms-ud-2.12-230717 | Raw text | 99.94 | 99.65 | 98.65 | 95.80 | 98.35 | 95.62 | 98.29 | 90.41 | 87.46 | 78.92 | 79.72 | -| afrikaans-afribooms-ud-2.12-230717 | Gold tokenization | — | — | 98.71 | 95.85 | 98.41 | 95.67 | 98.33 | 90.58 | 87.63 | 79.08 | 79.85 | -| ancient_greek-proiel-ud-2.12-230717 | Raw text | 99.98 | 49.19 | 97.63 | 97.93 | 92.13 | 90.72 | 94.76 | 81.74 | 77.92 | 61.96 | 66.22 | -| ancient_greek-proiel-ud-2.12-230717 | Gold tokenization | — | — | 97.87 | 98.14 | 92.58 | 91.30 | 94.82 | 86.59 | 82.72 | 68.31 | 72.13 | -| ancient_greek-perseus-ud-2.12-230717 | Raw text | 99.97 | 98.85 | 92.98 | 85.76 | 91.42 | 84.89 | 86.72 | 80.13 | 74.55 | 54.46 | 55.76 | -| ancient_greek-perseus-ud-2.12-230717 | Gold tokenization | — | — | 93.02 | 85.78 | 91.44 | 84.91 | 86.74 | 80.30 | 74.71 | 54.56 | 55.87 | -| ancient_hebrew-ptnk-ud-2.12-230717 | Raw text | 71.14 | 98.06 | 69.03 | 69.17 | 67.09 | 66.13 | 67.47 | 48.21 | 46.75 | 31.14 | 32.42 | -| ancient_hebrew-ptnk-ud-2.12-230717 | Gold tokenization | — | — | 96.70 | 96.86 | 90.36 | 89.22 | 96.60 | 91.29 | 87.61 | 68.26 | 76.48 | -| arabic-padt-ud-2.12-230717 | Raw text | 94.58 | 82.09 | 91.67 | 89.00 | 89.12 | 88.65 | 90.34 | 78.87 | 74.83 | 66.04 | 68.16 | -| arabic-padt-ud-2.12-230717 | Gold tokenization | — | — | 96.96 | 94.34 | 94.50 | 94.01 | 95.20 | 88.24 | 83.65 | 74.73 | 76.32 | -| armenian-armtdp-ud-2.12-230717 | Raw text | 99.28 | 95.70 | 96.29 | — | 91.22 | 90.21 | 94.93 | 86.55 | 81.73 | 68.65 | 73.67 | -| armenian-armtdp-ud-2.12-230717 | Gold tokenization | — | — | 96.86 | — | 91.86 | 90.71 | 95.54 | 88.21 | 83.30 | 69.30 | 74.60 | -| armenian-bsut-ud-2.12-230717 | Raw text | 99.79 | 98.73 | 97.38 | — | 91.96 | 91.20 | 96.79 | 90.06 | 85.83 | 71.10 | 79.05 | -| armenian-bsut-ud-2.12-230717 | Gold tokenization | — | — | 97.60 | — | 92.16 | 91.41 | 96.99 | 90.62 | 86.38 | 71.65 | 79.57 | -| basque-bdt-ud-2.12-230717 | Raw text | 99.94 | 99.83 | 96.33 | — | 93.32 | 91.44 | 96.38 | 87.81 | 84.80 | 74.93 | 79.49 | -| basque-bdt-ud-2.12-230717 | Gold tokenization | — | — | 96.39 | — | 93.37 | 91.48 | 96.41 | 87.88 | 84.86 | 74.97 | 79.52 | -| belarusian-hse-ud-2.12-230717 | Raw text | 99.37 | 86.58 | 98.21 | 97.59 | 94.52 | 93.57 | 93.25 | 87.32 | 85.41 | 76.98 | 76.66 | -| belarusian-hse-ud-2.12-230717 | Gold tokenization | — | — | 98.81 | 98.16 | 95.10 | 94.13 | 93.82 | 89.79 | 87.60 | 78.81 | 78.42 | -| bulgarian-btb-ud-2.12-230717 | Raw text | 99.91 | 94.17 | 99.20 | 97.25 | 97.96 | 96.87 | 98.01 | 94.41 | 91.71 | 85.92 | 86.61 | -| bulgarian-btb-ud-2.12-230717 | Gold tokenization | — | — | 99.33 | 97.37 | 98.09 | 96.99 | 98.09 | 95.22 | 92.48 | 86.61 | 87.35 | -| catalan-ancora-ud-2.12-230717 | Raw text | 99.95 | 99.08 | 99.11 | 97.25 | 98.72 | 96.98 | 99.40 | 94.92 | 93.43 | 88.06 | 89.51 | -| catalan-ancora-ud-2.12-230717 | Gold tokenization | — | — | 99.17 | 97.36 | 98.79 | 97.09 | 99.46 | 95.08 | 93.59 | 88.27 | 89.70 | -| chinese-gsdsimp-ud-2.12-230717 | Raw text | 90.29 | 99.10 | 87.23 | 87.15 | 89.70 | 86.47 | 90.23 | 72.74 | 70.28 | 63.38 | 66.93 | -| chinese-gsdsimp-ud-2.12-230717 | Gold tokenization | — | — | 96.10 | 96.00 | 99.43 | 95.33 | 99.93 | 87.08 | 83.97 | 78.23 | 82.55 | -| chinese-gsd-ud-2.12-230717 | Raw text | 90.27 | 99.10 | 87.18 | 87.09 | 89.68 | 86.42 | 90.20 | 72.57 | 70.14 | 63.02 | 66.73 | -| chinese-gsd-ud-2.12-230717 | Gold tokenization | — | — | 96.16 | 96.05 | 99.41 | 95.38 | 99.92 | 87.18 | 84.07 | 78.07 | 82.55 | -| classical_chinese-kyoto-ud-2.12-230717 | Raw text | 97.94 | 46.37 | 89.68 | 88.94 | 91.59 | 86.06 | 97.51 | 71.39 | 66.13 | 62.36 | 64.70 | -| classical_chinese-kyoto-ud-2.12-230717 | Gold tokenization | — | — | 93.43 | 92.14 | 94.63 | 89.89 | 99.52 | 84.71 | 79.40 | 75.55 | 78.24 | -| coptic-scriptorium-ud-2.12-230717 | Raw text | 75.26 | 33.63 | 73.02 | 72.92 | 73.15 | 72.08 | 73.90 | 51.82 | 50.05 | 37.34 | 39.84 | -| coptic-scriptorium-ud-2.12-230717 | Gold tokenization | — | — | 97.05 | 96.91 | 97.81 | 95.90 | 97.41 | 90.46 | 87.70 | 76.41 | 79.79 | -| croatian-set-ud-2.12-230717 | Raw text | 99.93 | 94.79 | 98.49 | 95.82 | 96.31 | 95.53 | 97.78 | 92.41 | 89.57 | 81.89 | 84.77 | -| croatian-set-ud-2.12-230717 | Gold tokenization | — | — | 98.57 | 95.90 | 96.39 | 95.62 | 97.83 | 92.91 | 90.07 | 82.34 | 85.21 | -| czech-pdt-ud-2.12-230717 | Raw text | 99.93 | 93.37 | 99.30 | 98.46 | 98.78 | 98.25 | 99.38 | 95.01 | 93.64 | 90.75 | 92.30 | -| czech-pdt-ud-2.12-230717 | Gold tokenization | — | — | 99.38 | 98.55 | 98.87 | 98.34 | 99.46 | 95.81 | 94.43 | 91.41 | 92.97 | -| czech-cac-ud-2.12-230717 | Raw text | 99.99 | 99.68 | 99.69 | 98.25 | 98.07 | 97.74 | 99.30 | 96.20 | 94.95 | 90.84 | 93.11 | -| czech-cac-ud-2.12-230717 | Gold tokenization | — | — | 99.70 | 98.26 | 98.08 | 97.74 | 99.31 | 96.20 | 94.95 | 90.86 | 93.13 | -| czech-cltt-ud-2.12-230717 | Raw text | 99.32 | 96.92 | 98.95 | 93.72 | 93.99 | 93.65 | 98.48 | 92.47 | 90.77 | 82.87 | 88.60 | -| czech-cltt-ud-2.12-230717 | Gold tokenization | — | — | 99.40 | 94.18 | 94.49 | 94.07 | 99.03 | 93.17 | 91.20 | 82.92 | 88.86 | -| czech-fictree-ud-2.12-230717 | Raw text | 99.99 | 98.95 | 99.14 | 97.07 | 97.93 | 96.88 | 99.29 | 96.17 | 94.67 | 89.47 | 92.40 | -| czech-fictree-ud-2.12-230717 | Gold tokenization | — | — | 99.16 | 97.08 | 97.95 | 96.89 | 99.30 | 96.24 | 94.73 | 89.57 | 92.50 | -| danish-ddt-ud-2.12-230717 | Raw text | 99.82 | 89.80 | 97.98 | — | 97.36 | 96.60 | 97.38 | 88.60 | 86.46 | 79.10 | 81.13 | -| danish-ddt-ud-2.12-230717 | Gold tokenization | — | — | 98.17 | — | 97.59 | 96.83 | 97.52 | 89.82 | 87.64 | 80.18 | 82.15 | -| dutch-alpino-ud-2.12-230717 | Raw text | 99.75 | 89.10 | 97.77 | 96.66 | 97.68 | 96.28 | 94.91 | 92.90 | 90.52 | 83.71 | 80.13 | -| dutch-alpino-ud-2.12-230717 | Gold tokenization | — | — | 98.02 | 96.82 | 97.89 | 96.45 | 95.16 | 94.31 | 91.90 | 85.01 | 81.32 | -| dutch-lassysmall-ud-2.12-230717 | Raw text | 99.77 | 74.71 | 97.33 | 96.15 | 96.86 | 95.50 | 96.18 | 90.99 | 88.49 | 80.77 | 79.51 | -| dutch-lassysmall-ud-2.12-230717 | Gold tokenization | — | — | 97.67 | 96.84 | 97.49 | 96.29 | 96.51 | 94.63 | 91.91 | 85.25 | 84.07 | -| english-ewt-ud-2.12-230717 | Raw text | 99.09 | 87.82 | 96.69 | 96.32 | 96.70 | 95.01 | 97.26 | 90.71 | 88.81 | 82.26 | 84.29 | -| english-ewt-ud-2.12-230717 | Gold tokenization | — | — | 97.50 | 97.17 | 97.56 | 95.84 | 98.08 | 93.03 | 91.04 | 84.70 | 86.64 | -| english-atis-ud-2.12-230717 | Raw text | 99.98 | 80.49 | 99.04 | — | 98.59 | 98.14 | 99.63 | 94.45 | 93.02 | 87.97 | 90.18 | -| english-atis-ud-2.12-230717 | Gold tokenization | — | — | 99.07 | — | 98.65 | 98.21 | 99.64 | 96.17 | 94.59 | 90.22 | 92.42 | -| english-eslspok-ud-2.12-230717 | Raw text | 100.00 | 92.21 | 98.59 | 98.54 | — | 98.10 | — | 95.37 | 93.87 | 91.26 | 93.01 | -| english-eslspok-ud-2.12-230717 | Gold tokenization | — | — | 98.68 | 98.68 | — | 98.19 | — | 96.20 | 94.79 | 91.98 | 93.74 | -| english-gum-ud-2.12-230717 | Raw text | 99.63 | 95.72 | 98.00 | 98.04 | 97.91 | 97.02 | 98.84 | 92.87 | 90.96 | 85.35 | 87.10 | -| english-gum-ud-2.12-230717 | Gold tokenization | — | — | 98.34 | 98.39 | 98.27 | 97.36 | 99.15 | 93.72 | 91.79 | 86.11 | 87.79 | -| english-lines-ud-2.12-230717 | Raw text | 99.92 | 87.45 | 97.68 | 96.86 | 97.03 | 94.48 | 98.33 | 91.32 | 88.55 | 80.61 | 83.70 | -| english-lines-ud-2.12-230717 | Gold tokenization | — | — | 97.78 | 96.95 | 97.13 | 94.58 | 98.39 | 92.18 | 89.38 | 81.36 | 84.51 | -| english-partut-ud-2.12-230717 | Raw text | 99.72 | 100.00 | 97.43 | 97.29 | 96.44 | 95.41 | 98.17 | 94.24 | 92.33 | 83.28 | 87.38 | -| english-partut-ud-2.12-230717 | Gold tokenization | — | — | 97.68 | 97.54 | 96.68 | 95.69 | 98.44 | 94.42 | 92.52 | 83.71 | 87.64 | -| erzya-jr-ud-2.12-230717 | Raw text | 99.18 | 94.15 | 87.94 | 87.38 | 78.90 | 73.49 | 84.89 | 72.92 | 63.24 | 41.32 | 48.34 | -| erzya-jr-ud-2.12-230717 | Gold tokenization | — | — | 88.66 | 88.07 | 79.54 | 74.02 | 85.52 | 74.08 | 64.24 | 41.90 | 48.91 | -| estonian-edt-ud-2.12-230717 | Raw text | 99.94 | 92.23 | 97.67 | 98.21 | 96.42 | 95.26 | 95.45 | 88.56 | 85.96 | 80.03 | 79.59 | -| estonian-edt-ud-2.12-230717 | Gold tokenization | — | — | 97.79 | 98.26 | 96.50 | 95.38 | 95.52 | 89.43 | 86.81 | 80.81 | 80.32 | -| estonian-ewt-ud-2.12-230717 | Raw text | 98.63 | 78.03 | 94.88 | 96.14 | 94.10 | 91.81 | 93.75 | 83.23 | 80.01 | 72.15 | 73.37 | -| estonian-ewt-ud-2.12-230717 | Gold tokenization | — | — | 96.22 | 97.46 | 95.35 | 93.07 | 95.00 | 87.27 | 83.71 | 74.95 | 76.20 | -| faroese-farpahc-ud-2.12-230717 | Raw text | 99.74 | 92.77 | 97.45 | 93.00 | 94.24 | 92.32 | 99.74 | 86.01 | 82.29 | 68.12 | 75.32 | -| faroese-farpahc-ud-2.12-230717 | Gold tokenization | — | — | 97.67 | 93.17 | 94.49 | 92.53 | 100.00 | 86.96 | 83.20 | 69.20 | 76.51 | -| finnish-tdt-ud-2.12-230717 | Raw text | 99.70 | 90.82 | 97.67 | 98.31 | 96.08 | 95.23 | 92.11 | 90.42 | 88.48 | 82.43 | 78.28 | -| finnish-tdt-ud-2.12-230717 | Gold tokenization | — | — | 98.00 | 98.59 | 96.39 | 95.54 | 92.39 | 91.72 | 89.75 | 83.44 | 79.26 | -| finnish-ftb-ud-2.12-230717 | Raw text | 99.91 | 86.84 | 96.70 | 95.08 | 96.78 | 94.01 | 95.76 | 90.17 | 87.35 | 80.13 | 80.78 | -| finnish-ftb-ud-2.12-230717 | Gold tokenization | — | — | 97.08 | 95.29 | 96.87 | 94.35 | 95.90 | 92.40 | 89.53 | 82.71 | 83.28 | -| french-gsd-ud-2.12-230717 | Raw text | 98.84 | 94.93 | 97.33 | — | 97.25 | 96.57 | 97.72 | 93.15 | 91.20 | 84.70 | 86.92 | -| french-gsd-ud-2.12-230717 | Gold tokenization | — | — | 98.48 | — | 98.32 | 97.65 | 98.85 | 94.94 | 93.11 | 86.58 | 88.24 | -| french-parisstories-ud-2.12-230717 | Raw text | 99.73 | 93.08 | 97.20 | — | 93.07 | 91.20 | 98.02 | 79.94 | 76.66 | 62.32 | 71.73 | -| french-parisstories-ud-2.12-230717 | Gold tokenization | — | — | 97.48 | — | 93.30 | 91.45 | 98.26 | 81.17 | 77.85 | 63.20 | 72.63 | -| french-partut-ud-2.12-230717 | Raw text | 99.42 | 98.64 | 97.43 | 96.97 | 95.28 | 94.51 | 97.89 | 94.44 | 92.83 | 82.58 | 87.48 | -| french-partut-ud-2.12-230717 | Gold tokenization | — | — | 98.12 | 97.62 | 95.89 | 95.12 | 98.50 | 95.35 | 93.89 | 83.73 | 88.49 | -| french-rhapsodie-ud-2.12-230717 | Raw text | 99.16 | 99.82 | 97.31 | 97.37 | 96.16 | 93.38 | 98.19 | 88.06 | 84.92 | 75.51 | 80.31 | -| french-rhapsodie-ud-2.12-230717 | Gold tokenization | — | — | 98.19 | 98.11 | 97.02 | 94.15 | 99.00 | 89.33 | 86.14 | 76.49 | 80.94 | -| french-sequoia-ud-2.12-230717 | Raw text | 99.15 | 89.53 | 98.40 | — | 97.19 | 96.84 | 98.30 | 94.06 | 92.75 | 86.39 | 89.39 | -| french-sequoia-ud-2.12-230717 | Gold tokenization | — | — | 99.25 | — | 98.01 | 97.63 | 99.14 | 95.63 | 94.37 | 88.23 | 90.53 | -| galician-treegal-ud-2.12-230717 | Raw text | 98.74 | 87.99 | 95.93 | 93.63 | 94.83 | 92.83 | 96.76 | 83.46 | 79.60 | 67.95 | 71.94 | -| galician-treegal-ud-2.12-230717 | Gold tokenization | — | — | 97.19 | 94.72 | 95.89 | 93.88 | 97.90 | 86.99 | 82.78 | 71.43 | 75.90 | -| galician-ctg-ud-2.12-230717 | Raw text | 99.22 | 97.22 | 97.24 | 97.07 | 99.05 | 96.65 | 98.12 | 85.14 | 82.72 | 71.07 | 75.57 | -| galician-ctg-ud-2.12-230717 | Gold tokenization | — | — | 97.97 | 97.79 | 99.83 | 97.35 | 98.86 | 86.86 | 84.31 | 73.03 | 77.52 | -| german-gsd-ud-2.12-230717 | Raw text | 99.76 | 82.68 | 96.16 | 97.53 | 90.78 | 88.15 | 96.91 | 87.04 | 83.20 | 65.39 | 75.45 | -| german-gsd-ud-2.12-230717 | Gold tokenization | — | — | 96.47 | 97.80 | 91.21 | 88.63 | 97.18 | 88.79 | 84.99 | 66.97 | 77.23 | -| german-hdt-ud-2.12-230717 | Raw text | 99.90 | 92.39 | 98.55 | 98.46 | 94.21 | 93.81 | 97.69 | 96.90 | 96.00 | 84.87 | 90.50 | -| german-hdt-ud-2.12-230717 | Gold tokenization | — | — | 98.66 | 98.59 | 94.34 | 93.95 | 97.79 | 97.60 | 96.71 | 85.54 | 91.20 | -| gothic-proiel-ud-2.12-230717 | Raw text | 100.00 | 31.12 | 96.17 | 96.70 | 89.88 | 87.80 | 94.71 | 78.72 | 72.74 | 58.81 | 63.43 | -| gothic-proiel-ud-2.12-230717 | Gold tokenization | — | — | 96.85 | 97.22 | 91.01 | 89.27 | 94.78 | 86.66 | 80.81 | 68.63 | 72.61 | -| greek-gdt-ud-2.12-230717 | Raw text | 99.87 | 90.19 | 98.19 | 98.21 | 95.72 | 95.10 | 96.09 | 92.99 | 91.18 | 81.67 | 81.75 | -| greek-gdt-ud-2.12-230717 | Gold tokenization | — | — | 98.32 | 98.34 | 95.83 | 95.20 | 96.17 | 93.80 | 91.91 | 82.25 | 82.38 | -| greek-gud-ud-2.12-230717 | Raw text | 99.92 | 94.98 | 97.01 | 96.26 | 94.24 | 90.55 | 95.76 | 92.94 | 90.06 | 75.94 | 80.42 | -| greek-gud-ud-2.12-230717 | Gold tokenization | — | — | 97.11 | 96.32 | 94.32 | 90.65 | 95.83 | 93.59 | 90.68 | 76.44 | 80.91 | -| hebrew-htb-ud-2.12-230717 | Raw text | 85.10 | 99.69 | 82.96 | 82.95 | 81.30 | 80.69 | 83.02 | 70.71 | 68.21 | 55.84 | 60.10 | -| hebrew-htb-ud-2.12-230717 | Gold tokenization | — | — | 97.64 | 97.62 | 95.79 | 95.27 | 97.36 | 92.45 | 89.94 | 79.28 | 82.12 | -| hebrew-iahltwiki-ud-2.12-230717 | Raw text | 88.54 | 97.16 | 85.97 | 85.97 | 81.45 | 80.46 | 87.15 | 76.11 | 74.26 | 57.99 | 67.30 | -| hebrew-iahltwiki-ud-2.12-230717 | Gold tokenization | — | — | 97.09 | 97.09 | 92.18 | 91.10 | 98.29 | 93.66 | 91.26 | 75.14 | 85.53 | -| hindi-hdtb-ud-2.12-230717 | Raw text | 100.00 | 98.72 | 97.74 | 97.35 | 94.23 | 92.39 | 98.93 | 95.31 | 92.43 | 79.64 | 87.78 | -| hindi-hdtb-ud-2.12-230717 | Gold tokenization | — | — | 97.74 | 97.34 | 94.25 | 92.40 | 98.94 | 95.43 | 92.55 | 79.77 | 87.94 | -| hungarian-szeged-ud-2.12-230717 | Raw text | 99.85 | 95.89 | 96.68 | — | 94.18 | 93.47 | 94.89 | 88.56 | 84.89 | 74.96 | 78.33 | -| hungarian-szeged-ud-2.12-230717 | Gold tokenization | — | — | 96.77 | — | 94.31 | 93.57 | 95.01 | 88.99 | 85.30 | 75.22 | 78.65 | -| icelandic-modern-ud-2.12-230717 | Raw text | 99.37 | 94.59 | 97.58 | 95.34 | 88.49 | 85.62 | 96.87 | 86.05 | 83.30 | 64.64 | 75.54 | -| icelandic-modern-ud-2.12-230717 | Gold tokenization | — | — | 98.15 | 95.92 | 88.93 | 86.07 | 97.45 | 87.03 | 84.17 | 65.30 | 76.47 | -| icelandic-gc-ud-2.12-230717 | Raw text | 99.72 | 94.64 | 94.72 | 82.28 | 85.01 | 79.83 | 91.64 | 83.22 | 78.78 | 58.56 | 68.85 | -| icelandic-gc-ud-2.12-230717 | Gold tokenization | — | — | 95.06 | 82.71 | 85.52 | 80.34 | 91.81 | 84.14 | 79.66 | 59.21 | 69.49 | -| icelandic-icepahc-ud-2.12-230717 | Raw text | 99.80 | 92.67 | 96.90 | 93.31 | 92.01 | 87.13 | 96.24 | 87.30 | 83.46 | 66.91 | 74.57 | -| icelandic-icepahc-ud-2.12-230717 | Gold tokenization | — | — | 97.08 | 93.55 | 92.18 | 87.35 | 96.39 | 87.85 | 83.95 | 67.40 | 75.14 | -| indonesian-gsd-ud-2.12-230717 | Raw text | 99.49 | 92.35 | 94.35 | 94.03 | 95.77 | 89.10 | 98.12 | 87.62 | 81.71 | 72.43 | 76.99 | -| indonesian-gsd-ud-2.12-230717 | Gold tokenization | — | — | 94.79 | 94.41 | 96.17 | 89.42 | 98.52 | 88.41 | 82.43 | 73.18 | 77.77 | -| indonesian-csui-ud-2.12-230717 | Raw text | 99.45 | 91.01 | 95.88 | 96.07 | 96.66 | 95.33 | 98.11 | 85.95 | 81.63 | 76.18 | 78.21 | -| indonesian-csui-ud-2.12-230717 | Gold tokenization | — | — | 96.34 | 96.58 | 97.15 | 95.78 | 98.74 | 87.26 | 82.71 | 77.14 | 79.20 | -| irish-idt-ud-2.12-230717 | Raw text | 99.88 | 97.58 | 96.04 | 94.90 | 90.84 | 87.69 | 95.85 | 86.48 | 80.95 | 64.53 | 71.46 | -| irish-idt-ud-2.12-230717 | Gold tokenization | — | — | 96.13 | 95.08 | 90.97 | 87.85 | 95.97 | 86.75 | 81.19 | 64.56 | 71.53 | -| irish-twittirish-ud-2.12-230717 | Raw text | 98.50 | 46.62 | 90.58 | — | — | 90.58 | 88.41 | 78.58 | 72.34 | 58.38 | 56.97 | -| irish-twittirish-ud-2.12-230717 | Gold tokenization | — | — | 91.80 | — | — | 91.80 | 89.57 | 85.75 | 79.31 | 66.72 | 64.16 | -| italian-isdt-ud-2.12-230717 | Raw text | 99.74 | 99.07 | 98.44 | 98.38 | 98.14 | 97.64 | 98.68 | 94.73 | 93.05 | 86.79 | 88.06 | -| italian-isdt-ud-2.12-230717 | Gold tokenization | — | — | 98.71 | 98.64 | 98.39 | 97.89 | 98.95 | 95.14 | 93.47 | 87.19 | 88.54 | -| italian-markit-ud-2.12-230717 | Raw text | 99.62 | 98.24 | 96.96 | 97.13 | 94.12 | 92.60 | 88.18 | 88.60 | 84.72 | 70.64 | 77.87 | -| italian-markit-ud-2.12-230717 | Gold tokenization | — | — | 97.35 | 97.52 | 94.39 | 92.88 | 88.50 | 89.39 | 85.51 | 71.25 | 78.65 | -| italian-parlamint-ud-2.12-230717 | Raw text | 99.42 | 94.12 | 98.59 | 97.96 | 97.95 | 97.05 | 98.63 | 91.93 | 89.97 | 84.20 | 86.02 | -| italian-parlamint-ud-2.12-230717 | Gold tokenization | — | — | 99.17 | 98.52 | 98.50 | 97.58 | 99.16 | 93.41 | 91.44 | 85.80 | 87.60 | -| italian-partut-ud-2.12-230717 | Raw text | 99.73 | 100.00 | 98.41 | 98.41 | 98.19 | 97.64 | 98.57 | 96.15 | 94.15 | 87.84 | 88.90 | -| italian-partut-ud-2.12-230717 | Gold tokenization | — | — | 98.52 | 98.52 | 98.27 | 97.72 | 98.82 | 96.18 | 94.09 | 87.68 | 88.80 | -| italian-postwita-ud-2.12-230717 | Raw text | 99.36 | 49.53 | 96.58 | 96.33 | 96.33 | 94.80 | 96.62 | 82.80 | 79.03 | 68.81 | 70.55 | -| italian-postwita-ud-2.12-230717 | Gold tokenization | — | — | 97.17 | 96.95 | 96.90 | 95.44 | 97.25 | 87.96 | 83.79 | 75.19 | 76.95 | -| italian-twittiro-ud-2.12-230717 | Raw text | 98.94 | 46.67 | 95.91 | 95.74 | 94.94 | 93.42 | 94.57 | 83.09 | 78.65 | 66.30 | 66.97 | -| italian-twittiro-ud-2.12-230717 | Gold tokenization | — | — | 96.84 | 96.57 | 95.87 | 94.15 | 95.43 | 88.34 | 83.43 | 71.82 | 72.62 | -| italian-vit-ud-2.12-230717 | Raw text | 99.74 | 94.87 | 98.11 | 97.29 | 97.65 | 96.15 | 98.87 | 92.22 | 89.43 | 81.28 | 84.07 | -| italian-vit-ud-2.12-230717 | Gold tokenization | — | — | 98.35 | 97.65 | 97.87 | 96.51 | 99.10 | 92.94 | 90.12 | 81.99 | 84.78 | -| japanese-gsdluw-ud-2.12-230717 | Raw text | 95.18 | 99.72 | 93.82 | 93.50 | 95.18 | 93.44 | 93.56 | 86.27 | 85.58 | 76.26 | 76.41 | -| japanese-gsdluw-ud-2.12-230717 | Gold tokenization | — | — | 98.36 | 98.01 | 100.00 | 97.90 | 97.78 | 95.16 | 94.19 | 86.28 | 84.89 | -| japanese-gsd-ud-2.12-230717 | Raw text | 96.17 | 100.00 | 94.97 | 94.18 | 96.16 | 93.85 | 95.03 | 87.91 | 87.07 | 80.80 | 80.98 | -| japanese-gsd-ud-2.12-230717 | Gold tokenization | — | — | 98.59 | 97.52 | 99.99 | 97.20 | 98.47 | 94.93 | 93.94 | 88.80 | 88.47 | -| korean-kaist-ud-2.12-230717 | Raw text | 100.00 | 100.00 | 96.19 | 87.78 | — | 87.58 | 94.18 | 88.85 | 86.92 | 82.77 | 80.35 | -| korean-kaist-ud-2.12-230717 | Gold tokenization | — | — | 96.19 | 87.78 | — | 87.58 | 94.18 | 88.85 | 86.92 | 82.77 | 80.35 | -| korean-gsd-ud-2.12-230717 | Raw text | 99.87 | 93.93 | 96.54 | 90.07 | 99.67 | 87.94 | 93.62 | 87.88 | 83.98 | 80.68 | 76.82 | -| korean-gsd-ud-2.12-230717 | Gold tokenization | — | — | 96.72 | 90.24 | 99.79 | 88.12 | 93.74 | 88.69 | 84.76 | 81.49 | 77.58 | -| latin-ittb-ud-2.12-230717 | Raw text | 99.99 | 91.21 | 99.01 | 96.65 | 97.07 | 95.62 | 99.16 | 90.25 | 88.31 | 82.53 | 85.95 | -| latin-ittb-ud-2.12-230717 | Gold tokenization | — | — | 99.03 | 96.66 | 97.12 | 95.64 | 99.18 | 91.28 | 89.35 | 83.17 | 86.55 | -| latin-llct-ud-2.12-230717 | Raw text | 99.99 | 99.49 | 99.75 | 97.14 | 97.15 | 96.87 | 97.76 | 95.37 | 94.37 | 89.06 | 90.39 | -| latin-llct-ud-2.12-230717 | Gold tokenization | — | — | 99.75 | 97.14 | 97.16 | 96.87 | 97.77 | 95.39 | 94.39 | 89.07 | 90.41 | -| latin-perseus-ud-2.12-230717 | Raw text | 99.95 | 98.99 | 92.88 | 81.11 | 84.60 | 77.45 | 88.86 | 78.92 | 71.78 | 53.50 | 59.27 | -| latin-perseus-ud-2.12-230717 | Gold tokenization | — | — | 92.95 | 81.14 | 84.65 | 77.49 | 88.89 | 79.08 | 71.91 | 53.59 | 59.31 | -| latin-proiel-ud-2.12-230717 | Raw text | 99.85 | 37.40 | 96.60 | 96.69 | 90.66 | 89.40 | 96.19 | 76.66 | 72.46 | 59.50 | 64.96 | -| latin-proiel-ud-2.12-230717 | Gold tokenization | — | — | 97.02 | 97.12 | 91.43 | 90.31 | 96.42 | 83.88 | 79.55 | 67.88 | 73.35 | -| latin-udante-ud-2.12-230717 | Raw text | 99.61 | 98.81 | 90.94 | 75.50 | 84.14 | 72.23 | 86.97 | 75.88 | 68.63 | 47.63 | 51.46 | -| latin-udante-ud-2.12-230717 | Gold tokenization | — | — | 91.18 | 75.56 | 84.38 | 72.29 | 87.20 | 75.95 | 68.65 | 47.73 | 51.44 | -| latvian-lvtb-ud-2.12-230717 | Raw text | 99.29 | 97.80 | 96.79 | 90.92 | 94.75 | 90.16 | 96.57 | 88.79 | 85.85 | 77.56 | 81.04 | -| latvian-lvtb-ud-2.12-230717 | Gold tokenization | — | — | 97.44 | 91.56 | 95.44 | 90.77 | 97.21 | 89.95 | 86.94 | 78.74 | 82.16 | -| lithuanian-alksnis-ud-2.12-230717 | Raw text | 99.91 | 87.87 | 95.95 | 90.31 | 91.09 | 89.50 | 93.45 | 82.74 | 78.94 | 68.03 | 71.20 | -| lithuanian-alksnis-ud-2.12-230717 | Gold tokenization | — | — | 96.08 | 90.44 | 91.27 | 89.64 | 93.56 | 83.94 | 80.08 | 68.93 | 72.06 | -| lithuanian-hse-ud-2.12-230717 | Raw text | 97.30 | 97.30 | 89.93 | 90.03 | 81.92 | 79.03 | 88.16 | 71.85 | 62.63 | 44.48 | 53.82 | -| lithuanian-hse-ud-2.12-230717 | Gold tokenization | — | — | 91.32 | 91.42 | 83.40 | 80.09 | 90.75 | 75.00 | 65.00 | 45.87 | 55.84 | -| maghrebi_arabic_french-arabizi-ud-2.12-230717 | Raw text | 91.65 | 7.00 | 78.81 | 71.59 | 82.65 | 69.81 | 50.63 | 57.90 | 49.93 | 36.22 | 24.59 | -| maghrebi_arabic_french-arabizi-ud-2.12-230717 | Gold tokenization | — | — | 86.69 | 78.89 | 90.82 | 77.88 | 54.66 | 76.32 | 65.86 | 47.72 | 31.57 | -| maltese-mudt-ud-2.12-230717 | Raw text | 99.84 | 86.29 | 95.73 | 95.79 | — | 95.31 | — | 85.08 | 80.25 | 68.88 | 73.05 | -| maltese-mudt-ud-2.12-230717 | Gold tokenization | — | — | 95.87 | 95.92 | — | 95.46 | — | 85.70 | 80.81 | 69.28 | 73.43 | -| manx-cadhan-ud-2.12-230717 | Raw text | 97.36 | 98.25 | 94.18 | — | 95.78 | 93.37 | 93.43 | 87.42 | 84.02 | 77.75 | 78.03 | -| manx-cadhan-ud-2.12-230717 | Gold tokenization | — | — | 96.77 | — | 98.39 | 95.93 | 95.98 | 92.47 | 89.13 | 83.16 | 82.07 | -| marathi-ufal-ud-2.12-230717 | Raw text | 94.16 | 92.63 | 82.73 | — | 74.21 | 70.80 | 84.91 | 68.13 | 60.83 | 39.75 | 47.62 | -| marathi-ufal-ud-2.12-230717 | Gold tokenization | — | — | 87.14 | — | 76.94 | 73.06 | 87.86 | 73.79 | 65.29 | 41.82 | 50.10 | -| naija-nsc-ud-2.12-230717 | Raw text | 99.95 | 100.00 | 98.04 | — | 98.92 | 97.53 | 99.33 | 93.02 | 90.46 | 87.48 | 88.98 | -| naija-nsc-ud-2.12-230717 | Gold tokenization | — | — | 98.08 | — | 98.96 | 97.56 | 99.39 | 93.10 | 90.53 | 87.55 | 89.03 | -| north_sami-giella-ud-2.12-230717 | Raw text | 99.87 | 98.79 | 91.63 | 93.51 | 89.29 | 85.24 | 86.91 | 75.78 | 70.85 | 60.16 | 58.42 | -| north_sami-giella-ud-2.12-230717 | Gold tokenization | — | — | 91.78 | 93.65 | 89.42 | 85.38 | 87.01 | 76.01 | 71.08 | 60.38 | 58.60 | -| norwegian-bokmaal-ud-2.12-230717 | Raw text | 99.82 | 97.27 | 98.38 | 98.94 | 97.47 | 96.81 | 98.58 | 93.88 | 92.64 | 86.95 | 88.78 | -| norwegian-bokmaal-ud-2.12-230717 | Gold tokenization | — | — | 98.58 | 99.14 | 97.65 | 97.01 | 98.78 | 94.54 | 93.28 | 87.54 | 89.41 | -| norwegian-nynorsk-ud-2.12-230717 | Raw text | 99.93 | 94.54 | 98.43 | 99.16 | 97.35 | 96.55 | 98.46 | 93.79 | 92.37 | 85.84 | 88.08 | -| norwegian-nynorsk-ud-2.12-230717 | Gold tokenization | — | — | 98.62 | 99.27 | 97.51 | 96.75 | 98.60 | 94.58 | 93.19 | 86.82 | 89.08 | -| old_church_slavonic-proiel-ud-2.12-230717 | Raw text | 100.00 | 40.05 | 96.11 | 96.39 | 89.72 | 88.01 | 90.18 | 78.06 | 73.64 | 60.96 | 61.97 | -| old_church_slavonic-proiel-ud-2.12-230717 | Gold tokenization | — | — | 96.66 | 96.98 | 90.32 | 88.99 | 90.19 | 85.05 | 80.53 | 68.59 | 69.15 | -| old_east_slavic-torot-ud-2.12-230717 | Raw text | 100.00 | 34.53 | 95.40 | 95.48 | 89.92 | 87.64 | 88.09 | 76.94 | 72.12 | 58.23 | 57.89 | -| old_east_slavic-torot-ud-2.12-230717 | Gold tokenization | — | — | 95.89 | 95.94 | 90.67 | 88.72 | 88.14 | 85.27 | 80.38 | 67.99 | 65.94 | -| old_east_slavic-birchbark-ud-2.12-230717 | Raw text | 99.99 | 16.66 | 88.50 | 99.37 | 76.09 | 72.03 | 65.58 | 64.39 | 57.66 | 32.84 | 27.49 | -| old_east_slavic-birchbark-ud-2.12-230717 | Gold tokenization | — | — | 89.13 | 99.38 | 76.70 | 72.84 | 65.59 | 76.43 | 69.19 | 41.15 | 32.90 | -| old_east_slavic-rnc-ud-2.12-230717 | Raw text | 97.64 | 60.48 | 92.65 | 86.46 | 78.10 | 68.76 | 77.22 | 64.58 | 60.03 | 36.90 | 37.52 | -| old_east_slavic-rnc-ud-2.12-230717 | Gold tokenization | — | — | 93.99 | 88.74 | 79.24 | 69.78 | 78.28 | 70.79 | 65.17 | 39.63 | 40.04 | -| old_french-srcmf-ud-2.12-230717 | Raw text | 99.70 | 100.00 | 96.74 | 96.58 | 97.76 | 95.78 | 99.66 | 90.92 | 87.17 | 80.65 | 84.10 | -| old_french-srcmf-ud-2.12-230717 | Gold tokenization | — | — | 97.06 | 96.91 | 98.07 | 96.08 | 99.96 | 91.34 | 87.62 | 81.11 | 84.56 | -| persian-perdt-ud-2.12-230717 | Raw text | 99.66 | 99.83 | 97.51 | 97.39 | 97.64 | 95.63 | 98.91 | 93.56 | 91.32 | 86.30 | 88.68 | -| persian-perdt-ud-2.12-230717 | Gold tokenization | — | — | 97.81 | 97.68 | 97.94 | 95.92 | 99.23 | 94.08 | 91.84 | 86.86 | 89.27 | -| persian-seraji-ud-2.12-230717 | Raw text | 99.65 | 98.75 | 97.89 | 97.91 | 97.90 | 97.44 | 98.26 | 91.78 | 88.92 | 84.47 | 84.56 | -| persian-seraji-ud-2.12-230717 | Gold tokenization | — | — | 98.22 | 98.23 | 98.22 | 97.74 | 98.54 | 92.47 | 89.62 | 85.12 | 85.22 | -| polish-pdb-ud-2.12-230717 | Raw text | 99.85 | 97.33 | 98.86 | 95.76 | 95.89 | 95.08 | 98.08 | 94.21 | 92.16 | 85.14 | 88.25 | -| polish-pdb-ud-2.12-230717 | Gold tokenization | — | — | 99.02 | 95.92 | 96.04 | 95.22 | 98.21 | 94.72 | 92.67 | 85.55 | 88.68 | -| polish-lfg-ud-2.12-230717 | Raw text | 99.85 | 99.65 | 98.97 | 96.08 | 96.49 | 95.15 | 98.24 | 96.78 | 95.40 | 89.61 | 92.26 | -| polish-lfg-ud-2.12-230717 | Gold tokenization | — | — | 99.13 | 96.26 | 96.67 | 95.32 | 98.38 | 97.19 | 95.81 | 90.02 | 92.60 | -| pomak-philotis-ud-2.12-230717 | Raw text | 99.98 | 94.49 | 98.80 | — | 95.54 | 95.26 | 96.71 | 88.17 | 83.04 | 70.60 | 73.82 | -| pomak-philotis-ud-2.12-230717 | Gold tokenization | — | — | 98.82 | — | 95.54 | 95.26 | 96.73 | 88.63 | 83.54 | 71.09 | 74.23 | -| portuguese-bosque-ud-2.12-230717 | Raw text | 99.68 | 89.73 | 97.88 | — | 96.86 | 95.87 | 98.27 | 92.13 | 89.83 | 80.75 | 84.19 | -| portuguese-bosque-ud-2.12-230717 | Gold tokenization | — | — | 98.16 | — | 97.11 | 96.11 | 98.57 | 93.28 | 90.94 | 81.78 | 85.38 | -| portuguese-cintil-ud-2.12-230717 | Raw text | 99.41 | 78.66 | 97.42 | 96.01 | 95.29 | 93.21 | 97.66 | 85.13 | 81.88 | 71.77 | 75.51 | -| portuguese-cintil-ud-2.12-230717 | Gold tokenization | — | — | 98.00 | 96.61 | 95.91 | 93.80 | 98.23 | 87.54 | 84.20 | 74.06 | 77.83 | -| portuguese-petrogold-ud-2.12-230717 | Raw text | 99.59 | 93.11 | 98.75 | — | 98.70 | 98.20 | 99.09 | 94.69 | 93.57 | 88.77 | 90.11 | -| portuguese-petrogold-ud-2.12-230717 | Gold tokenization | — | — | 99.05 | — | 99.00 | 98.46 | 99.51 | 95.62 | 94.42 | 89.68 | 91.18 | -| romanian-rrt-ud-2.12-230717 | Raw text | 99.70 | 95.50 | 97.88 | 97.14 | 97.39 | 96.91 | 98.00 | 91.92 | 88.46 | 81.93 | 83.37 | -| romanian-rrt-ud-2.12-230717 | Gold tokenization | — | — | 98.15 | 97.40 | 97.65 | 97.16 | 98.25 | 92.77 | 89.25 | 82.56 | 83.97 | -| romanian-nonstandard-ud-2.12-230717 | Raw text | 98.83 | 96.77 | 96.12 | 91.86 | 90.52 | 89.16 | 94.82 | 88.67 | 84.67 | 68.20 | 76.17 | -| romanian-nonstandard-ud-2.12-230717 | Gold tokenization | — | — | 97.25 | 92.87 | 91.51 | 90.13 | 95.88 | 90.46 | 86.41 | 69.79 | 77.51 | -| romanian-simonero-ud-2.12-230717 | Raw text | 99.84 | 100.00 | 98.41 | 97.97 | 97.51 | 97.20 | 98.89 | 93.95 | 92.03 | 85.30 | 88.19 | -| romanian-simonero-ud-2.12-230717 | Gold tokenization | — | — | 98.56 | 98.12 | 97.66 | 97.34 | 99.04 | 94.29 | 92.35 | 85.57 | 88.47 | -| russian-syntagrus-ud-2.12-230717 | Raw text | 99.67 | 98.31 | 98.50 | — | 94.02 | 93.76 | 98.19 | 93.80 | 91.66 | 82.80 | 88.87 | -| russian-syntagrus-ud-2.12-230717 | Gold tokenization | — | — | 98.83 | — | 94.33 | 94.07 | 98.48 | 94.51 | 92.33 | 83.33 | 89.40 | -| russian-gsd-ud-2.12-230717 | Raw text | 99.50 | 96.49 | 98.05 | 97.56 | 94.57 | 93.51 | 96.87 | 91.64 | 88.70 | 80.95 | 84.53 | -| russian-gsd-ud-2.12-230717 | Gold tokenization | — | — | 98.50 | 97.97 | 95.01 | 93.90 | 97.25 | 92.83 | 89.81 | 81.85 | 85.46 | -| russian-taiga-ud-2.12-230717 | Raw text | 98.07 | 86.01 | 95.59 | — | 93.02 | 92.05 | 94.62 | 82.89 | 79.47 | 70.47 | 73.61 | -| russian-taiga-ud-2.12-230717 | Gold tokenization | — | — | 97.25 | — | 94.80 | 93.67 | 96.34 | 85.53 | 81.91 | 72.70 | 76.02 | -| sanskrit-vedic-ud-2.12-230717 | Raw text | 100.00 | 27.18 | 89.20 | — | 81.19 | 76.40 | 87.11 | 61.05 | 50.02 | 41.42 | 44.77 | -| sanskrit-vedic-ud-2.12-230717 | Gold tokenization | — | — | 90.07 | — | 82.70 | 78.17 | 87.40 | 73.69 | 61.55 | 51.56 | 54.91 | -| scottish_gaelic-arcosg-ud-2.12-230717 | Raw text | 97.43 | 61.26 | 93.66 | 89.50 | 91.07 | 88.34 | 94.92 | 80.79 | 76.33 | 64.78 | 69.63 | -| scottish_gaelic-arcosg-ud-2.12-230717 | Gold tokenization | — | — | 96.40 | 92.49 | 94.04 | 91.41 | 97.47 | 87.25 | 82.84 | 71.75 | 76.18 | -| serbian-set-ud-2.12-230717 | Raw text | 99.99 | 93.00 | 99.03 | 95.94 | 96.13 | 95.71 | 97.82 | 93.48 | 90.99 | 83.45 | 86.89 | -| serbian-set-ud-2.12-230717 | Gold tokenization | — | — | 99.05 | 95.95 | 96.16 | 95.74 | 97.83 | 94.18 | 91.65 | 84.17 | 87.56 | -| slovak-snk-ud-2.12-230717 | Raw text | 100.00 | 81.69 | 97.68 | 90.26 | 93.35 | 89.42 | 96.51 | 91.48 | 89.73 | 80.19 | 84.62 | -| slovak-snk-ud-2.12-230717 | Gold tokenization | — | — | 97.83 | 90.34 | 93.44 | 89.56 | 96.54 | 93.88 | 92.00 | 82.42 | 86.89 | -| slovenian-ssj-ud-2.12-230717 | Raw text | 99.94 | 98.95 | 98.96 | 97.09 | 97.26 | 96.78 | 98.57 | 94.23 | 92.86 | 87.37 | 89.22 | -| slovenian-ssj-ud-2.12-230717 | Gold tokenization | — | — | 99.02 | 97.15 | 97.33 | 96.84 | 98.61 | 94.40 | 93.02 | 87.52 | 89.34 | -| slovenian-sst-ud-2.12-230717 | Raw text | 99.97 | 24.74 | 95.82 | 93.37 | 93.58 | 91.64 | 97.69 | 66.76 | 61.98 | 52.04 | 55.84 | -| slovenian-sst-ud-2.12-230717 | Gold tokenization | — | — | 96.10 | 93.49 | 93.79 | 91.91 | 97.73 | 78.52 | 73.20 | 63.83 | 68.34 | -| spanish-ancora-ud-2.12-230717 | Raw text | 99.95 | 98.78 | 99.06 | 96.12 | 98.76 | 95.71 | 99.39 | 93.68 | 91.92 | 86.79 | 88.29 | -| spanish-ancora-ud-2.12-230717 | Gold tokenization | — | — | 99.11 | 96.16 | 98.81 | 95.75 | 99.43 | 93.87 | 92.10 | 86.95 | 88.45 | -| spanish-gsd-ud-2.12-230717 | Raw text | 99.72 | 94.90 | 97.10 | — | 96.74 | 95.07 | 98.58 | 92.51 | 90.28 | 78.76 | 84.27 | -| spanish-gsd-ud-2.12-230717 | Gold tokenization | — | — | 97.36 | — | 97.01 | 95.32 | 98.83 | 93.38 | 91.10 | 79.58 | 85.17 | -| swedish-talbanken-ud-2.12-230717 | Raw text | 99.84 | 96.53 | 98.37 | 97.23 | 97.31 | 96.43 | 98.17 | 92.08 | 89.72 | 83.69 | 85.90 | -| swedish-talbanken-ud-2.12-230717 | Gold tokenization | — | — | 98.53 | 97.42 | 97.49 | 96.61 | 98.33 | 92.53 | 90.15 | 84.20 | 86.44 | -| swedish-lines-ud-2.12-230717 | Raw text | 99.96 | 88.00 | 97.61 | 95.49 | 90.93 | 88.18 | 97.82 | 90.48 | 87.14 | 71.66 | 81.94 | -| swedish-lines-ud-2.12-230717 | Gold tokenization | — | — | 97.71 | 95.50 | 90.96 | 88.23 | 97.86 | 91.19 | 87.78 | 72.25 | 82.63 | -| tamil-ttb-ud-2.12-230717 | Raw text | 94.26 | 97.52 | 84.29 | 82.92 | 84.09 | 77.76 | 88.79 | 70.63 | 61.98 | 50.53 | 54.69 | -| tamil-ttb-ud-2.12-230717 | Gold tokenization | — | — | 89.14 | 87.13 | 89.19 | 81.80 | 93.87 | 78.38 | 68.88 | 56.48 | 60.89 | -| telugu-mtg-ud-2.12-230717 | Raw text | 99.58 | 96.62 | 92.94 | 92.94 | 98.61 | 92.94 | — | 90.72 | 84.07 | 76.19 | 80.19 | -| telugu-mtg-ud-2.12-230717 | Gold tokenization | — | — | 93.48 | 93.48 | 99.03 | 93.48 | — | 91.68 | 85.02 | 77.22 | 81.03 | -| turkish-boun-ud-2.12-230717 | Raw text | 96.57 | 86.25 | 90.03 | 86.05 | 81.12 | 71.69 | 90.47 | 73.32 | 67.11 | 49.09 | 60.77 | -| turkish-boun-ud-2.12-230717 | Gold tokenization | — | — | 92.97 | 88.91 | 83.07 | 73.35 | 93.52 | 80.79 | 73.91 | 52.78 | 66.50 | -| turkish-atis-ud-2.12-230717 | Raw text | 100.00 | 80.20 | 99.02 | — | 98.57 | 98.40 | 99.11 | 89.03 | 87.24 | 84.91 | 85.71 | -| turkish-atis-ud-2.12-230717 | Gold tokenization | — | — | 99.04 | — | 98.57 | 98.42 | 99.11 | 90.96 | 89.09 | 86.83 | 87.69 | -| turkish-framenet-ud-2.12-230717 | Raw text | 100.00 | 100.00 | 96.52 | — | 94.75 | 93.87 | 96.39 | 93.73 | 84.80 | 74.12 | 77.96 | -| turkish-framenet-ud-2.12-230717 | Gold tokenization | — | — | 96.52 | — | 94.75 | 93.87 | 96.39 | 93.73 | 84.80 | 74.12 | 77.96 | -| turkish-imst-ud-2.12-230717 | Raw text | 97.94 | 97.70 | 93.70 | 93.46 | 90.63 | 88.13 | 94.46 | 75.22 | 66.28 | 55.69 | 60.80 | -| turkish-imst-ud-2.12-230717 | Gold tokenization | — | — | 95.46 | 95.27 | 92.41 | 89.75 | 96.32 | 78.94 | 69.42 | 57.62 | 62.96 | -| turkish-kenet-ud-2.12-230717 | Raw text | 100.00 | 98.12 | 93.80 | — | 92.10 | 90.85 | 93.50 | 83.94 | 71.51 | 62.32 | 65.35 | -| turkish-kenet-ud-2.12-230717 | Gold tokenization | — | — | 93.83 | — | 92.12 | 90.89 | 93.51 | 84.10 | 71.63 | 62.46 | 65.49 | -| turkish-penn-ud-2.12-230717 | Raw text | 99.34 | 80.59 | 95.50 | — | 94.48 | 93.29 | 94.14 | 84.40 | 71.73 | 62.08 | 64.36 | -| turkish-penn-ud-2.12-230717 | Gold tokenization | — | — | 96.14 | — | 95.12 | 93.94 | 94.71 | 86.84 | 73.89 | 63.46 | 65.77 | -| turkish-tourism-ud-2.12-230717 | Raw text | 99.96 | 99.86 | 98.92 | — | 94.98 | 94.67 | 98.27 | 97.04 | 91.43 | 81.58 | 87.09 | -| turkish-tourism-ud-2.12-230717 | Gold tokenization | — | — | 98.96 | — | 95.02 | 94.72 | 98.31 | 97.10 | 91.50 | 81.66 | 87.18 | -| turkish_german-sagt-ud-2.12-230717 | Raw text | 98.91 | 99.44 | 90.21 | — | 80.24 | 75.45 | 90.80 | 71.42 | 61.22 | 41.16 | 50.92 | -| turkish_german-sagt-ud-2.12-230717 | Gold tokenization | — | — | 91.09 | — | 80.82 | 75.93 | 91.49 | 72.76 | 62.20 | 41.63 | 51.55 | -| ukrainian-iu-ud-2.12-230717 | Raw text | 99.81 | 96.23 | 97.84 | 94.28 | 94.25 | 93.16 | 97.47 | 90.37 | 87.94 | 78.30 | 82.74 | -| ukrainian-iu-ud-2.12-230717 | Gold tokenization | — | — | 98.03 | 94.44 | 94.39 | 93.31 | 97.67 | 90.97 | 88.52 | 78.70 | 83.18 | -| urdu-udtb-ud-2.12-230717 | Raw text | 100.00 | 98.31 | 94.09 | 92.20 | 82.76 | 78.43 | 97.41 | 88.02 | 82.68 | 57.25 | 74.71 | -| urdu-udtb-ud-2.12-230717 | Gold tokenization | — | — | 94.06 | 92.19 | 82.76 | 78.41 | 97.41 | 88.13 | 82.81 | 57.30 | 74.88 | -| uyghur-udt-ud-2.12-230717 | Raw text | 99.54 | 81.87 | 89.77 | 91.72 | 88.23 | 80.82 | 94.71 | 75.32 | 64.44 | 50.04 | 57.14 | -| uyghur-udt-ud-2.12-230717 | Gold tokenization | — | — | 90.23 | 92.21 | 88.65 | 81.27 | 95.22 | 77.05 | 66.02 | 50.95 | 58.33 | -| vietnamese-vtb-ud-2.12-230717 | Raw text | 86.06 | 93.73 | 78.61 | 77.61 | — | 77.50 | 85.76 | 56.86 | 50.02 | 41.40 | 45.54 | -| vietnamese-vtb-ud-2.12-230717 | Gold tokenization | — | — | 90.02 | 88.88 | — | 88.73 | 99.50 | 76.31 | 65.90 | 55.36 | 61.24 | -| welsh-ccg-ud-2.12-230717 | Raw text | 99.46 | 97.68 | 95.27 | 94.28 | 89.57 | 87.25 | 94.43 | 86.73 | 80.81 | 63.09 | 69.84 | -| welsh-ccg-ud-2.12-230717 | Gold tokenization | — | — | 95.74 | 94.73 | 90.03 | 87.66 | 94.94 | 87.83 | 81.85 | 64.04 | 70.94 | -| western_armenian-armtdp-ud-2.12-230717 | Raw text | 99.89 | 98.68 | 96.67 | — | 92.31 | 91.63 | 97.14 | 89.26 | 84.68 | 69.80 | 76.24 | -| western_armenian-armtdp-ud-2.12-230717 | Gold tokenization | — | — | 96.75 | — | 92.40 | 91.71 | 97.23 | 89.51 | 84.91 | 69.99 | 76.44 | -| wolof-wtb-ud-2.12-230717 | Raw text | 99.23 | 91.95 | 94.16 | 94.17 | 93.56 | 91.48 | 95.18 | 83.75 | 78.61 | 66.55 | 69.91 | -| wolof-wtb-ud-2.12-230717 | Gold tokenization | — | — | 95.11 | 95.08 | 94.34 | 92.33 | 95.88 | 85.90 | 80.57 | 68.67 | 71.71 | diff --git a/udpipe2/docs/models_ud26.md b/udpipe2/docs/models_ud26.md deleted file mode 100644 index 1248207becd4a2a2a23a3ab69f032ebe12b879fb..0000000000000000000000000000000000000000 --- a/udpipe2/docs/models_ud26.md +++ /dev/null @@ -1,257 +0,0 @@ -## Universal Dependencies 2.6 Models #universal_dependencies_26_models - -Universal Dependencies 2.6 Models are distributed under the -[CC BY-NC-SA](https://creativecommons.org/licenses/by-nc-sa/4.0/) licence. -The models are based solely on [Universal Dependencies -2.6](https://hdl.handle.net/11234/1-3226) treebanks, and additionally -use [multilingual BERT](https://github.com/google-research/bert/blob/master/multilingual.md). - -The models require [UDPipe 2](https://ufal.mff.cuni.cz/udpipe/2). - -### Download - -The latest version 200831 of the Universal Dependencies 2.6 models -can be downloaded from [LINDAT/CLARIN repository](https://hdl.handle.net/11234/1-4798). - -The models are also available in the [REST service](https://lindat.mff.cuni.cz/services/udpipe/). - -### Acknowledgements #universal_dependencies_26_models_acknowledgements - -This work has been supported by the Ministry of Education, Youth and Sports of -the Czech Republic, Project No. LM2018101 LINDAT/CLARIAH-CZ. - -The models were trained on [Universal Dependencies 2.6](https://hdl.handle.net/11234/1-3226) treebanks. - -For the UD treebanks which do not contain original plain text version, -raw text is used to train the tokenizer instead. The plain texts -were taken from the [W2C -- Web to Corpus](https://hdl.handle.net/11858/00-097C-0000-0022-6133-9). - -Finally, [multilingual BERT](https://github.com/google-research/bert/blob/master/multilingual.md) -is used to provide contextualized word embeddings. - -### Publications - -- Milan Straka, Jana Straková, Jan Hajič (2019): [Evaluating Contextualized Embeddings on 54 Languages in POS Tagging, Lemmatization and Dependency Parsing](https://arxiv.org/abs/1908.07448). In: ArXiv.org Computing Research Repository, ISSN 2331-8422, 1904.02099 -- Milan Straka (2018): [UDPipe 2.0 Prototype at CoNLL 2018 UD Shared Task](https://www.aclweb.org/anthology/K18-2020/). In: Proceedings of CoNLL 2018: The SIGNLL Conference on Computational Natural Language Learning, pp. 197-207, Association for Computational Linguistics, Stroudsburg, PA, USA, ISBN 978-1-948087-72-8 - -### Model Description - -The Universal Dependencies 2.6 models contain 99 models of 63 languages, each consisting of -a tokenizer, tagger, lemmatizer and dependency parser, all trained using -the UD data. We used the original train-dev-test split, but for treebanks with -only train and no dev data we used last 10% of the train data as dev data. -We produce models only for treebanks with at least 1000 training words. - -The tokenizer is trained using the `SpaceAfter=No` features. If the features -are not present in the data, they can be filled in using raw text in the -language in question. - -The tagger, lemmatizer and parser are trained using gold UD data. - -### Model Performance - -We present the tokenizer, tagger, lemmatizer and parser performance, measured on -the testing portion of the data, evaluated both on the raw text and using the -gold tokenization. The results are F1 scores measured by the `conll18_ud_eval.py` -script. - -| Model | Mode | Words | Sents | UPOS | XPOS | UFeats | AllTags | Lemma | UAS | LAS | MLAS | BLEX | -|:------------------------|:-------------|-------:|-------:|------:|------:|--------:|--------:|-------:|------:|------:|------:|------:| -| afrikaans-afribooms-ud-2.6-200830 | Raw text | 99.82 | 98.25 | 98.55 | 95.42 | 98.27 | 95.33 | 97.52 | 90.34 | 87.93 | 80.33 | 79.91 | -| afrikaans-afribooms-ud-2.6-200830 | Gold tokenization | — | — | 98.70 | 95.56 | 98.41 | 95.48 | 97.61 | 90.80 | 88.40 | 80.78 | 80.31 | -| ancient_greek-perseus-ud-2.6-200830 | Raw text | 99.97 | 98.85 | 93.20 | 86.01 | 91.59 | 85.27 | 86.81 | 79.57 | 73.90 | 54.80 | 55.63 | -| ancient_greek-perseus-ud-2.6-200830 | Gold tokenization | — | — | 93.24 | 86.03 | 91.62 | 85.30 | 86.84 | 79.74 | 74.06 | 54.92 | 55.72 | -| ancient_greek-proiel-ud-2.6-200830 | Raw text | 100.00 | 48.02 | 97.73 | 98.04 | 92.36 | 91.01 | 94.71 | 79.98 | 75.99 | 60.15 | 65.88 | -| ancient_greek-proiel-ud-2.6-200830 | Gold tokenization | — | — | 97.91 | 98.18 | 92.59 | 91.30 | 94.76 | 85.95 | 81.90 | 66.72 | 71.84 | -| arabic-padt-ud-2.6-200830 | Raw text | 94.58 | 82.09 | 91.68 | 88.96 | 89.14 | 88.65 | 90.36 | 78.86 | 74.85 | 66.06 | 68.12 | -| arabic-padt-ud-2.6-200830 | Gold tokenization | — | — | 96.87 | 94.20 | 94.36 | 93.82 | 95.23 | 87.60 | 83.14 | 74.51 | 76.09 | -| armenian-armtdp-ud-2.6-200830 | Raw text | 99.34 | 97.85 | 95.64 | — | 90.30 | 88.94 | 94.45 | 85.07 | 79.97 | 66.54 | 71.61 | -| armenian-armtdp-ud-2.6-200830 | Gold tokenization | — | — | 96.11 | — | 90.90 | 89.37 | 95.04 | 86.31 | 81.18 | 67.00 | 72.22 | -| basque-bdt-ud-2.6-200830 | Raw text | 99.94 | 99.83 | 96.44 | — | 93.60 | 91.69 | 96.40 | 87.24 | 84.15 | 74.97 | 79.94 | -| basque-bdt-ud-2.6-200830 | Gold tokenization | — | — | 96.48 | — | 93.64 | 91.72 | 96.43 | 87.30 | 84.21 | 75.00 | 79.96 | -| belarusian-hse-ud-2.6-200830 | Raw text | 99.84 | 78.70 | 96.14 | 31.78 | 82.07 | 26.98 | 81.48 | 75.81 | 71.05 | 49.78 | 50.93 | -| belarusian-hse-ud-2.6-200830 | Gold tokenization | — | — | 96.39 | 31.85 | 82.19 | 27.08 | 81.53 | 80.18 | 75.18 | 52.92 | 53.60 | -| bulgarian-btb-ud-2.6-200830 | Raw text | 99.91 | 94.17 | 99.15 | 97.19 | 97.95 | 96.84 | 97.97 | 94.35 | 91.61 | 85.92 | 86.43 | -| bulgarian-btb-ud-2.6-200830 | Gold tokenization | — | — | 99.27 | 97.30 | 98.05 | 96.95 | 98.07 | 95.17 | 92.41 | 86.62 | 87.17 | -| catalan-ancora-ud-2.6-200830 | Raw text | 99.98 | 99.43 | 99.05 | 98.99 | 98.63 | 98.14 | 99.31 | 94.53 | 92.86 | 87.63 | 89.24 | -| catalan-ancora-ud-2.6-200830 | Gold tokenization | — | — | 99.09 | 99.03 | 98.67 | 98.18 | 99.34 | 94.60 | 92.95 | 87.73 | 89.35 | -| chinese-gsdsimp-ud-2.6-200830 | Raw text | 90.29 | 99.10 | 87.32 | 87.20 | 89.73 | 86.54 | 90.29 | 72.68 | 70.32 | 63.38 | 66.94 | -| chinese-gsdsimp-ud-2.6-200830 | Gold tokenization | — | — | 96.32 | 96.15 | 99.43 | 95.50 | 99.99 | 86.89 | 83.93 | 78.52 | 82.60 | -| chinese-gsd-ud-2.6-200830 | Raw text | 90.27 | 99.10 | 87.27 | 87.18 | 89.74 | 86.50 | 90.27 | 72.99 | 70.50 | 63.83 | 67.21 | -| chinese-gsd-ud-2.6-200830 | Gold tokenization | — | — | 96.30 | 96.16 | 99.42 | 95.45 | 99.99 | 87.30 | 84.22 | 78.63 | 82.84 | -| classical_chinese-kyoto-ud-2.6-200830 | Raw text | 99.46 | 46.22 | 90.91 | 90.91 | 93.43 | 88.00 | 99.42 | 72.75 | 67.18 | 63.67 | 66.02 | -| classical_chinese-kyoto-ud-2.6-200830 | Gold tokenization | — | — | 93.55 | 93.24 | 95.01 | 90.86 | 99.96 | 85.49 | 80.20 | 76.42 | 79.25 | -| coptic-scriptorium-ud-2.6-200830 | Raw text | 71.91 | 35.97 | 69.61 | 68.00 | 63.06 | 60.16 | 70.51 | 47.75 | 45.89 | 25.42 | 35.81 | -| coptic-scriptorium-ud-2.6-200830 | Gold tokenization | — | — | 96.15 | 92.53 | 87.75 | 81.98 | 96.70 | 89.14 | 85.79 | 57.57 | 76.42 | -| croatian-set-ud-2.6-200830 | Raw text | 99.95 | 94.41 | 98.18 | 95.91 | 96.40 | 95.27 | 97.58 | 92.20 | 88.40 | 80.16 | 83.07 | -| croatian-set-ud-2.6-200830 | Gold tokenization | — | — | 98.23 | 96.00 | 96.52 | 95.38 | 97.64 | 92.72 | 88.89 | 80.66 | 83.53 | -| czech-pdt-ud-2.6-200830 | Raw text | 99.93 | 93.35 | 99.23 | 97.61 | 97.59 | 97.13 | 99.09 | 93.81 | 92.03 | 87.79 | 89.88 | -| czech-pdt-ud-2.6-200830 | Gold tokenization | — | — | 99.30 | 97.71 | 97.70 | 97.24 | 99.17 | 94.60 | 92.81 | 88.45 | 90.57 | -| czech-cac-ud-2.6-200830 | Raw text | 99.98 | 99.68 | 99.52 | 97.33 | 97.05 | 96.64 | 98.93 | 94.31 | 92.48 | 87.56 | 89.76 | -| czech-cac-ud-2.6-200830 | Gold tokenization | — | — | 99.54 | 97.36 | 97.07 | 96.67 | 98.95 | 94.37 | 92.54 | 87.63 | 89.83 | -| czech-fictree-ud-2.6-200830 | Raw text | 99.99 | 98.95 | 98.68 | 95.80 | 96.79 | 95.38 | 99.20 | 94.83 | 92.66 | 85.35 | 89.58 | -| czech-fictree-ud-2.6-200830 | Gold tokenization | — | — | 98.69 | 95.82 | 96.80 | 95.40 | 99.21 | 94.92 | 92.74 | 85.47 | 89.69 | -| czech-cltt-ud-2.6-200830 | Raw text | 99.65 | 97.40 | 99.21 | 95.00 | 94.98 | 94.76 | 99.06 | 91.37 | 89.67 | 82.08 | 86.96 | -| czech-cltt-ud-2.6-200830 | Gold tokenization | — | — | 99.49 | 95.19 | 95.16 | 94.95 | 99.30 | 91.91 | 90.21 | 82.25 | 87.31 | -| danish-ddt-ud-2.6-200830 | Raw text | 99.81 | 89.78 | 98.01 | — | 97.52 | 96.72 | 97.31 | 88.56 | 86.46 | 79.62 | 81.12 | -| danish-ddt-ud-2.6-200830 | Gold tokenization | — | — | 98.26 | — | 97.73 | 96.99 | 97.53 | 89.82 | 87.67 | 80.73 | 82.27 | -| dutch-alpino-ud-2.6-200830 | Raw text | 99.83 | 88.59 | 97.41 | 95.98 | 97.02 | 95.36 | 97.32 | 92.79 | 90.38 | 81.53 | 83.18 | -| dutch-alpino-ud-2.6-200830 | Gold tokenization | — | — | 97.57 | 96.13 | 97.18 | 95.53 | 97.46 | 93.93 | 91.53 | 82.72 | 84.42 | -| dutch-lassysmall-ud-2.6-200830 | Raw text | 99.83 | 75.40 | 96.58 | 95.42 | 96.41 | 94.73 | 97.21 | 90.36 | 87.66 | 78.84 | 80.17 | -| dutch-lassysmall-ud-2.6-200830 | Gold tokenization | — | — | 96.79 | 96.05 | 96.97 | 95.40 | 97.33 | 94.26 | 91.24 | 83.56 | 84.84 | -| english-ewt-ud-2.6-200830 | Raw text | 98.95 | 86.60 | 96.36 | 96.06 | 96.56 | 94.88 | 97.64 | 89.55 | 87.43 | 80.50 | 83.29 | -| english-ewt-ud-2.6-200830 | Gold tokenization | — | — | 97.29 | 97.03 | 97.57 | 95.84 | 98.57 | 92.24 | 90.05 | 83.33 | 86.07 | -| english-gum-ud-2.6-200830 | Raw text | 99.81 | 83.66 | 96.79 | 96.76 | 97.55 | 95.88 | 97.35 | 90.02 | 87.52 | 79.41 | 80.43 | -| english-gum-ud-2.6-200830 | Gold tokenization | — | — | 96.99 | 96.93 | 97.75 | 96.09 | 97.56 | 91.93 | 89.36 | 81.20 | 82.25 | -| english-lines-ud-2.6-200830 | Raw text | 99.92 | 87.45 | 97.60 | 95.86 | 96.88 | 93.39 | 98.34 | 89.36 | 86.45 | 79.35 | 82.87 | -| english-lines-ud-2.6-200830 | Gold tokenization | — | — | 97.67 | 95.90 | 96.92 | 93.41 | 98.41 | 90.26 | 87.36 | 80.24 | 83.79 | -| english-partut-ud-2.6-200830 | Raw text | 99.72 | 100.00 | 97.37 | 97.08 | 96.29 | 95.38 | 98.23 | 94.12 | 92.09 | 83.04 | 87.20 | -| english-partut-ud-2.6-200830 | Gold tokenization | — | — | 97.62 | 97.33 | 96.54 | 95.63 | 98.50 | 94.40 | 92.37 | 83.44 | 87.48 | -| estonian-edt-ud-2.6-200830 | Raw text | 99.96 | 91.56 | 97.65 | 98.25 | 96.44 | 95.19 | 95.34 | 88.75 | 86.18 | 80.12 | 79.65 | -| estonian-edt-ud-2.6-200830 | Gold tokenization | — | — | 97.75 | 98.29 | 96.48 | 95.28 | 95.40 | 89.66 | 87.06 | 80.93 | 80.44 | -| estonian-ewt-ud-2.6-200830 | Raw text | 98.96 | 70.09 | 95.00 | 96.30 | 93.74 | 91.31 | 93.81 | 81.07 | 77.55 | 69.14 | 70.69 | -| estonian-ewt-ud-2.6-200830 | Gold tokenization | — | — | 96.22 | 97.37 | 94.65 | 92.37 | 94.83 | 86.37 | 82.55 | 73.03 | 74.39 | -| finnish-tdt-ud-2.6-200830 | Raw text | 99.70 | 88.64 | 97.63 | 98.25 | 96.05 | 95.11 | 92.06 | 90.11 | 88.10 | 82.04 | 77.91 | -| finnish-tdt-ud-2.6-200830 | Gold tokenization | — | — | 97.97 | 98.56 | 96.37 | 95.48 | 92.38 | 91.69 | 89.63 | 83.30 | 79.18 | -| finnish-ftb-ud-2.6-200830 | Raw text | 99.91 | 86.84 | 96.52 | 95.08 | 96.72 | 93.82 | 95.73 | 89.93 | 87.32 | 80.13 | 80.74 | -| finnish-ftb-ud-2.6-200830 | Gold tokenization | — | — | 96.85 | 95.31 | 96.87 | 94.16 | 95.83 | 91.99 | 89.34 | 82.64 | 83.05 | -| french-gsd-ud-2.6-200830 | Raw text | 98.87 | 94.67 | 97.23 | 98.86 | 96.65 | 96.00 | 97.69 | 92.77 | 90.82 | 83.14 | 86.08 | -| french-gsd-ud-2.6-200830 | Gold tokenization | — | — | 98.29 | 99.99 | 97.63 | 96.94 | 98.80 | 94.46 | 92.63 | 84.72 | 87.21 | -| french-sequoia-ud-2.6-200830 | Raw text | 99.09 | 87.50 | 98.33 | — | 97.25 | 96.79 | 98.16 | 93.90 | 92.45 | 86.54 | 89.25 | -| french-sequoia-ud-2.6-200830 | Gold tokenization | — | — | 99.32 | — | 98.19 | 97.78 | 99.09 | 95.80 | 94.43 | 88.78 | 90.78 | -| french-partut-ud-2.6-200830 | Raw text | 99.42 | 100.00 | 97.28 | 96.93 | 94.17 | 93.63 | 95.59 | 94.71 | 92.71 | 80.18 | 83.34 | -| french-partut-ud-2.6-200830 | Gold tokenization | — | — | 97.89 | 97.54 | 94.74 | 94.20 | 96.20 | 95.47 | 93.62 | 81.20 | 84.28 | -| french-spoken-ud-2.6-200830 | Raw text | 99.06 | 21.15 | 96.49 | 96.44 | — | 93.98 | 97.48 | 79.23 | 74.91 | 64.48 | 66.67 | -| french-spoken-ud-2.6-200830 | Gold tokenization | — | — | 97.63 | 97.31 | — | 95.00 | 98.28 | 87.27 | 82.51 | 74.23 | 75.56 | -| galician-ctg-ud-2.6-200830 | Raw text | 99.22 | 97.22 | 97.30 | 97.07 | 99.05 | 96.71 | 98.07 | 85.45 | 83.07 | 72.03 | 76.75 | -| galician-ctg-ud-2.6-200830 | Gold tokenization | — | — | 98.04 | 97.79 | 99.83 | 97.43 | 98.82 | 87.22 | 84.73 | 74.05 | 78.78 | -| galician-treegal-ud-2.6-200830 | Raw text | 98.74 | 87.99 | 95.99 | 93.58 | 94.72 | 92.63 | 96.71 | 83.26 | 79.23 | 67.54 | 71.73 | -| galician-treegal-ud-2.6-200830 | Gold tokenization | — | — | 97.23 | 94.65 | 95.76 | 93.73 | 97.89 | 86.57 | 82.30 | 71.04 | 75.71 | -| german-hdt-ud-2.6-200830 | Raw text | 99.91 | 92.34 | 98.51 | 98.45 | 94.09 | 93.69 | 97.23 | 96.88 | 95.96 | 84.87 | 90.41 | -| german-hdt-ud-2.6-200830 | Gold tokenization | — | — | 98.62 | 98.57 | 94.21 | 93.81 | 97.32 | 97.57 | 96.67 | 85.53 | 91.10 | -| german-gsd-ud-2.6-200830 | Raw text | 99.58 | 80.90 | 94.39 | 97.51 | 91.14 | 85.97 | 96.58 | 87.06 | 82.93 | 62.33 | 74.97 | -| german-gsd-ud-2.6-200830 | Gold tokenization | — | — | 94.73 | 97.96 | 91.65 | 86.51 | 96.95 | 89.36 | 85.31 | 64.33 | 77.26 | -| gothic-proiel-ud-2.6-200830 | Raw text | 100.00 | 31.12 | 96.39 | 96.90 | 90.18 | 88.05 | 94.70 | 74.10 | 68.48 | 55.16 | 62.26 | -| gothic-proiel-ud-2.6-200830 | Gold tokenization | — | — | 96.81 | 97.26 | 91.12 | 89.28 | 94.77 | 83.73 | 77.93 | 65.37 | 70.85 | -| greek-gdt-ud-2.6-200830 | Raw text | 99.87 | 90.19 | 97.99 | 98.00 | 95.57 | 94.91 | 95.53 | 93.00 | 91.16 | 81.28 | 80.73 | -| greek-gdt-ud-2.6-200830 | Gold tokenization | — | — | 98.14 | 98.14 | 95.69 | 95.02 | 95.61 | 93.82 | 91.95 | 82.03 | 81.53 | -| hebrew-htb-ud-2.6-200830 | Raw text | 85.04 | 99.39 | 82.79 | 82.76 | 81.31 | 80.57 | 82.97 | 69.85 | 67.39 | 54.79 | 59.16 | -| hebrew-htb-ud-2.6-200830 | Gold tokenization | — | — | 97.48 | 97.48 | 96.03 | 95.36 | 97.23 | 91.83 | 89.25 | 78.52 | 81.02 | -| hindi-hdtb-ud-2.6-200830 | Raw text | 100.00 | 98.90 | 97.64 | 97.29 | 94.18 | 92.32 | 98.78 | 95.32 | 92.37 | 79.24 | 87.69 | -| hindi-hdtb-ud-2.6-200830 | Gold tokenization | — | — | 97.65 | 97.29 | 94.21 | 92.35 | 98.78 | 95.44 | 92.49 | 79.41 | 87.84 | -| hungarian-szeged-ud-2.6-200830 | Raw text | 99.85 | 95.89 | 96.77 | — | 94.32 | 93.51 | 94.97 | 87.78 | 84.24 | 74.80 | 77.84 | -| hungarian-szeged-ud-2.6-200830 | Gold tokenization | — | — | 96.87 | — | 94.45 | 93.61 | 95.09 | 88.28 | 84.73 | 75.27 | 78.26 | -| indonesian-gsd-ud-2.6-200830 | Raw text | 100.00 | 94.13 | 93.89 | 94.28 | 95.55 | 89.00 | 99.61 | 86.07 | 79.97 | 69.25 | 77.74 | -| indonesian-gsd-ud-2.6-200830 | Gold tokenization | — | — | 93.90 | 94.26 | 95.52 | 88.98 | 99.61 | 86.32 | 80.18 | 69.51 | 78.00 | -| irish-idt-ud-2.6-200830 | Raw text | 99.71 | 97.36 | 94.35 | 94.30 | 73.43 | 70.38 | 93.18 | 84.47 | 77.88 | 40.78 | 65.74 | -| irish-idt-ud-2.6-200830 | Gold tokenization | — | — | 94.59 | 94.60 | 73.65 | 70.63 | 93.41 | 84.98 | 78.30 | 40.94 | 65.87 | -| italian-isdt-ud-2.6-200830 | Raw text | 99.84 | 98.76 | 98.52 | 98.44 | 98.23 | 97.66 | 98.65 | 94.77 | 93.12 | 86.91 | 87.85 | -| italian-isdt-ud-2.6-200830 | Gold tokenization | — | — | 98.68 | 98.60 | 98.38 | 97.81 | 98.81 | 95.07 | 93.44 | 87.20 | 88.19 | -| italian-partut-ud-2.6-200830 | Raw text | 99.73 | 100.00 | 98.41 | 98.52 | 98.27 | 97.77 | 98.74 | 96.07 | 93.90 | 87.45 | 88.95 | -| italian-partut-ud-2.6-200830 | Gold tokenization | — | — | 98.54 | 98.65 | 98.38 | 97.88 | 98.93 | 96.18 | 93.98 | 87.48 | 89.15 | -| italian-postwita-ud-2.6-200830 | Raw text | 99.47 | 30.49 | 96.53 | 96.28 | 96.43 | 94.89 | 96.76 | 80.97 | 76.94 | 65.79 | 67.44 | -| italian-postwita-ud-2.6-200830 | Gold tokenization | — | — | 97.06 | 96.79 | 96.89 | 95.41 | 97.18 | 88.04 | 83.76 | 75.23 | 76.98 | -| italian-twittiro-ud-2.6-200830 | Raw text | 99.06 | 36.80 | 95.99 | 95.86 | 95.22 | 93.37 | 94.68 | 81.69 | 77.38 | 64.34 | 65.32 | -| italian-twittiro-ud-2.6-200830 | Gold tokenization | — | — | 97.01 | 96.77 | 96.14 | 94.42 | 95.50 | 87.84 | 83.43 | 71.64 | 72.68 | -| italian-vit-ud-2.6-200830 | Raw text | 99.69 | 94.69 | 97.86 | 97.07 | 97.38 | 95.76 | 98.64 | 92.03 | 89.20 | 80.39 | 83.83 | -| italian-vit-ud-2.6-200830 | Gold tokenization | — | — | 98.16 | 97.49 | 97.66 | 96.16 | 98.92 | 92.77 | 89.91 | 81.15 | 84.53 | -| japanese-gsd-ud-2.6-200830 | Raw text | 95.34 | 94.61 | 93.67 | 93.56 | 95.32 | 92.74 | 95.02 | 85.11 | 84.01 | 76.23 | 77.83 | -| japanese-gsd-ud-2.6-200830 | Gold tokenization | — | — | 98.03 | 97.71 | 99.99 | 96.83 | 99.61 | 94.73 | 93.41 | 87.64 | 89.28 | -| korean-kaist-ud-2.6-200830 | Raw text | 99.95 | 100.00 | 95.89 | 87.82 | — | 87.62 | 94.23 | 89.41 | 87.58 | 82.32 | 80.34 | -| korean-kaist-ud-2.6-200830 | Gold tokenization | — | — | 95.94 | 87.85 | — | 87.66 | 94.27 | 89.51 | 87.67 | 82.42 | 80.42 | -| korean-gsd-ud-2.6-200830 | Raw text | 99.87 | 93.93 | 96.61 | 90.19 | 99.69 | 88.03 | 93.51 | 88.68 | 85.04 | 80.93 | 77.36 | -| korean-gsd-ud-2.6-200830 | Gold tokenization | — | — | 96.74 | 90.32 | 99.82 | 88.16 | 93.64 | 89.50 | 85.84 | 81.76 | 78.14 | -| latin-ittb-ud-2.6-200830 | Raw text | 99.99 | 92.44 | 98.54 | 96.35 | 96.92 | 95.12 | 98.94 | 90.31 | 88.16 | 82.19 | 85.37 | -| latin-ittb-ud-2.6-200830 | Gold tokenization | — | — | 98.52 | 96.37 | 96.92 | 95.11 | 98.93 | 91.24 | 89.07 | 82.62 | 85.88 | -| latin-llct-ud-2.6-200830 | Raw text | 100.00 | 99.49 | 99.60 | 97.13 | 97.11 | 96.63 | 97.68 | 95.48 | 94.35 | 89.31 | 90.44 | -| latin-llct-ud-2.6-200830 | Gold tokenization | — | — | 99.60 | 97.14 | 97.11 | 96.63 | 97.68 | 95.54 | 94.40 | 89.40 | 90.53 | -| latin-proiel-ud-2.6-200830 | Raw text | 99.87 | 36.81 | 96.67 | 96.81 | 90.71 | 89.59 | 96.16 | 74.44 | 69.97 | 57.51 | 64.96 | -| latin-proiel-ud-2.6-200830 | Gold tokenization | — | — | 97.07 | 97.16 | 91.53 | 90.52 | 96.42 | 83.78 | 79.04 | 67.58 | 73.88 | -| latin-perseus-ud-2.6-200830 | Raw text | 100.00 | 98.46 | 91.65 | 81.18 | 86.33 | 78.75 | 88.05 | 78.09 | 68.97 | 52.82 | 56.03 | -| latin-perseus-ud-2.6-200830 | Gold tokenization | — | — | 91.64 | 81.17 | 86.33 | 78.74 | 88.04 | 78.21 | 69.07 | 52.84 | 55.99 | -| latvian-lvtb-ud-2.6-200830 | Raw text | 99.32 | 98.74 | 96.28 | 89.64 | 93.79 | 88.84 | 95.81 | 88.31 | 85.26 | 75.23 | 79.56 | -| latvian-lvtb-ud-2.6-200830 | Gold tokenization | — | — | 96.92 | 90.24 | 94.40 | 89.43 | 96.45 | 89.33 | 86.23 | 76.29 | 80.60 | -| lithuanian-alksnis-ud-2.6-200830 | Raw text | 99.91 | 87.87 | 95.97 | 90.37 | 91.07 | 89.41 | 93.61 | 82.54 | 78.70 | 67.95 | 71.30 | -| lithuanian-alksnis-ud-2.6-200830 | Gold tokenization | — | — | 96.04 | 90.40 | 91.18 | 89.49 | 93.70 | 83.93 | 80.08 | 69.02 | 72.43 | -| lithuanian-hse-ud-2.6-200830 | Raw text | 97.30 | 97.30 | 89.66 | 89.28 | 81.45 | 77.07 | 87.98 | 70.92 | 62.53 | 44.26 | 53.76 | -| lithuanian-hse-ud-2.6-200830 | Gold tokenization | — | — | 91.23 | 91.32 | 83.21 | 78.40 | 90.28 | 73.77 | 64.53 | 45.25 | 54.68 | -| maltese-mudt-ud-2.6-200830 | Raw text | 99.84 | 86.29 | 95.77 | 95.66 | — | 95.30 | — | 84.76 | 79.76 | 68.39 | 72.24 | -| maltese-mudt-ud-2.6-200830 | Gold tokenization | — | — | 95.88 | 95.77 | — | 95.40 | — | 85.46 | 80.38 | 68.69 | 72.66 | -| marathi-ufal-ud-2.6-200830 | Raw text | 90.25 | 92.63 | 78.50 | — | 65.25 | 61.50 | 80.00 | 61.25 | 53.50 | 31.73 | 40.92 | -| marathi-ufal-ud-2.6-200830 | Gold tokenization | — | — | 84.22 | — | 68.69 | 63.83 | 80.10 | 70.39 | 60.92 | 31.95 | 42.32 | -| naija-nsc-ud-2.6-200830 | Raw text | 100.00 | 99.56 | 98.14 | — | 99.16 | 97.77 | 99.27 | 92.46 | 89.81 | 84.18 | 86.20 | -| naija-nsc-ud-2.6-200830 | Gold tokenization | — | — | 98.14 | — | 99.16 | 97.77 | 99.27 | 92.50 | 89.84 | 84.25 | 86.26 | -| north_sami-giella-ud-2.6-200830 | Raw text | 99.87 | 98.79 | 92.35 | 93.57 | 89.40 | 85.61 | 86.85 | 76.66 | 71.84 | 60.71 | 58.95 | -| north_sami-giella-ud-2.6-200830 | Gold tokenization | — | — | 92.47 | 93.70 | 89.56 | 85.75 | 86.96 | 76.97 | 72.16 | 60.95 | 59.19 | -| norwegian-bokmaal-ud-2.6-200830 | Raw text | 99.83 | 95.63 | 98.37 | — | 97.52 | 96.86 | 98.55 | 93.74 | 92.26 | 87.03 | 88.76 | -| norwegian-bokmaal-ud-2.6-200830 | Gold tokenization | — | — | 98.57 | — | 97.71 | 97.05 | 98.75 | 94.48 | 93.00 | 87.67 | 89.43 | -| norwegian-nynorsk-ud-2.6-200830 | Raw text | 99.91 | 94.11 | 98.36 | — | 97.38 | 96.67 | 98.37 | 93.86 | 92.11 | 86.07 | 88.11 | -| norwegian-nynorsk-ud-2.6-200830 | Gold tokenization | — | — | 98.50 | — | 97.51 | 96.80 | 98.50 | 94.66 | 92.93 | 86.95 | 89.01 | -| norwegian-nynorsklia-ud-2.6-200830 | Raw text | 99.91 | 99.53 | 96.45 | — | 95.71 | 93.62 | 98.05 | 80.90 | 76.53 | 65.74 | 69.55 | -| norwegian-nynorsklia-ud-2.6-200830 | Gold tokenization | — | — | 96.55 | — | 95.79 | 93.72 | 98.14 | 81.15 | 76.76 | 65.94 | 69.80 | -| old_church_slavonic-proiel-ud-2.6-200830 | Raw text | 100.00 | 41.43 | 96.58 | 96.83 | 90.44 | 89.17 | 93.19 | 77.42 | 73.57 | 63.51 | 68.53 | -| old_church_slavonic-proiel-ud-2.6-200830 | Gold tokenization | — | — | 96.89 | 97.09 | 91.22 | 89.97 | 93.20 | 87.95 | 83.81 | 73.92 | 77.26 | -| old_french-srcmf-ud-2.6-200830 | Raw text | 99.93 | 100.00 | 96.40 | 96.27 | 97.80 | 95.58 | — | 92.28 | 87.74 | 81.08 | 84.17 | -| old_french-srcmf-ud-2.6-200830 | Gold tokenization | — | — | 96.47 | 96.33 | 97.86 | 95.64 | — | 92.36 | 87.81 | 81.17 | 84.26 | -| old_russian-torot-ud-2.6-200830 | Raw text | 100.00 | 29.60 | 94.33 | 94.39 | 87.51 | 85.16 | 85.82 | 70.66 | 65.18 | 51.26 | 53.18 | -| old_russian-torot-ud-2.6-200830 | Gold tokenization | — | — | 94.93 | 94.99 | 88.44 | 86.35 | 85.77 | 83.15 | 77.17 | 63.78 | 62.66 | -| old_russian-rnc-ud-2.6-200830 | Raw text | 98.15 | 85.46 | 91.80 | 87.74 | 75.83 | 66.63 | 74.94 | 63.08 | 57.53 | 33.85 | 35.04 | -| old_russian-rnc-ud-2.6-200830 | Gold tokenization | — | — | 93.34 | 89.43 | 77.09 | 67.76 | 76.13 | 66.86 | 60.73 | 36.05 | 37.07 | -| persian-seraji-ud-2.6-200830 | Raw text | 99.65 | 98.75 | 97.69 | 97.66 | 97.75 | 97.29 | 96.67 | 91.09 | 88.15 | 83.43 | 82.26 | -| persian-seraji-ud-2.6-200830 | Gold tokenization | — | — | 97.98 | 97.97 | 98.07 | 97.60 | 96.94 | 91.74 | 88.76 | 84.00 | 82.82 | -| polish-pdb-ud-2.6-200830 | Raw text | 99.85 | 97.33 | 98.88 | 95.73 | 95.84 | 95.03 | 98.05 | 94.02 | 92.01 | 84.93 | 88.08 | -| polish-pdb-ud-2.6-200830 | Gold tokenization | — | — | 99.04 | 95.88 | 95.99 | 95.18 | 98.19 | 94.51 | 92.50 | 85.36 | 88.52 | -| polish-lfg-ud-2.6-200830 | Raw text | 99.85 | 99.65 | 98.92 | 95.99 | 96.51 | 95.06 | 98.27 | 96.89 | 95.52 | 89.73 | 92.45 | -| polish-lfg-ud-2.6-200830 | Gold tokenization | — | — | 99.09 | 96.18 | 96.70 | 95.25 | 98.41 | 97.29 | 95.91 | 90.12 | 92.77 | -| portuguese-gsd-ud-2.6-200830 | Raw text | 99.84 | 97.50 | 98.53 | 98.52 | 99.71 | 98.43 | 99.33 | 94.57 | 93.47 | 88.69 | 90.02 | -| portuguese-gsd-ud-2.6-200830 | Gold tokenization | — | — | 98.69 | 98.69 | 99.87 | 98.59 | 99.49 | 94.94 | 93.82 | 89.11 | 90.36 | -| portuguese-bosque-ud-2.6-200830 | Raw text | 99.55 | 90.64 | 97.19 | — | 96.17 | 94.79 | 97.98 | 92.32 | 89.72 | 79.29 | 84.22 | -| portuguese-bosque-ud-2.6-200830 | Gold tokenization | — | — | 97.60 | — | 96.49 | 95.11 | 98.42 | 93.53 | 90.80 | 80.42 | 85.51 | -| romanian-rrt-ud-2.6-200830 | Raw text | 99.69 | 95.28 | 97.79 | 97.18 | 97.32 | 96.81 | 98.20 | 91.83 | 87.56 | 80.00 | 82.17 | -| romanian-rrt-ud-2.6-200830 | Gold tokenization | — | — | 98.08 | 97.44 | 97.60 | 97.08 | 98.49 | 92.74 | 88.38 | 80.82 | 82.88 | -| romanian-nonstandard-ud-2.6-200830 | Raw text | 98.35 | 96.73 | 95.61 | 91.38 | 90.03 | 88.67 | 94.23 | 88.89 | 84.47 | 67.59 | 75.81 | -| romanian-nonstandard-ud-2.6-200830 | Gold tokenization | — | — | 97.21 | 92.90 | 91.53 | 90.13 | 95.74 | 91.00 | 86.49 | 69.53 | 77.28 | -| russian-syntagrus-ud-2.6-200830 | Raw text | 99.60 | 98.80 | 98.86 | — | 97.60 | 97.38 | 98.33 | 94.22 | 92.97 | 89.27 | 90.35 | -| russian-syntagrus-ud-2.6-200830 | Gold tokenization | — | — | 99.27 | — | 97.98 | 97.76 | 98.68 | 94.99 | 93.72 | 89.90 | 90.95 | -| russian-gsd-ud-2.6-200830 | Raw text | 99.50 | 96.22 | 98.03 | 97.51 | 94.76 | 93.60 | 96.89 | 91.66 | 88.38 | 80.67 | 84.18 | -| russian-gsd-ud-2.6-200830 | Gold tokenization | — | — | 98.49 | 97.98 | 95.17 | 93.97 | 97.27 | 92.77 | 89.43 | 81.44 | 85.05 | -| russian-taiga-ud-2.6-200830 | Raw text | 97.16 | 82.69 | 94.13 | 95.72 | 90.01 | 87.50 | 93.05 | 81.17 | 76.99 | 65.28 | 69.94 | -| russian-taiga-ud-2.6-200830 | Gold tokenization | — | — | 96.47 | 98.56 | 92.72 | 89.87 | 95.68 | 85.57 | 80.81 | 68.93 | 73.90 | -| sanskrit-vedic-ud-2.6-200830 | Raw text | 100.00 | 27.18 | 89.50 | — | 81.72 | 77.12 | 87.11 | 60.79 | 49.75 | 41.65 | 44.67 | -| sanskrit-vedic-ud-2.6-200830 | Gold tokenization | — | — | 90.01 | — | 83.11 | 78.58 | 87.24 | 73.34 | 61.55 | 51.87 | 54.91 | -| scottish_gaelic-arcosg-ud-2.6-200830 | Raw text | 99.58 | 55.57 | 93.63 | 87.07 | 89.78 | 85.43 | 95.41 | 77.66 | 71.86 | 55.15 | 60.51 | -| scottish_gaelic-arcosg-ud-2.6-200830 | Gold tokenization | — | — | 94.26 | 87.84 | 90.23 | 86.30 | 95.85 | 83.77 | 77.61 | 62.05 | 68.26 | -| serbian-set-ud-2.6-200830 | Raw text | 99.99 | 93.00 | 98.98 | 95.75 | 95.92 | 95.35 | 97.82 | 93.66 | 91.18 | 83.18 | 86.80 | -| serbian-set-ud-2.6-200830 | Gold tokenization | — | — | 99.01 | 95.78 | 95.94 | 95.39 | 97.83 | 94.33 | 91.82 | 83.84 | 87.45 | -| slovak-snk-ud-2.6-200830 | Raw text | 100.00 | 85.28 | 97.19 | 87.79 | 92.66 | 86.71 | 96.52 | 91.71 | 89.60 | 78.75 | 84.54 | -| slovak-snk-ud-2.6-200830 | Gold tokenization | — | — | 97.30 | 88.06 | 92.84 | 86.98 | 96.60 | 93.68 | 91.57 | 80.55 | 86.59 | -| slovenian-ssj-ud-2.6-200830 | Raw text | 97.99 | 67.98 | 96.93 | 94.35 | 94.56 | 93.95 | 96.59 | 88.09 | 86.65 | 80.90 | 83.58 | -| slovenian-ssj-ud-2.6-200830 | Gold tokenization | — | — | 98.86 | 96.44 | 96.69 | 96.01 | 98.54 | 94.41 | 92.96 | 86.68 | 89.30 | -| slovenian-sst-ud-2.6-200830 | Raw text | 99.85 | 23.14 | 94.70 | 92.70 | 92.52 | 89.74 | 97.14 | 64.23 | 59.57 | 49.25 | 52.95 | -| slovenian-sst-ud-2.6-200830 | Gold tokenization | — | — | 95.71 | 93.11 | 92.94 | 90.90 | 97.46 | 77.81 | 72.24 | 62.71 | 67.18 | -| spanish-ancora-ud-2.6-200830 | Raw text | 99.95 | 98.32 | 99.09 | 99.02 | 98.87 | 98.33 | 99.36 | 93.62 | 91.78 | 86.82 | 88.06 | -| spanish-ancora-ud-2.6-200830 | Gold tokenization | — | — | 99.14 | 99.06 | 98.91 | 98.37 | 99.40 | 93.83 | 91.97 | 87.01 | 88.24 | -| spanish-gsd-ud-2.6-200830 | Raw text | 99.76 | 94.54 | 97.17 | — | 97.05 | 95.32 | 98.80 | 92.00 | 89.70 | 79.23 | 84.49 | -| spanish-gsd-ud-2.6-200830 | Gold tokenization | — | — | 97.40 | — | 97.27 | 95.54 | 99.02 | 92.73 | 90.38 | 79.93 | 85.13 | -| swedish-talbanken-ud-2.6-200830 | Raw text | 99.89 | 96.13 | 98.41 | 97.26 | 97.33 | 96.46 | 98.19 | 91.99 | 89.68 | 83.63 | 85.82 | -| swedish-talbanken-ud-2.6-200830 | Gold tokenization | — | — | 98.51 | 97.38 | 97.44 | 96.57 | 98.30 | 92.46 | 90.14 | 84.12 | 86.33 | -| swedish-lines-ud-2.6-200830 | Raw text | 99.96 | 87.20 | 97.71 | 95.47 | 90.89 | 88.10 | 97.76 | 89.14 | 85.80 | 71.44 | 81.67 | -| swedish-lines-ud-2.6-200830 | Gold tokenization | — | — | 97.75 | 95.48 | 90.91 | 88.09 | 97.79 | 89.91 | 86.52 | 72.13 | 82.48 | -| tamil-ttb-ud-2.6-200830 | Raw text | 94.51 | 97.52 | 88.39 | 82.92 | 85.30 | 82.11 | 89.15 | 70.28 | 64.91 | 54.93 | 58.46 | -| tamil-ttb-ud-2.6-200830 | Gold tokenization | — | — | 93.36 | 87.28 | 90.10 | 86.22 | 93.97 | 78.03 | 71.79 | 61.09 | 64.80 | -| telugu-mtg-ud-2.6-200830 | Raw text | 99.58 | 96.62 | 93.63 | 93.63 | 98.48 | 93.63 | — | 90.17 | 83.52 | 76.00 | 79.62 | -| telugu-mtg-ud-2.6-200830 | Gold tokenization | — | — | 94.04 | 94.04 | 98.89 | 94.04 | — | 91.12 | 84.47 | 76.84 | 80.46 | -| turkish-imst-ud-2.6-200830 | Raw text | 98.30 | 96.97 | 94.48 | 93.69 | 92.06 | 89.95 | 94.41 | 72.63 | 66.80 | 58.31 | 61.57 | -| turkish-imst-ud-2.6-200830 | Gold tokenization | — | — | 96.10 | 95.32 | 93.66 | 91.50 | 96.00 | 76.10 | 69.93 | 60.33 | 63.83 | -| ukrainian-iu-ud-2.6-200830 | Raw text | 99.81 | 96.61 | 97.89 | 94.22 | 94.18 | 93.13 | 97.39 | 90.59 | 88.24 | 78.76 | 83.19 | -| ukrainian-iu-ud-2.6-200830 | Gold tokenization | — | — | 98.10 | 94.42 | 94.34 | 93.30 | 97.56 | 91.11 | 88.75 | 79.11 | 83.59 | -| urdu-udtb-ud-2.6-200830 | Raw text | 100.00 | 98.31 | 94.10 | 92.27 | 82.89 | 78.41 | 97.38 | 88.27 | 82.63 | 56.79 | 74.77 | -| urdu-udtb-ud-2.6-200830 | Gold tokenization | — | — | 94.08 | 92.26 | 82.92 | 78.43 | 97.39 | 88.37 | 82.74 | 56.90 | 74.92 | -| uyghur-udt-ud-2.6-200830 | Raw text | 99.54 | 81.81 | 89.24 | 91.70 | 88.47 | 80.04 | 94.76 | 76.58 | 64.72 | 46.67 | 55.08 | -| uyghur-udt-ud-2.6-200830 | Gold tokenization | — | — | 89.67 | 92.21 | 88.92 | 80.47 | 95.27 | 78.39 | 66.27 | 47.53 | 56.23 | -| vietnamese-vtb-ud-2.6-200830 | Raw text | 85.37 | 93.46 | 78.19 | 76.69 | 85.11 | 76.53 | 85.15 | 52.80 | 47.90 | 41.56 | 44.31 | -| vietnamese-vtb-ud-2.6-200830 | Gold tokenization | — 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o=4)return!1;if(e.listIndent>=0&&e.sCount[r]-e.listIndent>=4&&e.sCount[r]=e.blkIndent&&(I=!0),(q=o(e,r))>=0){if(h=!0,S=e.bMarks[r]+e.tShift[r],k=Number(e.src.substr(S,q-S-1)),I&&1!==k)return!1}else{if(!((q=s(e,r))>=0))return!1;h=!1}if(I&&e.skipSpaces(q)>=e.eMarks[r])return!1;if(g=e.src.charCodeAt(q-1),n)return!0;for(_=e.tokens.length,h?(T=e.push("ordered_list_open","ol",1),1!==k&&(T.attrs=[["start",k]])):T=e.push("bullet_list_open","ul",1),T.map=m=[r,0],T.markup=String.fromCharCode(g),v=r,F=!1,z=e.md.block.ruler.getRules("list"),x=e.parentType,e.parentType="list";v=b?1:C-p)>4&&(u=1),l=p+u,(T=e.push("list_item_open","li",1)).markup=String.fromCharCode(g),T.map=f=[r,0],D=e.tight,w=e.tShift[r],A=e.sCount[r],y=e.listIndent,e.listIndent=e.blkIndent,e.blkIndent=l,e.tight=!0,e.tShift[r]=a-e.bMarks[r],e.sCount[r]=C,a>=b&&e.isEmpty(r+1)?e.line=Math.min(e.line+2,t):e.md.block.tokenize(e,r,t,!0),e.tight&&!F||(R=!1),F=e.line-r>1&&e.isEmpty(e.line-1),e.blkIndent=e.listIndent,e.listIndent=y,e.tShift[r]=w,e.sCount[r]=A,e.tight=D,(T=e.push("list_item_close","li",-1)).markup=String.fromCharCode(g),v=r=e.line,f[1]=v,a=e.bMarks[r],v>=t)break;if(e.sCount[v]=4)break;for(L=!1,c=0,d=z.length;c3||e.sCount[c]<0)){for(n=!1,s=0,o=l.length;s=4)return!1;if(91!==e.src.charCodeAt(x))return!1;for(;++x3||e.sCount[w]<0)){for(b=!1,p=0,h=v.length;p0&&this.level++,this.tokens.push(s),s},o.prototype.isEmpty=function(e){return this.bMarks[e]+this.tShift[e]>=this.eMarks[e]},o.prototype.skipEmptyLines=function(e){for(var r=this.lineMax;er;)if(!s(this.src.charCodeAt(--e)))return e+1;return e},o.prototype.skipChars=function(e,r){for(var t=this.src.length;et;)if(r!==this.src.charCodeAt(--e))return e+1;return e},o.prototype.getLines=function(e,r,t,n){var o,i,a,c,l,u,p,h=e;if(e>=r)return"";for(u=new Array(r-e),o=0;ht?new Array(i-t+1).join(" ")+this.src.slice(c,l):this.src.slice(c,l)}return u.join("")},o.prototype.Token=n,r.exports=o},{"../common/utils":4,"../token":51}],29:[function(e,r,t){"use strict";var n=e("../common/utils").isSpace;function s(e,r){var t=e.bMarks[r]+e.blkIndent,n=e.eMarks[r];return e.src.substr(t,n-t)}function o(e){var r,t=[],n=0,s=e.length,o=0,i=0,a=!1,c=0;for(r=e.charCodeAt(n);nt)return!1;if(p=r+1,e.sCount[p]=4)return!1;if((l=e.bMarks[p]+e.tShift[p])>=e.eMarks[p])return!1;if(124!==(a=e.src.charCodeAt(l++))&&45!==a&&58!==a)return!1;for(;l=4)return!1;if((f=(h=o(c.replace(/^\||\|$/g,""))).length)>m.length)return!1;if(i)return!0;for((d=e.push("table_open","table",1)).map=g=[r,0],(d=e.push("thead_open","thead",1)).map=[r,r+1],(d=e.push("tr_open","tr",1)).map=[r,r+1],u=0;u=4);p++){for(h=o(c.replace(/^\||\|$/g,"")),d=e.push("tr_open","tr",1),u=0;u/i.test(e)}r.exports=function(e){var r,t,o,i,a,c,l,u,p,h,f,d,m,_,g,k,b,v,C=e.tokens;if(e.md.options.linkify)for(t=0,o=C.length;t=0;r--)if("link_close"!==(c=i[r]).type){if("html_inline"===c.type&&(v=c.content,/^\s]/i.test(v)&&m>0&&m--,s(c.content)&&m++),!(m>0)&&"text"===c.type&&e.md.linkify.test(c.content)){for(p=c.content,b=e.md.linkify.match(p),l=[],d=c.level,f=0,u=0;uf&&((a=new e.Token("text","",0)).content=p.slice(f,h),a.level=d,l.push(a)),(a=new e.Token("link_open","a",1)).attrs=[["href",g]],a.level=d++,a.markup="linkify",a.info="auto",l.push(a),(a=new e.Token("text","",0)).content=k,a.level=d,l.push(a),(a=new e.Token("link_close","a",-1)).level=--d,a.markup="linkify",a.info="auto",l.push(a),f=b[u].lastIndex);f=0;r--)"text"!==(t=e[r]).type||n||(t.content=t.content.replace(o,a)),"link_open"===t.type&&"auto"===t.info&&n--,"link_close"===t.type&&"auto"===t.info&&n++}function l(e){var r,t,s=0;for(r=e.length-1;r>=0;r--)"text"!==(t=e[r]).type||s||n.test(t.content)&&(t.content=t.content.replace(/\+-/g,"\xb1").replace(/\.{2,}/g,"\u2026").replace(/([?!])\u2026/g,"$1..").replace(/([?!]){4,}/g,"$1$1$1").replace(/,{2,}/g,",").replace(/(^|[^-])---(?=[^-]|$)/gm,"$1\u2014").replace(/(^|\s)--(?=\s|$)/gm,"$1\u2013").replace(/(^|[^-\s])--(?=[^-\s]|$)/gm,"$1\u2013")),"link_open"===t.type&&"auto"===t.info&&s--,"link_close"===t.type&&"auto"===t.info&&s++}r.exports=function(e){var r;if(e.md.options.typographer)for(r=e.tokens.length-1;r>=0;r--)"inline"===e.tokens[r].type&&(s.test(e.tokens[r].content)&&c(e.tokens[r].children),n.test(e.tokens[r].content)&&l(e.tokens[r].children))}},{}],35:[function(e,r,t){"use strict";var n=e("../common/utils").isWhiteSpace,s=e("../common/utils").isPunctChar,o=e("../common/utils").isMdAsciiPunct,i=/['"]/,a=/['"]/g;function c(e,r,t){return e.substr(0,r)+t+e.substr(r+1)}function l(e,r){var t,i,l,u,p,h,f,d,m,_,g,k,b,v,C,y,x,A,w,D,E;for(w=[],t=0;t=0&&!(w[x].level<=f);x--);if(w.length=x+1,"text"===i.type){p=0,h=(l=i.content).length;e:for(;p=0)m=l.charCodeAt(u.index-1);else for(x=t-1;x>=0&&("softbreak"!==e[x].type&&"hardbreak"!==e[x].type);x--)if(e[x].content){m=e[x].content.charCodeAt(e[x].content.length-1);break}if(_=32,p=48&&m<=57&&(y=C=!1),C&&y&&(C=g,y=k),C||y){if(y)for(x=w.length-1;x>=0&&(d=w[x],!(w[x].level=0;r--)"inline"===e.tokens[r].type&&i.test(e.tokens[r].content)&&l(e.tokens[r].children,e)}},{"../common/utils":4}],36:[function(e,r,t){"use strict";var n=e("../token");function s(e,r,t){this.src=e,this.env=t,this.tokens=[],this.inlineMode=!1,this.md=r}s.prototype.Token=n,r.exports=s},{"../token":51}],37:[function(e,r,t){"use strict";var n=/^<([a-zA-Z0-9.!#$%&'*+\/=?^_`{|}~-]+@[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(?:\.[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?)*)>/,s=/^<([a-zA-Z][a-zA-Z0-9+.\-]{1,31}):([^<>\x00-\x20]*)>/;r.exports=function(e,r){var t,o,i,a,c,l,u=e.pos;return 60===e.src.charCodeAt(u)&&(!((t=e.src.slice(u)).indexOf(">")<0)&&(s.test(t)?(a=(o=t.match(s))[0].slice(1,-1),c=e.md.normalizeLink(a),!!e.md.validateLink(c)&&(r||((l=e.push("link_open","a",1)).attrs=[["href",c]],l.markup="autolink",l.info="auto",(l=e.push("text","",0)).content=e.md.normalizeLinkText(a),(l=e.push("link_close","a",-1)).markup="autolink",l.info="auto"),e.pos+=o[0].length,!0)):!!n.test(t)&&(a=(i=t.match(n))[0].slice(1,-1),c=e.md.normalizeLink("mailto:"+a),!!e.md.validateLink(c)&&(r||((l=e.push("link_open","a",1)).attrs=[["href",c]],l.markup="autolink",l.info="auto",(l=e.push("text","",0)).content=e.md.normalizeLinkText(a),(l=e.push("link_close","a",-1)).markup="autolink",l.info="auto"),e.pos+=i[0].length,!0))))}},{}],38:[function(e,r,t){"use strict";r.exports=function(e,r){var t,n,s,o,i,a,c=e.pos;if(96!==e.src.charCodeAt(c))return!1;for(t=c,c++,n=e.posMax;ci;n-=o.jump+1)if((o=r[n]).marker===s.marker&&(-1===a&&(a=n),o.open&&o.end<0&&(c=!1,(o.close||s.open)&&(o.length+s.length)%3==0&&(o.length%3==0&&s.length%3==0||(c=!0)),!c))){l=n>0&&!r[n-1].open?r[n-1].jump+1:0,s.jump=t-n+l,s.open=!1,o.end=t,o.jump=l,o.close=!1,a=-1;break}-1!==a&&(u[s.marker][(s.length||0)%3]=a)}}r.exports=function(e){var r,t=e.tokens_meta,s=e.tokens_meta.length;for(n(0,e.delimiters),r=0;r=0;t--)95!==(n=r[t]).marker&&42!==n.marker||-1!==n.end&&(s=r[n.end],a=t>0&&r[t-1].end===n.end+1&&r[t-1].token===n.token-1&&r[n.end+1].token===s.token+1&&r[t-1].marker===n.marker,i=String.fromCharCode(n.marker),(o=e.tokens[n.token]).type=a?"strong_open":"em_open",o.tag=a?"strong":"em",o.nesting=1,o.markup=a?i+i:i,o.content="",(o=e.tokens[s.token]).type=a?"strong_close":"em_close",o.tag=a?"strong":"em",o.nesting=-1,o.markup=a?i+i:i,o.content="",a&&(e.tokens[r[t-1].token].content="",e.tokens[r[n.end+1].token].content="",t--))}r.exports.tokenize=function(e,r){var t,n,s=e.pos,o=e.src.charCodeAt(s);if(r)return!1;if(95!==o&&42!==o)return!1;for(n=e.scanDelims(e.pos,42===o),t=0;t?@[]^_`{|}~-".split("").forEach((function(e){s[e.charCodeAt(0)]=1})),r.exports=function(e,r){var t,o=e.pos,i=e.posMax;if(92!==e.src.charCodeAt(o))return!1;if(++o=o)&&(!(33!==(t=e.src.charCodeAt(i+1))&&63!==t&&47!==t&&!function(e){var r=32|e;return r>=97&&r<=122}(t))&&(!!(s=e.src.slice(i).match(n))&&(r||(e.push("html_inline","",0).content=e.src.slice(i,i+s[0].length)),e.pos+=s[0].length,!0))))}},{"../common/html_re":3}],44:[function(e,r,t){"use strict";var n=e("../common/utils").normalizeReference,s=e("../common/utils").isSpace;r.exports=function(e,r){var t,o,i,a,c,l,u,p,h,f,d,m,_,g="",k=e.pos,b=e.posMax;if(33!==e.src.charCodeAt(e.pos))return!1;if(91!==e.src.charCodeAt(e.pos+1))return!1;if(l=e.pos+2,(c=e.md.helpers.parseLinkLabel(e,e.pos+1,!1))<0)return!1;if((u=c+1)=b)return!1;for(_=u,(h=e.md.helpers.parseLinkDestination(e.src,u,e.posMax)).ok&&(g=e.md.normalizeLink(h.str),e.md.validateLink(g)?u=h.pos:g=""),_=u;u=b||41!==e.src.charCodeAt(u))return e.pos=k,!1;u++}else{if(void 0===e.env.references)return!1;if(u=0?a=e.src.slice(_,u++):u=c+1):u=c+1,a||(a=e.src.slice(l,c)),!(p=e.env.references[n(a)]))return e.pos=k,!1;g=p.href,f=p.title}return r||(i=e.src.slice(l,c),e.md.inline.parse(i,e.md,e.env,m=[]),(d=e.push("image","img",0)).attrs=t=[["src",g],["alt",""]],d.children=m,d.content=i,f&&t.push(["title",f])),e.pos=u,e.posMax=b,!0}},{"../common/utils":4}],45:[function(e,r,t){"use strict";var n=e("../common/utils").normalizeReference,s=e("../common/utils").isSpace;r.exports=function(e,r){var t,o,i,a,c,l,u,p,h,f="",d=e.pos,m=e.posMax,_=e.pos,g=!0;if(91!==e.src.charCodeAt(e.pos))return!1;if(c=e.pos+1,(a=e.md.helpers.parseLinkLabel(e,e.pos,!0))<0)return!1;if((l=a+1)=m)return!1;for(_=l,(u=e.md.helpers.parseLinkDestination(e.src,l,e.posMax)).ok&&(f=e.md.normalizeLink(u.str),e.md.validateLink(f)?l=u.pos:f=""),_=l;l=m||41!==e.src.charCodeAt(l))&&(g=!0),l++}if(g){if(void 0===e.env.references)return!1;if(l=0?i=e.src.slice(_,l++):l=a+1):l=a+1,i||(i=e.src.slice(c,a)),!(p=e.env.references[n(i)]))return e.pos=d,!1;f=p.href,h=p.title}return r||(e.pos=c,e.posMax=a,e.push("link_open","a",1).attrs=t=[["href",f]],h&&t.push(["title",h]),e.md.inline.tokenize(e),e.push("link_close","a",-1)),e.pos=l,e.posMax=m,!0}},{"../common/utils":4}],46:[function(e,r,t){"use strict";var n=e("../common/utils").isSpace;r.exports=function(e,r){var t,s,o=e.pos;if(10!==e.src.charCodeAt(o))return!1;for(t=e.pending.length-1,s=e.posMax,r||(t>=0&&32===e.pending.charCodeAt(t)?t>=1&&32===e.pending.charCodeAt(t-1)?(e.pending=e.pending.replace(/ +$/,""),e.push("hardbreak","br",0)):(e.pending=e.pending.slice(0,-1),e.push("softbreak","br",0)):e.push("softbreak","br",0)),o++;o0&&(this.level++,this._prev_delimiters.push(this.delimiters),this.delimiters=[],o={delimiters:this.delimiters}),this.pendingLevel=this.level,this.tokens.push(s),this.tokens_meta.push(o),s},a.prototype.scanDelims=function(e,r){var t,n,a,c,l,u,p,h,f,d=e,m=!0,_=!0,g=this.posMax,k=this.src.charCodeAt(e);for(t=e>0?this.src.charCodeAt(e-1):32;d0&&n++,"text"===s[r].type&&r+1=0&&(t=this.attrs[r][1]),t},n.prototype.attrJoin=function(e,r){var t=this.attrIndex(e);t<0?this.attrPush([e,r]):this.attrs[t][1]=this.attrs[t][1]+" "+r},r.exports=n},{}],52:[function(e,r,t){r.exports={Aacute:"\xc1",aacute:"\xe1",Abreve:"\u0102",abreve:"\u0103",ac:"\u223e",acd:"\u223f",acE:"\u223e\u0333",Acirc:"\xc2",acirc:"\xe2",acute:"\xb4",Acy:"\u0410",acy:"\u0430",AElig:"\xc6",aelig:"\xe6",af:"\u2061",Afr:"\ud835\udd04",afr:"\ud835\udd1e",Agrave:"\xc0",agrave:"\xe0",alefsym:"\u2135",aleph:"\u2135",Alpha:"\u0391",alpha:"\u03b1",Amacr:"\u0100",amacr:"\u0101",amalg:"\u2a3f",amp:"&",AMP:"&",andand:"\u2a55",And:"\u2a53",and:"\u2227",andd:"\u2a5c",andslope:"\u2a58",andv:"\u2a5a",ang:"\u2220",ange:"\u29a4",angle:"\u2220",angmsdaa:"\u29a8",angmsdab:"\u29a9",angmsdac:"\u29aa",angmsdad:"\u29ab",angmsdae:"\u29ac",angmsdaf:"\u29ad",angmsdag:"\u29ae",angmsdah:"\u29af",angmsd:"\u2221",angrt:"\u221f",angrtvb:"\u22be",angrtvbd:"\u299d",angsph:"\u2222",angst:"\xc5",angzarr:"\u237c",Aogon:"\u0104",aogon:"\u0105",Aopf:"\ud835\udd38",aopf:"\ud835\udd52",apacir:"\u2a6f",ap:"\u2248",apE:"\u2a70",ape:"\u224a",apid:"\u224b",apos:"'",ApplyFunction:"\u2061",approx:"\u2248",approxeq:"\u224a",Aring:"\xc5",aring:"\xe5",Ascr:"\ud835\udc9c",ascr:"\ud835\udcb6",Assign:"\u2254",ast:"*",asymp:"\u2248",asympeq:"\u224d",Atilde:"\xc3",atilde:"\xe3",Auml:"\xc4",auml:"\xe4",awconint:"\u2233",awint:"\u2a11",backcong:"\u224c",backepsilon:"\u03f6",backprime:"\u2035",backsim:"\u223d",backsimeq:"\u22cd",Backslash:"\u2216",Barv:"\u2ae7",barvee:"\u22bd",barwed:"\u2305",Barwed:"\u2306",barwedge:"\u2305",bbrk:"\u23b5",bbrktbrk:"\u23b6",bcong:"\u224c",Bcy:"\u0411",bcy:"\u0431",bdquo:"\u201e",becaus:"\u2235",because:"\u2235",Because:"\u2235",bemptyv:"\u29b0",bepsi:"\u03f6",bernou:"\u212c",Bernoullis:"\u212c",Beta:"\u0392",beta:"\u03b2",beth:"\u2136",between:"\u226c",Bfr:"\ud835\udd05",bfr:"\ud835\udd1f",bigcap:"\u22c2",bigcirc:"\u25ef",bigcup:"\u22c3",bigodot:"\u2a00",bigoplus:"\u2a01",bigotimes:"\u2a02",bigsqcup:"\u2a06",bigstar:"\u2605",bigtriangledown:"\u25bd",bigtriangleup:"\u25b3",biguplus:"\u2a04",bigvee:"\u22c1",bigwedge:"\u22c0",bkarow:"\u290d",blacklozenge:"\u29eb",blacksquare:"\u25aa",blacktriangle:"\u25b4",blacktriangledown:"\u25be",blacktriangleleft:"\u25c2",blacktriangleright:"\u25b8",blank:"\u2423",blk12:"\u2592",blk14:"\u2591",blk34:"\u2593",block:"\u2588",bne:"=\u20e5",bnequiv:"\u2261\u20e5",bNot:"\u2aed",bnot:"\u2310",Bopf:"\ud835\udd39",bopf:"\ud835\udd53",bot:"\u22a5",bottom:"\u22a5",bowtie:"\u22c8",boxbox:"\u29c9",boxdl:"\u2510",boxdL:"\u2555",boxDl:"\u2556",boxDL:"\u2557",boxdr:"\u250c",boxdR:"\u2552",boxDr:"\u2553",boxDR:"\u2554",boxh:"\u2500",boxH:"\u2550",boxhd:"\u252c",boxHd:"\u2564",boxhD:"\u2565",boxHD:"\u2566",boxhu:"\u2534",boxHu:"\u2567",boxhU:"\u2568",boxHU:"\u2569",boxminus:"\u229f",boxplus:"\u229e",boxtimes:"\u22a0",boxul:"\u2518",boxuL:"\u255b",boxUl:"\u255c",boxUL:"\u255d",boxur:"\u2514",boxuR:"\u2558",boxUr:"\u2559",boxUR:"\u255a",boxv:"\u2502",boxV:"\u2551",boxvh:"\u253c",boxvH:"\u256a",boxVh:"\u256b",boxVH:"\u256c",boxvl:"\u2524",boxvL:"\u2561",boxVl:"\u2562",boxVL:"\u2563",boxvr:"\u251c",boxvR:"\u255e",boxVr:"\u255f",boxVR:"\u2560",bprime:"\u2035",breve:"\u02d8",Breve:"\u02d8",brvbar:"\xa6",bscr:"\ud835\udcb7",Bscr:"\u212c",bsemi:"\u204f",bsim:"\u223d",bsime:"\u22cd",bsolb:"\u29c5",bsol:"\\",bsolhsub:"\u27c8",bull:"\u2022",bullet:"\u2022",bump:"\u224e",bumpE:"\u2aae",bumpe:"\u224f",Bumpeq:"\u224e",bumpeq:"\u224f",Cacute:"\u0106",cacute:"\u0107",capand:"\u2a44",capbrcup:"\u2a49",capcap:"\u2a4b",cap:"\u2229",Cap:"\u22d2",capcup:"\u2a47",capdot:"\u2a40",CapitalDifferentialD:"\u2145",caps:"\u2229\ufe00",caret:"\u2041",caron:"\u02c7",Cayleys:"\u212d",ccaps:"\u2a4d",Ccaron:"\u010c",ccaron:"\u010d",Ccedil:"\xc7",ccedil:"\xe7",Ccirc:"\u0108",ccirc:"\u0109",Cconint:"\u2230",ccups:"\u2a4c",ccupssm:"\u2a50",Cdot:"\u010a",cdot:"\u010b",cedil:"\xb8",Cedilla:"\xb8",cemptyv:"\u29b2",cent:"\xa2",centerdot:"\xb7",CenterDot:"\xb7",cfr:"\ud835\udd20",Cfr:"\u212d",CHcy:"\u0427",chcy:"\u0447",check:"\u2713",checkmark:"\u2713",Chi:"\u03a7",chi:"\u03c7",circ:"\u02c6",circeq:"\u2257",circlearrowleft:"\u21ba",circlearrowright:"\u21bb",circledast:"\u229b",circledcirc:"\u229a",circleddash:"\u229d",CircleDot:"\u2299",circledR:"\xae",circledS:"\u24c8",CircleMinus:"\u2296",CirclePlus:"\u2295",CircleTimes:"\u2297",cir:"\u25cb",cirE:"\u29c3",cire:"\u2257",cirfnint:"\u2a10",cirmid:"\u2aef",cirscir:"\u29c2",ClockwiseContourIntegral:"\u2232",CloseCurlyDoubleQuote:"\u201d",CloseCurlyQuote:"\u2019",clubs:"\u2663",clubsuit:"\u2663",colon:":",Colon:"\u2237",Colone:"\u2a74",colone:"\u2254",coloneq:"\u2254",comma:",",commat:"@",comp:"\u2201",compfn:"\u2218",complement:"\u2201",complexes:"\u2102",cong:"\u2245",congdot:"\u2a6d",Congruent:"\u2261",conint:"\u222e",Conint:"\u222f",ContourIntegral:"\u222e",copf:"\ud835\udd54",Copf:"\u2102",coprod:"\u2210",Coproduct:"\u2210",copy:"\xa9",COPY:"\xa9",copysr:"\u2117",CounterClockwiseContourIntegral:"\u2233",crarr:"\u21b5",cross:"\u2717",Cross:"\u2a2f",Cscr:"\ud835\udc9e",cscr:"\ud835\udcb8",csub:"\u2acf",csube:"\u2ad1",csup:"\u2ad0",csupe:"\u2ad2",ctdot:"\u22ef",cudarrl:"\u2938",cudarrr:"\u2935",cuepr:"\u22de",cuesc:"\u22df",cularr:"\u21b6",cularrp:"\u293d",cupbrcap:"\u2a48",cupcap:"\u2a46",CupCap:"\u224d",cup:"\u222a",Cup:"\u22d3",cupcup:"\u2a4a",cupdot:"\u228d",cupor:"\u2a45",cups:"\u222a\ufe00",curarr:"\u21b7",curarrm:"\u293c",curlyeqprec:"\u22de",curlyeqsucc:"\u22df",curlyvee:"\u22ce",curlywedge:"\u22cf",curren:"\xa4",curvearrowleft:"\u21b6",curvearrowright:"\u21b7",cuvee:"\u22ce",cuwed:"\u22cf",cwconint:"\u2232",cwint:"\u2231",cylcty:"\u232d",dagger:"\u2020",Dagger:"\u2021",daleth:"\u2138",darr:"\u2193",Darr:"\u21a1",dArr:"\u21d3",dash:"\u2010",Dashv:"\u2ae4",dashv:"\u22a3",dbkarow:"\u290f",dblac:"\u02dd",Dcaron:"\u010e",dcaron:"\u010f",Dcy:"\u0414",dcy:"\u0434",ddagger:"\u2021",ddarr:"\u21ca",DD:"\u2145",dd:"\u2146",DDotrahd:"\u2911",ddotseq:"\u2a77",deg:"\xb0",Del:"\u2207",Delta:"\u0394",delta:"\u03b4",demptyv:"\u29b1",dfisht:"\u297f",Dfr:"\ud835\udd07",dfr:"\ud835\udd21",dHar:"\u2965",dharl:"\u21c3",dharr:"\u21c2",DiacriticalAcute:"\xb4",DiacriticalDot:"\u02d9",DiacriticalDoubleAcute:"\u02dd",DiacriticalGrave:"`",DiacriticalTilde:"\u02dc",diam:"\u22c4",diamond:"\u22c4",Diamond:"\u22c4",diamondsuit:"\u2666",diams:"\u2666",die:"\xa8",DifferentialD:"\u2146",digamma:"\u03dd",disin:"\u22f2",div:"\xf7",divide:"\xf7",divideontimes:"\u22c7",divonx:"\u22c7",DJcy:"\u0402",djcy:"\u0452",dlcorn:"\u231e",dlcrop:"\u230d",dollar:"$",Dopf:"\ud835\udd3b",dopf:"\ud835\udd55",Dot:"\xa8",dot:"\u02d9",DotDot:"\u20dc",doteq:"\u2250",doteqdot:"\u2251",DotEqual:"\u2250",dotminus:"\u2238",dotplus:"\u2214",dotsquare:"\u22a1",doublebarwedge:"\u2306",DoubleContourIntegral:"\u222f",DoubleDot:"\xa8",DoubleDownArrow:"\u21d3",DoubleLeftArrow:"\u21d0",DoubleLeftRightArrow:"\u21d4",DoubleLeftTee:"\u2ae4",DoubleLongLeftArrow:"\u27f8",DoubleLongLeftRightArrow:"\u27fa",DoubleLongRightArrow:"\u27f9",DoubleRightArrow:"\u21d2",DoubleRightTee:"\u22a8",DoubleUpArrow:"\u21d1",DoubleUpDownArrow:"\u21d5",DoubleVerticalBar:"\u2225",DownArrowBar:"\u2913",downarrow:"\u2193",DownArrow:"\u2193",Downarrow:"\u21d3",DownArrowUpArrow:"\u21f5",DownBreve:"\u0311",downdownarrows:"\u21ca",downharpoonleft:"\u21c3",downharpoonright:"\u21c2",DownLeftRightVector:"\u2950",DownLeftTeeVector:"\u295e",DownLeftVectorBar:"\u2956",DownLeftVector:"\u21bd",DownRightTeeVector:"\u295f",DownRightVectorBar:"\u2957",DownRightVector:"\u21c1",DownTeeArrow:"\u21a7",DownTee:"\u22a4",drbkarow:"\u2910",drcorn:"\u231f",drcrop:"\u230c",Dscr:"\ud835\udc9f",dscr:"\ud835\udcb9",DScy:"\u0405",dscy:"\u0455",dsol:"\u29f6",Dstrok:"\u0110",dstrok:"\u0111",dtdot:"\u22f1",dtri:"\u25bf",dtrif:"\u25be",duarr:"\u21f5",duhar:"\u296f",dwangle:"\u29a6",DZcy:"\u040f",dzcy:"\u045f",dzigrarr:"\u27ff",Eacute:"\xc9",eacute:"\xe9",easter:"\u2a6e",Ecaron:"\u011a",ecaron:"\u011b",Ecirc:"\xca",ecirc:"\xea",ecir:"\u2256",ecolon:"\u2255",Ecy:"\u042d",ecy:"\u044d",eDDot:"\u2a77",Edot:"\u0116",edot:"\u0117",eDot:"\u2251",ee:"\u2147",efDot:"\u2252",Efr:"\ud835\udd08",efr:"\ud835\udd22",eg:"\u2a9a",Egrave:"\xc8",egrave:"\xe8",egs:"\u2a96",egsdot:"\u2a98",el:"\u2a99",Element:"\u2208",elinters:"\u23e7",ell:"\u2113",els:"\u2a95",elsdot:"\u2a97",Emacr:"\u0112",emacr:"\u0113",empty:"\u2205",emptyset:"\u2205",EmptySmallSquare:"\u25fb",emptyv:"\u2205",EmptyVerySmallSquare:"\u25ab",emsp13:"\u2004",emsp14:"\u2005",emsp:"\u2003",ENG:"\u014a",eng:"\u014b",ensp:"\u2002",Eogon:"\u0118",eogon:"\u0119",Eopf:"\ud835\udd3c",eopf:"\ud835\udd56",epar:"\u22d5",eparsl:"\u29e3",eplus:"\u2a71",epsi:"\u03b5",Epsilon:"\u0395",epsilon:"\u03b5",epsiv:"\u03f5",eqcirc:"\u2256",eqcolon:"\u2255",eqsim:"\u2242",eqslantgtr:"\u2a96",eqslantless:"\u2a95",Equal:"\u2a75",equals:"=",EqualTilde:"\u2242",equest:"\u225f",Equilibrium:"\u21cc",equiv:"\u2261",equivDD:"\u2a78",eqvparsl:"\u29e5",erarr:"\u2971",erDot:"\u2253",escr:"\u212f",Escr:"\u2130",esdot:"\u2250",Esim:"\u2a73",esim:"\u2242",Eta:"\u0397",eta:"\u03b7",ETH:"\xd0",eth:"\xf0",Euml:"\xcb",euml:"\xeb",euro:"\u20ac",excl:"!",exist:"\u2203",Exists:"\u2203",expectation:"\u2130",exponentiale:"\u2147",ExponentialE:"\u2147",fallingdotseq:"\u2252",Fcy:"\u0424",fcy:"\u0444",female:"\u2640",ffilig:"\ufb03",fflig:"\ufb00",ffllig:"\ufb04",Ffr:"\ud835\udd09",ffr:"\ud835\udd23",filig:"\ufb01",FilledSmallSquare:"\u25fc",FilledVerySmallSquare:"\u25aa",fjlig:"fj",flat:"\u266d",fllig:"\ufb02",fltns:"\u25b1",fnof:"\u0192",Fopf:"\ud835\udd3d",fopf:"\ud835\udd57",forall:"\u2200",ForAll:"\u2200",fork:"\u22d4",forkv:"\u2ad9",Fouriertrf:"\u2131",fpartint:"\u2a0d",frac12:"\xbd",frac13:"\u2153",frac14:"\xbc",frac15:"\u2155",frac16:"\u2159",frac18:"\u215b",frac23:"\u2154",frac25:"\u2156",frac34:"\xbe",frac35:"\u2157",frac38:"\u215c",frac45:"\u2158",frac56:"\u215a",frac58:"\u215d",frac78:"\u215e",frasl:"\u2044",frown:"\u2322",fscr:"\ud835\udcbb",Fscr:"\u2131",gacute:"\u01f5",Gamma:"\u0393",gamma:"\u03b3",Gammad:"\u03dc",gammad:"\u03dd",gap:"\u2a86",Gbreve:"\u011e",gbreve:"\u011f",Gcedil:"\u0122",Gcirc:"\u011c",gcirc:"\u011d",Gcy:"\u0413",gcy:"\u0433",Gdot:"\u0120",gdot:"\u0121",ge:"\u2265",gE:"\u2267",gEl:"\u2a8c",gel:"\u22db",geq:"\u2265",geqq:"\u2267",geqslant:"\u2a7e",gescc:"\u2aa9",ges:"\u2a7e",gesdot:"\u2a80",gesdoto:"\u2a82",gesdotol:"\u2a84",gesl:"\u22db\ufe00",gesles:"\u2a94",Gfr:"\ud835\udd0a",gfr:"\ud835\udd24",gg:"\u226b",Gg:"\u22d9",ggg:"\u22d9",gimel:"\u2137",GJcy:"\u0403",gjcy:"\u0453",gla:"\u2aa5",gl:"\u2277",glE:"\u2a92",glj:"\u2aa4",gnap:"\u2a8a",gnapprox:"\u2a8a",gne:"\u2a88",gnE:"\u2269",gneq:"\u2a88",gneqq:"\u2269",gnsim:"\u22e7",Gopf:"\ud835\udd3e",gopf:"\ud835\udd58",grave:"`",GreaterEqual:"\u2265",GreaterEqualLess:"\u22db",GreaterFullEqual:"\u2267",GreaterGreater:"\u2aa2",GreaterLess:"\u2277",GreaterSlantEqual:"\u2a7e",GreaterTilde:"\u2273",Gscr:"\ud835\udca2",gscr:"\u210a",gsim:"\u2273",gsime:"\u2a8e",gsiml:"\u2a90",gtcc:"\u2aa7",gtcir:"\u2a7a",gt:">",GT:">",Gt:"\u226b",gtdot:"\u22d7",gtlPar:"\u2995",gtquest:"\u2a7c",gtrapprox:"\u2a86",gtrarr:"\u2978",gtrdot:"\u22d7",gtreqless:"\u22db",gtreqqless:"\u2a8c",gtrless:"\u2277",gtrsim:"\u2273",gvertneqq:"\u2269\ufe00",gvnE:"\u2269\ufe00",Hacek:"\u02c7",hairsp:"\u200a",half:"\xbd",hamilt:"\u210b",HARDcy:"\u042a",hardcy:"\u044a",harrcir:"\u2948",harr:"\u2194",hArr:"\u21d4",harrw:"\u21ad",Hat:"^",hbar:"\u210f",Hcirc:"\u0124",hcirc:"\u0125",hearts:"\u2665",heartsuit:"\u2665",hellip:"\u2026",hercon:"\u22b9",hfr:"\ud835\udd25",Hfr:"\u210c",HilbertSpace:"\u210b",hksearow:"\u2925",hkswarow:"\u2926",hoarr:"\u21ff",homtht:"\u223b",hookleftarrow:"\u21a9",hookrightarrow:"\u21aa",hopf:"\ud835\udd59",Hopf:"\u210d",horbar:"\u2015",HorizontalLine:"\u2500",hscr:"\ud835\udcbd",Hscr:"\u210b",hslash:"\u210f",Hstrok:"\u0126",hstrok:"\u0127",HumpDownHump:"\u224e",HumpEqual:"\u224f",hybull:"\u2043",hyphen:"\u2010",Iacute:"\xcd",iacute:"\xed",ic:"\u2063",Icirc:"\xce",icirc:"\xee",Icy:"\u0418",icy:"\u0438",Idot:"\u0130",IEcy:"\u0415",iecy:"\u0435",iexcl:"\xa1",iff:"\u21d4",ifr:"\ud835\udd26",Ifr:"\u2111",Igrave:"\xcc",igrave:"\xec",ii:"\u2148",iiiint:"\u2a0c",iiint:"\u222d",iinfin:"\u29dc",iiota:"\u2129",IJlig:"\u0132",ijlig:"\u0133",Imacr:"\u012a",imacr:"\u012b",image:"\u2111",ImaginaryI:"\u2148",imagline:"\u2110",imagpart:"\u2111",imath:"\u0131",Im:"\u2111",imof:"\u22b7",imped:"\u01b5",Implies:"\u21d2",incare:"\u2105",in:"\u2208",infin:"\u221e",infintie:"\u29dd",inodot:"\u0131",intcal:"\u22ba",int:"\u222b",Int:"\u222c",integers:"\u2124",Integral:"\u222b",intercal:"\u22ba",Intersection:"\u22c2",intlarhk:"\u2a17",intprod:"\u2a3c",InvisibleComma:"\u2063",InvisibleTimes:"\u2062",IOcy:"\u0401",iocy:"\u0451",Iogon:"\u012e",iogon:"\u012f",Iopf:"\ud835\udd40",iopf:"\ud835\udd5a",Iota:"\u0399",iota:"\u03b9",iprod:"\u2a3c",iquest:"\xbf",iscr:"\ud835\udcbe",Iscr:"\u2110",isin:"\u2208",isindot:"\u22f5",isinE:"\u22f9",isins:"\u22f4",isinsv:"\u22f3",isinv:"\u2208",it:"\u2062",Itilde:"\u0128",itilde:"\u0129",Iukcy:"\u0406",iukcy:"\u0456",Iuml:"\xcf",iuml:"\xef",Jcirc:"\u0134",jcirc:"\u0135",Jcy:"\u0419",jcy:"\u0439",Jfr:"\ud835\udd0d",jfr:"\ud835\udd27",jmath:"\u0237",Jopf:"\ud835\udd41",jopf:"\ud835\udd5b",Jscr:"\ud835\udca5",jscr:"\ud835\udcbf",Jsercy:"\u0408",jsercy:"\u0458",Jukcy:"\u0404",jukcy:"\u0454",Kappa:"\u039a",kappa:"\u03ba",kappav:"\u03f0",Kcedil:"\u0136",kcedil:"\u0137",Kcy:"\u041a",kcy:"\u043a",Kfr:"\ud835\udd0e",kfr:"\ud835\udd28",kgreen:"\u0138",KHcy:"\u0425",khcy:"\u0445",KJcy:"\u040c",kjcy:"\u045c",Kopf:"\ud835\udd42",kopf:"\ud835\udd5c",Kscr:"\ud835\udca6",kscr:"\ud835\udcc0",lAarr:"\u21da",Lacute:"\u0139",lacute:"\u013a",laemptyv:"\u29b4",lagran:"\u2112",Lambda:"\u039b",lambda:"\u03bb",lang:"\u27e8",Lang:"\u27ea",langd:"\u2991",langle:"\u27e8",lap:"\u2a85",Laplacetrf:"\u2112",laquo:"\xab",larrb:"\u21e4",larrbfs:"\u291f",larr:"\u2190",Larr:"\u219e",lArr:"\u21d0",larrfs:"\u291d",larrhk:"\u21a9",larrlp:"\u21ab",larrpl:"\u2939",larrsim:"\u2973",larrtl:"\u21a2",latail:"\u2919",lAtail:"\u291b",lat:"\u2aab",late:"\u2aad",lates:"\u2aad\ufe00",lbarr:"\u290c",lBarr:"\u290e",lbbrk:"\u2772",lbrace:"{",lbrack:"[",lbrke:"\u298b",lbrksld:"\u298f",lbrkslu:"\u298d",Lcaron:"\u013d",lcaron:"\u013e",Lcedil:"\u013b",lcedil:"\u013c",lceil:"\u2308",lcub:"{",Lcy:"\u041b",lcy:"\u043b",ldca:"\u2936",ldquo:"\u201c",ldquor:"\u201e",ldrdhar:"\u2967",ldrushar:"\u294b",ldsh:"\u21b2",le:"\u2264",lE:"\u2266",LeftAngleBracket:"\u27e8",LeftArrowBar:"\u21e4",leftarrow:"\u2190",LeftArrow:"\u2190",Leftarrow:"\u21d0",LeftArrowRightArrow:"\u21c6",leftarrowtail:"\u21a2",LeftCeiling:"\u2308",LeftDoubleBracket:"\u27e6",LeftDownTeeVector:"\u2961",LeftDownVectorBar:"\u2959",LeftDownVector:"\u21c3",LeftFloor:"\u230a",leftharpoondown:"\u21bd",leftharpoonup:"\u21bc",leftleftarrows:"\u21c7",leftrightarrow:"\u2194",LeftRightArrow:"\u2194",Leftrightarrow:"\u21d4",leftrightarrows:"\u21c6",leftrightharpoons:"\u21cb",leftrightsquigarrow:"\u21ad",LeftRightVector:"\u294e",LeftTeeArrow:"\u21a4",LeftTee:"\u22a3",LeftTeeVector:"\u295a",leftthreetimes:"\u22cb",LeftTriangleBar:"\u29cf",LeftTriangle:"\u22b2",LeftTriangleEqual:"\u22b4",LeftUpDownVector:"\u2951",LeftUpTeeVector:"\u2960",LeftUpVectorBar:"\u2958",LeftUpVector:"\u21bf",LeftVectorBar:"\u2952",LeftVector:"\u21bc",lEg:"\u2a8b",leg:"\u22da",leq:"\u2264",leqq:"\u2266",leqslant:"\u2a7d",lescc:"\u2aa8",les:"\u2a7d",lesdot:"\u2a7f",lesdoto:"\u2a81",lesdotor:"\u2a83",lesg:"\u22da\ufe00",lesges:"\u2a93",lessapprox:"\u2a85",lessdot:"\u22d6",lesseqgtr:"\u22da",lesseqqgtr:"\u2a8b",LessEqualGreater:"\u22da",LessFullEqual:"\u2266",LessGreater:"\u2276",lessgtr:"\u2276",LessLess:"\u2aa1",lesssim:"\u2272",LessSlantEqual:"\u2a7d",LessTilde:"\u2272",lfisht:"\u297c",lfloor:"\u230a",Lfr:"\ud835\udd0f",lfr:"\ud835\udd29",lg:"\u2276",lgE:"\u2a91",lHar:"\u2962",lhard:"\u21bd",lharu:"\u21bc",lharul:"\u296a",lhblk:"\u2584",LJcy:"\u0409",ljcy:"\u0459",llarr:"\u21c7",ll:"\u226a",Ll:"\u22d8",llcorner:"\u231e",Lleftarrow:"\u21da",llhard:"\u296b",lltri:"\u25fa",Lmidot:"\u013f",lmidot:"\u0140",lmoustache:"\u23b0",lmoust:"\u23b0",lnap:"\u2a89",lnapprox:"\u2a89",lne:"\u2a87",lnE:"\u2268",lneq:"\u2a87",lneqq:"\u2268",lnsim:"\u22e6",loang:"\u27ec",loarr:"\u21fd",lobrk:"\u27e6",longleftarrow:"\u27f5",LongLeftArrow:"\u27f5",Longleftarrow:"\u27f8",longleftrightarrow:"\u27f7",LongLeftRightArrow:"\u27f7",Longleftrightarrow:"\u27fa",longmapsto:"\u27fc",longrightarrow:"\u27f6",LongRightArrow:"\u27f6",Longrightarrow:"\u27f9",looparrowleft:"\u21ab",looparrowright:"\u21ac",lopar:"\u2985",Lopf:"\ud835\udd43",lopf:"\ud835\udd5d",loplus:"\u2a2d",lotimes:"\u2a34",lowast:"\u2217",lowbar:"_",LowerLeftArrow:"\u2199",LowerRightArrow:"\u2198",loz:"\u25ca",lozenge:"\u25ca",lozf:"\u29eb",lpar:"(",lparlt:"\u2993",lrarr:"\u21c6",lrcorner:"\u231f",lrhar:"\u21cb",lrhard:"\u296d",lrm:"\u200e",lrtri:"\u22bf",lsaquo:"\u2039",lscr:"\ud835\udcc1",Lscr:"\u2112",lsh:"\u21b0",Lsh:"\u21b0",lsim:"\u2272",lsime:"\u2a8d",lsimg:"\u2a8f",lsqb:"[",lsquo:"\u2018",lsquor:"\u201a",Lstrok:"\u0141",lstrok:"\u0142",ltcc:"\u2aa6",ltcir:"\u2a79",lt:"<",LT:"<",Lt:"\u226a",ltdot:"\u22d6",lthree:"\u22cb",ltimes:"\u22c9",ltlarr:"\u2976",ltquest:"\u2a7b",ltri:"\u25c3",ltrie:"\u22b4",ltrif:"\u25c2",ltrPar:"\u2996",lurdshar:"\u294a",luruhar:"\u2966",lvertneqq:"\u2268\ufe00",lvnE:"\u2268\ufe00",macr:"\xaf",male:"\u2642",malt:"\u2720",maltese:"\u2720",Map:"\u2905",map:"\u21a6",mapsto:"\u21a6",mapstodown:"\u21a7",mapstoleft:"\u21a4",mapstoup:"\u21a5",marker:"\u25ae",mcomma:"\u2a29",Mcy:"\u041c",mcy:"\u043c",mdash:"\u2014",mDDot:"\u223a",measuredangle:"\u2221",MediumSpace:"\u205f",Mellintrf:"\u2133",Mfr:"\ud835\udd10",mfr:"\ud835\udd2a",mho:"\u2127",micro:"\xb5",midast:"*",midcir:"\u2af0",mid:"\u2223",middot:"\xb7",minusb:"\u229f",minus:"\u2212",minusd:"\u2238",minusdu:"\u2a2a",MinusPlus:"\u2213",mlcp:"\u2adb",mldr:"\u2026",mnplus:"\u2213",models:"\u22a7",Mopf:"\ud835\udd44",mopf:"\ud835\udd5e",mp:"\u2213",mscr:"\ud835\udcc2",Mscr:"\u2133",mstpos:"\u223e",Mu:"\u039c",mu:"\u03bc",multimap:"\u22b8",mumap:"\u22b8",nabla:"\u2207",Nacute:"\u0143",nacute:"\u0144",nang:"\u2220\u20d2",nap:"\u2249",napE:"\u2a70\u0338",napid:"\u224b\u0338",napos:"\u0149",napprox:"\u2249",natural:"\u266e",naturals:"\u2115",natur:"\u266e",nbsp:"\xa0",nbump:"\u224e\u0338",nbumpe:"\u224f\u0338",ncap:"\u2a43",Ncaron:"\u0147",ncaron:"\u0148",Ncedil:"\u0145",ncedil:"\u0146",ncong:"\u2247",ncongdot:"\u2a6d\u0338",ncup:"\u2a42",Ncy:"\u041d",ncy:"\u043d",ndash:"\u2013",nearhk:"\u2924",nearr:"\u2197",neArr:"\u21d7",nearrow:"\u2197",ne:"\u2260",nedot:"\u2250\u0338",NegativeMediumSpace:"\u200b",NegativeThickSpace:"\u200b",NegativeThinSpace:"\u200b",NegativeVeryThinSpace:"\u200b",nequiv:"\u2262",nesear:"\u2928",nesim:"\u2242\u0338",NestedGreaterGreater:"\u226b",NestedLessLess:"\u226a",NewLine:"\n",nexist:"\u2204",nexists:"\u2204",Nfr:"\ud835\udd11",nfr:"\ud835\udd2b",ngE:"\u2267\u0338",nge:"\u2271",ngeq:"\u2271",ngeqq:"\u2267\u0338",ngeqslant:"\u2a7e\u0338",nges:"\u2a7e\u0338",nGg:"\u22d9\u0338",ngsim:"\u2275",nGt:"\u226b\u20d2",ngt:"\u226f",ngtr:"\u226f",nGtv:"\u226b\u0338",nharr:"\u21ae",nhArr:"\u21ce",nhpar:"\u2af2",ni:"\u220b",nis:"\u22fc",nisd:"\u22fa",niv:"\u220b",NJcy:"\u040a",njcy:"\u045a",nlarr:"\u219a",nlArr:"\u21cd",nldr:"\u2025",nlE:"\u2266\u0338",nle:"\u2270",nleftarrow:"\u219a",nLeftarrow:"\u21cd",nleftrightarrow:"\u21ae",nLeftrightarrow:"\u21ce",nleq:"\u2270",nleqq:"\u2266\u0338",nleqslant:"\u2a7d\u0338",nles:"\u2a7d\u0338",nless:"\u226e",nLl:"\u22d8\u0338",nlsim:"\u2274",nLt:"\u226a\u20d2",nlt:"\u226e",nltri:"\u22ea",nltrie:"\u22ec",nLtv:"\u226a\u0338",nmid:"\u2224",NoBreak:"\u2060",NonBreakingSpace:"\xa0",nopf:"\ud835\udd5f",Nopf:"\u2115",Not:"\u2aec",not:"\xac",NotCongruent:"\u2262",NotCupCap:"\u226d",NotDoubleVerticalBar:"\u2226",NotElement:"\u2209",NotEqual:"\u2260",NotEqualTilde:"\u2242\u0338",NotExists:"\u2204",NotGreater:"\u226f",NotGreaterEqual:"\u2271",NotGreaterFullEqual:"\u2267\u0338",NotGreaterGreater:"\u226b\u0338",NotGreaterLess:"\u2279",NotGreaterSlantEqual:"\u2a7e\u0338",NotGreaterTilde:"\u2275",NotHumpDownHump:"\u224e\u0338",NotHumpEqual:"\u224f\u0338",notin:"\u2209",notindot:"\u22f5\u0338",notinE:"\u22f9\u0338",notinva:"\u2209",notinvb:"\u22f7",notinvc:"\u22f6",NotLeftTriangleBar:"\u29cf\u0338",NotLeftTriangle:"\u22ea",NotLeftTriangleEqual:"\u22ec",NotLess:"\u226e",NotLessEqual:"\u2270",NotLessGreater:"\u2278",NotLessLess:"\u226a\u0338",NotLessSlantEqual:"\u2a7d\u0338",NotLessTilde:"\u2274",NotNestedGreaterGreater:"\u2aa2\u0338",NotNestedLessLess:"\u2aa1\u0338",notni:"\u220c",notniva:"\u220c",notnivb:"\u22fe",notnivc:"\u22fd",NotPrecedes:"\u2280",NotPrecedesEqual:"\u2aaf\u0338",NotPrecedesSlantEqual:"\u22e0",NotReverseElement:"\u220c",NotRightTriangleBar:"\u29d0\u0338",NotRightTriangle:"\u22eb",NotRightTriangleEqual:"\u22ed",NotSquareSubset:"\u228f\u0338",NotSquareSubsetEqual:"\u22e2",NotSquareSuperset:"\u2290\u0338",NotSquareSupersetEqual:"\u22e3",NotSubset:"\u2282\u20d2",NotSubsetEqual:"\u2288",NotSucceeds:"\u2281",NotSucceedsEqual:"\u2ab0\u0338",NotSucceedsSlantEqual:"\u22e1",NotSucceedsTilde:"\u227f\u0338",NotSuperset:"\u2283\u20d2",NotSupersetEqual:"\u2289",NotTilde:"\u2241",NotTildeEqual:"\u2244",NotTildeFullEqual:"\u2247",NotTildeTilde:"\u2249",NotVerticalBar:"\u2224",nparallel:"\u2226",npar:"\u2226",nparsl:"\u2afd\u20e5",npart:"\u2202\u0338",npolint:"\u2a14",npr:"\u2280",nprcue:"\u22e0",nprec:"\u2280",npreceq:"\u2aaf\u0338",npre:"\u2aaf\u0338",nrarrc:"\u2933\u0338",nrarr:"\u219b",nrArr:"\u21cf",nrarrw:"\u219d\u0338",nrightarrow:"\u219b",nRightarrow:"\u21cf",nrtri:"\u22eb",nrtrie:"\u22ed",nsc:"\u2281",nsccue:"\u22e1",nsce:"\u2ab0\u0338",Nscr:"\ud835\udca9",nscr:"\ud835\udcc3",nshortmid:"\u2224",nshortparallel:"\u2226",nsim:"\u2241",nsime:"\u2244",nsimeq:"\u2244",nsmid:"\u2224",nspar:"\u2226",nsqsube:"\u22e2",nsqsupe:"\u22e3",nsub:"\u2284",nsubE:"\u2ac5\u0338",nsube:"\u2288",nsubset:"\u2282\u20d2",nsubseteq:"\u2288",nsubseteqq:"\u2ac5\u0338",nsucc:"\u2281",nsucceq:"\u2ab0\u0338",nsup:"\u2285",nsupE:"\u2ac6\u0338",nsupe:"\u2289",nsupset:"\u2283\u20d2",nsupseteq:"\u2289",nsupseteqq:"\u2ac6\u0338",ntgl:"\u2279",Ntilde:"\xd1",ntilde:"\xf1",ntlg:"\u2278",ntriangleleft:"\u22ea",ntrianglelefteq:"\u22ec",ntriangleright:"\u22eb",ntrianglerighteq:"\u22ed",Nu:"\u039d",nu:"\u03bd",num:"#",numero:"\u2116",numsp:"\u2007",nvap:"\u224d\u20d2",nvdash:"\u22ac",nvDash:"\u22ad",nVdash:"\u22ae",nVDash:"\u22af",nvge:"\u2265\u20d2",nvgt:">\u20d2",nvHarr:"\u2904",nvinfin:"\u29de",nvlArr:"\u2902",nvle:"\u2264\u20d2",nvlt:"<\u20d2",nvltrie:"\u22b4\u20d2",nvrArr:"\u2903",nvrtrie:"\u22b5\u20d2",nvsim:"\u223c\u20d2",nwarhk:"\u2923",nwarr:"\u2196",nwArr:"\u21d6",nwarrow:"\u2196",nwnear:"\u2927",Oacute:"\xd3",oacute:"\xf3",oast:"\u229b",Ocirc:"\xd4",ocirc:"\xf4",ocir:"\u229a",Ocy:"\u041e",ocy:"\u043e",odash:"\u229d",Odblac:"\u0150",odblac:"\u0151",odiv:"\u2a38",odot:"\u2299",odsold:"\u29bc",OElig:"\u0152",oelig:"\u0153",ofcir:"\u29bf",Ofr:"\ud835\udd12",ofr:"\ud835\udd2c",ogon:"\u02db",Ograve:"\xd2",ograve:"\xf2",ogt:"\u29c1",ohbar:"\u29b5",ohm:"\u03a9",oint:"\u222e",olarr:"\u21ba",olcir:"\u29be",olcross:"\u29bb",oline:"\u203e",olt:"\u29c0",Omacr:"\u014c",omacr:"\u014d",Omega:"\u03a9",omega:"\u03c9",Omicron:"\u039f",omicron:"\u03bf",omid:"\u29b6",ominus:"\u2296",Oopf:"\ud835\udd46",oopf:"\ud835\udd60",opar:"\u29b7",OpenCurlyDoubleQuote:"\u201c",OpenCurlyQuote:"\u2018",operp:"\u29b9",oplus:"\u2295",orarr:"\u21bb",Or:"\u2a54",or:"\u2228",ord:"\u2a5d",order:"\u2134",orderof:"\u2134",ordf:"\xaa",ordm:"\xba",origof:"\u22b6",oror:"\u2a56",orslope:"\u2a57",orv:"\u2a5b",oS:"\u24c8",Oscr:"\ud835\udcaa",oscr:"\u2134",Oslash:"\xd8",oslash:"\xf8",osol:"\u2298",Otilde:"\xd5",otilde:"\xf5",otimesas:"\u2a36",Otimes:"\u2a37",otimes:"\u2297",Ouml:"\xd6",ouml:"\xf6",ovbar:"\u233d",OverBar:"\u203e",OverBrace:"\u23de",OverBracket:"\u23b4",OverParenthesis:"\u23dc",para:"\xb6",parallel:"\u2225",par:"\u2225",parsim:"\u2af3",parsl:"\u2afd",part:"\u2202",PartialD:"\u2202",Pcy:"\u041f",pcy:"\u043f",percnt:"%",period:".",permil:"\u2030",perp:"\u22a5",pertenk:"\u2031",Pfr:"\ud835\udd13",pfr:"\ud835\udd2d",Phi:"\u03a6",phi:"\u03c6",phiv:"\u03d5",phmmat:"\u2133",phone:"\u260e",Pi:"\u03a0",pi:"\u03c0",pitchfork:"\u22d4",piv:"\u03d6",planck:"\u210f",planckh:"\u210e",plankv:"\u210f",plusacir:"\u2a23",plusb:"\u229e",pluscir:"\u2a22",plus:"+",plusdo:"\u2214",plusdu:"\u2a25",pluse:"\u2a72",PlusMinus:"\xb1",plusmn:"\xb1",plussim:"\u2a26",plustwo:"\u2a27",pm:"\xb1",Poincareplane:"\u210c",pointint:"\u2a15",popf:"\ud835\udd61",Popf:"\u2119",pound:"\xa3",prap:"\u2ab7",Pr:"\u2abb",pr:"\u227a",prcue:"\u227c",precapprox:"\u2ab7",prec:"\u227a",preccurlyeq:"\u227c",Precedes:"\u227a",PrecedesEqual:"\u2aaf",PrecedesSlantEqual:"\u227c",PrecedesTilde:"\u227e",preceq:"\u2aaf",precnapprox:"\u2ab9",precneqq:"\u2ab5",precnsim:"\u22e8",pre:"\u2aaf",prE:"\u2ab3",precsim:"\u227e",prime:"\u2032",Prime:"\u2033",primes:"\u2119",prnap:"\u2ab9",prnE:"\u2ab5",prnsim:"\u22e8",prod:"\u220f",Product:"\u220f",profalar:"\u232e",profline:"\u2312",profsurf:"\u2313",prop:"\u221d",Proportional:"\u221d",Proportion:"\u2237",propto:"\u221d",prsim:"\u227e",prurel:"\u22b0",Pscr:"\ud835\udcab",pscr:"\ud835\udcc5",Psi:"\u03a8",psi:"\u03c8",puncsp:"\u2008",Qfr:"\ud835\udd14",qfr:"\ud835\udd2e",qint:"\u2a0c",qopf:"\ud835\udd62",Qopf:"\u211a",qprime:"\u2057",Qscr:"\ud835\udcac",qscr:"\ud835\udcc6",quaternions:"\u210d",quatint:"\u2a16",quest:"?",questeq:"\u225f",quot:'"',QUOT:'"',rAarr:"\u21db",race:"\u223d\u0331",Racute:"\u0154",racute:"\u0155",radic:"\u221a",raemptyv:"\u29b3",rang:"\u27e9",Rang:"\u27eb",rangd:"\u2992",range:"\u29a5",rangle:"\u27e9",raquo:"\xbb",rarrap:"\u2975",rarrb:"\u21e5",rarrbfs:"\u2920",rarrc:"\u2933",rarr:"\u2192",Rarr:"\u21a0",rArr:"\u21d2",rarrfs:"\u291e",rarrhk:"\u21aa",rarrlp:"\u21ac",rarrpl:"\u2945",rarrsim:"\u2974",Rarrtl:"\u2916",rarrtl:"\u21a3",rarrw:"\u219d",ratail:"\u291a",rAtail:"\u291c",ratio:"\u2236",rationals:"\u211a",rbarr:"\u290d",rBarr:"\u290f",RBarr:"\u2910",rbbrk:"\u2773",rbrace:"}",rbrack:"]",rbrke:"\u298c",rbrksld:"\u298e",rbrkslu:"\u2990",Rcaron:"\u0158",rcaron:"\u0159",Rcedil:"\u0156",rcedil:"\u0157",rceil:"\u2309",rcub:"}",Rcy:"\u0420",rcy:"\u0440",rdca:"\u2937",rdldhar:"\u2969",rdquo:"\u201d",rdquor:"\u201d",rdsh:"\u21b3",real:"\u211c",realine:"\u211b",realpart:"\u211c",reals:"\u211d",Re:"\u211c",rect:"\u25ad",reg:"\xae",REG:"\xae",ReverseElement:"\u220b",ReverseEquilibrium:"\u21cb",ReverseUpEquilibrium:"\u296f",rfisht:"\u297d",rfloor:"\u230b",rfr:"\ud835\udd2f",Rfr:"\u211c",rHar:"\u2964",rhard:"\u21c1",rharu:"\u21c0",rharul:"\u296c",Rho:"\u03a1",rho:"\u03c1",rhov:"\u03f1",RightAngleBracket:"\u27e9",RightArrowBar:"\u21e5",rightarrow:"\u2192",RightArrow:"\u2192",Rightarrow:"\u21d2",RightArrowLeftArrow:"\u21c4",rightarrowtail:"\u21a3",RightCeiling:"\u2309",RightDoubleBracket:"\u27e7",RightDownTeeVector:"\u295d",RightDownVectorBar:"\u2955",RightDownVector:"\u21c2",RightFloor:"\u230b",rightharpoondown:"\u21c1",rightharpoonup:"\u21c0",rightleftarrows:"\u21c4",rightleftharpoons:"\u21cc",rightrightarrows:"\u21c9",rightsquigarrow:"\u219d",RightTeeArrow:"\u21a6",RightTee:"\u22a2",RightTeeVector:"\u295b",rightthreetimes:"\u22cc",RightTriangleBar:"\u29d0",RightTriangle:"\u22b3",RightTriangleEqual:"\u22b5",RightUpDownVector:"\u294f",RightUpTeeVector:"\u295c",RightUpVectorBar:"\u2954",RightUpVector:"\u21be",RightVectorBar:"\u2953",RightVector:"\u21c0",ring:"\u02da",risingdotseq:"\u2253",rlarr:"\u21c4",rlhar:"\u21cc",rlm:"\u200f",rmoustache:"\u23b1",rmoust:"\u23b1",rnmid:"\u2aee",roang:"\u27ed",roarr:"\u21fe",robrk:"\u27e7",ropar:"\u2986",ropf:"\ud835\udd63",Ropf:"\u211d",roplus:"\u2a2e",rotimes:"\u2a35",RoundImplies:"\u2970",rpar:")",rpargt:"\u2994",rppolint:"\u2a12",rrarr:"\u21c9",Rrightarrow:"\u21db",rsaquo:"\u203a",rscr:"\ud835\udcc7",Rscr:"\u211b",rsh:"\u21b1",Rsh:"\u21b1",rsqb:"]",rsquo:"\u2019",rsquor:"\u2019",rthree:"\u22cc",rtimes:"\u22ca",rtri:"\u25b9",rtrie:"\u22b5",rtrif:"\u25b8",rtriltri:"\u29ce",RuleDelayed:"\u29f4",ruluhar:"\u2968",rx:"\u211e",Sacute:"\u015a",sacute:"\u015b",sbquo:"\u201a",scap:"\u2ab8",Scaron:"\u0160",scaron:"\u0161",Sc:"\u2abc",sc:"\u227b",sccue:"\u227d",sce:"\u2ab0",scE:"\u2ab4",Scedil:"\u015e",scedil:"\u015f",Scirc:"\u015c",scirc:"\u015d",scnap:"\u2aba",scnE:"\u2ab6",scnsim:"\u22e9",scpolint:"\u2a13",scsim:"\u227f",Scy:"\u0421",scy:"\u0441",sdotb:"\u22a1",sdot:"\u22c5",sdote:"\u2a66",searhk:"\u2925",searr:"\u2198",seArr:"\u21d8",searrow:"\u2198",sect:"\xa7",semi:";",seswar:"\u2929",setminus:"\u2216",setmn:"\u2216",sext:"\u2736",Sfr:"\ud835\udd16",sfr:"\ud835\udd30",sfrown:"\u2322",sharp:"\u266f",SHCHcy:"\u0429",shchcy:"\u0449",SHcy:"\u0428",shcy:"\u0448",ShortDownArrow:"\u2193",ShortLeftArrow:"\u2190",shortmid:"\u2223",shortparallel:"\u2225",ShortRightArrow:"\u2192",ShortUpArrow:"\u2191",shy:"\xad",Sigma:"\u03a3",sigma:"\u03c3",sigmaf:"\u03c2",sigmav:"\u03c2",sim:"\u223c",simdot:"\u2a6a",sime:"\u2243",simeq:"\u2243",simg:"\u2a9e",simgE:"\u2aa0",siml:"\u2a9d",simlE:"\u2a9f",simne:"\u2246",simplus:"\u2a24",simrarr:"\u2972",slarr:"\u2190",SmallCircle:"\u2218",smallsetminus:"\u2216",smashp:"\u2a33",smeparsl:"\u29e4",smid:"\u2223",smile:"\u2323",smt:"\u2aaa",smte:"\u2aac",smtes:"\u2aac\ufe00",SOFTcy:"\u042c",softcy:"\u044c",solbar:"\u233f",solb:"\u29c4",sol:"/",Sopf:"\ud835\udd4a",sopf:"\ud835\udd64",spades:"\u2660",spadesuit:"\u2660",spar:"\u2225",sqcap:"\u2293",sqcaps:"\u2293\ufe00",sqcup:"\u2294",sqcups:"\u2294\ufe00",Sqrt:"\u221a",sqsub:"\u228f",sqsube:"\u2291",sqsubset:"\u228f",sqsubseteq:"\u2291",sqsup:"\u2290",sqsupe:"\u2292",sqsupset:"\u2290",sqsupseteq:"\u2292",square:"\u25a1",Square:"\u25a1",SquareIntersection:"\u2293",SquareSubset:"\u228f",SquareSubsetEqual:"\u2291",SquareSuperset:"\u2290",SquareSupersetEqual:"\u2292",SquareUnion:"\u2294",squarf:"\u25aa",squ:"\u25a1",squf:"\u25aa",srarr:"\u2192",Sscr:"\ud835\udcae",sscr:"\ud835\udcc8",ssetmn:"\u2216",ssmile:"\u2323",sstarf:"\u22c6",Star:"\u22c6",star:"\u2606",starf:"\u2605",straightepsilon:"\u03f5",straightphi:"\u03d5",strns:"\xaf",sub:"\u2282",Sub:"\u22d0",subdot:"\u2abd",subE:"\u2ac5",sube:"\u2286",subedot:"\u2ac3",submult:"\u2ac1",subnE:"\u2acb",subne:"\u228a",subplus:"\u2abf",subrarr:"\u2979",subset:"\u2282",Subset:"\u22d0",subseteq:"\u2286",subseteqq:"\u2ac5",SubsetEqual:"\u2286",subsetneq:"\u228a",subsetneqq:"\u2acb",subsim:"\u2ac7",subsub:"\u2ad5",subsup:"\u2ad3",succapprox:"\u2ab8",succ:"\u227b",succcurlyeq:"\u227d",Succeeds:"\u227b",SucceedsEqual:"\u2ab0",SucceedsSlantEqual:"\u227d",SucceedsTilde:"\u227f",succeq:"\u2ab0",succnapprox:"\u2aba",succneqq:"\u2ab6",succnsim:"\u22e9",succsim:"\u227f",SuchThat:"\u220b",sum:"\u2211",Sum:"\u2211",sung:"\u266a",sup1:"\xb9",sup2:"\xb2",sup3:"\xb3",sup:"\u2283",Sup:"\u22d1",supdot:"\u2abe",supdsub:"\u2ad8",supE:"\u2ac6",supe:"\u2287",supedot:"\u2ac4",Superset:"\u2283",SupersetEqual:"\u2287",suphsol:"\u27c9",suphsub:"\u2ad7",suplarr:"\u297b",supmult:"\u2ac2",supnE:"\u2acc",supne:"\u228b",supplus:"\u2ac0",supset:"\u2283",Supset:"\u22d1",supseteq:"\u2287",supseteqq:"\u2ac6",supsetneq:"\u228b",supsetneqq:"\u2acc",supsim:"\u2ac8",supsub:"\u2ad4",supsup:"\u2ad6",swarhk:"\u2926",swarr:"\u2199",swArr:"\u21d9",swarrow:"\u2199",swnwar:"\u292a",szlig:"\xdf",Tab:"\t",target:"\u2316",Tau:"\u03a4",tau:"\u03c4",tbrk:"\u23b4",Tcaron:"\u0164",tcaron:"\u0165",Tcedil:"\u0162",tcedil:"\u0163",Tcy:"\u0422",tcy:"\u0442",tdot:"\u20db",telrec:"\u2315",Tfr:"\ud835\udd17",tfr:"\ud835\udd31",there4:"\u2234",therefore:"\u2234",Therefore:"\u2234",Theta:"\u0398",theta:"\u03b8",thetasym:"\u03d1",thetav:"\u03d1",thickapprox:"\u2248",thicksim:"\u223c",ThickSpace:"\u205f\u200a",ThinSpace:"\u2009",thinsp:"\u2009",thkap:"\u2248",thksim:"\u223c",THORN:"\xde",thorn:"\xfe",tilde:"\u02dc",Tilde:"\u223c",TildeEqual:"\u2243",TildeFullEqual:"\u2245",TildeTilde:"\u2248",timesbar:"\u2a31",timesb:"\u22a0",times:"\xd7",timesd:"\u2a30",tint:"\u222d",toea:"\u2928",topbot:"\u2336",topcir:"\u2af1",top:"\u22a4",Topf:"\ud835\udd4b",topf:"\ud835\udd65",topfork:"\u2ada",tosa:"\u2929",tprime:"\u2034",trade:"\u2122",TRADE:"\u2122",triangle:"\u25b5",triangledown:"\u25bf",triangleleft:"\u25c3",trianglelefteq:"\u22b4",triangleq:"\u225c",triangleright:"\u25b9",trianglerighteq:"\u22b5",tridot:"\u25ec",trie:"\u225c",triminus:"\u2a3a",TripleDot:"\u20db",triplus:"\u2a39",trisb:"\u29cd",tritime:"\u2a3b",trpezium:"\u23e2",Tscr:"\ud835\udcaf",tscr:"\ud835\udcc9",TScy:"\u0426",tscy:"\u0446",TSHcy:"\u040b",tshcy:"\u045b",Tstrok:"\u0166",tstrok:"\u0167",twixt:"\u226c",twoheadleftarrow:"\u219e",twoheadrightarrow:"\u21a0",Uacute:"\xda",uacute:"\xfa",uarr:"\u2191",Uarr:"\u219f",uArr:"\u21d1",Uarrocir:"\u2949",Ubrcy:"\u040e",ubrcy:"\u045e",Ubreve:"\u016c",ubreve:"\u016d",Ucirc:"\xdb",ucirc:"\xfb",Ucy:"\u0423",ucy:"\u0443",udarr:"\u21c5",Udblac:"\u0170",udblac:"\u0171",udhar:"\u296e",ufisht:"\u297e",Ufr:"\ud835\udd18",ufr:"\ud835\udd32",Ugrave:"\xd9",ugrave:"\xf9",uHar:"\u2963",uharl:"\u21bf",uharr:"\u21be",uhblk:"\u2580",ulcorn:"\u231c",ulcorner:"\u231c",ulcrop:"\u230f",ultri:"\u25f8",Umacr:"\u016a",umacr:"\u016b",uml:"\xa8",UnderBar:"_",UnderBrace:"\u23df",UnderBracket:"\u23b5",UnderParenthesis:"\u23dd",Union:"\u22c3",UnionPlus:"\u228e",Uogon:"\u0172",uogon:"\u0173",Uopf:"\ud835\udd4c",uopf:"\ud835\udd66",UpArrowBar:"\u2912",uparrow:"\u2191",UpArrow:"\u2191",Uparrow:"\u21d1",UpArrowDownArrow:"\u21c5",updownarrow:"\u2195",UpDownArrow:"\u2195",Updownarrow:"\u21d5",UpEquilibrium:"\u296e",upharpoonleft:"\u21bf",upharpoonright:"\u21be",uplus:"\u228e",UpperLeftArrow:"\u2196",UpperRightArrow:"\u2197",upsi:"\u03c5",Upsi:"\u03d2",upsih:"\u03d2",Upsilon:"\u03a5",upsilon:"\u03c5",UpTeeArrow:"\u21a5",UpTee:"\u22a5",upuparrows:"\u21c8",urcorn:"\u231d",urcorner:"\u231d",urcrop:"\u230e",Uring:"\u016e",uring:"\u016f",urtri:"\u25f9",Uscr:"\ud835\udcb0",uscr:"\ud835\udcca",utdot:"\u22f0",Utilde:"\u0168",utilde:"\u0169",utri:"\u25b5",utrif:"\u25b4",uuarr:"\u21c8",Uuml:"\xdc",uuml:"\xfc",uwangle:"\u29a7",vangrt:"\u299c",varepsilon:"\u03f5",varkappa:"\u03f0",varnothing:"\u2205",varphi:"\u03d5",varpi:"\u03d6",varpropto:"\u221d",varr:"\u2195",vArr:"\u21d5",varrho:"\u03f1",varsigma:"\u03c2",varsubsetneq:"\u228a\ufe00",varsubsetneqq:"\u2acb\ufe00",varsupsetneq:"\u228b\ufe00",varsupsetneqq:"\u2acc\ufe00",vartheta:"\u03d1",vartriangleleft:"\u22b2",vartriangleright:"\u22b3",vBar:"\u2ae8",Vbar:"\u2aeb",vBarv:"\u2ae9",Vcy:"\u0412",vcy:"\u0432",vdash:"\u22a2",vDash:"\u22a8",Vdash:"\u22a9",VDash:"\u22ab",Vdashl:"\u2ae6",veebar:"\u22bb",vee:"\u2228",Vee:"\u22c1",veeeq:"\u225a",vellip:"\u22ee",verbar:"|",Verbar:"\u2016",vert:"|",Vert:"\u2016",VerticalBar:"\u2223",VerticalLine:"|",VerticalSeparator:"\u2758",VerticalTilde:"\u2240",VeryThinSpace:"\u200a",Vfr:"\ud835\udd19",vfr:"\ud835\udd33",vltri:"\u22b2",vnsub:"\u2282\u20d2",vnsup:"\u2283\u20d2",Vopf:"\ud835\udd4d",vopf:"\ud835\udd67",vprop:"\u221d",vrtri:"\u22b3",Vscr:"\ud835\udcb1",vscr:"\ud835\udccb",vsubnE:"\u2acb\ufe00",vsubne:"\u228a\ufe00",vsupnE:"\u2acc\ufe00",vsupne:"\u228b\ufe00",Vvdash:"\u22aa",vzigzag:"\u299a",Wcirc:"\u0174",wcirc:"\u0175",wedbar:"\u2a5f",wedge:"\u2227",Wedge:"\u22c0",wedgeq:"\u2259",weierp:"\u2118",Wfr:"\ud835\udd1a",wfr:"\ud835\udd34",Wopf:"\ud835\udd4e",wopf:"\ud835\udd68",wp:"\u2118",wr:"\u2240",wreath:"\u2240",Wscr:"\ud835\udcb2",wscr:"\ud835\udccc",xcap:"\u22c2",xcirc:"\u25ef",xcup:"\u22c3",xdtri:"\u25bd",Xfr:"\ud835\udd1b",xfr:"\ud835\udd35",xharr:"\u27f7",xhArr:"\u27fa",Xi:"\u039e",xi:"\u03be",xlarr:"\u27f5",xlArr:"\u27f8",xmap:"\u27fc",xnis:"\u22fb",xodot:"\u2a00",Xopf:"\ud835\udd4f",xopf:"\ud835\udd69",xoplus:"\u2a01",xotime:"\u2a02",xrarr:"\u27f6",xrArr:"\u27f9",Xscr:"\ud835\udcb3",xscr:"\ud835\udccd",xsqcup:"\u2a06",xuplus:"\u2a04",xutri:"\u25b3",xvee:"\u22c1",xwedge:"\u22c0",Yacute:"\xdd",yacute:"\xfd",YAcy:"\u042f",yacy:"\u044f",Ycirc:"\u0176",ycirc:"\u0177",Ycy:"\u042b",ycy:"\u044b",yen:"\xa5",Yfr:"\ud835\udd1c",yfr:"\ud835\udd36",YIcy:"\u0407",yicy:"\u0457",Yopf:"\ud835\udd50",yopf:"\ud835\udd6a",Yscr:"\ud835\udcb4",yscr:"\ud835\udcce",YUcy:"\u042e",yucy:"\u044e",yuml:"\xff",Yuml:"\u0178",Zacute:"\u0179",zacute:"\u017a",Zcaron:"\u017d",zcaron:"\u017e",Zcy:"\u0417",zcy:"\u0437",Zdot:"\u017b",zdot:"\u017c",zeetrf:"\u2128",ZeroWidthSpace:"\u200b",Zeta:"\u0396",zeta:"\u03b6",zfr:"\ud835\udd37",Zfr:"\u2128",ZHcy:"\u0416",zhcy:"\u0436",zigrarr:"\u21dd",zopf:"\ud835\udd6b",Zopf:"\u2124",Zscr:"\ud835\udcb5",zscr:"\ud835\udccf",zwj:"\u200d",zwnj:"\u200c"}},{}],53:[function(e,r,t){"use 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\xA0\u1680\u2000-\u200A\u2028\u2029\u202F\u205F\u3000]/},{}],65:[function(e,r,t){"use strict";t.Any=e("./properties/Any/regex"),t.Cc=e("./categories/Cc/regex"),t.Cf=e("./categories/Cf/regex"),t.P=e("./categories/P/regex"),t.Z=e("./categories/Z/regex")},{"./categories/Cc/regex":61,"./categories/Cf/regex":62,"./categories/P/regex":63,"./categories/Z/regex":64,"./properties/Any/regex":66}],66:[function(e,r,t){r.exports=/[\0-\uD7FF\uE000-\uFFFF]|[\uD800-\uDBFF][\uDC00-\uDFFF]|[\uD800-\uDBFF](?![\uDC00-\uDFFF])|(?:[^\uD800-\uDBFF]|^)[\uDC00-\uDFFF]/},{}],67:[function(e,r,t){"use strict";r.exports=e("./lib/")},{"./lib/":9}]},{},[67])(67)})); diff --git a/udpipe2/docs/scripts/md b/udpipe2/docs/scripts/md deleted file mode 100644 index 39de725e0a5903646456dbd036b44ea8f8f4301d..0000000000000000000000000000000000000000 --- a/udpipe2/docs/scripts/md +++ /dev/null @@ -1,47 +0,0 @@ -#!/usr/bin/env nodejs -'use strict'; - -var fs = require('fs'); - -var args = process.argv.slice(2); - -// Initialize markdown-it and plugins -var md = require('./markdown-it.min.js')({ - html: true, - xhtmlOut: false, - breaks: false, - linkify: true, - typographer: false -}); -md.renderer.rules.table_open = function () { return '\n'; }; - -// Main callback -function process_data(err, data) { - if (err) { - console.error(err.stack); - process.exit(1); - } - - var output; - try { - output = md.render(data); - } catch (err) { - console.error(err.stack); - process.exit(1); - } - - if (args.length >= 2) { - fs.writeFileSync(args[1], output); - } else { - process.stdout.write(output); - } -} - -// Load and process data -if (args.length >= 1) { - fs.readFile(args[0], 'utf-8', process_data); -} else { - var chunks = []; - process.stdin.on('data', function (chunk) { chunks.push(chunk); }); - process.stdin.on('end', function () { process_data(null, Buffer.concat(chunks).toString('utf-8')); }); -} diff --git a/udpipe2/docs/scripts/preprocess b/udpipe2/docs/scripts/preprocess deleted file mode 100644 index e94e247b54c40e27b9f09b569fca1352e20e9de7..0000000000000000000000000000000000000000 --- a/udpipe2/docs/scripts/preprocess +++ /dev/null @@ -1,3 +0,0 @@ -#!/bin/sh - -perl -ple '$_ = "$2" if /^(#+)\s*(.*?)\s*#(\S+)$/' diff --git a/udpipe2/docs/udpipe_2.md b/udpipe2/docs/udpipe_2.md deleted file mode 100644 index 1f297d9a0fc58fde90d260181e3a2f904bedb72f..0000000000000000000000000000000000000000 --- a/udpipe2/docs/udpipe_2.md +++ /dev/null @@ -1,58 +0,0 @@ -# UDPipe 2 - -UDPipe 2 is a Python prototype, capable of performing tagging, lemmatization and -syntactic analysis of CoNLL-U input. It took part in several competitions, -reaching excellent results in all of them: -- one of the winners of the [CoNLL 2018 Shared Task](https://www.aclweb.org/anthology/K18-2001/) -- one of the winners of the [SIGMORPHON 2019 Shared Task 2](https://www.aclweb.org/anthology/W19-4226/) -- the winner of [EvaLatin 2020](https://www.aclweb.org/anthology/2020.lt4hala-1.16/) - -Compared to UDPipe 1, it is Python-only, it does not perform tokenization, and -the models require more computation power. - -## REST Service - -The UDPipe 2 models are currently available from the -[LINDAT UDPipe REST Service](https://lindat.mff.cuni.cz/services/udpipe/). -Apart from the web interface, you can use the -[udpipe2_client.py](https://github.com/ufal/udpipe/blob/udpipe-2/udpipe2_client.py) -script to process your files through the service. - -## Getting UDPipe 2 - -You can get UDPipe 2 sources from the -[udpipe-2 branch of the UDPipe repository](https://github.com/ufal/udpipe/tree/udpipe-2). -The sources can be used to both train a new model and to run a local REST server -for inference. - -## Models - -The available models are described [on a separate page](/udpipe/2/models). - -

Acknowledgements

- -This work has been supported by the Ministry of Education, Youth and Sports of -the Czech Republic, Project No. LM2018101 LINDAT/CLARIAH-CZ. - -## Publications - -- Milan Straka (2018): [UDPipe 2.0 Prototype at CoNLL 2018 UD Shared Task](https://www.aclweb.org/anthology/K18-2020/). In: Proceedings of CoNLL 2018: The SIGNLL Conference on Computational Natural Language Learning, pp. 197-207, Association for Computational Linguistics, Stroudsburg, PA, USA, ISBN 978-1-948087-72-8 -- Milan Straka, Jana Straková, Jan Hajič (2019): [UDPipe at SIGMORPHON 2019: Contextualized Embeddings, Regularization with Morphological Categories, Corpora Merging](https://www.aclweb.org/anthology/W19-4212/). In: Proceedings of the 16th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology, pp. 95-103, Association for Computational Linguistics, Stroudsburg, PA, USA, ISBN 978-1-950737-36-9 -- Milan Straka, Jana Straková, Jan Hajič (2019): [Evaluating Contextualized Embeddings on 54 Languages in POS Tagging, Lemmatization and Dependency Parsing](https://arxiv.org/abs/1908.07448). In: ArXiv.org Computing Research Repository, ISSN 2331-8422, 1904.02099 - -## Bibtex for Referencing - -``` -@InProceedings{straka-2018-udpipe, - title = "{UDP}ipe 2.0 Prototype at {C}o{NLL} 2018 {UD} Shared Task", - author = "Straka, Milan", - booktitle = "Proceedings of the {C}o{NLL} 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies", - month = oct, - year = "2018", - address = "Brussels, Belgium", - publisher = "Association for Computational Linguistics", - url = "https://www.aclweb.org/anthology/K18-2020", - doi = "10.18653/v1/K18-2020", - pages = "197--207", -} -``` diff --git a/udpipe2/models-2.10/.gitignore b/udpipe2/models-2.10/.gitignore deleted file mode 100644 index 423c60b314ebc1b32817babf3d899e62daecc0b3..0000000000000000000000000000000000000000 --- a/udpipe2/models-2.10/.gitignore +++ /dev/null @@ -1 +0,0 @@ -/*-220711.model diff --git a/udpipe2/models-2.10/EVALUATION-logs.txt b/udpipe2/models-2.10/EVALUATION-logs.txt deleted file mode 100644 index 65dc208796806d5465a798899fe5a46a23ec0e90..0000000000000000000000000000000000000000 --- a/udpipe2/models-2.10/EVALUATION-logs.txt +++ /dev/null @@ -1,124 +0,0 @@ -af_afribooms UPOS: 98.77, XPOS: 95.63, UFeats: 98.31, AllTags: 95.51, Lemmas: 97.53, UAS: 90.72, LAS: 87.80, CLAS: 82.75, MLAS: 79.21, BLEX: 78.99 -ar_padt UPOS: 97.02, XPOS: 94.38, UFeats: 94.53, AllTags: 94.07, Lemmas: 95.31, UAS: 88.13, LAS: 83.51, CLAS: 80.41, MLAS: 74.57, BLEX: 76.16 -be_hse UPOS: 98.81, XPOS: 96.74, UFeats: 94.94, AllTags: 92.87, Lemmas: 93.86, UAS: 89.55, LAS: 87.39, CLAS: 84.54, MLAS: 78.23, BLEX: 78.12 -bg_btb UPOS: 99.29, XPOS: 97.30, UFeats: 98.06, AllTags: 96.95, Lemmas: 98.09, UAS: 95.24, LAS: 92.44, CLAS: 89.62, MLAS: 86.49, BLEX: 87.03 -ca_ancora UPOS: 99.14, XPOS: 97.32, UFeats: 98.79, AllTags: 97.07, Lemmas: 99.46, UAS: 95.02, LAS: 93.31, CLAS: 89.78, MLAS: 87.69, BLEX: 89.13 -cop_scriptorium UPOS: 96.94, XPOS: 96.78, UFeats: 97.51, AllTags: 95.50, Lemmas: 97.01, UAS: 90.48, LAS: 87.70, CLAS: 81.89, MLAS: 76.04, BLEX: 79.55 -cs_cac UPOS: 99.73, XPOS: 98.58, UFeats: 98.38, AllTags: 98.13, Lemmas: 99.19, UAS: 96.09, LAS: 94.73, CLAS: 93.70, MLAS: 91.09, BLEX: 92.67 -cs_cltt UPOS: 99.47, XPOS: 95.47, UFeats: 95.59, AllTags: 95.15, Lemmas: 99.47, UAS: 91.28, LAS: 89.73, CLAS: 87.29, MLAS: 81.76, BLEX: 86.82 -cs_fictree UPOS: 99.18, XPOS: 97.08, UFeats: 97.83, AllTags: 96.87, Lemmas: 99.36, UAS: 96.46, LAS: 94.97, CLAS: 93.80, MLAS: 89.69, BLEX: 92.92 -cs_pdt UPOS: 99.45, XPOS: 98.47, UFeats: 98.40, AllTags: 98.08, Lemmas: 99.28, UAS: 95.64, LAS: 94.23, CLAS: 93.35, MLAS: 90.86, BLEX: 92.50 -cu_proiel UPOS: 97.08, XPOS: 97.28, UFeats: 91.05, AllTags: 89.88, Lemmas: 93.14, UAS: 88.30, LAS: 84.19, CLAS: 82.97, MLAS: 73.96, BLEX: 77.40 -cy_ccg UPOS: 95.84, XPOS: 94.87, UFeats: 90.27, AllTags: 88.01, Lemmas: 94.44, UAS: 87.85, LAS: 81.82, CLAS: 76.78, MLAS: 64.29, BLEX: 70.19 -da_ddt UPOS: 98.16, XPOS: 100.00, UFeats: 97.55, AllTags: 96.77, Lemmas: 97.45, UAS: 89.46, LAS: 87.42, CLAS: 85.26, MLAS: 80.41, BLEX: 82.17 -de_gsd UPOS: 95.94, XPOS: 97.87, UFeats: 90.62, AllTags: 87.59, Lemmas: 96.96, UAS: 89.28, LAS: 85.04, CLAS: 80.96, MLAS: 65.30, BLEX: 76.75 -de_hdt UPOS: 98.62, XPOS: 98.58, UFeats: 94.22, AllTags: 93.83, Lemmas: 97.26, UAS: 97.63, LAS: 96.75, CLAS: 94.81, MLAS: 85.45, BLEX: 91.08 -el_gdt UPOS: 98.23, XPOS: 98.24, UFeats: 95.83, AllTags: 95.15, Lemmas: 95.70, UAS: 93.85, LAS: 92.04, CLAS: 88.57, MLAS: 82.21, BLEX: 81.75 -en_atis UPOS: 98.97, XPOS: 100.00, UFeats: 98.56, AllTags: 98.13, Lemmas: 99.94, UAS: 95.88, LAS: 94.26, CLAS: 92.50, MLAS: 89.80, BLEX: 92.40 -en_ewt UPOS: 97.35, XPOS: 97.06, UFeats: 97.51, AllTags: 95.71, Lemmas: 98.07, UAS: 92.62, LAS: 90.57, CLAS: 88.15, MLAS: 84.02, BLEX: 85.98 -en_gum UPOS: 98.27, XPOS: 98.26, UFeats: 98.21, AllTags: 97.23, Lemmas: 99.09, UAS: 93.17, LAS: 91.19, CLAS: 88.17, MLAS: 85.40, BLEX: 87.04 -en_lines UPOS: 97.79, XPOS: 96.84, UFeats: 97.07, AllTags: 94.48, Lemmas: 98.47, UAS: 92.09, LAS: 89.17, CLAS: 86.34, MLAS: 81.03, BLEX: 84.34 -en_partut UPOS: 97.48, XPOS: 97.36, UFeats: 96.57, AllTags: 95.51, Lemmas: 98.42, UAS: 94.48, LAS: 92.46, CLAS: 89.24, MLAS: 83.68, BLEX: 87.62 -es_ancora UPOS: 99.11, XPOS: 96.07, UFeats: 98.80, AllTags: 95.62, Lemmas: 99.42, UAS: 93.88, LAS: 91.97, CLAS: 88.73, MLAS: 86.57, BLEX: 88.04 -es_gsd UPOS: 97.39, XPOS: 100.00, UFeats: 97.20, AllTags: 95.53, Lemmas: 98.97, UAS: 92.65, LAS: 90.32, CLAS: 86.46, MLAS: 79.43, BLEX: 85.05 -et_edt UPOS: 97.81, XPOS: 98.36, UFeats: 96.36, AllTags: 95.17, Lemmas: 95.43, UAS: 89.71, LAS: 87.03, CLAS: 85.53, MLAS: 80.74, BLEX: 80.37 -et_ewt UPOS: 96.65, XPOS: 97.43, UFeats: 95.17, AllTags: 93.07, Lemmas: 94.97, UAS: 86.76, LAS: 83.24, CLAS: 80.95, MLAS: 74.74, BLEX: 75.57 -eu_bdt UPOS: 96.30, XPOS: 100.00, UFeats: 93.50, AllTags: 91.48, Lemmas: 96.39, UAS: 87.48, LAS: 84.36, CLAS: 83.23, MLAS: 74.75, BLEX: 79.86 -fa_perdt UPOS: 97.78, XPOS: 97.65, UFeats: 97.91, AllTags: 95.88, Lemmas: 99.19, UAS: 94.18, LAS: 91.95, CLAS: 90.15, MLAS: 86.72, BLEX: 89.23 -fa_seraji UPOS: 98.24, XPOS: 98.29, UFeats: 98.29, AllTags: 97.77, Lemmas: 96.80, UAS: 92.36, LAS: 89.48, CLAS: 86.84, MLAS: 84.78, BLEX: 83.40 -fi_ftb UPOS: 97.00, XPOS: 95.36, UFeats: 96.91, AllTags: 94.28, Lemmas: 95.67, UAS: 91.91, LAS: 89.22, CLAS: 86.82, MLAS: 82.47, BLEX: 82.83 -fi_tdt UPOS: 97.92, XPOS: 98.49, UFeats: 96.30, AllTags: 95.41, Lemmas: 92.45, UAS: 91.51, LAS: 89.46, CLAS: 87.88, MLAS: 83.17, BLEX: 79.15 -fo_farpahc UPOS: 97.63, XPOS: 93.28, UFeats: 94.72, AllTags: 92.70, Lemmas: 100.00, UAS: 86.82, LAS: 83.10, CLAS: 76.51, MLAS: 69.17, BLEX: 76.51 -fr_gsd UPOS: 98.44, XPOS: 100.00, UFeats: 98.47, AllTags: 97.71, Lemmas: 98.75, UAS: 94.55, LAS: 92.71, CLAS: 89.22, MLAS: 86.34, BLEX: 87.59 -fr_parisstories UPOS: 96.81, XPOS: 100.00, UFeats: 94.89, AllTags: 92.68, Lemmas: 97.98, UAS: 81.66, LAS: 76.48, CLAS: 70.99, MLAS: 62.48, BLEX: 69.32 -fr_partut UPOS: 97.89, XPOS: 97.35, UFeats: 95.27, AllTags: 94.51, Lemmas: 97.89, UAS: 95.62, LAS: 93.85, CLAS: 90.63, MLAS: 82.18, BLEX: 87.24 -fr_rhapsodie UPOS: 98.00, XPOS: 98.13, UFeats: 96.88, AllTags: 93.95, Lemmas: 98.99, UAS: 89.90, LAS: 86.09, CLAS: 81.29, MLAS: 75.88, BLEX: 80.35 -fr_sequoia UPOS: 99.24, XPOS: 100.00, UFeats: 97.95, AllTags: 97.54, Lemmas: 99.13, UAS: 95.43, LAS: 94.11, CLAS: 91.57, MLAS: 88.00, BLEX: 90.34 -fro_srcmf UPOS: 96.99, XPOS: 96.82, UFeats: 98.01, AllTags: 96.01, Lemmas: 99.95, UAS: 91.58, LAS: 87.82, CLAS: 84.91, MLAS: 81.15, BLEX: 84.85 -ga_idt UPOS: 95.89, XPOS: 95.07, UFeats: 90.62, AllTags: 87.43, Lemmas: 95.54, UAS: 87.28, LAS: 81.64, CLAS: 76.56, MLAS: 64.55, BLEX: 71.85 -gd_arcosg UPOS: 96.62, XPOS: 92.24, UFeats: 94.09, AllTags: 91.39, Lemmas: 97.59, UAS: 87.34, LAS: 81.66, CLAS: 77.44, MLAS: 69.22, BLEX: 75.23 -gl_ctg UPOS: 98.01, XPOS: 97.78, UFeats: 99.84, AllTags: 97.41, Lemmas: 98.79, UAS: 87.31, LAS: 84.80, CLAS: 80.10, MLAS: 74.04, BLEX: 78.88 -gl_treegal UPOS: 97.19, XPOS: 94.83, UFeats: 95.91, AllTags: 93.86, Lemmas: 97.86, UAS: 86.75, LAS: 82.40, CLAS: 77.86, MLAS: 71.24, BLEX: 75.54 -got_proiel UPOS: 96.97, XPOS: 97.42, UFeats: 90.97, AllTags: 89.31, Lemmas: 94.71, UAS: 84.11, LAS: 78.29, CLAS: 75.45, MLAS: 65.70, BLEX: 71.10 -grc_perseus UPOS: 92.88, XPOS: 85.60, UFeats: 91.47, AllTags: 84.90, Lemmas: 86.70, UAS: 80.32, LAS: 74.53, CLAS: 68.75, MLAS: 54.71, BLEX: 55.87 -grc_proiel UPOS: 97.87, XPOS: 98.14, UFeats: 92.48, AllTags: 91.22, Lemmas: 94.73, UAS: 86.05, LAS: 82.14, CLAS: 77.52, MLAS: 66.97, BLEX: 71.90 -hbo_ptnk UPOS: 68.02, XPOS: 67.97, UFeats: 67.11, AllTags: 56.05, Lemmas: 53.36, UAS: 63.32, LAS: 51.61, CLAS: 43.47, MLAS: 28.00, BLEX: 24.34 -he_htb UPOS: 97.44, XPOS: 97.42, UFeats: 95.76, AllTags: 95.03, Lemmas: 97.34, UAS: 92.71, LAS: 90.24, CLAS: 85.28, MLAS: 78.74, BLEX: 81.80 -he_iahltwiki UPOS: 97.09, XPOS: 97.10, UFeats: 91.58, AllTags: 90.40, Lemmas: 98.24, UAS: 93.88, LAS: 91.45, CLAS: 87.75, MLAS: 74.25, BLEX: 85.44 -hi_hdtb UPOS: 97.58, XPOS: 97.14, UFeats: 94.19, AllTags: 92.25, Lemmas: 98.92, UAS: 95.42, LAS: 92.44, CLAS: 89.10, MLAS: 79.35, BLEX: 87.81 -hr_set UPOS: 98.54, XPOS: 95.80, UFeats: 96.31, AllTags: 95.54, Lemmas: 97.68, UAS: 92.67, LAS: 89.75, CLAS: 87.55, MLAS: 81.90, BLEX: 84.69 -hu_szeged UPOS: 96.79, XPOS: 100.00, UFeats: 94.35, AllTags: 93.56, Lemmas: 95.04, UAS: 89.30, LAS: 85.53, CLAS: 84.34, MLAS: 75.39, BLEX: 78.45 -hy_armtdp UPOS: 96.63, XPOS: 100.00, UFeats: 92.05, AllTags: 90.61, Lemmas: 95.70, UAS: 88.50, LAS: 83.81, CLAS: 79.54, MLAS: 70.02, BLEX: 75.42 -hy_bsut UPOS: 97.53, XPOS: 100.00, UFeats: 92.25, AllTags: 91.44, Lemmas: 96.82, UAS: 90.55, LAS: 86.28, CLAS: 82.61, MLAS: 71.68, BLEX: 79.32 -hyw_armtdp UPOS: 96.90, XPOS: 100.00, UFeats: 92.66, AllTags: 91.86, Lemmas: 97.22, UAS: 89.64, LAS: 84.88, CLAS: 79.34, MLAS: 70.04, BLEX: 76.22 -id_csui UPOS: 96.56, XPOS: 96.72, UFeats: 97.43, AllTags: 95.97, Lemmas: 98.87, UAS: 87.76, LAS: 83.27, CLAS: 80.93, MLAS: 77.55, BLEX: 79.90 -id_gsd UPOS: 94.66, XPOS: 94.26, UFeats: 96.00, AllTags: 89.13, Lemmas: 98.53, UAS: 88.56, LAS: 82.41, CLAS: 79.03, MLAS: 73.15, BLEX: 77.87 -is_icepahc UPOS: 97.08, XPOS: 93.45, UFeats: 91.47, AllTags: 86.61, Lemmas: 96.15, UAS: 87.77, LAS: 83.87, CLAS: 78.62, MLAS: 66.49, BLEX: 74.83 -is_modern UPOS: 99.14, XPOS: 98.21, UFeats: 98.45, AllTags: 97.95, Lemmas: 98.98, UAS: 94.50, LAS: 93.26, CLAS: 91.23, MLAS: 89.41, BLEX: 90.16 -it_isdt UPOS: 98.72, XPOS: 98.65, UFeats: 98.42, AllTags: 97.83, Lemmas: 98.95, UAS: 94.96, LAS: 93.34, CLAS: 89.84, MLAS: 86.95, BLEX: 88.38 -it_markit UPOS: 97.15, XPOS: 97.40, UFeats: 94.09, AllTags: 92.34, Lemmas: 88.54, UAS: 89.14, LAS: 85.26, CLAS: 81.14, MLAS: 70.49, BLEX: 78.46 -it_partut UPOS: 98.54, XPOS: 98.57, UFeats: 98.49, AllTags: 97.69, Lemmas: 98.93, UAS: 96.26, LAS: 94.15, CLAS: 90.51, MLAS: 87.68, BLEX: 89.07 -it_postwita UPOS: 97.04, XPOS: 96.83, UFeats: 96.80, AllTags: 95.29, Lemmas: 97.31, UAS: 88.36, LAS: 84.20, CLAS: 79.86, MLAS: 75.33, BLEX: 77.34 -it_twittiro UPOS: 96.91, XPOS: 96.61, UFeats: 95.93, AllTags: 94.09, Lemmas: 95.19, UAS: 88.07, LAS: 83.53, CLAS: 78.14, MLAS: 71.77, BLEX: 72.69 -it_vit UPOS: 98.36, XPOS: 97.71, UFeats: 97.85, AllTags: 96.52, Lemmas: 99.10, UAS: 92.88, LAS: 89.97, CLAS: 85.60, MLAS: 81.89, BLEX: 84.63 -ja_gsd UPOS: 98.55, XPOS: 97.50, UFeats: 99.99, AllTags: 97.13, Lemmas: 98.47, UAS: 94.73, LAS: 93.75, CLAS: 90.40, MLAS: 88.50, BLEX: 88.34 -ja_gsdluw UPOS: 98.36, XPOS: 98.05, UFeats: 100.00, AllTags: 97.93, Lemmas: 97.89, UAS: 95.23, LAS: 94.18, CLAS: 88.82, MLAS: 86.38, BLEX: 85.19 -ko_gsd UPOS: 96.73, XPOS: 90.43, UFeats: 99.82, AllTags: 88.19, Lemmas: 93.69, UAS: 89.32, LAS: 85.66, CLAS: 83.79, MLAS: 81.52, BLEX: 77.99 -ko_kaist UPOS: 95.88, XPOS: 87.74, UFeats: 100.00, AllTags: 87.56, Lemmas: 94.17, UAS: 89.34, LAS: 87.48, CLAS: 85.38, MLAS: 82.15, BLEX: 80.14 -la_perseus UPOS: 91.85, XPOS: 80.66, UFeats: 86.20, AllTags: 78.48, Lemmas: 88.15, UAS: 78.13, LAS: 68.71, CLAS: 64.31, MLAS: 52.28, BLEX: 55.56 -la_udante UPOS: 90.82, XPOS: 75.70, UFeats: 81.53, AllTags: 71.65, Lemmas: 87.44, UAS: 75.47, LAS: 67.94, CLAS: 59.71, MLAS: 43.88, BLEX: 50.47 -la_ittb UPOS: 98.92, XPOS: 96.57, UFeats: 96.79, AllTags: 95.18, Lemmas: 99.18, UAS: 91.51, LAS: 89.51, CLAS: 87.10, MLAS: 82.60, BLEX: 86.59 -la_llct UPOS: 99.68, XPOS: 97.15, UFeats: 97.25, AllTags: 96.89, Lemmas: 97.78, UAS: 95.55, LAS: 94.57, CLAS: 93.62, MLAS: 89.80, BLEX: 90.98 -la_proiel UPOS: 97.12, XPOS: 97.32, UFeats: 91.24, AllTags: 90.24, Lemmas: 96.44, UAS: 83.20, LAS: 78.50, CLAS: 75.46, MLAS: 66.31, BLEX: 73.00 -lt_alksnis UPOS: 96.04, XPOS: 90.52, UFeats: 91.23, AllTags: 89.61, Lemmas: 93.69, UAS: 83.70, LAS: 79.89, CLAS: 77.74, MLAS: 68.90, BLEX: 72.37 -lt_hse UPOS: 91.23, XPOS: 92.36, UFeats: 85.28, AllTags: 80.09, Lemmas: 90.57, UAS: 73.96, LAS: 64.53, CLAS: 62.53, MLAS: 47.54, BLEX: 56.10 -lv_lvtb UPOS: 97.14, XPOS: 90.44, UFeats: 94.49, AllTags: 89.64, Lemmas: 96.55, UAS: 89.84, LAS: 86.82, CLAS: 84.71, MLAS: 77.05, BLEX: 81.31 -lzh_kyoto UPOS: 92.31, XPOS: 90.88, UFeats: 93.87, AllTags: 88.08, Lemmas: 99.47, UAS: 83.15, LAS: 77.63, CLAS: 76.82, MLAS: 73.05, BLEX: 76.42 -mr_ufal UPOS: 82.52, XPOS: 100.00, UFeats: 68.20, AllTags: 62.86, Lemmas: 80.83, UAS: 68.93, LAS: 58.50, CLAS: 51.45, MLAS: 29.46, BLEX: 38.17 -mt_mudt UPOS: 95.95, XPOS: 95.92, UFeats: 100.00, AllTags: 95.48, Lemmas: 100.00, UAS: 85.65, LAS: 80.69, CLAS: 73.31, MLAS: 69.38, BLEX: 73.31 -nl_alpino UPOS: 97.97, XPOS: 96.87, UFeats: 97.89, AllTags: 96.39, Lemmas: 95.26, UAS: 94.01, LAS: 91.64, CLAS: 87.74, MLAS: 84.12, BLEX: 80.85 -nl_lassysmall UPOS: 97.26, XPOS: 96.43, UFeats: 97.31, AllTags: 95.79, Lemmas: 95.97, UAS: 94.51, LAS: 91.66, CLAS: 88.45, MLAS: 84.41, BLEX: 83.08 -no_bokmaal UPOS: 98.61, XPOS: 100.00, UFeats: 97.69, AllTags: 97.07, Lemmas: 98.82, UAS: 94.40, LAS: 92.92, CLAS: 90.96, MLAS: 87.60, BLEX: 89.44 -no_nynorsk UPOS: 98.41, XPOS: 100.00, UFeats: 97.49, AllTags: 96.71, Lemmas: 98.53, UAS: 94.63, LAS: 92.93, CLAS: 91.06, MLAS: 86.89, BLEX: 89.20 -no_nynorsklia UPOS: 96.72, XPOS: 100.00, UFeats: 95.80, AllTags: 93.84, Lemmas: 98.14, UAS: 81.41, LAS: 76.83, CLAS: 71.80, MLAS: 66.22, BLEX: 69.89 -orv_birchbark UPOS: 89.37, XPOS: 99.37, UFeats: 76.61, AllTags: 72.61, Lemmas: 66.04, UAS: 76.32, LAS: 69.00, CLAS: 62.28, MLAS: 41.54, BLEX: 33.60 -orv_rnc UPOS: 93.29, XPOS: 88.93, UFeats: 78.51, AllTags: 68.69, Lemmas: 76.77, UAS: 67.15, LAS: 61.10, CLAS: 55.87, MLAS: 37.11, BLEX: 37.26 -orv_torot UPOS: 95.06, XPOS: 95.30, UFeats: 88.54, AllTags: 86.56, Lemmas: 85.96, UAS: 83.29, LAS: 77.23, CLAS: 74.03, MLAS: 64.03, BLEX: 62.93 -pcm_nsc UPOS: 98.08, XPOS: 100.00, UFeats: 99.00, AllTags: 97.57, Lemmas: 99.38, UAS: 93.74, LAS: 91.07, CLAS: 90.15, MLAS: 88.21, BLEX: 89.67 -pl_lfg UPOS: 99.17, XPOS: 96.25, UFeats: 96.75, AllTags: 95.32, Lemmas: 98.38, UAS: 97.25, LAS: 95.90, CLAS: 94.64, MLAS: 90.19, BLEX: 92.68 -pl_pdb UPOS: 99.05, XPOS: 96.03, UFeats: 96.23, AllTags: 95.38, Lemmas: 98.23, UAS: 94.73, LAS: 92.70, CLAS: 90.86, MLAS: 85.82, BLEX: 88.80 -pt_bosque UPOS: 98.22, XPOS: 100.00, UFeats: 97.23, AllTags: 96.27, Lemmas: 98.66, UAS: 93.50, LAS: 91.16, CLAS: 87.67, MLAS: 82.44, BLEX: 85.87 -pt_gsd UPOS: 98.65, XPOS: 98.64, UFeats: 99.89, AllTags: 98.55, Lemmas: 99.40, UAS: 94.90, LAS: 93.81, CLAS: 90.98, MLAS: 89.23, BLEX: 90.36 -qpm_philotis UPOS: 98.90, XPOS: 100.00, UFeats: 95.67, AllTags: 95.32, Lemmas: 96.69, UAS: 88.68, LAS: 83.75, CLAS: 78.35, MLAS: 71.48, BLEX: 74.42 -qtd_sagt UPOS: 91.09, XPOS: 100.00, UFeats: 80.84, AllTags: 76.02, Lemmas: 91.52, UAS: 72.70, LAS: 62.06, CLAS: 56.88, MLAS: 41.62, BLEX: 51.71 -ro_nonstandard UPOS: 97.30, XPOS: 92.86, UFeats: 91.51, AllTags: 90.08, Lemmas: 95.99, UAS: 90.58, LAS: 86.50, CLAS: 82.00, MLAS: 69.63, BLEX: 77.69 -ro_rrt UPOS: 98.19, XPOS: 97.45, UFeats: 97.67, AllTags: 97.21, Lemmas: 98.22, UAS: 92.72, LAS: 89.13, CLAS: 85.64, MLAS: 82.13, BLEX: 83.69 -ro_simonero UPOS: 98.60, XPOS: 98.12, UFeats: 97.70, AllTags: 97.37, Lemmas: 99.07, UAS: 94.41, LAS: 92.44, CLAS: 89.67, MLAS: 85.72, BLEX: 88.60 -ru_gsd UPOS: 98.58, XPOS: 97.98, UFeats: 95.15, AllTags: 93.97, Lemmas: 97.43, UAS: 92.67, LAS: 89.69, CLAS: 88.71, MLAS: 81.94, BLEX: 85.64 -ru_syntagrus UPOS: 98.79, XPOS: 100.00, UFeats: 94.30, AllTags: 94.00, Lemmas: 98.46, UAS: 94.56, LAS: 92.40, CLAS: 91.22, MLAS: 83.25, BLEX: 89.44 -ru_taiga UPOS: 97.34, XPOS: 100.00, UFeats: 94.89, AllTags: 93.63, Lemmas: 96.37, UAS: 85.64, LAS: 81.92, CLAS: 79.70, MLAS: 72.73, BLEX: 76.10 -sa_vedic UPOS: 89.97, XPOS: 100.00, UFeats: 83.05, AllTags: 78.27, Lemmas: 87.33, UAS: 73.74, LAS: 62.01, CLAS: 61.49, MLAS: 51.98, BLEX: 55.41 -sk_snk UPOS: 97.88, XPOS: 90.55, UFeats: 93.69, AllTags: 89.78, Lemmas: 96.50, UAS: 93.89, LAS: 92.05, CLAS: 90.98, MLAS: 82.79, BLEX: 86.92 -sl_ssj UPOS: 99.03, XPOS: 97.02, UFeats: 97.24, AllTags: 96.69, Lemmas: 98.63, UAS: 94.15, LAS: 92.76, CLAS: 90.59, MLAS: 86.97, BLEX: 89.02 -sl_sst UPOS: 95.62, XPOS: 93.09, UFeats: 92.91, AllTags: 90.82, Lemmas: 97.55, UAS: 78.41, LAS: 73.09, CLAS: 69.96, MLAS: 63.33, BLEX: 68.33 -sme_giella UPOS: 91.92, XPOS: 93.67, UFeats: 89.46, AllTags: 85.43, Lemmas: 87.13, UAS: 75.48, LAS: 70.77, CLAS: 68.68, MLAS: 60.05, BLEX: 58.58 -sr_set UPOS: 99.13, XPOS: 96.01, UFeats: 96.20, AllTags: 95.74, Lemmas: 97.78, UAS: 94.26, LAS: 91.80, CLAS: 90.34, MLAS: 84.30, BLEX: 87.60 -sv_lines UPOS: 97.73, XPOS: 95.52, UFeats: 90.87, AllTags: 88.13, Lemmas: 97.76, UAS: 91.44, LAS: 88.20, CLAS: 85.49, MLAS: 72.49, BLEX: 82.96 -sv_talbanken UPOS: 98.61, XPOS: 97.52, UFeats: 97.48, AllTags: 96.70, Lemmas: 98.32, UAS: 92.68, LAS: 90.31, CLAS: 88.54, MLAS: 84.43, BLEX: 86.55 -ta_ttb UPOS: 89.29, XPOS: 87.78, UFeats: 89.94, AllTags: 82.65, Lemmas: 94.42, UAS: 78.13, LAS: 68.78, CLAS: 65.74, MLAS: 56.80, BLEX: 61.48 -te_mtg UPOS: 94.04, XPOS: 94.17, UFeats: 99.03, AllTags: 93.90, Lemmas: 100.00, UAS: 91.68, LAS: 85.58, CLAS: 81.98, MLAS: 77.98, BLEX: 81.98 -tr_atis UPOS: 99.02, XPOS: 100.00, UFeats: 98.52, AllTags: 98.32, Lemmas: 99.13, UAS: 91.13, LAS: 89.32, CLAS: 88.73, MLAS: 87.00, BLEX: 87.95 -tr_boun UPOS: 92.53, XPOS: 93.47, UFeats: 92.77, AllTags: 87.21, Lemmas: 94.26, UAS: 81.07, LAS: 74.73, CLAS: 72.30, MLAS: 61.25, BLEX: 66.92 -tr_framenet UPOS: 96.86, XPOS: 100.00, UFeats: 94.82, AllTags: 94.07, Lemmas: 96.66, UAS: 93.39, LAS: 84.25, CLAS: 81.02, MLAS: 73.89, BLEX: 77.64 -tr_imst UPOS: 95.94, XPOS: 95.49, UFeats: 92.38, AllTags: 89.91, Lemmas: 96.13, UAS: 78.07, LAS: 72.09, CLAS: 68.54, MLAS: 60.22, BLEX: 65.33 -tr_kenet UPOS: 93.72, XPOS: 100.00, UFeats: 92.12, AllTags: 90.71, Lemmas: 93.33, UAS: 84.07, LAS: 71.29, CLAS: 70.60, MLAS: 61.80, BLEX: 64.89 -tr_penn UPOS: 96.30, XPOS: 100.00, UFeats: 95.08, AllTags: 93.88, Lemmas: 95.01, UAS: 86.76, LAS: 73.92, CLAS: 70.55, MLAS: 63.59, BLEX: 66.03 -tr_tourism UPOS: 98.85, XPOS: 100.00, UFeats: 95.12, AllTags: 94.72, Lemmas: 98.40, UAS: 97.25, LAS: 91.58, CLAS: 89.07, MLAS: 82.03, BLEX: 87.45 -ug_udt UPOS: 89.71, XPOS: 92.31, UFeats: 88.64, AllTags: 80.51, Lemmas: 95.14, UAS: 78.38, LAS: 66.50, CLAS: 60.23, MLAS: 47.83, BLEX: 56.57 -uk_iu UPOS: 98.08, XPOS: 94.54, UFeats: 94.35, AllTags: 93.26, Lemmas: 97.53, UAS: 91.12, LAS: 88.72, CLAS: 86.01, MLAS: 79.14, BLEX: 83.36 -ur_udtb UPOS: 93.93, XPOS: 92.17, UFeats: 82.92, AllTags: 78.42, Lemmas: 97.41, UAS: 88.23, LAS: 82.57, CLAS: 77.05, MLAS: 56.64, BLEX: 74.76 -vi_vtb UPOS: 90.36, XPOS: 88.56, UFeats: 99.74, AllTags: 88.33, Lemmas: 99.59, UAS: 72.88, LAS: 65.41, CLAS: 62.99, MLAS: 58.76, BLEX: 62.51 -wo_wtb UPOS: 95.17, XPOS: 95.07, UFeats: 94.28, AllTags: 92.24, Lemmas: 95.96, UAS: 86.28, LAS: 80.76, CLAS: 75.46, MLAS: 68.69, BLEX: 72.07 -zh_gsd UPOS: 96.21, XPOS: 96.08, UFeats: 99.40, AllTags: 95.34, Lemmas: 99.99, UAS: 87.15, LAS: 83.95, CLAS: 82.60, MLAS: 78.42, BLEX: 82.59 -zh_gsdsimp UPOS: 96.15, XPOS: 96.04, UFeats: 99.45, AllTags: 95.30, Lemmas: 99.99, UAS: 87.28, LAS: 84.07, CLAS: 82.65, MLAS: 78.56, BLEX: 82.64 -************ ***** 96.49, XPOS: 96.10, UFeats: 94.35, AllTags: 91.83, Lemmas: 95.84, UAS: 88.77, LAS: 84.76, CLAS: 81.51, MLAS: 74.47, BLEX: 77.84 diff --git a/udpipe2/models-2.10/EVALUATION-server.txt b/udpipe2/models-2.10/EVALUATION-server.txt deleted file mode 100644 index 58b7d822e56414d55ba0512df9766d46ac567617..0000000000000000000000000000000000000000 --- a/udpipe2/models-2.10/EVALUATION-server.txt +++ /dev/null @@ -1,246 +0,0 @@ -afrikaans-afribooms-ud-2.10-220711 raw_text Tokens:99.78 Sentences:98.59 Words:99.78 UPOS:98.58 XPOS:95.46 UFeats:98.13 AllTags:95.33 Lemmas:97.43 UAS:90.10 LAS:87.23 CLAS:82.16 MLAS:78.64 BLEX:78.59 -afrikaans-afribooms-ud-2.10-220711 gold_tok Tokens:100.00 Sentences:100.00 Words:100.00 UPOS:98.77 XPOS:95.62 UFeats:98.31 AllTags:95.50 Lemmas:97.53 UAS:90.72 LAS:87.80 CLAS:82.75 MLAS:79.23 BLEX:78.99 -ancient_greek-perseus-ud-2.10-220711 raw_text Tokens:99.97 Sentences:98.85 Words:99.97 UPOS:92.83 XPOS:85.55 UFeats:91.45 AllTags:84.87 Lemmas:86.68 UAS:80.13 LAS:74.36 CLAS:68.58 MLAS:54.62 BLEX:55.72 -ancient_greek-perseus-ud-2.10-220711 gold_tok Tokens:100.00 Sentences:100.00 Words:100.00 UPOS:92.88 XPOS:85.60 UFeats:91.47 AllTags:84.90 Lemmas:86.70 UAS:80.32 LAS:74.53 CLAS:68.75 MLAS:54.73 BLEX:55.87 -ancient_greek-proiel-ud-2.10-220711 raw_text Tokens:100.00 Sentences:48.02 Words:100.00 UPOS:97.77 XPOS:98.05 UFeats:92.35 AllTags:91.05 Lemmas:94.71 UAS:79.82 LAS:76.06 CLAS:70.68 MLAS:60.08 BLEX:65.75 -ancient_greek-proiel-ud-2.10-220711 gold_tok Tokens:100.00 Sentences:100.00 Words:100.00 UPOS:97.87 XPOS:98.14 UFeats:92.49 AllTags:91.26 Lemmas:94.73 UAS:86.05 LAS:82.14 CLAS:77.52 MLAS:67.03 BLEX:71.90 -ancient_hebrew-ptnk-ud-2.10-220711 raw_text Tokens:100.00 Sentences:98.06 Words:68.76 UPOS:56.80 XPOS:56.94 UFeats:55.13 AllTags:50.80 Lemmas:49.88 UAS:38.73 LAS:34.67 CLAS:24.33 MLAS:18.47 BLEX:17.69 -ancient_hebrew-ptnk-ud-2.10-220711 gold_tok Tokens:100.00 Sentences:100.00 Words:100.00 UPOS:68.03 XPOS:67.97 UFeats:66.97 AllTags:56.15 Lemmas:53.35 UAS:63.31 LAS:51.61 CLAS:43.47 MLAS:28.08 BLEX:24.34 -arabic-padt-ud-2.10-220711 raw_text Tokens:99.98 Sentences:82.09 Words:94.58 UPOS:91.72 XPOS:89.01 UFeats:89.14 AllTags:88.69 Lemmas:90.41 UAS:78.63 LAS:74.54 CLAS:71.29 MLAS:65.84 BLEX:67.88 -arabic-padt-ud-2.10-220711 gold_tok Tokens:100.00 Sentences:100.00 Words:100.00 UPOS:97.02 XPOS:94.38 UFeats:94.53 AllTags:94.08 Lemmas:95.31 UAS:88.11 LAS:83.49 CLAS:80.38 MLAS:74.57 BLEX:76.13 -armenian-armtdp-ud-2.10-220711 raw_text Tokens:99.86 Sentences:95.70 Words:99.28 UPOS:96.07 XPOS:99.28 UFeats:91.39 AllTags:90.28 Lemmas:95.04 UAS:86.84 LAS:82.22 CLAS:78.45 MLAS:69.53 BLEX:74.39 -armenian-armtdp-ud-2.10-220711 gold_tok Tokens:100.00 Sentences:100.00 Words:100.00 UPOS:96.63 XPOS:100.00 UFeats:92.03 AllTags:90.77 Lemmas:95.70 UAS:88.50 LAS:83.81 CLAS:79.54 MLAS:70.18 BLEX:75.42 -armenian-bsut-ud-2.10-220711 raw_text Tokens:99.94 Sentences:98.73 Words:99.79 UPOS:97.31 XPOS:99.79 UFeats:92.01 AllTags:91.24 Lemmas:96.62 UAS:90.02 LAS:85.75 CLAS:82.13 MLAS:71.20 BLEX:78.86 -armenian-bsut-ud-2.10-220711 gold_tok Tokens:100.00 Sentences:100.00 Words:100.00 UPOS:97.53 XPOS:100.00 UFeats:92.24 AllTags:91.48 Lemmas:96.82 UAS:90.56 LAS:86.29 CLAS:82.62 MLAS:71.73 BLEX:79.32 -basque-bdt-ud-2.10-220711 raw_text Tokens:99.94 Sentences:99.83 Words:99.94 UPOS:96.25 XPOS:99.94 UFeats:92.69 AllTags:90.69 Lemmas:96.36 UAS:87.40 LAS:84.28 CLAS:83.16 MLAS:73.94 BLEX:79.81 -basque-bdt-ud-2.10-220711 gold_tok Tokens:100.00 Sentences:100.00 Words:100.00 UPOS:96.30 XPOS:100.00 UFeats:92.73 AllTags:90.72 Lemmas:96.39 UAS:87.48 LAS:84.36 CLAS:83.23 MLAS:73.99 BLEX:79.86 -belarusian-hse-ud-2.10-220711 raw_text Tokens:99.47 Sentences:83.97 Words:99.47 UPOS:98.30 XPOS:96.26 UFeats:94.38 AllTags:92.37 Lemmas:93.35 UAS:86.84 LAS:84.85 CLAS:82.19 MLAS:76.00 BLEX:76.01 -belarusian-hse-ud-2.10-220711 gold_tok Tokens:100.00 Sentences:100.00 Words:100.00 UPOS:98.81 XPOS:96.74 UFeats:94.94 AllTags:92.87 Lemmas:93.86 UAS:89.55 LAS:87.38 CLAS:84.53 MLAS:78.23 BLEX:78.11 -bulgarian-btb-ud-2.10-220711 raw_text Tokens:99.91 Sentences:94.17 Words:99.91 UPOS:99.19 XPOS:97.20 UFeats:97.97 AllTags:96.85 Lemmas:97.99 UAS:94.41 LAS:91.67 CLAS:88.89 MLAS:85.89 BLEX:86.31 -bulgarian-btb-ud-2.10-220711 gold_tok Tokens:100.00 Sentences:100.00 Words:100.00 UPOS:99.29 XPOS:97.30 UFeats:98.07 AllTags:96.96 Lemmas:98.09 UAS:95.24 LAS:92.44 CLAS:89.62 MLAS:86.52 BLEX:87.03 -catalan-ancora-ud-2.10-220711 raw_text Tokens:99.97 Sentences:99.08 Words:99.95 UPOS:99.07 XPOS:97.21 UFeats:98.70 AllTags:96.96 Lemmas:99.40 UAS:94.86 LAS:93.14 CLAS:89.56 MLAS:87.45 BLEX:88.92 -catalan-ancora-ud-2.10-220711 gold_tok Tokens:100.00 Sentences:100.00 Words:100.00 UPOS:99.14 XPOS:97.32 UFeats:98.78 AllTags:97.07 Lemmas:99.46 UAS:95.02 LAS:93.30 CLAS:89.77 MLAS:87.69 BLEX:89.13 -chinese-gsdsimp-ud-2.10-220711 raw_text Tokens:90.29 Sentences:99.10 Words:90.29 UPOS:87.21 XPOS:87.16 UFeats:89.74 AllTags:86.42 Lemmas:90.29 UAS:73.11 LAS:70.62 CLAS:67.09 MLAS:63.58 BLEX:67.09 -chinese-gsdsimp-ud-2.10-220711 gold_tok Tokens:100.00 Sentences:100.00 Words:100.00 UPOS:96.14 XPOS:96.04 UFeats:99.45 AllTags:95.30 Lemmas:99.99 UAS:87.28 LAS:84.07 CLAS:82.65 MLAS:78.56 BLEX:82.64 -chinese-gsd-ud-2.10-220711 raw_text Tokens:90.27 Sentences:99.10 Words:90.27 UPOS:87.15 XPOS:87.05 UFeats:89.71 AllTags:86.36 Lemmas:90.27 UAS:72.85 LAS:70.29 CLAS:66.89 MLAS:63.41 BLEX:66.89 -chinese-gsd-ud-2.10-220711 gold_tok Tokens:100.00 Sentences:100.00 Words:100.00 UPOS:96.21 XPOS:96.08 UFeats:99.40 AllTags:95.34 Lemmas:99.99 UAS:87.15 LAS:83.96 CLAS:82.60 MLAS:78.41 BLEX:82.59 -classical_chinese-kyoto-ud-2.10-220711 raw_text Tokens:97.26 Sentences:40.71 Words:97.26 UPOS:87.40 XPOS:86.48 UFeats:89.97 AllTags:83.27 Lemmas:96.78 UAS:67.56 LAS:62.17 CLAS:60.83 MLAS:58.02 BLEX:60.60 -classical_chinese-kyoto-ud-2.10-220711 gold_tok Tokens:100.00 Sentences:100.00 Words:100.00 UPOS:92.30 XPOS:90.87 UFeats:93.94 AllTags:88.19 Lemmas:99.47 UAS:83.16 LAS:77.63 CLAS:76.82 MLAS:73.15 BLEX:76.42 -coptic-scriptorium-ud-2.10-220711 raw_text Tokens:100.00 Sentences:33.87 Words:74.49 UPOS:72.43 XPOS:72.34 UFeats:72.53 AllTags:71.54 Lemmas:72.91 UAS:51.25 LAS:49.43 CLAS:39.61 MLAS:36.55 BLEX:39.14 -coptic-scriptorium-ud-2.10-220711 gold_tok Tokens:100.00 Sentences:100.00 Words:100.00 UPOS:96.94 XPOS:96.78 UFeats:97.49 AllTags:95.50 Lemmas:97.02 UAS:90.48 LAS:87.70 CLAS:81.89 MLAS:76.04 BLEX:79.57 -croatian-set-ud-2.10-220711 raw_text Tokens:99.93 Sentences:94.79 Words:99.93 UPOS:98.48 XPOS:95.72 UFeats:96.23 AllTags:95.49 Lemmas:97.60 UAS:92.17 LAS:89.27 CLAS:87.09 MLAS:81.53 BLEX:84.23 -croatian-set-ud-2.10-220711 gold_tok Tokens:100.00 Sentences:100.00 Words:100.00 UPOS:98.54 XPOS:95.80 UFeats:96.30 AllTags:95.56 Lemmas:97.68 UAS:92.67 LAS:89.75 CLAS:87.55 MLAS:81.92 BLEX:84.69 -czech-pdt-ud-2.10-220711 raw_text Tokens:99.94 Sentences:93.74 Words:99.94 UPOS:99.37 XPOS:98.40 UFeats:98.33 AllTags:98.02 Lemmas:99.21 UAS:94.90 LAS:93.50 CLAS:92.72 MLAS:90.28 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-ukrainian-iu-ud-2.10-220711 gold_tok Tokens:100.00 Sentences:100.00 Words:100.00 UPOS:98.08 XPOS:94.54 UFeats:94.34 AllTags:93.29 Lemmas:97.53 UAS:91.12 LAS:88.72 CLAS:86.01 MLAS:79.21 BLEX:83.36 -urdu-udtb-ud-2.10-220711 raw_text Tokens:100.00 Sentences:98.31 Words:100.00 UPOS:93.91 XPOS:92.15 UFeats:82.83 AllTags:78.40 Lemmas:97.41 UAS:88.15 LAS:82.49 CLAS:76.96 MLAS:56.62 BLEX:74.68 -urdu-udtb-ud-2.10-220711 gold_tok Tokens:100.00 Sentences:100.00 Words:100.00 UPOS:93.93 XPOS:92.17 UFeats:82.86 AllTags:78.43 Lemmas:97.41 UAS:88.23 LAS:82.58 CLAS:77.07 MLAS:56.67 BLEX:74.77 -uyghur-udt-ud-2.10-220711 raw_text Tokens:99.54 Sentences:81.81 Words:99.54 UPOS:89.33 XPOS:91.75 UFeats:88.12 AllTags:79.98 Lemmas:94.67 UAS:76.66 LAS:64.87 CLAS:58.89 MLAS:46.84 BLEX:55.29 -uyghur-udt-ud-2.10-220711 gold_tok Tokens:100.00 Sentences:100.00 Words:100.00 UPOS:89.71 XPOS:92.30 UFeats:88.59 AllTags:80.50 Lemmas:95.14 UAS:78.38 LAS:66.49 CLAS:60.22 MLAS:47.83 BLEX:56.56 -vietnamese-vtb-ud-2.10-220711 raw_text Tokens:85.37 Sentences:93.46 Words:85.37 UPOS:78.21 XPOS:76.76 UFeats:85.12 AllTags:76.57 Lemmas:85.16 UAS:52.68 LAS:47.84 CLAS:44.45 MLAS:41.55 BLEX:44.29 -vietnamese-vtb-ud-2.10-220711 gold_tok Tokens:100.00 Sentences:100.00 Words:100.00 UPOS:90.36 XPOS:88.55 UFeats:99.72 AllTags:88.32 Lemmas:99.59 UAS:72.88 LAS:65.41 CLAS:62.99 MLAS:58.76 BLEX:62.51 -welsh-ccg-ud-2.10-220711 raw_text Tokens:99.87 Sentences:97.37 Words:99.42 UPOS:95.33 XPOS:94.40 UFeats:89.82 AllTags:87.61 Lemmas:93.93 UAS:86.61 LAS:80.67 CLAS:75.50 MLAS:63.31 BLEX:69.02 -welsh-ccg-ud-2.10-220711 gold_tok Tokens:100.00 Sentences:100.00 Words:100.00 UPOS:95.84 XPOS:94.87 UFeats:90.31 AllTags:88.07 Lemmas:94.44 UAS:87.85 LAS:81.83 CLAS:76.80 MLAS:64.36 BLEX:70.21 -western_armenian-armtdp-ud-2.10-220711 raw_text Tokens:99.98 Sentences:98.68 Words:99.89 UPOS:96.82 XPOS:99.89 UFeats:92.51 AllTags:91.83 Lemmas:97.14 UAS:89.39 LAS:84.66 CLAS:79.12 MLAS:69.84 BLEX:76.01 -western_armenian-armtdp-ud-2.10-220711 gold_tok Tokens:100.00 Sentences:100.00 Words:100.00 UPOS:96.90 XPOS:100.00 UFeats:92.60 AllTags:91.93 Lemmas:97.22 UAS:89.64 LAS:84.89 CLAS:79.36 MLAS:70.07 BLEX:76.23 -wolof-wtb-ud-2.10-220711 raw_text Tokens:99.96 Sentences:91.95 Words:99.23 UPOS:94.20 XPOS:94.15 UFeats:93.50 AllTags:91.41 Lemmas:95.20 UAS:84.15 LAS:78.69 CLAS:73.47 MLAS:66.75 BLEX:70.23 -wolof-wtb-ud-2.10-220711 gold_tok Tokens:100.00 Sentences:100.00 Words:100.00 UPOS:95.17 XPOS:95.07 UFeats:94.32 AllTags:92.31 Lemmas:95.96 UAS:86.27 LAS:80.75 CLAS:75.45 MLAS:68.70 BLEX:72.06 diff --git a/udpipe2/models-2.10/eval_all.sh b/udpipe2/models-2.10/eval_all.sh deleted file mode 100644 index 63be9abfea3876fe8d2921253ac31101669481e6..0000000000000000000000000000000000000000 --- a/udpipe2/models-2.10/eval_all.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/bin/sh - -sh models_list.sh | while read names path treebank ack; do - model=${names%%:*} - for mode in "raw_text" "gold_tok"; do - conllu=../ud-2.10/$treebank/$treebank-ud-test.conllu - case $mode in - raw_text) txt=${conllu%.conllu}.txt;; - *) txt=; - esac - echo $model $mode $(bash ../scripts/eval_via_server.sh $model $conllu "$txt" | tail -n+3 | awk 'BEGIN{ORS=" "}{print $1 ":" $7}') - done -done diff --git a/udpipe2/models-2.10/eval_has_feature.sh b/udpipe2/models-2.10/eval_has_feature.sh deleted file mode 100644 index c356cffb01fbeacc15ad0aa2e36839c4ba40a70b..0000000000000000000000000000000000000000 --- a/udpipe2/models-2.10/eval_has_feature.sh +++ /dev/null @@ -1,14 +0,0 @@ -#!/bin/sh - -for d in ../ud-2.10/*/; do - case $d in - *_all/) continue;; - esac - - has_xpos=$(grep -P "^\d+\t" $d$(basename ${d%/})-ud-test.conllu | cut -f5 | uniq | wc -l | perl -ple '$_=$_ > 1 ? 1 : 0') - has_feats=$(grep -P "^\d+\t" $d$(basename ${d%/})-ud-test.conllu | cut -f6 | uniq | wc -l | perl -ple '$_=$_ > 1 ? 1 : 0') - has_lemmas=$(grep -P "^\d+\t" $d$(basename ${d%/})-ud-test.conllu | cut -f3 | uniq | wc -l | perl -ple '$_=$_ > 1 ? 1 : 0') - [ $has_xpos = 0 ] && echo $d: no XPOS - [ $has_feats = 0 ] && echo $d: no UFeats - [ $has_lemmas = 0 ] && echo $d: no Lemmas -done diff --git a/udpipe2/models-2.10/models_copy.sh b/udpipe2/models-2.10/models_copy.sh deleted file mode 100644 index 6953cb03b536802dcd65904e575ffd337f652932..0000000000000000000000000000000000000000 --- a/udpipe2/models-2.10/models_copy.sh +++ /dev/null @@ -1,22 +0,0 @@ -#!/bin/sh - -[ $# -ge 2 ] || { echo Usage $0 datadir date >&2; exit 1; } -data="$1"; shift -date="$1"; shift - -for treebank in $(cut -f1 ../$data/langs_sizes); do - case $treebank in *_all) continue; esac - - lang=${treebank%%_*} - if [ -d ../$data/${lang}_all ] && ! grep -q "^$treebank\$" models_non_shared.txt; then - rsync -av -m --delete --exclude *.v2 --exclude training.log ../models/$data-${lang}_all/ ${lang}_all-$data-$date.model - ln -fsn ${lang}_all-$data-$date.model $treebank-$data-$date.model - else - rsync -av -m --delete --exclude *.v2 --exclude training.log ../models/$data-$treebank/ $treebank-$data-$date.model - fi -done - -for treebank in $(cut -f1 ../$data/langs_sizes); do - case $treebank in *_all) continue; esac - cp ~/repos/udpipe/training/models-ud-2.10-tokenizer-chosen/$treebank/$treebank.model $treebank-$data-$date.model/$treebank.tokenizer -done diff --git a/udpipe2/models-2.10/models_list.sh b/udpipe2/models-2.10/models_list.sh deleted file mode 100644 index c6024173a9b816149920691cf07b26b7ae1dd1b2..0000000000000000000000000000000000000000 --- a/udpipe2/models-2.10/models_list.sh +++ /dev/null @@ -1,129 +0,0 @@ -#!/bin/sh - -ack='https://ufal.mff.cuni.cz/udpipe/2/models#universal_dependencies_210_models' - -cat <&2; exit 1; } -prefix="$1"; shift - -for d in $prefix-*_all; do - for t in $d/*/; do - t=$(basename $t) - echo $t - for s in Dev Test; do - grep "$s.*epoch 20, lr 0.0001" $prefix-$t/training.log - grep "$s.*$t epoch 20, lr 0.0001" $d/training.log - done - done -done diff --git a/udpipe2/models-2.12/compare_shared_summary.py b/udpipe2/models-2.12/compare_shared_summary.py deleted file mode 100644 index b5cdf02ab2857c16a990d92657c744b66759a4ca..0000000000000000000000000000000000000000 --- a/udpipe2/models-2.12/compare_shared_summary.py +++ /dev/null @@ -1,21 +0,0 @@ -#!/usr/bin/env python3 -import sys - -treebank, results = None, [] -while (line := sys.stdin.readline()): - parts = line.rstrip("\r\n").split(" ") - if len(parts) == 1: - if results: - print(treebank, *results) - treebank = parts[0] - results = [] - else: - values = [] - for i in range(2): - if i: - parts = sys.stdin.readline().rstrip("\r\n").split(" ") - values.append(sum(float(parts[parts.index(f"{key}:") + 1].rstrip(",")) for key in ["AllTags", "Lemmas", "LAS"])) - results.append(f"{values[1] - values[0]:.3f}") - -if results: - print(treebank, *results) diff --git a/udpipe2/models-2.12/eval_all.sh b/udpipe2/models-2.12/eval_all.sh deleted file mode 100644 index c34130b074449963c6129fbf27d496367c80299c..0000000000000000000000000000000000000000 --- a/udpipe2/models-2.12/eval_all.sh +++ /dev/null @@ -1,17 +0,0 @@ -#!/bin/sh - -[ $# -ge 1 ] || { echo "Usage $0 datadir [server]" >&2; exit 1; } -data="$1"; shift -server="$1" - -sh models_list.sh | while read names path treebank ack; do - model=${names%%:*} - for mode in "raw_text" "gold_tok"; do - conllu=../$data/$treebank/$treebank-ud-test.conllu - case $mode in - raw_text) txt=${conllu%.conllu}.txt;; - *) txt=; - esac - echo $model $mode $(bash ../scripts/eval_via_server.sh $model $conllu "$txt" $server | tail -n+3 | awk 'BEGIN{ORS=" "}{print $1 ":" $7}') - done -done diff --git a/udpipe2/models-2.12/eval_has_feature.sh b/udpipe2/models-2.12/eval_has_feature.sh deleted file mode 100644 index 61a3e8fce6670128e13531923fb9e36e6741291e..0000000000000000000000000000000000000000 --- a/udpipe2/models-2.12/eval_has_feature.sh +++ /dev/null @@ -1,17 +0,0 @@ -#!/bin/sh - -[ $# -ge 1 ] || { echo Usage $0 datadir >&2; exit 1; } -data="$1"; shift - -for d in ../$data/*/; do - case $d in - *_all/) continue;; - esac - - has_xpos=$(grep -P "^\d+\t" $d$(basename ${d%/})-ud-test.conllu | cut -f5 | uniq | wc -l | perl -ple '$_=$_ > 1 ? 1 : 0') - has_feats=$(grep -P "^\d+\t" $d$(basename ${d%/})-ud-test.conllu | cut -f6 | uniq | wc -l | perl -ple '$_=$_ > 1 ? 1 : 0') - has_lemmas=$(grep -P "^\d+\t" $d$(basename ${d%/})-ud-test.conllu | cut -f3 | uniq | wc -l | perl -ple '$_=$_ > 1 ? 1 : 0') - [ $has_xpos = 0 ] && echo $d: no XPOS - [ $has_feats = 0 ] && echo $d: no UFeats - [ $has_lemmas = 0 ] && echo $d: no Lemmas -done diff --git a/udpipe2/models-2.12/models_copy.sh b/udpipe2/models-2.12/models_copy.sh deleted file mode 100644 index 59bc5a5ca79371386a3dcd6bf2e07213b35b530e..0000000000000000000000000000000000000000 --- a/udpipe2/models-2.12/models_copy.sh +++ /dev/null @@ -1,26 +0,0 @@ -#!/bin/sh - -[ $# -ge 2 ] || { echo Usage $0 datadir date >&2; exit 1; } -data="$1"; shift -date="$1"; shift - -for treebank in $(cut -f1 ../$data/langs_sizes); do - case $treebank in *_all) continue; esac - - lang=${treebank%%_*} - if [ -d ../$data/${lang}_all ] && ! grep -q "^$treebank\$" models_non_shared.txt; then - rsync -av -m --delete --exclude *.v2 --exclude training.log ../models/$data-${lang}_all/ ${lang}_all-$data-$date.model - ln -fsn ${lang}_all-$data-$date.model $treebank-$data-$date.model - else - rsync -av -m --delete --exclude *.v2 --exclude training.log ../models/$data-$treebank/ $treebank-$data-$date.model - fi -done - -for treebank in $(cut -f1 ../$data/langs_sizes); do - case $treebank in *_all) continue; esac - - cp ~/repos/udpipe/training/models-$data-tokenizer-chosen/$treebank/$treebank.model $treebank-$data-$date.model/$treebank.tokenizer - - consistent_feats=$treebank-$data-$date.model/consistent_feats.table - [ -f "$consistent_feats " ] || python3 ../scripts/gen_consistent_feats_tables.py ${treebank%%_*} $(dirname $consistent_feats) -done diff --git a/udpipe2/models-2.12/models_list.sh b/udpipe2/models-2.12/models_list.sh deleted file mode 100644 index c9843e511d002160522092b4e98acaaa20f3bebc..0000000000000000000000000000000000000000 --- a/udpipe2/models-2.12/models_list.sh +++ /dev/null @@ -1,137 +0,0 @@ -#!/bin/sh - -ack='https://ufal.mff.cuni.cz/udpipe/2/models#universal_dependencies_212_models' - -cat < 1 ? 1 : 0') - has_feats=$(grep -P "^\d+\t" $d$(basename ${d%/})-ud-test.conllu | cut -f6 | uniq | wc -l | perl -ple '$_=$_ > 1 ? 1 : 0') - has_lemmas=$(grep -P "^\d+\t" $d$(basename ${d%/})-ud-test.conllu | cut -f3 | uniq | wc -l | perl -ple '$_=$_ > 1 ? 1 : 0') - [ $has_xpos = 0 ] && echo $d: no XPOS - [ $has_feats = 0 ] && echo $d: no UFeats - [ $has_lemmas = 0 ] && echo $d: no Lemmas -done diff --git a/udpipe2/models-2.6/models_copy.sh b/udpipe2/models-2.6/models_copy.sh deleted file mode 100644 index d6f5c9329a83fbe5bd35b1f92218257337035643..0000000000000000000000000000000000000000 --- a/udpipe2/models-2.6/models_copy.sh +++ /dev/null @@ -1,16 +0,0 @@ -#!/bin/sh - -[ $# -ge 1 ] || { echo Usage $0 datadir >&2; exit 1; } -data="$1"; shift - -for treebank in $(cut -f1 ../$data/langs_sizes); do - case $treebank in *_all) continue; esac - - lang=${treebank%%_*} - if [ -d ../$data/${lang}_all ] && ! grep -q "^$treebank\$" models_non_shared.txt; then - rsync -av -m --delete --exclude *.v2 --exclude training.log ../models/$data-${lang}_all/ ${lang}_all-$data-200830.model - ln -fsn ${lang}_all-$data-200830.model $treebank-$data-200830.model - else - rsync -av -m --delete --exclude *.v2 --exclude training.log ../models/$data-$treebank/ $treebank-$data-200830.model - fi -done diff --git a/udpipe2/models-2.6/models_list.sh b/udpipe2/models-2.6/models_list.sh deleted file mode 100644 index 87c1dc8fe2205374b5232c40d9c00b9718f6ea47..0000000000000000000000000000000000000000 --- a/udpipe2/models-2.6/models_list.sh +++ /dev/null @@ -1,105 +0,0 @@ -#!/bin/sh - -ack='https://ufal.mff.cuni.cz/udpipe/2/models#universal_dependencies_26_models' - -cat <) { - /Epoch (\d+).*heldout tokens: (\S+), sentences: (\S+)/ and $s[$1] = "$2 $3 "; - /Choosing parameters from epoch (\d+)/ and print $s[$1]; - } - close $f; - } else { - print "0P/0R/0 0P/0R/0 "; - } - } - #extract_from_log(); - - #/No SpaceAfter/ && extract_from_log(); - #/multiwords/ && next; - /^Tokenizer (?:words|sentences).*precision: ([0-9.]*%).*recall: ([0-9.]*%).*f1: ([0-9.]*%)/ && print "$1P/$2R/$3 "; - #/ sentences/ && extract_from_log(); - ' $base.dev $base.test - echo $base -# done | sort -srV -t/ -k5 - done | perl -aF'/[\s\/]/' -ne 'push @l,$_; $l{$_}=2*$F[3]+$F[6];END{foreach (sort {$l{$b}<=>$l{$a}} @l) {print}}' - echo -done | perl -pe 's/(\d+\.\d+)%/sprintf "%.2f", $1/ge' | column -te | less -S diff --git a/udpipe2/models-evalatin20/la_evalatin20_tokenizer/train.sh b/udpipe2/models-evalatin20/la_evalatin20_tokenizer/train.sh deleted file mode 100644 index 986b4f2b17ad59c4256f229e79c01a6af2982d66..0000000000000000000000000000000000000000 --- a/udpipe2/models-evalatin20/la_evalatin20_tokenizer/train.sh +++ /dev/null @@ -1,17 +0,0 @@ -#!/bin/bash - -set -e - -lang="$1"; shift -id="$1"; shift -args="$1"; shift - -mkdir -p $lang -exec >$lang/$id.log; exec 2>&1 -echo "$args" >$lang/$id.cmd - -./udpipe --train --tokenizer="$args" --tagger=none --parser=none --heldout=../ud-2.6/$lang/$lang-dev.conllu $lang/$id.model ../ud-2.6/$lang/$lang-train.conllu -./udpipe --accuracy --tokenize "$lang/$id.model" <../ud-2.6/$lang/$lang-dev.conllu >"$lang/$id.dev" -./udpipe --accuracy --tokenize "$lang/$id.model" <../ud-2.6/$lang/$lang-test.conllu >"$lang/$id.test" - -echo Done diff --git a/udpipe2/models-evalatin20/la_evalatin20_tokenizer/train_all.sh b/udpipe2/models-evalatin20/la_evalatin20_tokenizer/train_all.sh deleted file mode 100644 index 7137350a2feeaabc779a820625ed2daadce2ced3..0000000000000000000000000000000000000000 --- a/udpipe2/models-evalatin20/la_evalatin20_tokenizer/train_all.sh +++ /dev/null @@ -1,31 +0,0 @@ -#!/bin/bash - -[ -x udpipe ] || { echo Missing udpipe >&2; exit 1; } - -index=1 -while [ -f train_all_$index.cmds ]; do - index=`expr $index + 1` -done -cmd_file=train_all_$index.cmds - ->$cmd_file -for d in ${@:-la_evalatin20}; do - lang=`basename "$d"` - for args in dimension={24,64}\;epochs=100\;segment_size={50,200}\;initialization_range={0.2,0.1}\;batch_size=50\;learning_rate={0.01,0.005,0.002}\;learning_rate_final={0,0.001}\;dropout={0.1,0.2,0.3}\;early_stopping=1; do - id="${args// /_}" - id="${id//\//_}" - id="${id//,/-}" - id="${id//;/-}" - id="${id//:/}" - id="${id//dimension/dim}" - id="${id//batch_size/bs}" - id="${id//learning_rate_final/lf}" - id="${id//learning_rate/l}" - id="${id//dropout/d}" - - echo ./train.sh $lang "$id" "$args" >>$cmd_file - done -done - -tasks=$(wc -l <$cmd_file) -echo qsub -q ${Q:-cpu-troja.q@*} $SGE_ARGS -cwd -b y -o $cmd_file.log -j y -t 1-$tasks -tc ${SGE_CONCURRENT:-400} arrayjob_runner $cmd_file diff --git a/udpipe2/models-evalatin20/models_list.sh b/udpipe2/models-evalatin20/models_list.sh deleted file mode 100644 index 5b9d313c73d9e0f03d368167fe8a9e2b1ab1e3cd..0000000000000000000000000000000000000000 --- a/udpipe2/models-evalatin20/models_list.sh +++ /dev/null @@ -1,7 +0,0 @@ -#!/bin/sh - -ack='https://ufal.mff.cuni.cz/udpipe/2/models#evalatin20_models' - -cat <&2; exit 1; } -server="$1" - -for dataset in pdt-a pdt-m pcedt-m pdtsc-m faust-m; do - gold=../pdtc-1.0/pdtc-1.0-$dataset/pdtc-1.0-$dataset-test.conllu - [ -f ${gold%.conllu}.paragraphs.conllu ] && gold=${gold%.conllu}.paragraphs.conllu - for mode in raw_text gold_tok; do - case $mode in - raw_text) perl ../ud-2.12/conllu_to_text.pl $gold >tmp.txt; data="-F data=@tmp.txt -F tokenizer=";; - gold_tok) data="-F data=@$gold -F input=conllu";; - esac - curl $data -F tagger= -F parser= $server/process | jq -j .result >tmp.conllu - - for eval in exact-match lemmaid-match; do - case $eval in - exact-match) process_conllu="cat";; - lemmaid-match) process_conllu="python3 strip_lemma_info.py";; - esac - echo $dataset $mode $eval $(python3 ../udpipe2_eval.py --no_single_root --verbose <($process_conllu $gold) <($process_conllu tmp.conllu) | tail -n+3 | awk 'BEGIN{ORS=" "}{print $1 ":" $7}') - done - done -done | sh evaluation_merge_em.sh -rm tmp.txt tmp.conllu diff --git a/udpipe2/models-pdtc1.0/evaluation_merge_em.sh b/udpipe2/models-pdtc1.0/evaluation_merge_em.sh deleted file mode 100644 index e908aaa8d2c8fe806cf0e33d2d9f516d00e55e44..0000000000000000000000000000000000000000 --- a/udpipe2/models-pdtc1.0/evaluation_merge_em.sh +++ /dev/null @@ -1,6 +0,0 @@ -#!/bin/sh - -awk ' - /exact-match/ { lemma_em=substr($0, match($0, /Lemmas:[0-9.]*/)); sub(/ .*$/, "", lemma_em); sub(/Lemmas:/, "LemmasEM:", lemma_em) } - /lemmaid-match/ { sub(/ lemmaid-match/, ""); sub(/UAS:/, lemma_em " UAS:"); print } -' "$@" diff --git a/udpipe2/models-pdtc1.0/models_list.sh b/udpipe2/models-pdtc1.0/models_list.sh deleted file mode 100644 index 50d368812366a3e12124784b0a9bc3b3c69ff1fe..0000000000000000000000000000000000000000 --- a/udpipe2/models-pdtc1.0/models_list.sh +++ /dev/null @@ -1,7 +0,0 @@ -#!/bin/sh - -ack='https://ufal.mff.cuni.cz/udpipe/2/models#czech_pdtc1.0_model' - -cat < $d/$d-$t.conllu - done -done diff --git a/udpipe2/pdtc-1.0/compose_deprel.py b/udpipe2/pdtc-1.0/compose_deprel.py deleted file mode 100644 index 1206f8cc66f89d2821db5a9cdfdc459954bca41f..0000000000000000000000000000000000000000 --- a/udpipe2/pdtc-1.0/compose_deprel.py +++ /dev/null @@ -1,68 +0,0 @@ -#!/usr/bin/env python3 -import argparse -import sys - -IS_MEMBER, IS_PARENTHESIS_ROOT = 5, 3 - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("paths", default=[], nargs="*", help="Input paths") - parser.add_argument("--is_member", default=False, action="store_true", help="Add is_member") - parser.add_argument("--is_parenthesis_root", default=False, action="store_true", help="Add is_parenthesis_root") - args = parser.parse_args() - - for path in args.paths: - with open(path, "r", encoding="utf-8") as conllu_file: - block = [] - for line in conllu_file: - line = line.rstrip("\n") - if not line: - assert block - # Process block - heads, deps = {}, {} - for entry in block: - columns = entry.split("\t") - if len(columns) == 10: - assert int(columns[0]) == len(heads) + 1 - deps[len(heads) + 1] = columns[7] - heads[len(heads) + 1] = columns[6] - - for i in range(len(block)): - columns = block[i].split("\t") - if len(columns) == 10: - if args.is_member and columns[IS_MEMBER] == "1": - parent = int(columns[0]) - while parent and deps[parent] not in ("Apos", "Coord"): - parent = int(heads[parent]) - - if deps[parent] == "Apos": - columns[7] += "_Ap" - elif deps[parent] == "Coord": - columns[7] += "_Co" - else: - print("Did not find correct parent of IsMember {} in the below sentence".format(block[i]), *block, file=sys.stderr, sep="\n") - columns[IS_MEMBER] = "_" - - if args.is_parenthesis_root and columns[IS_PARENTHESIS_ROOT] == "1": - columns[7] += "_Pa" - columns[IS_PARENTHESIS_ROOT] = "_" - - block[i] = "\t".join(columns) - -# if args.is_member and columns[IS_MEMBER] == "1": -# columns[7] += "_IsMember" -# columns[IS_MEMBER] = "_" -# -# if args.is_parenthesis_root and columns[IS_PARENTHESIS_ROOT] == "1": -# columns[7] += "_Pa" -# columns[IS_PARENTHESIS_ROOT] = "_" -# -# line = "\t".join(columns) -# -# print(line) - - print(*block, sep="\n", end="\n\n") - block = [] - else: - block.append(line) - assert not block diff --git a/udpipe2/pdtc-1.0/gen_lists b/udpipe2/pdtc-1.0/gen_lists deleted file mode 100644 index b1b840042d9b180122ab7d8a85a98a0415a7bf2b..0000000000000000000000000000000000000000 --- a/udpipe2/pdtc-1.0/gen_lists +++ /dev/null @@ -1,28 +0,0 @@ -#!/bin/bash - -for l in a m onlym; do - mkdir -p pdtc-1.0-pdt-$l - case $l in - onlym) src=mw; l1=m;; - *) src=*$l*; l1=$l;; - esac - ls data/PDT/pml/$src/train*/*.$l1.gz | sed 's/\.[am]\.gz$//' > pdtc-1.0-pdt-$l/files_train - ls data/PDT/pml/$src/dtest*/*.$l1.gz | sed 's/\.[am]\.gz$//' > pdtc-1.0-pdt-$l/files_dev - ls data/PDT/pml/$src/etest*/*.$l1.gz | sed 's/\.[am]\.gz$//' > pdtc-1.0-pdt-$l/files_test -done - -mkdir -p pdtc-1.0-pcedt-m -ls data/PCEDT/pml/wsj{0[0-9],1[0-8]}*.m.gz | sed 's/\.[am]\.gz$//' > pdtc-1.0-pcedt-m/files_train -ls data/PCEDT/pml/wsj{19,2[01]}*.m.gz | sed 's/\.[am]\.gz$//' > pdtc-1.0-pcedt-m/files_dev -ls data/PCEDT/pml/wsj2[234]*.m.gz | sed 's/\.[am]\.gz$//' > pdtc-1.0-pcedt-m/files_test - -for t in Faust PDTSC; do - mkdir -p pdtc-1.0-${t,,}-m - ls data/$t/pml/*_{[0-9],[0-9][0-9]}[^67].*m.gz | sed 's/\.[am]\.gz$//' > pdtc-1.0-${t,,}-m/files_train - ls data/$t/pml/*_{[0-9],[0-9][0-9]}6.*m.gz | sed 's/\.[am]\.gz$//' > pdtc-1.0-${t,,}-m/files_dev - ls data/$t/pml/*_{[0-9],[0-9][0-9]}7.*m.gz | sed 's/\.[am]\.gz$//' > pdtc-1.0-${t,,}-m/files_test -done - -for d in pdtc-1.0-*/; do - cat $d/files_train $d/files_dev $d/files_test >$d/files_all -done diff --git a/udpipe2/pdtc-1.0/langs_sizes b/udpipe2/pdtc-1.0/langs_sizes deleted file mode 100644 index f2b8a98d2b151b5653f557f08deb1260b328efc3..0000000000000000000000000000000000000000 --- a/udpipe2/pdtc-1.0/langs_sizes +++ /dev/null @@ -1,4 +0,0 @@ -pdtc-1.0-a_all 1171193 -pdtc-1.0-als_all 1171193 -pdtc-1.0-pa_all 3071901 -pdtc-1.0-pals_all 3071901 diff --git a/udpipe2/pdtc-1.0/parse.sh b/udpipe2/pdtc-1.0/parse.sh deleted file mode 100644 index d7af11f1c1d1a486db60198ebe3def0aa7e9d565..0000000000000000000000000000000000000000 --- a/udpipe2/pdtc-1.0/parse.sh +++ /dev/null @@ -1,7 +0,0 @@ -#!/bin/bash - -[ $# -eq 1 ] || { echo: "Usage: $0 treebank >&2"; exit 1; } -input="$1" - -MODEL=models/pdtc-1.0-pdtc-1.0-a_all -qsub -q gpu-troja.q -l gpu=1 -j y -o "${input%.conllu}".predicted-conllu.log withcuda101 venv-tf2/bin/python udpipe2.py "$MODEL" --threads=4 --predict --predict_input="$input" --predict_output="${input%.conllu}".predicted-conllu diff --git a/udpipe2/pdtc-1.0/pdtc-1.0-faust-m/.gitignore b/udpipe2/pdtc-1.0/pdtc-1.0-faust-m/.gitignore deleted file mode 100644 index c62a1e1911f42a6574f048cbbf7e76c35df0a42c..0000000000000000000000000000000000000000 --- a/udpipe2/pdtc-1.0/pdtc-1.0-faust-m/.gitignore +++ /dev/null @@ -1,4 +0,0 @@ -*.conllu -*.log -*.npz -*.predicted-conllu diff --git a/udpipe2/pdtc-1.0/pdtc-1.0-faust-m/files_all b/udpipe2/pdtc-1.0/pdtc-1.0-faust-m/files_all deleted file mode 100644 index a743f3b1b591008c278046275dbd735f29cb6323..0000000000000000000000000000000000000000 --- a/udpipe2/pdtc-1.0/pdtc-1.0-faust-m/files_all +++ /dev/null @@ -1,60 +0,0 @@ -data/Faust/pml/faust_2010_07_es_01 -data/Faust/pml/faust_2010_07_es_02 -data/Faust/pml/faust_2010_07_es_03 -data/Faust/pml/faust_2010_07_es_04 -data/Faust/pml/faust_2010_07_es_05 -data/Faust/pml/faust_2010_07_es_08 -data/Faust/pml/faust_2010_07_es_09 -data/Faust/pml/faust_2010_07_es_10 -data/Faust/pml/faust_2010_07_es_11 -data/Faust/pml/faust_2010_07_es_12 -data/Faust/pml/faust_2010_07_es_13 -data/Faust/pml/faust_2010_07_es_14 -data/Faust/pml/faust_2010_07_es_15 -data/Faust/pml/faust_2010_07_es_18 -data/Faust/pml/faust_2010_07_es_19 -data/Faust/pml/faust_2010_07_es_20 -data/Faust/pml/faust_2010_07_jh_01 -data/Faust/pml/faust_2010_07_jh_02 -data/Faust/pml/faust_2010_07_jh_03 -data/Faust/pml/faust_2010_07_jh_04 -data/Faust/pml/faust_2010_07_jh_05 -data/Faust/pml/faust_2010_07_jh_08 -data/Faust/pml/faust_2010_07_jh_09 -data/Faust/pml/faust_2010_07_jh_10 -data/Faust/pml/faust_2010_07_jh_11 -data/Faust/pml/faust_2010_07_jh_12 -data/Faust/pml/faust_2010_07_jh_13 -data/Faust/pml/faust_2010_07_jh_14 -data/Faust/pml/faust_2010_07_jh_15 -data/Faust/pml/faust_2010_07_jh_18 -data/Faust/pml/faust_2010_07_jh_19 -data/Faust/pml/faust_2010_07_jh_20 -data/Faust/pml/faust_2010_07_mu_01 -data/Faust/pml/faust_2010_07_mu_02 -data/Faust/pml/faust_2010_07_mu_03 -data/Faust/pml/faust_2010_07_mu_04 -data/Faust/pml/faust_2010_07_mu_05 -data/Faust/pml/faust_2010_07_mu_08 -data/Faust/pml/faust_2010_07_mu_09 -data/Faust/pml/faust_2010_07_mu_10 -data/Faust/pml/faust_2010_07_mu_11 -data/Faust/pml/faust_2010_07_mu_12 -data/Faust/pml/faust_2010_07_mu_13 -data/Faust/pml/faust_2010_07_mu_14 -data/Faust/pml/faust_2010_07_mu_15 -data/Faust/pml/faust_2010_07_mu_18 -data/Faust/pml/faust_2010_07_mu_19 -data/Faust/pml/faust_2010_07_mu_20 -data/Faust/pml/faust_2010_07_es_06 -data/Faust/pml/faust_2010_07_es_16 -data/Faust/pml/faust_2010_07_jh_06 -data/Faust/pml/faust_2010_07_jh_16 -data/Faust/pml/faust_2010_07_mu_06 -data/Faust/pml/faust_2010_07_mu_16 -data/Faust/pml/faust_2010_07_es_07 -data/Faust/pml/faust_2010_07_es_17 -data/Faust/pml/faust_2010_07_jh_07 -data/Faust/pml/faust_2010_07_jh_17 -data/Faust/pml/faust_2010_07_mu_07 -data/Faust/pml/faust_2010_07_mu_17 diff --git a/udpipe2/pdtc-1.0/pdtc-1.0-faust-m/files_dev b/udpipe2/pdtc-1.0/pdtc-1.0-faust-m/files_dev deleted file mode 100644 index fbab3dd4dba7bd63d3a7f2c9a1b904b54f29e710..0000000000000000000000000000000000000000 --- a/udpipe2/pdtc-1.0/pdtc-1.0-faust-m/files_dev +++ /dev/null @@ -1,6 +0,0 @@ -data/Faust/pml/faust_2010_07_es_06 -data/Faust/pml/faust_2010_07_es_16 -data/Faust/pml/faust_2010_07_jh_06 -data/Faust/pml/faust_2010_07_jh_16 -data/Faust/pml/faust_2010_07_mu_06 -data/Faust/pml/faust_2010_07_mu_16 diff --git a/udpipe2/pdtc-1.0/pdtc-1.0-faust-m/files_test b/udpipe2/pdtc-1.0/pdtc-1.0-faust-m/files_test deleted file mode 100644 index 07de6e55d4a0570e40ec183e11bfda4721b5d4e9..0000000000000000000000000000000000000000 --- a/udpipe2/pdtc-1.0/pdtc-1.0-faust-m/files_test +++ /dev/null @@ -1,6 +0,0 @@ -data/Faust/pml/faust_2010_07_es_07 -data/Faust/pml/faust_2010_07_es_17 -data/Faust/pml/faust_2010_07_jh_07 -data/Faust/pml/faust_2010_07_jh_17 -data/Faust/pml/faust_2010_07_mu_07 -data/Faust/pml/faust_2010_07_mu_17 diff --git a/udpipe2/pdtc-1.0/pdtc-1.0-faust-m/files_train b/udpipe2/pdtc-1.0/pdtc-1.0-faust-m/files_train deleted file mode 100644 index e1d7ac7bf174b81b9c1a2cfae39c8391d57bd712..0000000000000000000000000000000000000000 --- a/udpipe2/pdtc-1.0/pdtc-1.0-faust-m/files_train +++ /dev/null @@ -1,48 +0,0 @@ -data/Faust/pml/faust_2010_07_es_01 -data/Faust/pml/faust_2010_07_es_02 -data/Faust/pml/faust_2010_07_es_03 -data/Faust/pml/faust_2010_07_es_04 -data/Faust/pml/faust_2010_07_es_05 -data/Faust/pml/faust_2010_07_es_08 -data/Faust/pml/faust_2010_07_es_09 -data/Faust/pml/faust_2010_07_es_10 -data/Faust/pml/faust_2010_07_es_11 -data/Faust/pml/faust_2010_07_es_12 -data/Faust/pml/faust_2010_07_es_13 -data/Faust/pml/faust_2010_07_es_14 -data/Faust/pml/faust_2010_07_es_15 -data/Faust/pml/faust_2010_07_es_18 -data/Faust/pml/faust_2010_07_es_19 -data/Faust/pml/faust_2010_07_es_20 -data/Faust/pml/faust_2010_07_jh_01 -data/Faust/pml/faust_2010_07_jh_02 -data/Faust/pml/faust_2010_07_jh_03 -data/Faust/pml/faust_2010_07_jh_04 -data/Faust/pml/faust_2010_07_jh_05 -data/Faust/pml/faust_2010_07_jh_08 -data/Faust/pml/faust_2010_07_jh_09 -data/Faust/pml/faust_2010_07_jh_10 -data/Faust/pml/faust_2010_07_jh_11 -data/Faust/pml/faust_2010_07_jh_12 -data/Faust/pml/faust_2010_07_jh_13 -data/Faust/pml/faust_2010_07_jh_14 -data/Faust/pml/faust_2010_07_jh_15 -data/Faust/pml/faust_2010_07_jh_18 -data/Faust/pml/faust_2010_07_jh_19 -data/Faust/pml/faust_2010_07_jh_20 -data/Faust/pml/faust_2010_07_mu_01 -data/Faust/pml/faust_2010_07_mu_02 -data/Faust/pml/faust_2010_07_mu_03 -data/Faust/pml/faust_2010_07_mu_04 -data/Faust/pml/faust_2010_07_mu_05 -data/Faust/pml/faust_2010_07_mu_08 -data/Faust/pml/faust_2010_07_mu_09 -data/Faust/pml/faust_2010_07_mu_10 -data/Faust/pml/faust_2010_07_mu_11 -data/Faust/pml/faust_2010_07_mu_12 -data/Faust/pml/faust_2010_07_mu_13 -data/Faust/pml/faust_2010_07_mu_14 -data/Faust/pml/faust_2010_07_mu_15 -data/Faust/pml/faust_2010_07_mu_18 -data/Faust/pml/faust_2010_07_mu_19 -data/Faust/pml/faust_2010_07_mu_20 diff --git a/udpipe2/pdtc-1.0/pdtc-1.0-pcedt-m/.gitignore b/udpipe2/pdtc-1.0/pdtc-1.0-pcedt-m/.gitignore deleted file mode 100644 index c62a1e1911f42a6574f048cbbf7e76c35df0a42c..0000000000000000000000000000000000000000 --- a/udpipe2/pdtc-1.0/pdtc-1.0-pcedt-m/.gitignore +++ /dev/null @@ -1,4 +0,0 @@ -*.conllu -*.log -*.npz -*.predicted-conllu diff --git a/udpipe2/pdtc-1.0/pdtc-1.0-pcedt-m/files_all b/udpipe2/pdtc-1.0/pdtc-1.0-pcedt-m/files_all deleted file mode 100644 index b4e4fbbc4ececac13f0c8363ea631e84e90f455a..0000000000000000000000000000000000000000 --- a/udpipe2/pdtc-1.0/pdtc-1.0-pcedt-m/files_all +++ /dev/null @@ -1,2312 +0,0 @@ -data/PCEDT/pml/wsj0001.cz -data/PCEDT/pml/wsj0002.cz -data/PCEDT/pml/wsj0003.cz -data/PCEDT/pml/wsj0004.cz -data/PCEDT/pml/wsj0005.cz -data/PCEDT/pml/wsj0006.cz -data/PCEDT/pml/wsj0007.cz -data/PCEDT/pml/wsj0008.cz -data/PCEDT/pml/wsj0009.cz -data/PCEDT/pml/wsj0010.cz 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re.compile(r"(?<=.)(?:`|_|-[^0-9]).*$") - -for line in fileinput.input(): - line = line.rstrip("\n") - columns = line.split("\t") - if not line.startswith("#") and len(columns) == 10: - columns[2] = lemma_strip.sub("", columns[2]) - line = "\t".join(columns) - print(line) diff --git a/udpipe2/pdtc-1.0/to_conllu.sh b/udpipe2/pdtc-1.0/to_conllu.sh deleted file mode 100644 index 00030ea5b032ceb770dd0e85e174ba52276a1528..0000000000000000000000000000000000000000 --- a/udpipe2/pdtc-1.0/to_conllu.sh +++ /dev/null @@ -1,29 +0,0 @@ -#!/bin/bash - -[ $# -ge 2 ] || { echo "Usage: $0 target_dir layer files" >&2; exit 1; } - -target="$1"; shift -layer="$1"; shift - -resources="$(dirname $0)/resources" - -for file in "$@"; do - treex -Lcs Read::PDT schema_dir=$resources from=$file.$layer top_layer=$layer Write::CoNLLU to=$target/$file.conllu use_tree_id_as_sent_id=1 upos=is_parenthesis_root xpos=tag feats=is_member deprel=afun - gawk -i inplace ' - BEGIN {s = 1; p = 1; last_pid = -1} - /^# sent_id = / { - split($4, parts, /-/); p_s = parts[length(parts)]; if (match(p_s, /^p[0-9]+s[0-9]+$/) > 0) { - pid = substr(p_s, 0, index(p_s, "s")); if (pid != last_pid) { print "# newpar id = '"${file//\//-}"'-p" p; p += 1; last_pid = pid}} - $4 = "s" s; s += 1} - {print} - ' $target/$file.conllu - sed ' - 1i# newdoc id = '"${file//\//-}"' - s@^# sent_id = @# sent_id = '"${file//\//-}"'-@ - ' -i $target/$file.conllu - if [ "$layer" = m ]; then - sed ' - s/^\([0-9][0-9]*\t\([^\t]*\t\)\{5\}\)0\(\(\t[^\t]*\)\{3\}\)$/\1_\3/ - ' -i $target/$file.conllu - fi -done diff --git a/udpipe2/pdtc-1.0/to_conllu_all.sh b/udpipe2/pdtc-1.0/to_conllu_all.sh deleted file mode 100644 index 1d6282e6c99dc41a785ff691fa3abaf2ce705450..0000000000000000000000000000000000000000 --- a/udpipe2/pdtc-1.0/to_conllu_all.sh +++ /dev/null @@ -1,13 +0,0 @@ -#!/bin/bash - -# 50 is a good number -[ $# -ge 1 ] || { echo "Usage: $0 jobs_per_treebank" >&2; exit 1; } -N=$1 - -for d in pdtc-1.0-*/; do - d=${d%/} - layer=$(echo -n $d | tail -c1) - for i in $(seq 1 $N); do - qsub -q cpu-*.q -o $d/conllu.log -j y bash $(dirname $0)/to_conllu.sh $d/conllu $layer $(split -n l/$i/$N $d/files_all) - done -done diff --git a/udpipe2/requirements.txt b/udpipe2/requirements.txt deleted file mode 100644 index 79f61bc1cd4effb0b62a164017e19d4b2c4f5f54..0000000000000000000000000000000000000000 --- a/udpipe2/requirements.txt +++ /dev/null @@ -1,4 +0,0 @@ -tensorflow_gpu~=1.15.4 # Note that for just inference, you can use TF 2.* too. -ufal.chu_liu_edmonds -ufal.udpipe>=1.3,<2 -# For the Czech PDT-C models, you also need the `ufal.morphodita>=1.11.2` package. diff --git a/udpipe2/scripts/compute_embeddings.sh b/udpipe2/scripts/compute_embeddings.sh deleted file mode 100644 index 12ece18b96a4c43d61e6a5c314ea495b3d1b47c4..0000000000000000000000000000000000000000 --- a/udpipe2/scripts/compute_embeddings.sh +++ /dev/null @@ -1,17 +0,0 @@ -#!/bin/sh - -[ $# -ge 1 ] || { echo Usage: $0 data_directory embedding_args... >&2; exit 1; } -data="$1"; shift - -#for d in $data/*1.0-[a-z]*/; do -for d in $data/*/; do - case $d in - */cs_*) model=robeczech-base-last4;; - *) model=bert-base-multilingual-uncased-last4;; - esac - - for f in $d*.conllu; do - [ $f.npz -nt $f ] && continue - sbatch -p gpu-ms,gpu-troja -G 1 -C "gpu_cc6.1|gpu_cc7.5" --mem=16G -o $f.log run withcuda101 wembedding_service/venv/bin/python wembedding_service/compute_wembeddings.py --format=conllu $f $f.npz --model=$model "$@" - done -done diff --git a/udpipe2/scripts/eval_via_server.sh b/udpipe2/scripts/eval_via_server.sh deleted file mode 100644 index 6ff759a832e75baa99745f03d3c340a59719a3f5..0000000000000000000000000000000000000000 --- a/udpipe2/scripts/eval_via_server.sh +++ /dev/null @@ -1,9 +0,0 @@ -#!/bin/bash - -[ $# -ge 2 ] || { echo "Usage: $0 model conllu_data [plain_text] [server]" >&2; exit 1; } -model="$1" -data_conllu="$2" -data_txt="$3" -server="${4:-https://lindat.mff.cuni.cz/services/udpipe/api}" - -python3 $(dirname $0)/../udpipe2_eval.py -v $data_conllu <(sed "${data_txt:+p;d;}"'s/\t[^\t]*\t[^\t]*\t[^\t]*\t[^\t]*\t[^\t]*\t[^\t]*\t[^\t]*\t[^\t]*$/\t_\t_\t_\t_\t_\t_\t_\t_/' ${data_txt:-$data_conllu} | curl -F model=$model -F data=@- ${data_txt:+-F tokenizer=} -F tagger= -F parser= $server/process | jq -j .result) diff --git a/udpipe2/scripts/frontend_rest_server.py b/udpipe2/scripts/frontend_rest_server.py deleted file mode 100644 index 181714d74f21657cc765cb314972735066d8a5a2..0000000000000000000000000000000000000000 --- a/udpipe2/scripts/frontend_rest_server.py +++ /dev/null @@ -1,304 +0,0 @@ -#!/usr/bin/env python3 -# coding=utf-8 -# -# Copyright 2020 Institute of Formal and Applied Linguistics, Faculty of -# Mathematics and Physics, Charles University, Czech Republic. -# -# This Source Code Form is subject to the terms of the Mozilla Public -# License, v. 2.0. If a copy of the MPL was not distributed with this -# file, You can obtain one at http://mozilla.org/MPL/2.0/. - -"""Word embeddings server class.""" - -import email.parser -import http.server -import json -import random -import socketserver -import sys -import threading -import time -import urllib.error -import urllib.parse -import urllib.request - - -class FrontendRESTServer(socketserver.TCPServer): - class Backend(): - def __init__(self, server): - self._server = server - - with self.request("/models") as response: - data = json.loads(response.read()) - - assert "models" in data and isinstance(data["models"], dict) - self.models = data["models"] - - assert "default_model" in data and isinstance(data["default_model"], str) - self.default_model = data["default_model"] - - def request(self, url, data=None, data_content_type=None): - return urllib.request.urlopen(urllib.request.Request( - url="http://{}{}".format(self._server, url), - data=data, - headers={} if data is None else {"Content-Type": data_content_type}, - )) - - - class FrontendRESTServer(http.server.BaseHTTPRequestHandler): - protocol_version = "HTTP/1.1" - - format_for_log_table = str.maketrans("\n", "\r", "\r") - def format_for_log(request, data, limit=None): - if limit is not None: - if limit <= 0: - data = "[{}B]".format(len(data)) - elif len(data) > limit: - data = data[:limit // 2] + " ... " + data[min(-1, -limit // 2):] - return data.translate(request.format_for_log_table) - - def respond(request, content_type, code=200, additional_headers={}): - request.close_connection = True - request.send_response(code) - request.send_header("Connection", "close") - request.send_header("Content-Type", content_type) - request.send_header("Access-Control-Allow-Origin", "*") - for key, value in additional_headers.items(): - request.send_header(key, value) - request.end_headers() - - def respond_error(request, message, code=400): - request.respond("text/plain", code) - request.wfile.write(message.encode("utf-8")) - - def handle_expect_100(request): - try: - request_too_long = int(request.headers["Content-Length"]) > request.server._args.max_request_size - except: - request_too_long = False - - if request_too_long: - request.respond_error("The payload size is too large.") - return False - return super().handle_expect_100() - - def do_GET(request): - # Parse the model from URL/body - params, body, body_content_type = {}, None, None - try: - encoded_path = request.path.encode("iso-8859-1").decode("utf-8") - url = urllib.parse.urlparse(encoded_path) - for name, value in urllib.parse.parse_qsl(url.query, encoding="utf-8", keep_blank_values=True, errors="strict"): - params[name] = value - except: - return request.respond_error("Cannot parse request URL.") - - # Parse the body of a POST request - if request.command == "POST": - if request.headers.get("Transfer-Encoding", "identity").lower() != "identity": - return request.respond_error("Only 'identity' Transfer-Encoding of payload is supported for now.") - - try: - content_length = int(request.headers["Content-Length"]) - except: - return request.respond_error("The Content-Length of payload is required.") - - if content_length > request.server._args.max_request_size: - while content_length: - read = request.rfile.read(min(content_length, 65536)) - content_length -= len(read) if read else content_length - return request.respond_error("The payload size is too large.") - - body = request.rfile.read(content_length) - body_content_type = request.headers.get("Content-Type", "") - - # multipart/form-data - if request.headers.get("Content-Type", "").startswith("multipart/form-data"): - try: - parser = email.parser.BytesFeedParser() - parser.feed(b"Content-Type: " + request.headers["Content-Type"].encode("ascii") + b"\r\n\r\n") - parser.feed(body) - for part in parser.close().get_payload(): - name = part.get_param("name", header="Content-Disposition") - if name: - params[name] = part.get_payload(decode=True).decode("utf-8") - except: - return request.respond_error("Cannot parse the multipart/form-data payload.") - # x-www-form-urlencoded - elif request.headers.get("Content-Type", "").startswith("application/x-www-form-urlencoded"): - try: - for name, value in urllib.parse.parse_qsl( - body.decode("utf-8"), encoding="utf-8", keep_blank_values=True, errors="strict"): - params[name] = value - except: - return request.respond_error("Cannot parse the application/x-www-form-urlencoded payload.") - - # Log if required - if request.server._args.log_data: - print(url.path, " ".join(request.headers.get_all("X-Forwarded-For", [])), - *["{}:{}".format(key, request.format_for_log(value)) for key, value in params.items() if key != "data"], - "data:" + request.format_for_log(params.get("data", ""), request.server._args.log_data), - sep="\t", file=sys.stderr, flush=True) - - # Handle /models - if url.path == "/models": - response = { - "models": {name: value for backend in request.server.backends for name, value in backend.models.items()}, - "default_model": request.server.backends[0].default_model, - } - request.respond("application/json") - request.wfile.write(json.dumps(response, indent=1).encode("utf-8")) - # Handle everything else - else: - # Start by finding appropriate backends - backends = request.server.backends.copy() - model = params.get("model", request.server.backends[0].default_model) - if model in request.server.aliases: - resolved_model = request.server.aliases[model] - backends = [backend for backend in request.server.backends if resolved_model in backend.models] or backends - - # Forward the request to the backend - started_responding = False - try: - assert backends, "No backends found!" - while backends: - backend = random.choice(backends) if len(backends) > 1 else backends[0] - backends.remove(backend) - try: - with backend.request(request.path, body, body_content_type) as response: - while True: - data = response.read(32768) - if not started_responding: - started_responding = True - billing_infclen = response.getheader("X-Billing-Input-NFC-Len", None) - headers = {"X-Billing-Input-NFC-Len": billing_infclen} if billing_infclen is not None else {} - request.respond(response.getheader("Content-Type", "application/json"), code=response.code, - additional_headers=headers) - if len(data) == 0: break - request.wfile.write(data) - except urllib.error.HTTPError as error: - if not started_responding: - started_responding = True - request.respond(error.headers.get("Content-Type", "text/plain"), code=error.code) - request.wfile.write(error.file.read()) - break - raise - except: - if backends and not started_responding: - import traceback - traceback.print_exc(file=sys.stderr) - print("The above error occurred during request processing on '{}',".format(backend._server), - "but more backends are available, retrying.", file=sys.stderr, flush=True) - continue - raise - break - except: - import traceback - traceback.print_exc(file=sys.stderr) - sys.stderr.flush() - - if not started_responding: - request.respond_error("An internal error occurred during processing.") - else: - request.wfile.write(b'",\n"An internal error occurred during processing, producing incorrect JSON!"') - - def do_POST(request): - return request.do_GET() - - def __init__(self, args): - self._args = args - - # Initialize all backends - self.backends = [self.Backend(backend) for backend in args.backends] - - # Initialize the aliases - self.aliases = {} - if args.aliases is not None: - with open(args.aliases, "r", encoding="utf-8") as aliases_file: - for line in aliases_file: - line = line.rstrip("\r\n") - if not line or line.startswith("#"): - continue - parts = line.split() - assert len(parts) in [3, 4], "Expected 3-4 columns in the aliases file: line '{}'".format(line) - names = parts[0].split(":") - for name in names: - parts = name.split("-") - for prefix in ("-".join(parts[:None if not i else -i]) for i in range(len(parts))): - self.aliases.setdefault(prefix, names[0]) - - # Initialize the server - self._threads = [] - super().__init__(("", self._args.port), self.FrontendRESTServer) - - def server_bind(self): - import socket - self.socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) - self.socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1) - super().server_bind() - - def server_activate(self): - self.socket.listen(256) - - def process_request_thread(self, request, client_address): - try: - self.finish_request(request, client_address) - except Exception: - self.handle_error(request, client_address) - finally: - self.shutdown_request(request) - - def process_request(self, request, client_address): - thread = threading.Thread(target=self.process_request_thread, args=(request, client_address), daemon=False) - self._threads.append(thread) - thread.start() - - def service_actions(self): - if len(self._threads) >= self._args.max_concurrency: - self._threads = [thread for thread in self._threads if thread.is_alive()] - - while len(self._threads) >= self._args.max_concurrency: - time.sleep(0.1) - self._threads = [thread for thread in self._threads if thread.is_alive()] - - def server_close(self): - super().server_close() - for thread in self._threads: - thread.join() - - -if __name__ == "__main__": - import argparse - import signal - - # Parse server arguments - parser = argparse.ArgumentParser() - parser.add_argument("port", type=int, help="Port to use") - parser.add_argument("backends", type=str, nargs="+", help="Backends to use") - parser.add_argument("--aliases", default=None, type=str, help="Path to model aliases") - parser.add_argument("--logfile", default=None, type=str, help="Log path") - parser.add_argument("--log_data", default=None, type=int, help="Log that much bytes of every request data") - parser.add_argument("--max_concurrency", default=256, type=int, help="Maximum concurrency") - parser.add_argument("--max_request_size", default=4096*1024, type=int, help="Maximum request size") - args = parser.parse_args() - - # Log stderr to logfile if given - if args.logfile is not None: - sys.stderr = open(args.logfile, "a", encoding="utf-8") - - # Create the server - server = FrontendRESTServer(args) - server_thread = threading.Thread(target=server.serve_forever, daemon=True) - server_thread.start() - - print("Started Frontend REST server on port {}.".format(args.port), file=sys.stderr) - print("To stop it gracefully, either send SIGINT (Ctrl+C) or SIGUSR1.", file=sys.stderr, flush=True) - - # Wait until the server should be closed - signal.pthread_sigmask(signal.SIG_BLOCK, [signal.SIGINT, signal.SIGUSR1]) - signal.sigwait([signal.SIGINT, signal.SIGUSR1]) - print("Initiating shutdown of the Frontend REST server.", file=sys.stderr, flush=True) - server.shutdown() - print("Stopped handling new requests, processing all current ones.", file=sys.stderr, flush=True) - server.server_close() - print("Finished shutdown of the Frontend REST server.", file=sys.stderr, flush=True) diff --git a/udpipe2/scripts/gen_consistent_feats_tables.py b/udpipe2/scripts/gen_consistent_feats_tables.py deleted file mode 100644 index 352c4c556ba7ffca47238e82b26a1eb2732a5ba6..0000000000000000000000000000000000000000 --- a/udpipe2/scripts/gen_consistent_feats_tables.py +++ /dev/null @@ -1,50 +0,0 @@ -#!/usr/bin/env python3 -import argparse -import gzip -import json -import os -import sys -import pickle - -import numpy as np - -sys.path.append(os.path.join(os.path.dirname(__file__), "..")) -import udpipe2_dataset - -def load_consistency(): - path = os.path.join(os.path.dirname(__file__), "docs-automation") - if not os.path.exists(path): - os.system("git -C '{}' clone --depth=1 https://github.com/UniversalDependencies/docs-automation".format(os.path.dirname(__file__))) - - with open(os.path.join(path, "valrules", "feats.json"), "r", encoding="utf-8") as feats_file: - return json.load(feats_file) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("lang", type=str, help="Language to generate") - parser.add_argument("model", type=str, help="Model path") - args = parser.parse_args() - - consistency = load_consistency()["features"][args.lang] - mappings = udpipe2_dataset.UDPipe2Dataset.load_mappings(os.path.join(args.model, "mappings.pickle")) - upos = mappings.factors[mappings.UPOS].words - feats = mappings.factors[mappings.FEATS].words - - allowed = np.ones([len(upos), len(feats)], dtype=bool) - for i, feat in enumerate(feats): - if feat in ["", "", "", "_"]: - continue - for feature_value in feat.split("|"): - feature, values = feature_value.split("=") - if feature not in consistency: - print("Did not find feature '{}' in consistency table!".format(feature), file=sys.stderr) - else: - for j in range(len(upos)): - if upos[j] not in consistency[feature]["byupos"] or \ - any(value not in consistency[feature]["byupos"][upos[j]] for value in values.split(",")) or \ - any(consistency[feature]["byupos"][upos[j]][value] == 0 for value in values.split(",")): - allowed[j][i] = False - - with gzip.open(os.path.join(args.model, "consistent_feats.table"), "wb") as consistent_feats_file: - np.save(consistent_feats_file, allowed, allow_pickle=False) diff --git a/udpipe2/scripts/results.sh b/udpipe2/scripts/results.sh deleted file mode 100644 index fe1dff83b1420cd8710c7b8ac755dd0ce8748570..0000000000000000000000000000000000000000 --- a/udpipe2/scripts/results.sh +++ /dev/null @@ -1,20 +0,0 @@ -#!/bin/sh - -[ $# -ge 3 ] || { echo Usage $0 treebank dev_or_test column_to_sort >&2; exit 1; } -treebank="$1" -dataset="$2" -column="$3" - -{ - -for log in models/*$treebank${EXP:+-$EXP}/log; do - [ -f "$log" ] || continue - echo $(basename $(dirname $log)) $(grep -i "^$dataset " $log | tail -n 1 | cut -d, -f3-) -done - -for log in models/*${treebank%%_*}_all${EXP:+-$EXP}/log; do - [ -f "$log" ] || continue - echo $(basename $(dirname $log)) $(grep -i "^$dataset $treebank " $log | tail -n 1 | cut -d, -f3-) -done - -} | sort -t: -rnk$column diff --git a/udpipe2/scripts/results_avg.py b/udpipe2/scripts/results_avg.py deleted file mode 100644 index c95fe79d69fb002a180de1f8f0b7a0391d718303..0000000000000000000000000000000000000000 --- a/udpipe2/scripts/results_avg.py +++ /dev/null @@ -1,29 +0,0 @@ -#!/usr/bin/env python3 -import re -import sys - -re_number = re.compile(r"([0-9]+(?:[.][0-9]*)?)") - -sums = [] -entries = 0 -for line in sys.stdin: - line = line.rstrip("\n") - for i, piece in enumerate(re_number.split(line)): - if i == len(sums): - sums.append(float(piece) if re_number.fullmatch(piece) else piece) - elif isinstance(sums[i], float): - sums[i] += float(piece) - assert i + 1 == len(sums) - for piece in re_number.split(line): - print("{:.2f}".format(float(piece)) if re_number.fullmatch(piece) else piece, end="") - print() - entries += 1 - -first = True -for entry in sums: - if isinstance(entry, float): - print("{:.2f}".format(entry / entries), end="") - first = False - else: - print(re.sub(r"\S", "*", entry) if first else entry, end="") -print() diff --git a/udpipe2/scripts/results_diff.py b/udpipe2/scripts/results_diff.py deleted file mode 100644 index 1285411f93acd21a7e63d965c3ba9aee5a299a91..0000000000000000000000000000000000000000 --- a/udpipe2/scripts/results_diff.py +++ /dev/null @@ -1,18 +0,0 @@ -#!/usr/bin/env python3 -import re -import sys - -re_number = re.compile(r"(?:^|(?<=[:\s]\s))([0-9]+(?:[.][0-9]*)?)") - -line = sys.stdin.readline().rstrip("\n") -print(line) -references = [float(piece) if re_number.fullmatch(piece) else None for piece in re_number.split(line)] - -for line in sys.stdin: - line = line.rstrip("\n") - for i, piece in enumerate(re_number.split(line)): - if i >= len(references): continue - if references[i] is not None and re_number.fullmatch(piece): - piece = "{:.2f}".format(float(piece) - references[i]) - print(piece, end="") - print() diff --git a/udpipe2/scripts/results_overview.sh b/udpipe2/scripts/results_overview.sh deleted file mode 100644 index 6d073c55b49f7ff784f60153160a4db9723fc022..0000000000000000000000000000000000000000 --- a/udpipe2/scripts/results_overview.sh +++ /dev/null @@ -1,11 +0,0 @@ -#!/bin/sh - -[ $# -ge 1 ] || { echo Usage $0 modeldir >&2; exit 1; } -modeldir="$1"; shift - -for d in $modeldir/*/; do - [ -L ${d%/} ] && continue - for f in $d/*.tokenizer; do - echo $(basename $f .tokenizer) $(grep -e '^Test ' -e "^Test $(basename $f .tokenizer) " $d/log | grep -o 'UPOS.*$') - done -done | column -t diff --git a/udpipe2/scripts/results_shared.sh b/udpipe2/scripts/results_shared.sh deleted file mode 100644 index 58f6811d7c2503245a79e9b989b808e9bfaa43ca..0000000000000000000000000000000000000000 --- a/udpipe2/scripts/results_shared.sh +++ /dev/null @@ -1,14 +0,0 @@ -#!/bin/sh - -[ $# -ge 2 ] || { echo Usage $0 datadir dev_or_test >&2; exit 1; } -data="$1"; shift -dataset="$1"; shift - -for lang in ${@:-$data/*_all/}; do - lang=$(basename $lang _all) - for treebank in $data/${lang}_*/; do - treebank=$(basename $treebank) - [ "${treebank##*_}" = all ] && continue - echo $treebank $(sh $(dirname $0)/results.sh $treebank $dataset 1 | sed 's/_all/-all/' | sort -r | $(dirname $0)/results_diff.py | sed -n 2p) - done -done diff --git a/udpipe2/scripts/train.sh b/udpipe2/scripts/train.sh deleted file mode 100644 index fab2198fc31347a1c987078c99cf7911fdcf2c93..0000000000000000000000000000000000000000 --- a/udpipe2/scripts/train.sh +++ /dev/null @@ -1,44 +0,0 @@ -#!/bin/bash - -[ $# -ge 2 ] || { echo Usage: $0 datadir treebank args... >&2; exit 1; } -data="$1"; shift -treebank="$1"; shift - -mkdir -p models/$data-$treebank${EXP:+-$EXP} -size=$(grep -P "^$treebank\t" $data/langs_sizes | cut -f2) -if [ "$size" -ge 400000 ]; then - args=--batch_size=64 -else - args=--batch_size=32 -fi -if [ "$size" -ge 50000 ]; then - args="$args --rnn_cell_dim=512" -else - args="$args --rnn_cell_dim=384" -fi -case $treebank in - cs*) args="$args --wembedding_model=robeczech-base-last4";; - *) args="$args --wembedding_model=bert-base-multilingual-uncased-last4";; -esac -case $treebank in - *_all) - evaluation_data="--dev" - for dev in $data/${treebank%_all}_*/*-dev.conllu; do - evaluation_data="$evaluation_data $(basename $(dirname $dev)):$dev" - done - evaluation_data="$evaluation_data --test" - for test in $data/${treebank%_all}_*/*-test.conllu; do - evaluation_data="$evaluation_data $(basename $(dirname $test)):$test" - done - ;; - la_evalatin20) - args="$args --tags=UPOS,LEMMAS --parse=0 --rnn_layers=3" - evaluation_data="--dev=$data/la_evalatin20/la_evalatin20-dev.conllu --test" - for kind in "" "-crossgenre" "-crosstime"; do - evaluation_data="$evaluation_data ${kind:+la_evalatin20$kind}:$data/la_evalatin20/la_evalatin20-test$kind.conllu" - done - ;; - *) evaluation_data="--dev=$data/$treebank/$treebank-ud-dev.conllu --test=$data/$treebank/$treebank-ud-test.conllu" -esac - -sbatch $SLURM_ARGS -p gpu-ms,gpu-troja -G 1 -C "gpu_cc6.1|gpu_cc7.5" --mem=24G -o models/$data-$treebank${EXP:+-$EXP}/training.log run withcuda100 venv/bin/python udpipe2.py models/$data-$treebank${EXP:+-$EXP} --train $data/$treebank/$treebank-*train.conllu $evaluation_data $args "$@" diff --git a/udpipe2/scripts/train_all.sh b/udpipe2/scripts/train_all.sh deleted file mode 100644 index 364ddf0e8bf6b70a6cccca8f48bf235ad30b542a..0000000000000000000000000000000000000000 --- a/udpipe2/scripts/train_all.sh +++ /dev/null @@ -1,9 +0,0 @@ -#!/bin/sh - -[ $# -ge 1 ] || { echo Usage: $0 datadir args... >&2; exit 1; } -data="$1"; shift - -for treebank in $(cut -f1 $data/langs_sizes); do - echo Running training of $data $treebank - sh $(dirname $0)/train.sh $data $treebank "$@" -done diff --git a/udpipe2/ud-2.10/.gitignore b/udpipe2/ud-2.10/.gitignore deleted file mode 100644 index 6bcdf5849c073a01182eb2de1ff3774b32b25b24..0000000000000000000000000000000000000000 --- a/udpipe2/ud-2.10/.gitignore +++ /dev/null @@ -1,153 +0,0 @@ -af_afribooms/ -ar_padt/ -be_hse/ -bg_btb/ -ca_ancora/ -cop_scriptorium/ -cs_all/ -cs_cac/ -cs_cltt/ -cs_fictree/ -cs_pdt/ -cu_proiel/ -cy_ccg/ -da_ddt/ -de_all/ -de_gsd/ -de_hdt/ -el_gdt/ -en_all/ -en_atis/ -en_ewt/ -en_gum/ -en_lines/ -en_partut/ -es_all/ -es_ancora/ -es_gsd/ -et_all/ -et_edt/ -et_ewt/ -eu_bdt/ -fa_all/ -fa_perdt/ -fa_seraji/ -fi_all/ -fi_ftb/ -fi_tdt/ -fo_farpahc/ -fr_all/ -fr_gsd/ -fr_parisstories/ -fr_partut/ -fr_rhapsodie/ -fr_sequoia/ -fro_srcmf/ -ga_idt/ -gd_arcosg/ -gl_all/ -gl_ctg/ -gl_treegal/ -got_proiel/ -grc_all/ -grc_perseus/ -grc_proiel/ -hbo_ptnk/ -he_all/ -he_htb/ -he_iahltwiki/ -hi_hdtb/ -hr_set/ -hu_szeged/ -hy_all/ -hy_armtdp/ -hy_bsut/ -hyw_armtdp/ -id_all/ -id_csui/ -id_gsd/ -is_all/ -is_icepahc/ -is_modern/ -it_all/ -it_isdt/ -it_markit/ -it_partut/ -it_postwita/ -it_twittiro/ -it_vit/ -ja_all/ -ja_gsd/ -ja_gsdluw/ -ko_all/ -ko_gsd/ -ko_kaist/ -la_all/ -la_ittb/ -la_llct/ -la_perseus/ -la_proiel/ -la_udante/ -lt_alksnis/ -lt_all/ -lt_hse/ -lv_lvtb/ -lzh_kyoto/ -mr_ufal/ -mt_mudt/ -nl_all/ -nl_alpino/ -nl_lassysmall/ -no_all/ -no_bokmaal/ -no_nynorsk/ -no_nynorsklia/ -orv_all/ -orv_birchbark/ -orv_rnc/ -orv_torot/ -pcm_nsc/ -pl_all/ -pl_lfg/ -pl_pdb/ -pt_all/ -pt_bosque/ -pt_gsd/ -qpm_philotis/ -qtd_sagt/ -ro_all/ -ro_nonstandard/ -ro_rrt/ -ro_simonero/ -ru_all/ -ru_gsd/ -ru_syntagrus/ -ru_taiga/ -sa_vedic/ -sk_snk/ -sl_all/ -sl_ssj/ -sl_sst/ -sme_giella/ -sr_set/ -sv_all/ -sv_lines/ -sv_talbanken/ -ta_ttb/ -te_mtg/ -tr_all/ -tr_atis/ -tr_boun/ -tr_framenet/ -tr_imst/ -tr_kenet/ -tr_penn/ -tr_tourism/ -ug_udt/ -uk_iu/ -ur_udtb/ -vi_vtb/ -wo_wtb/ -zh_all/ -zh_gsd/ -zh_gsdsimp/ diff --git a/udpipe2/ud-2.10/conllu_split.pl b/udpipe2/ud-2.10/conllu_split.pl deleted file mode 100644 index 75746712235a4a7aecef6606681582a02f50bc18..0000000000000000000000000000000000000000 --- a/udpipe2/ud-2.10/conllu_split.pl +++ /dev/null @@ -1,42 +0,0 @@ -#!/usr/bin/perl -use warnings; -use strict; -use utf8; -use open qw(:std :utf8); - -use File::Copy; - -sub write_sentences { - my ($target_file, @sentences) = @_; - - open (my $f, ">", $target_file) or die "Cannot open file $target_file: $!"; - foreach my $sentence (@sentences) { - print $f "$sentence\n"; - } - close $f; -} - - -@ARGV >= 2 or die "Usage: $0 target_directory lang ) { - chomp; - if (/^$/) { - push @sentences, $sentence; - $sentence = ""; - } else { - $sentence .= $_ . "\n"; - } -} -die "Unfinished sentence" if $sentence; - -# Generate everything from train_file -my $train_end = int(@sentences * 90 / 100); -die "Zero sentences for dev_file" if $train_end == int(@sentences); - -write_sentences("$target/$lang-ud-train.conllu", @sentences[0..$train_end-1]); -write_sentences("$target/$lang-ud-dev.conllu", @sentences[$train_end..$#sentences]); diff --git a/udpipe2/ud-2.10/conllu_to_text.pl b/udpipe2/ud-2.10/conllu_to_text.pl deleted file mode 100644 index e6df8824ddca3744769cbbcd78a70c3797c0abb3..0000000000000000000000000000000000000000 --- a/udpipe2/ud-2.10/conllu_to_text.pl +++ /dev/null @@ -1,250 +0,0 @@ -#!/usr/bin/env perl -# Extracts raw text from CoNLL-U file. Uses newdoc and newpar tags when available. -# Copyright © 2017 Dan Zeman -# License: GNU GPL - -use utf8; -use open ':utf8'; -binmode(STDIN, ':utf8'); -binmode(STDOUT, ':utf8'); -binmode(STDERR, ':utf8'); -use Getopt::Long; - -# Language code 'zh' or 'ja' will trigger Chinese-like text formatting. -my $language = 'en'; -GetOptions -( - 'language=s' => \$language -); -my $chinese = $language =~ m/^(zh|ja)(_|$)/; - -my @sentence = (); -my $text = ''; # from the text attribute of the sentence -my $ftext = ''; # from the word forms of the tokens -my $newpar = 0; -my $newdoc = 0; -my $buffer = ''; -my $start = 1; -my $mwtlast; -while(<>) -{ - push(@sentence, $_); - if(m/^\#\s*text\s*=\s*(.+)/) - { - $text = $1; - } - elsif(m/^\#\s*newpar(\s|$)/i) - { - $newpar = 1; - } - elsif(m/^\#\s*newdoc(\s|$)/i) - { - $newdoc = 1; - } - elsif(m/^\d+-(\d+)\t/) - { - $mwtlast = $1; - my @f = split(/\t/, $_); - # Paragraphs may start in the middle of a sentence (bulleted lists, verse etc.) - # The first token of the new paragraph has "NewPar=Yes" in the MISC column. - # Multi-word tokens have this in the token-introducing line. - if($f[9] =~ m/NewPar=Yes/i) - { - # Empty line between documents and paragraphs. (There may have been - # a paragraph break before the first part of this sentence as well!) - $buffer = print_new_paragraph_if_needed($start, $newdoc, $newpar, $buffer); - $buffer .= $ftext; - # Line breaks at word boundaries after at most 80 characters. - $buffer = print_lines_from_buffer($buffer, 80, $chinese); - print("$buffer\n\n"); - $buffer = ''; - # Start is only true until we write the first sentence of the input stream. - $start = 0; - $newdoc = 0; - $newpar = 0; - $text = ''; - $ftext = ''; - } - $ftext .= $f[1]; - $ftext .= ' ' unless($f[9] =~ m/SpaceAfter=No/); - } - elsif(m/^(\d+)\t/ && !(defined($mwtlast) && $1<=$mwtlast)) - { - $mwtlast = undef; - my @f = split(/\t/, $_); - # Paragraphs may start in the middle of a sentence (bulleted lists, verse etc.) - # The first token of the new paragraph has "NewPar=Yes" in the MISC column. - # Multi-word tokens have this in the token-introducing line. - if($f[9] =~ m/NewPar=Yes/i) - { - # Empty line between documents and paragraphs. (There may have been - # a paragraph break before the first part of this sentence as well!) - $buffer = print_new_paragraph_if_needed($start, $newdoc, $newpar, $buffer); - $buffer .= $ftext; - # Line breaks at word boundaries after at most 80 characters. - $buffer = print_lines_from_buffer($buffer, 80, $chinese); - print("$buffer\n\n"); - $buffer = ''; - # Start is only true until we write the first sentence of the input stream. - $start = 0; - $newdoc = 0; - $newpar = 0; - $text = ''; - $ftext = ''; - } - $ftext .= $f[1]; - $ftext .= ' ' unless($f[9] =~ m/SpaceAfter=No/); - } - elsif(m/^\s*$/) - { - # In a valid CoNLL-U file, $text should be equal to $ftext except for the - # space after the last token. However, if there have been intra-sentential - # paragraph breaks, $ftext contains only the part after the last such - # break, and $text is empty. Hence we currently use $ftext everywhere - # and ignore $text, even though we note it when seeing the text attribute. - # $text .= ' ' unless($chinese); - # Empty line between documents and paragraphs. - $buffer = print_new_paragraph_if_needed($start, $newdoc, $newpar, $buffer); - $buffer .= $ftext; - # Line breaks at word boundaries after at most 80 characters. - $buffer = print_lines_from_buffer($buffer, 80, $chinese); - # Start is only true until we write the first sentence of the input stream. - $start = 0; - $newdoc = 0; - $newpar = 0; - $text = ''; - $ftext = ''; - $mwtlast = undef; - } -} -# There may be unflushed buffer contents after the last sentence, less than 80 characters -# (otherwise we would have already dealt with it), so just flush it. -if($buffer ne '') -{ - print("$buffer\n"); -} - - - -#------------------------------------------------------------------------------ -# Checks whether we have to print an extra line to separate paragraphs. Does it -# if necessary. Returns the updated buffer. -#------------------------------------------------------------------------------ -sub print_new_paragraph_if_needed -{ - my $start = shift; - my $newdoc = shift; - my $newpar = shift; - my $buffer = shift; - if(!$start && ($newdoc || $newpar)) - { - if($buffer ne '') - { - print("$buffer\n"); - $buffer = ''; - } - print("\n"); - } - return $buffer; -} - - - -#------------------------------------------------------------------------------ -# Prints as many complete lines of text as there are in the buffer. Returns the -# remaining contents of the buffer. -#------------------------------------------------------------------------------ -sub print_lines_from_buffer -{ - my $buffer = shift; - # Maximum number of characters allowed on one line, not counting the line - # break character(s), which also replace any number of trailing spaces. - # Exception: If there is a word longer than the limit, it will be printed - # on one line. - # Note that this algorithm is not suitable for Chinese and Japanese. - my $limit = shift; - # We need a different algorithm for Chinese and Japanese. - my $chinese = shift; - if($chinese) - { - return print_chinese_lines_from_buffer($buffer, $limit); - } - if(length($buffer) >= $limit) - { - my @cbuffer = split(//, $buffer); - # There may be more than one new line waiting in the buffer. - while(scalar(@cbuffer) >= $limit) - { - ###!!! We could make it simpler if we ignored multi-space sequences - ###!!! between words. It sounds OK to ignore them because at the - ###!!! line break we do not respect original spacing anyway. - my $i; - my $ilastspace; - for($i = 0; $i<=$#cbuffer; $i++) - { - if($i>$limit && defined($ilastspace)) - { - last; - } - if($cbuffer[$i] =~ m/\s/) - { - $ilastspace = $i; - } - } - if(defined($ilastspace) && $ilastspace>0) - { - my @out = @cbuffer[0..($ilastspace-1)]; - splice(@cbuffer, 0, $ilastspace+1); - print(join('', @out), "\n"); - } - else - { - print(join('', @cbuffer), "\n"); - splice(@cbuffer); - } - } - $buffer = join('', @cbuffer); - } - return $buffer; -} - - - -#------------------------------------------------------------------------------ -# Prints as many complete lines of text as there are in the buffer. Returns the -# remaining contents of the buffer. Assumes that there are no spaces between -# words and lines can be broken between any two characters, as is the custom in -# Chinese and Japanese. -#------------------------------------------------------------------------------ -sub print_chinese_lines_from_buffer -{ - my $buffer = shift; - # Maximum number of characters allowed on one line, not counting the line - # break character(s). - my $limit = shift; - # We cannot simply print the first $limit characters from the buffer, - # followed by a line break. There could be embedded Latin words or - # numbers and we do not want to insert a line break in the middle of - # a foreign word. - my @cbuffer = split(//, $buffer); - while(scalar(@cbuffer) >= $limit) - { - my $nprint = 0; - for(my $i = 0; $i <= $#cbuffer; $i++) - { - if($i > $limit && $nprint > 0) - { - last; - } - unless($i < $#cbuffer && $cbuffer[$i] =~ m/[\p{Latin}0-9]/ && $cbuffer[$i+1] =~ m/[\p{Latin}0-9]/) - { - $nprint = $i+1; - } - } - my @out = @cbuffer[0..($nprint-1)]; - splice(@cbuffer, 0, $nprint); - print(join('', @out), "\n"); - } - $buffer = join('', @cbuffer); - return $buffer; -} diff --git a/udpipe2/ud-2.10/get.sh b/udpipe2/ud-2.10/get.sh deleted file mode 100644 index 36ff8d38e84a829ada093ecc1661ff5e8c044e8a..0000000000000000000000000000000000000000 --- a/udpipe2/ud-2.10/get.sh +++ /dev/null @@ -1,41 +0,0 @@ -#!/bin/sh - -#wget https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/1-4758/ud-treebanks-v2.10.tgz -#tar xf ud-treebanks-v2.10.tgz -rm ud-treebanks-v2.10.tgz -for tb in ud-treebanks-v2.10/*/*-ud-train.conllu; do - dir=`dirname $tb` - long_name=`basename $dir` - code=`basename $tb` - code=${code%%-*} - - case $long_name in - UD_Arabic-NYUAD|UD_English-ESL|UD_English-GUMReddit|UD_French-FTB|UD_Hindi_English-HIENCS|UD_Japanese-BCCWJ|UD_Japanese-BCCWJLUW) echo Skipping treebank $long_name without forms; continue;; - esac - - mkdir -p "$code" - cp "$dir"/* "$code" - if [ -f "$dir"/"$code"-ud-dev.conllu ]; then - cp "$dir"/"$code"-ud-train.conllu "$dir"/"$code"-ud-dev.conllu "$code" - else - perl conllu_split.pl "$code" "$code" <"$dir"/"$code"-ud-train.conllu - fi - - train_words=$(grep -c '^[0-9]' $code/*-ud-train.conllu) - [ "$train_words" -lt 1000 ] && { echo Skipping treebank $code with $train_words training words. >&2; rm -r "$code/"; continue; } - echo $code $long_name | tee -a iso_names.txt - - for conllu in "$code"/*.conllu; do - sed "1i# variant = $code" -i "$conllu" - case "$conllu" in - *train.conllu) rm "${conllu%.conllu}.txt";; - *) perl conllu_to_text.pl --language="$code" <"$conllu" >"${conllu%.conllu}.txt";; - esac - done -done -rm -r ud-treebanks-v2.10 - -for code in $(for d in */; do echo ${d%%[_/]*}; done | sort | uniq -d); do - mkdir "${code}_all" - cat "$code"*/*train.conllu >"${code}_all/${code}_all-ud-train.conllu" -done diff --git a/udpipe2/ud-2.10/iso_names.txt b/udpipe2/ud-2.10/iso_names.txt deleted file mode 100644 index 8a00587c58aa040c89885b6dfe7531e2e7f6842e..0000000000000000000000000000000000000000 --- a/udpipe2/ud-2.10/iso_names.txt +++ /dev/null @@ -1,123 +0,0 @@ -af_afribooms UD_Afrikaans-AfriBooms -grc_proiel UD_Ancient_Greek-PROIEL -grc_perseus UD_Ancient_Greek-Perseus -hbo_ptnk UD_Ancient_Hebrew-PTNK -ar_padt UD_Arabic-PADT -hy_armtdp UD_Armenian-ArmTDP -hy_bsut UD_Armenian-BSUT -eu_bdt UD_Basque-BDT -be_hse UD_Belarusian-HSE -bg_btb UD_Bulgarian-BTB -ca_ancora UD_Catalan-AnCora -zh_gsd UD_Chinese-GSD -zh_gsdsimp UD_Chinese-GSDSimp -lzh_kyoto UD_Classical_Chinese-Kyoto -cop_scriptorium UD_Coptic-Scriptorium -hr_set UD_Croatian-SET -cs_cac UD_Czech-CAC -cs_cltt UD_Czech-CLTT -cs_fictree UD_Czech-FicTree -cs_pdt UD_Czech-PDT -da_ddt UD_Danish-DDT -nl_alpino UD_Dutch-Alpino -nl_lassysmall UD_Dutch-LassySmall -en_atis UD_English-Atis -en_ewt UD_English-EWT -en_gum UD_English-GUM -en_lines UD_English-LinES -en_partut UD_English-ParTUT -et_edt UD_Estonian-EDT -et_ewt UD_Estonian-EWT -fo_farpahc UD_Faroese-FarPaHC -fi_ftb UD_Finnish-FTB -fi_tdt UD_Finnish-TDT -fr_gsd UD_French-GSD -fr_partut UD_French-ParTUT -fr_parisstories UD_French-ParisStories -fr_rhapsodie UD_French-Rhapsodie -fr_sequoia UD_French-Sequoia -gl_ctg UD_Galician-CTG -gl_treegal UD_Galician-TreeGal -de_gsd UD_German-GSD -de_hdt UD_German-HDT -got_proiel UD_Gothic-PROIEL -el_gdt UD_Greek-GDT -he_htb UD_Hebrew-HTB -he_iahltwiki UD_Hebrew-IAHLTwiki -hi_hdtb UD_Hindi-HDTB -hu_szeged UD_Hungarian-Szeged -is_icepahc UD_Icelandic-IcePaHC -is_modern UD_Icelandic-Modern -id_csui UD_Indonesian-CSUI -id_gsd UD_Indonesian-GSD -ga_idt UD_Irish-IDT -it_isdt UD_Italian-ISDT -it_markit UD_Italian-MarkIT -it_partut UD_Italian-ParTUT -it_postwita UD_Italian-PoSTWITA -it_twittiro UD_Italian-TWITTIRO -it_vit UD_Italian-VIT -ja_gsd UD_Japanese-GSD -ja_gsdluw UD_Japanese-GSDLUW -ko_gsd UD_Korean-GSD -ko_kaist UD_Korean-Kaist -la_ittb UD_Latin-ITTB -la_llct UD_Latin-LLCT -la_proiel UD_Latin-PROIEL -la_perseus UD_Latin-Perseus -la_udante UD_Latin-UDante -lv_lvtb UD_Latvian-LVTB -lt_alksnis UD_Lithuanian-ALKSNIS -lt_hse UD_Lithuanian-HSE -mt_mudt UD_Maltese-MUDT -mr_ufal UD_Marathi-UFAL -pcm_nsc UD_Naija-NSC -sme_giella UD_North_Sami-Giella -no_bokmaal UD_Norwegian-Bokmaal -no_nynorsk UD_Norwegian-Nynorsk -no_nynorsklia UD_Norwegian-NynorskLIA -cu_proiel UD_Old_Church_Slavonic-PROIEL -orv_birchbark UD_Old_East_Slavic-Birchbark -orv_rnc UD_Old_East_Slavic-RNC -orv_torot UD_Old_East_Slavic-TOROT -fro_srcmf UD_Old_French-SRCMF -fa_perdt UD_Persian-PerDT -fa_seraji UD_Persian-Seraji -pl_lfg UD_Polish-LFG -pl_pdb UD_Polish-PDB -qpm_philotis UD_Pomak-Philotis -pt_bosque UD_Portuguese-Bosque -pt_gsd UD_Portuguese-GSD -ro_nonstandard UD_Romanian-Nonstandard -ro_rrt UD_Romanian-RRT -ro_simonero UD_Romanian-SiMoNERo -ru_gsd UD_Russian-GSD -ru_syntagrus UD_Russian-SynTagRus -ru_taiga UD_Russian-Taiga -sa_vedic UD_Sanskrit-Vedic -gd_arcosg UD_Scottish_Gaelic-ARCOSG -sr_set UD_Serbian-SET -sk_snk UD_Slovak-SNK -sl_ssj UD_Slovenian-SSJ -sl_sst UD_Slovenian-SST -es_ancora UD_Spanish-AnCora -es_gsd UD_Spanish-GSD -sv_lines UD_Swedish-LinES -sv_talbanken UD_Swedish-Talbanken -ta_ttb UD_Tamil-TTB -te_mtg UD_Telugu-MTG -tr_atis UD_Turkish-Atis -tr_boun UD_Turkish-BOUN -tr_framenet UD_Turkish-FrameNet -tr_imst UD_Turkish-IMST -tr_kenet UD_Turkish-Kenet -tr_penn UD_Turkish-Penn -tr_tourism UD_Turkish-Tourism -qtd_sagt UD_Turkish_German-SAGT -uk_iu UD_Ukrainian-IU -ur_udtb UD_Urdu-UDTB -ug_udt UD_Uyghur-UDT -vi_vtb UD_Vietnamese-VTB -cy_ccg UD_Welsh-CCG -hyw_armtdp UD_Western_Armenian-ArmTDP -wo_wtb UD_Wolof-WTB diff --git a/udpipe2/ud-2.10/langs_sizes b/udpipe2/ud-2.10/langs_sizes deleted file mode 100644 index 9b252505b8df88370a08d598e76b5d2d813deb16..0000000000000000000000000000000000000000 --- a/udpipe2/ud-2.10/langs_sizes +++ /dev/null @@ -1,153 +0,0 @@ -de_all 3062863 -de_hdt 2799069 -cs_all 1807289 -ru_all 1456171 -ru_syntagrus 1204640 -cs_pdt 1173282 -es_all 835172 -ro_all 834851 -is_all 829620 -la_all 803889 -is_icepahc 705754 -it_all 692936 -fr_all 624821 -fa_all 573560 -tr_all 569473 -ro_nonstandard 532881 -no_all 524423 -en_all 478957 -cs_cac 472609 -es_ancora 452738 -fa_perdt 452496 -ca_ancora 429141 -pt_all 427088 -et_all 399686 -la_ittb 390785 -pl_all 386486 -es_gsd 382434 -fr_gsd 354516 -ko_all 353133 -grc_all 346928 -et_edt 344613 -ja_all 298617 -ko_kaist 296446 -fi_all 290416 -pl_pdb 281736 -hi_hdtb 281057 -it_isdt 276018 -be_hse 273172 -de_gsd 263794 -nl_all 261160 -he_all 258541 -pt_gsd 255312 -no_nynorsk 245330 -no_bokmaal 243886 -lzh_kyoto 236067 -sl_all 232442 -it_vit 224822 -ar_padt 223881 -sl_ssj 215155 -lv_lvtb 214983 -en_ewt 204578 -zh_all 197232 -la_llct 194143 -grc_proiel 187033 -nl_alpino 186026 -ro_rrt 185113 -ru_taiga 176631 -la_proiel 172133 -pt_bosque 171776 -ja_gsd 168333 -tr_penn 166514 -fi_tdt 162814 -grc_perseus 159895 -hy_all 159236 -fro_srcmf 158620 -hr_set 152857 -tr_kenet 143287 -orv_all 138948 -he_htb 137717 -cs_fictree 133637 -ja_gsdluw 130284 -fi_ftb 127602 -en_gum 124849 -bg_btb 124336 -is_modern 123866 -sv_all 122097 -fa_seraji 121064 -he_iahltwiki 120824 -orv_torot 118630 -ro_simonero 116857 -id_all 113959 -pcm_nsc 111877 -ur_udtb 108690 -pl_lfg 104750 -it_postwita 99537 -zh_gsdsimp 98616 -zh_gsd 98616 -tr_boun 98214 -id_gsd 97604 -hyw_armtdp 95921 -ga_idt 95881 -gl_all 93273 -uk_iu 92401 -sk_snk 80628 -da_ddt 80378 -gl_ctg 79327 -nl_lassysmall 75134 -ru_gsd 74900 -sr_set 74259 -eu_bdt 72974 -tr_tourism 71141 -qpm_philotis 69223 -gd_arcosg 68775 -sv_talbanken 66646 -en_lines 57372 -ko_gsd 56687 -sv_lines 55451 -et_ewt 55073 -lt_all 50851 -fr_sequoia 50505 -it_partut 48934 -en_atis 48655 -lt_alksnis 47641 -en_partut 43503 -el_gdt 42326 -hy_armtdp 42069 -tr_imst 37784 -cu_proiel 37432 -tr_atis 36200 -no_nynorsklia 35207 -got_proiel 35024 -af_afribooms 33880 -cop_scriptorium 30836 -la_udante 30441 -cs_cltt 27761 -fr_partut 24113 -it_twittiro 23634 -wo_wtb 23561 -fo_farpahc 23094 -mt_mudt 22880 -hy_bsut 21246 -vi_vtb 20285 -hu_szeged 20166 -it_markit 19991 -ug_udt 19262 -fr_rhapsodie 19144 -cy_ccg 18862 -hbo_ptnk 18278 -fr_parisstories 17923 -sl_sst 17287 -la_perseus 16387 -id_csui 16355 -tr_framenet 16333 -sa_vedic 15671 -sme_giella 14254 -gl_treegal 13946 -orv_rnc 13062 -qtd_sagt 10081 -orv_birchbark 7256 -ta_ttb 6329 -te_mtg 5082 -lt_hse 3210 -mr_ufal 2997 diff --git a/udpipe2/ud-2.10/langs_sizes.sh b/udpipe2/ud-2.10/langs_sizes.sh deleted file mode 100644 index 5dfcd9a049bb589d5e647cf719701c894e556142..0000000000000000000000000000000000000000 --- a/udpipe2/ud-2.10/langs_sizes.sh +++ /dev/null @@ -1,6 +0,0 @@ -#!/bin/sh - -for d in */; do - echo $d >&2 - echo "${d%/} $(grep -cP "^\d+\t" $d*train.conllu)" -done | sort -rnk2 >langs_sizes diff --git a/udpipe2/ud-2.12/.gitignore b/udpipe2/ud-2.12/.gitignore deleted file mode 100644 index 1a4d3646fcf888c15bc62bfd4168de8822d7224b..0000000000000000000000000000000000000000 --- a/udpipe2/ud-2.12/.gitignore +++ /dev/null @@ -1,163 +0,0 @@ -af_afribooms/ -ar_padt/ -be_hse/ -bg_btb/ -ca_ancora/ -cop_scriptorium/ -cs_all/ -cs_cac/ -cs_cltt/ -cs_fictree/ -cs_pdt/ -cu_proiel/ -cy_ccg/ -da_ddt/ -de_all/ -de_gsd/ -de_hdt/ -el_all/ -el_gdt/ -el_gud/ -en_all/ -en_atis/ -en_eslspok/ -en_ewt/ -en_gum/ -en_lines/ -en_partut/ -es_all/ -es_ancora/ -es_gsd/ -et_all/ -et_edt/ -et_ewt/ -eu_bdt/ -fa_all/ -fa_perdt/ -fa_seraji/ -fi_all/ -fi_ftb/ -fi_tdt/ -fo_farpahc/ -fr_all/ -fr_gsd/ -fr_parisstories/ -fr_partut/ -fr_rhapsodie/ -fr_sequoia/ -fro_srcmf/ -ga_all/ -ga_idt/ -ga_twittirish/ -gd_arcosg/ -gl_all/ -gl_ctg/ -gl_treegal/ -got_proiel/ -grc_all/ -grc_perseus/ -grc_proiel/ -gv_cadhan/ -hbo_ptnk/ -he_all/ -he_htb/ -he_iahltwiki/ -hi_hdtb/ -hr_set/ -hu_szeged/ -hy_all/ -hy_armtdp/ -hy_bsut/ -hyw_armtdp/ -id_all/ -id_csui/ -id_gsd/ -is_all/ -is_gc/ -is_icepahc/ -is_modern/ -it_all/ -it_isdt/ -it_markit/ -it_parlamint/ -it_partut/ -it_postwita/ -it_twittiro/ -it_vit/ -ja_all/ -ja_gsd/ -ja_gsdluw/ -ko_all/ -ko_gsd/ -ko_kaist/ -la_all/ -la_ittb/ -la_llct/ -la_perseus/ -la_proiel/ -la_udante/ -lt_alksnis/ -lt_all/ -lt_hse/ -lv_lvtb/ -lzh_kyoto/ -mr_ufal/ -mt_mudt/ -myv_jr/ -nl_all/ -nl_alpino/ -nl_lassysmall/ -no_all/ -no_bokmaal/ -no_nynorsk/ -orv_all/ -orv_birchbark/ -orv_rnc/ -orv_torot/ -pcm_nsc/ -pl_all/ -pl_lfg/ -pl_pdb/ -pt_all/ -pt_bosque/ -pt_cintil/ -pt_petrogold/ -qaf_arabizi/ -qpm_philotis/ -qtd_sagt/ -ro_all/ -ro_nonstandard/ -ro_rrt/ -ro_simonero/ -ru_all/ -ru_gsd/ -ru_syntagrus/ -ru_taiga/ -sa_vedic/ -sk_snk/ -sl_all/ -sl_ssj/ -sl_sst/ -sme_giella/ -sr_set/ -sv_all/ -sv_lines/ -sv_talbanken/ -ta_ttb/ -te_mtg/ -tr_all/ -tr_atis/ -tr_boun/ -tr_framenet/ -tr_imst/ -tr_kenet/ -tr_penn/ -tr_tourism/ -ug_udt/ -uk_iu/ -ur_udtb/ -vi_vtb/ -wo_wtb/ -zh_all/ -zh_gsd/ -zh_gsdsimp/ diff --git a/udpipe2/ud-2.12/conllu_split.pl b/udpipe2/ud-2.12/conllu_split.pl deleted file mode 100644 index 75746712235a4a7aecef6606681582a02f50bc18..0000000000000000000000000000000000000000 --- a/udpipe2/ud-2.12/conllu_split.pl +++ /dev/null @@ -1,42 +0,0 @@ -#!/usr/bin/perl -use warnings; -use strict; -use utf8; -use open qw(:std :utf8); - -use File::Copy; - -sub write_sentences { - my ($target_file, @sentences) = @_; - - open (my $f, ">", $target_file) or die "Cannot open file $target_file: $!"; - foreach my $sentence (@sentences) { - print $f "$sentence\n"; - } - close $f; -} - - -@ARGV >= 2 or die "Usage: $0 target_directory lang ) { - chomp; - if (/^$/) { - push @sentences, $sentence; - $sentence = ""; - } else { - $sentence .= $_ . "\n"; - } -} -die "Unfinished sentence" if $sentence; - -# Generate everything from train_file -my $train_end = int(@sentences * 90 / 100); -die "Zero sentences for dev_file" if $train_end == int(@sentences); - -write_sentences("$target/$lang-ud-train.conllu", @sentences[0..$train_end-1]); -write_sentences("$target/$lang-ud-dev.conllu", @sentences[$train_end..$#sentences]); diff --git a/udpipe2/ud-2.12/conllu_to_text.pl b/udpipe2/ud-2.12/conllu_to_text.pl deleted file mode 100644 index e6df8824ddca3744769cbbcd78a70c3797c0abb3..0000000000000000000000000000000000000000 --- a/udpipe2/ud-2.12/conllu_to_text.pl +++ /dev/null @@ -1,250 +0,0 @@ -#!/usr/bin/env perl -# Extracts raw text from CoNLL-U file. Uses newdoc and newpar tags when available. -# Copyright © 2017 Dan Zeman -# License: GNU GPL - -use utf8; -use open ':utf8'; -binmode(STDIN, ':utf8'); -binmode(STDOUT, ':utf8'); -binmode(STDERR, ':utf8'); -use Getopt::Long; - -# Language code 'zh' or 'ja' will trigger Chinese-like text formatting. -my $language = 'en'; -GetOptions -( - 'language=s' => \$language -); -my $chinese = $language =~ m/^(zh|ja)(_|$)/; - -my @sentence = (); -my $text = ''; # from the text attribute of the sentence -my $ftext = ''; # from the word forms of the tokens -my $newpar = 0; -my $newdoc = 0; -my $buffer = ''; -my $start = 1; -my $mwtlast; -while(<>) -{ - push(@sentence, $_); - if(m/^\#\s*text\s*=\s*(.+)/) - { - $text = $1; - } - elsif(m/^\#\s*newpar(\s|$)/i) - { - $newpar = 1; - } - elsif(m/^\#\s*newdoc(\s|$)/i) - { - $newdoc = 1; - } - elsif(m/^\d+-(\d+)\t/) - { - $mwtlast = $1; - my @f = split(/\t/, $_); - # Paragraphs may start in the middle of a sentence (bulleted lists, verse etc.) - # The first token of the new paragraph has "NewPar=Yes" in the MISC column. - # Multi-word tokens have this in the token-introducing line. - if($f[9] =~ m/NewPar=Yes/i) - { - # Empty line between documents and paragraphs. (There may have been - # a paragraph break before the first part of this sentence as well!) - $buffer = print_new_paragraph_if_needed($start, $newdoc, $newpar, $buffer); - $buffer .= $ftext; - # Line breaks at word boundaries after at most 80 characters. - $buffer = print_lines_from_buffer($buffer, 80, $chinese); - print("$buffer\n\n"); - $buffer = ''; - # Start is only true until we write the first sentence of the input stream. - $start = 0; - $newdoc = 0; - $newpar = 0; - $text = ''; - $ftext = ''; - } - $ftext .= $f[1]; - $ftext .= ' ' unless($f[9] =~ m/SpaceAfter=No/); - } - elsif(m/^(\d+)\t/ && !(defined($mwtlast) && $1<=$mwtlast)) - { - $mwtlast = undef; - my @f = split(/\t/, $_); - # Paragraphs may start in the middle of a sentence (bulleted lists, verse etc.) - # The first token of the new paragraph has "NewPar=Yes" in the MISC column. - # Multi-word tokens have this in the token-introducing line. - if($f[9] =~ m/NewPar=Yes/i) - { - # Empty line between documents and paragraphs. (There may have been - # a paragraph break before the first part of this sentence as well!) - $buffer = print_new_paragraph_if_needed($start, $newdoc, $newpar, $buffer); - $buffer .= $ftext; - # Line breaks at word boundaries after at most 80 characters. - $buffer = print_lines_from_buffer($buffer, 80, $chinese); - print("$buffer\n\n"); - $buffer = ''; - # Start is only true until we write the first sentence of the input stream. - $start = 0; - $newdoc = 0; - $newpar = 0; - $text = ''; - $ftext = ''; - } - $ftext .= $f[1]; - $ftext .= ' ' unless($f[9] =~ m/SpaceAfter=No/); - } - elsif(m/^\s*$/) - { - # In a valid CoNLL-U file, $text should be equal to $ftext except for the - # space after the last token. However, if there have been intra-sentential - # paragraph breaks, $ftext contains only the part after the last such - # break, and $text is empty. Hence we currently use $ftext everywhere - # and ignore $text, even though we note it when seeing the text attribute. - # $text .= ' ' unless($chinese); - # Empty line between documents and paragraphs. - $buffer = print_new_paragraph_if_needed($start, $newdoc, $newpar, $buffer); - $buffer .= $ftext; - # Line breaks at word boundaries after at most 80 characters. - $buffer = print_lines_from_buffer($buffer, 80, $chinese); - # Start is only true until we write the first sentence of the input stream. - $start = 0; - $newdoc = 0; - $newpar = 0; - $text = ''; - $ftext = ''; - $mwtlast = undef; - } -} -# There may be unflushed buffer contents after the last sentence, less than 80 characters -# (otherwise we would have already dealt with it), so just flush it. -if($buffer ne '') -{ - print("$buffer\n"); -} - - - -#------------------------------------------------------------------------------ -# Checks whether we have to print an extra line to separate paragraphs. Does it -# if necessary. Returns the updated buffer. -#------------------------------------------------------------------------------ -sub print_new_paragraph_if_needed -{ - my $start = shift; - my $newdoc = shift; - my $newpar = shift; - my $buffer = shift; - if(!$start && ($newdoc || $newpar)) - { - if($buffer ne '') - { - print("$buffer\n"); - $buffer = ''; - } - print("\n"); - } - return $buffer; -} - - - -#------------------------------------------------------------------------------ -# Prints as many complete lines of text as there are in the buffer. Returns the -# remaining contents of the buffer. -#------------------------------------------------------------------------------ -sub print_lines_from_buffer -{ - my $buffer = shift; - # Maximum number of characters allowed on one line, not counting the line - # break character(s), which also replace any number of trailing spaces. - # Exception: If there is a word longer than the limit, it will be printed - # on one line. - # Note that this algorithm is not suitable for Chinese and Japanese. - my $limit = shift; - # We need a different algorithm for Chinese and Japanese. - my $chinese = shift; - if($chinese) - { - return print_chinese_lines_from_buffer($buffer, $limit); - } - if(length($buffer) >= $limit) - { - my @cbuffer = split(//, $buffer); - # There may be more than one new line waiting in the buffer. - while(scalar(@cbuffer) >= $limit) - { - ###!!! We could make it simpler if we ignored multi-space sequences - ###!!! between words. It sounds OK to ignore them because at the - ###!!! line break we do not respect original spacing anyway. - my $i; - my $ilastspace; - for($i = 0; $i<=$#cbuffer; $i++) - { - if($i>$limit && defined($ilastspace)) - { - last; - } - if($cbuffer[$i] =~ m/\s/) - { - $ilastspace = $i; - } - } - if(defined($ilastspace) && $ilastspace>0) - { - my @out = @cbuffer[0..($ilastspace-1)]; - splice(@cbuffer, 0, $ilastspace+1); - print(join('', @out), "\n"); - } - else - { - print(join('', @cbuffer), "\n"); - splice(@cbuffer); - } - } - $buffer = join('', @cbuffer); - } - return $buffer; -} - - - -#------------------------------------------------------------------------------ -# Prints as many complete lines of text as there are in the buffer. Returns the -# remaining contents of the buffer. Assumes that there are no spaces between -# words and lines can be broken between any two characters, as is the custom in -# Chinese and Japanese. -#------------------------------------------------------------------------------ -sub print_chinese_lines_from_buffer -{ - my $buffer = shift; - # Maximum number of characters allowed on one line, not counting the line - # break character(s). - my $limit = shift; - # We cannot simply print the first $limit characters from the buffer, - # followed by a line break. There could be embedded Latin words or - # numbers and we do not want to insert a line break in the middle of - # a foreign word. - my @cbuffer = split(//, $buffer); - while(scalar(@cbuffer) >= $limit) - { - my $nprint = 0; - for(my $i = 0; $i <= $#cbuffer; $i++) - { - if($i > $limit && $nprint > 0) - { - last; - } - unless($i < $#cbuffer && $cbuffer[$i] =~ m/[\p{Latin}0-9]/ && $cbuffer[$i+1] =~ m/[\p{Latin}0-9]/) - { - $nprint = $i+1; - } - } - my @out = @cbuffer[0..($nprint-1)]; - splice(@cbuffer, 0, $nprint); - print(join('', @out), "\n"); - } - $buffer = join('', @cbuffer); - return $buffer; -} diff --git a/udpipe2/ud-2.12/get.sh b/udpipe2/ud-2.12/get.sh deleted file mode 100644 index 63b847cf17003bd27c66d5c1d73797ac1d5b070e..0000000000000000000000000000000000000000 --- a/udpipe2/ud-2.12/get.sh +++ /dev/null @@ -1,41 +0,0 @@ -#!/bin/sh - -wget https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/1-5150/ud-treebanks-v2.12.tgz -tar xf ud-treebanks-v2.12.tgz -rm ud-treebanks-v2.12.tgz -for tb in ud-treebanks-v2.12/*/*-ud-train.conllu; do - dir=`dirname $tb` - long_name=`basename $dir` - code=`basename $tb` - code=${code%%-*} - - case $long_name in - UD_Arabic-NYUAD|UD_English-GUMReddit|UD_Japanese-BCCWJ|UD_Japanese-BCCWJLUW) echo Skipping treebank $long_name without forms; continue;; - esac - - mkdir -p "$code" - cp "$dir"/* "$code" - if [ -f "$dir"/"$code"-ud-dev.conllu ]; then - cp "$dir"/"$code"-ud-train.conllu "$dir"/"$code"-ud-dev.conllu "$code" - else - perl conllu_split.pl "$code" "$code" <"$dir"/"$code"-ud-train.conllu - fi - - train_words=$(grep -c '^[0-9]' $code/*-ud-train.conllu) - [ "$train_words" -lt 1000 ] && { echo Skipping treebank $code with $train_words training words. >&2; rm -r "$code/"; continue; } - echo $code $long_name | tee -a iso_names.txt - - for conllu in "$code"/*.conllu; do - sed "1i# variant = $code" -i "$conllu" - case "$conllu" in - *train.conllu) rm "${conllu%.conllu}.txt";; - *) perl conllu_to_text.pl --language="$code" <"$conllu" >"${conllu%.conllu}.txt";; - esac - done -done -rm -r ud-treebanks-v2.12 - -for code in $(for d in */; do echo ${d%%[_/]*}; done | sort | uniq -d); do - mkdir "${code}_all" - cat "$code"*/*train.conllu >"${code}_all/${code}_all-ud-train.conllu" -done diff --git a/udpipe2/ud-2.12/iso_names.txt b/udpipe2/ud-2.12/iso_names.txt deleted file mode 100644 index 8bd301da4bf5e225e61de882cb8719b8ea8267af..0000000000000000000000000000000000000000 --- a/udpipe2/ud-2.12/iso_names.txt +++ /dev/null @@ -1,131 +0,0 @@ -af_afribooms UD_Afrikaans-AfriBooms -grc_proiel UD_Ancient_Greek-PROIEL -grc_perseus UD_Ancient_Greek-Perseus -hbo_ptnk UD_Ancient_Hebrew-PTNK -ar_padt UD_Arabic-PADT -hy_armtdp UD_Armenian-ArmTDP -hy_bsut UD_Armenian-BSUT -eu_bdt UD_Basque-BDT -be_hse UD_Belarusian-HSE -bg_btb UD_Bulgarian-BTB -ca_ancora UD_Catalan-AnCora -zh_gsd UD_Chinese-GSD -zh_gsdsimp UD_Chinese-GSDSimp -lzh_kyoto UD_Classical_Chinese-Kyoto -cop_scriptorium UD_Coptic-Scriptorium -hr_set UD_Croatian-SET -cs_cac UD_Czech-CAC -cs_cltt UD_Czech-CLTT -cs_fictree UD_Czech-FicTree -cs_pdt UD_Czech-PDT -da_ddt UD_Danish-DDT -nl_alpino UD_Dutch-Alpino -nl_lassysmall UD_Dutch-LassySmall -en_atis UD_English-Atis -en_eslspok UD_English-ESLSpok -en_ewt UD_English-EWT -en_gum UD_English-GUM -en_lines UD_English-LinES -en_partut UD_English-ParTUT -myv_jr UD_Erzya-JR -et_edt UD_Estonian-EDT -et_ewt UD_Estonian-EWT -fo_farpahc UD_Faroese-FarPaHC -fi_ftb UD_Finnish-FTB -fi_tdt UD_Finnish-TDT -fr_gsd UD_French-GSD -fr_partut UD_French-ParTUT -fr_parisstories UD_French-ParisStories -fr_rhapsodie UD_French-Rhapsodie -fr_sequoia UD_French-Sequoia -gl_ctg UD_Galician-CTG -gl_treegal UD_Galician-TreeGal -de_gsd UD_German-GSD -de_hdt UD_German-HDT -got_proiel UD_Gothic-PROIEL -el_gdt UD_Greek-GDT -el_gud UD_Greek-GUD -he_htb UD_Hebrew-HTB -he_iahltwiki UD_Hebrew-IAHLTwiki -hi_hdtb UD_Hindi-HDTB -hu_szeged UD_Hungarian-Szeged -is_gc UD_Icelandic-GC -is_icepahc UD_Icelandic-IcePaHC -is_modern UD_Icelandic-Modern -id_csui UD_Indonesian-CSUI -id_gsd UD_Indonesian-GSD -ga_idt UD_Irish-IDT -ga_twittirish UD_Irish-TwittIrish -it_isdt UD_Italian-ISDT -it_markit UD_Italian-MarkIT -it_partut UD_Italian-ParTUT -it_parlamint UD_Italian-ParlaMint -it_postwita UD_Italian-PoSTWITA -it_twittiro UD_Italian-TWITTIRO -it_vit UD_Italian-VIT -ja_gsd UD_Japanese-GSD -ja_gsdluw UD_Japanese-GSDLUW -ko_gsd UD_Korean-GSD -ko_kaist UD_Korean-Kaist -la_ittb UD_Latin-ITTB -la_llct UD_Latin-LLCT -la_proiel UD_Latin-PROIEL -la_perseus UD_Latin-Perseus -la_udante UD_Latin-UDante -lv_lvtb UD_Latvian-LVTB -lt_alksnis UD_Lithuanian-ALKSNIS -lt_hse UD_Lithuanian-HSE -qaf_arabizi UD_Maghrebi_Arabic_French-Arabizi -mt_mudt UD_Maltese-MUDT -gv_cadhan UD_Manx-Cadhan -mr_ufal UD_Marathi-UFAL -pcm_nsc UD_Naija-NSC -sme_giella UD_North_Sami-Giella -no_bokmaal UD_Norwegian-Bokmaal -no_nynorsk UD_Norwegian-Nynorsk -cu_proiel UD_Old_Church_Slavonic-PROIEL -orv_birchbark UD_Old_East_Slavic-Birchbark -orv_rnc UD_Old_East_Slavic-RNC -orv_torot UD_Old_East_Slavic-TOROT -fro_srcmf UD_Old_French-SRCMF -fa_perdt UD_Persian-PerDT -fa_seraji UD_Persian-Seraji -pl_lfg UD_Polish-LFG -pl_pdb UD_Polish-PDB -qpm_philotis UD_Pomak-Philotis -pt_bosque UD_Portuguese-Bosque -pt_cintil UD_Portuguese-CINTIL -pt_petrogold UD_Portuguese-PetroGold -ro_nonstandard UD_Romanian-Nonstandard -ro_rrt UD_Romanian-RRT -ro_simonero UD_Romanian-SiMoNERo -ru_gsd UD_Russian-GSD -ru_syntagrus UD_Russian-SynTagRus -ru_taiga UD_Russian-Taiga -sa_vedic UD_Sanskrit-Vedic -gd_arcosg UD_Scottish_Gaelic-ARCOSG -sr_set UD_Serbian-SET -sk_snk UD_Slovak-SNK -sl_ssj UD_Slovenian-SSJ -sl_sst UD_Slovenian-SST -es_ancora UD_Spanish-AnCora -es_gsd UD_Spanish-GSD -sv_lines UD_Swedish-LinES -sv_talbanken UD_Swedish-Talbanken -ta_ttb UD_Tamil-TTB -te_mtg UD_Telugu-MTG -tr_atis UD_Turkish-Atis -tr_boun UD_Turkish-BOUN -tr_framenet UD_Turkish-FrameNet -tr_imst UD_Turkish-IMST -tr_kenet UD_Turkish-Kenet -tr_penn UD_Turkish-Penn -tr_tourism UD_Turkish-Tourism -qtd_sagt UD_Turkish_German-SAGT -uk_iu UD_Ukrainian-IU -ur_udtb UD_Urdu-UDTB -ug_udt UD_Uyghur-UDT -vi_vtb UD_Vietnamese-VTB -cy_ccg UD_Welsh-CCG -hyw_armtdp UD_Western_Armenian-ArmTDP -wo_wtb UD_Wolof-WTB diff --git a/udpipe2/ud-2.12/langs_sizes b/udpipe2/ud-2.12/langs_sizes deleted file mode 100644 index 14b47e21a552a2b82102d30b82b34692385b9d94..0000000000000000000000000000000000000000 --- a/udpipe2/ud-2.12/langs_sizes +++ /dev/null @@ -1,163 +0,0 @@ -de_all 3062860 -de_hdt 2799069 -cs_all 1793425 -ru_all 1456171 -ru_syntagrus 1204640 -cs_pdt 1173285 -is_all 846134 -es_all 835171 -ro_all 834863 -la_all 809364 -pt_all 717123 -is_icepahc 705749 -it_all 702085 -fr_all 629181 -fa_all 573560 -tr_all 572270 -ro_nonstandard 532881 -en_all 518854 -no_all 489216 -cs_cac 472609 -es_ancora 452738 -fa_perdt 452496 -ca_ancora 429141 -et_all 411995 -la_ittb 390785 -pl_all 386486 -es_gsd 382433 -lzh_kyoto 374559 -fr_gsd 354567 -ko_all 353133 -grc_all 346934 -pt_cintil 346576 -et_edt 344564 -ja_all 298617 -ko_kaist 296446 -fi_all 290416 -pl_pdb 281736 -hi_hdtb 281057 -it_isdt 276013 -be_hse 273181 -de_gsd 263791 -nl_all 261161 -he_all 257325 -no_nynorsk 245330 -no_bokmaal 243886 -sl_all 232442 -it_vit 225279 -ar_padt 223881 -lv_lvtb 221315 -orv_all 216114 -sl_ssj 215155 -en_ewt 204576 -pt_petrogold 198771 -zh_all 197232 -la_llct 194143 -orv_torot 191350 -grc_proiel 187039 -nl_alpino 186027 -ro_rrt 185125 -la_proiel 177558 -ru_taiga 176631 -pt_bosque 171776 -ja_gsd 168333 -tr_penn 166514 -fi_tdt 162814 -grc_perseus 159895 -cu_proiel 159710 -hy_all 159236 -fro_srcmf 158620 -hr_set 152857 -en_gum 147869 -tr_kenet 143287 -he_htb 136501 -cs_fictree 133637 -ja_gsdluw 130284 -fi_ftb 127602 -bg_btb 124336 -sv_all 122097 -fa_seraji 121064 -he_iahltwiki 120824 -ro_simonero 116857 -id_all 113957 -pcm_nsc 111897 -ga_all 111658 -ur_udtb 108690 -pl_lfg 104750 -tr_boun 100713 -it_postwita 99531 -zh_gsdsimp 98616 -zh_gsd 98616 -id_gsd 97602 -hyw_armtdp 95921 -ga_idt 95881 -gl_all 93273 -uk_iu 92927 -sk_snk 80628 -da_ddt 80378 -gl_ctg 79327 -is_gc 78568 -nl_lassysmall 75134 -ru_gsd 74900 -sr_set 74259 -eu_bdt 72974 -tr_tourism 71141 -qpm_philotis 69223 -gd_arcosg 68775 -et_ewt 67431 -sv_talbanken 66646 -is_modern 61817 -en_lines 57372 -ko_gsd 56687 -el_all 55648 -sv_lines 55451 -lt_all 50851 -fr_sequoia 50503 -it_partut 48934 -en_atis 48655 -lt_alksnis 47641 -en_partut 43503 -el_gdt 42326 -hy_armtdp 42069 -tr_imst 38082 -tr_atis 36200 -got_proiel 35024 -cop_scriptorium 34526 -af_afribooms 33880 -la_udante 30451 -fr_partut 24103 -it_twittiro 23631 -wo_wtb 23561 -fo_farpahc 23094 -mt_mudt 22880 -cy_ccg 22381 -fr_parisstories 22244 -hy_bsut 21246 -vi_vtb 20215 -hu_szeged 20166 -it_markit 19893 -ug_udt 19262 -fr_rhapsodie 19144 -hbo_ptnk 18278 -orv_rnc 17508 -sl_sst 17287 -en_eslspok 16879 -la_perseus 16427 -id_csui 16355 -tr_framenet 16333 -ga_twittirish 15777 -sa_vedic 15671 -qaf_arabizi 15404 -sme_giella 14254 -gl_treegal 13946 -cs_cltt 13894 -el_gud 13322 -qtd_sagt 10081 -gv_cadhan 9566 -myv_jr 9112 -it_parlamint 8804 -orv_birchbark 7256 -ta_ttb 6329 -te_mtg 5082 -lt_hse 3210 -mr_ufal 2997 diff --git a/udpipe2/ud-2.12/langs_sizes.sh b/udpipe2/ud-2.12/langs_sizes.sh deleted file mode 100644 index 5dfcd9a049bb589d5e647cf719701c894e556142..0000000000000000000000000000000000000000 --- a/udpipe2/ud-2.12/langs_sizes.sh +++ /dev/null @@ -1,6 +0,0 @@ -#!/bin/sh - -for d in */; do - echo $d >&2 - echo "${d%/} $(grep -cP "^\d+\t" $d*train.conllu)" -done | sort -rnk2 >langs_sizes diff --git a/udpipe2/ud-2.6/.gitignore b/udpipe2/ud-2.6/.gitignore deleted file mode 100644 index dbae25c19ae7a288b99070c4db04bd8117b9945c..0000000000000000000000000000000000000000 --- a/udpipe2/ud-2.6/.gitignore +++ /dev/null @@ -1,122 +0,0 @@ -af_afribooms/ -ar_padt/ -be_hse/ -bg_btb/ -ca_ancora/ -cop_scriptorium/ -cs_all/ -cs_cac/ -cs_cltt/ -cs_fictree/ -cs_pdt/ -cu_proiel/ -cy_ccg/ -da_ddt/ -de_all/ -de_gsd/ -de_hdt/ -el_gdt/ -en_all/ -en_ewt/ -en_gum/ -en_lines/ -en_partut/ -es_all/ -es_ancora/ -es_gsd/ -et_all/ -et_edt/ -et_ewt/ -eu_bdt/ -fa_seraji/ -fi_all/ -fi_ftb/ -fi_tdt/ -fr_all/ -fr_gsd/ -fr_partut/ -fr_sequoia/ -fr_spoken/ -fro_srcmf/ -ga_idt/ -gd_arcosg/ -gl_all/ -gl_ctg/ -gl_treegal/ -got_proiel/ -grc_all/ -grc_perseus/ -grc_proiel/ -he_htb/ -hi_hdtb/ -hr_set/ -hu_szeged/ -hy_armtdp/ -id_gsd/ -it_all/ -it_isdt/ -it_partut/ -it_postwita/ -it_twittiro/ -it_vit/ -ja_gsd/ -ko_all/ -ko_gsd/ -ko_kaist/ -la_all/ -la_ittb/ -la_llct/ -la_perseus/ -la_proiel/ -lt_alksnis/ -lt_all/ -lt_hse/ -lv_lvtb/ -lzh_kyoto/ -mr_ufal/ -mt_mudt/ -nl_all/ -nl_alpino/ -nl_lassysmall/ -no_all/ -no_bokmaal/ -no_nynorsk/ -no_nynorsklia/ -orv_all/ -orv_rnc/ -orv_torot/ -pcm_nsc/ -pl_all/ -pl_lfg/ -pl_pdb/ -pt_all/ -pt_bosque/ -pt_gsd/ -ro_all/ -ro_nonstandard/ -ro_rrt/ -ru_all/ -ru_gsd/ -ru_syntagrus/ -ru_taiga/ -sa_vedic/ -sk_snk/ -sl_all/ -sl_ssj/ -sl_sst/ -sme_giella/ -sr_set/ -sv_all/ -sv_lines/ -sv_talbanken/ -ta_ttb/ -te_mtg/ -tr_imst/ -ug_udt/ -uk_iu/ -ur_udtb/ -vi_vtb/ -wo_wtb/ -zh_all/ -zh_gsd/ -zh_gsdsimp/ diff --git a/udpipe2/ud-2.6/conllu_split.pl b/udpipe2/ud-2.6/conllu_split.pl deleted file mode 100644 index 75746712235a4a7aecef6606681582a02f50bc18..0000000000000000000000000000000000000000 --- a/udpipe2/ud-2.6/conllu_split.pl +++ /dev/null @@ -1,42 +0,0 @@ -#!/usr/bin/perl -use warnings; -use strict; -use utf8; -use open qw(:std :utf8); - -use File::Copy; - -sub write_sentences { - my ($target_file, @sentences) = @_; - - open (my $f, ">", $target_file) or die "Cannot open file $target_file: $!"; - foreach my $sentence (@sentences) { - print $f "$sentence\n"; - } - close $f; -} - - -@ARGV >= 2 or die "Usage: $0 target_directory lang ) { - chomp; - if (/^$/) { - push @sentences, $sentence; - $sentence = ""; - } else { - $sentence .= $_ . "\n"; - } -} -die "Unfinished sentence" if $sentence; - -# Generate everything from train_file -my $train_end = int(@sentences * 90 / 100); -die "Zero sentences for dev_file" if $train_end == int(@sentences); - -write_sentences("$target/$lang-ud-train.conllu", @sentences[0..$train_end-1]); -write_sentences("$target/$lang-ud-dev.conllu", @sentences[$train_end..$#sentences]); diff --git a/udpipe2/ud-2.6/conllu_to_text.pl b/udpipe2/ud-2.6/conllu_to_text.pl deleted file mode 100644 index e6df8824ddca3744769cbbcd78a70c3797c0abb3..0000000000000000000000000000000000000000 --- a/udpipe2/ud-2.6/conllu_to_text.pl +++ /dev/null @@ -1,250 +0,0 @@ -#!/usr/bin/env perl -# Extracts raw text from CoNLL-U file. Uses newdoc and newpar tags when available. -# Copyright © 2017 Dan Zeman -# License: GNU GPL - -use utf8; -use open ':utf8'; -binmode(STDIN, ':utf8'); -binmode(STDOUT, ':utf8'); -binmode(STDERR, ':utf8'); -use Getopt::Long; - -# Language code 'zh' or 'ja' will trigger Chinese-like text formatting. -my $language = 'en'; -GetOptions -( - 'language=s' => \$language -); -my $chinese = $language =~ m/^(zh|ja)(_|$)/; - -my @sentence = (); -my $text = ''; # from the text attribute of the sentence -my $ftext = ''; # from the word forms of the tokens -my $newpar = 0; -my $newdoc = 0; -my $buffer = ''; -my $start = 1; -my $mwtlast; -while(<>) -{ - push(@sentence, $_); - if(m/^\#\s*text\s*=\s*(.+)/) - { - $text = $1; - } - elsif(m/^\#\s*newpar(\s|$)/i) - { - $newpar = 1; - } - elsif(m/^\#\s*newdoc(\s|$)/i) - { - $newdoc = 1; - } - elsif(m/^\d+-(\d+)\t/) - { - $mwtlast = $1; - my @f = split(/\t/, $_); - # Paragraphs may start in the middle of a sentence (bulleted lists, verse etc.) - # The first token of the new paragraph has "NewPar=Yes" in the MISC column. - # Multi-word tokens have this in the token-introducing line. - if($f[9] =~ m/NewPar=Yes/i) - { - # Empty line between documents and paragraphs. (There may have been - # a paragraph break before the first part of this sentence as well!) - $buffer = print_new_paragraph_if_needed($start, $newdoc, $newpar, $buffer); - $buffer .= $ftext; - # Line breaks at word boundaries after at most 80 characters. - $buffer = print_lines_from_buffer($buffer, 80, $chinese); - print("$buffer\n\n"); - $buffer = ''; - # Start is only true until we write the first sentence of the input stream. - $start = 0; - $newdoc = 0; - $newpar = 0; - $text = ''; - $ftext = ''; - } - $ftext .= $f[1]; - $ftext .= ' ' unless($f[9] =~ m/SpaceAfter=No/); - } - elsif(m/^(\d+)\t/ && !(defined($mwtlast) && $1<=$mwtlast)) - { - $mwtlast = undef; - my @f = split(/\t/, $_); - # Paragraphs may start in the middle of a sentence (bulleted lists, verse etc.) - # The first token of the new paragraph has "NewPar=Yes" in the MISC column. - # Multi-word tokens have this in the token-introducing line. - if($f[9] =~ m/NewPar=Yes/i) - { - # Empty line between documents and paragraphs. (There may have been - # a paragraph break before the first part of this sentence as well!) - $buffer = print_new_paragraph_if_needed($start, $newdoc, $newpar, $buffer); - $buffer .= $ftext; - # Line breaks at word boundaries after at most 80 characters. - $buffer = print_lines_from_buffer($buffer, 80, $chinese); - print("$buffer\n\n"); - $buffer = ''; - # Start is only true until we write the first sentence of the input stream. - $start = 0; - $newdoc = 0; - $newpar = 0; - $text = ''; - $ftext = ''; - } - $ftext .= $f[1]; - $ftext .= ' ' unless($f[9] =~ m/SpaceAfter=No/); - } - elsif(m/^\s*$/) - { - # In a valid CoNLL-U file, $text should be equal to $ftext except for the - # space after the last token. However, if there have been intra-sentential - # paragraph breaks, $ftext contains only the part after the last such - # break, and $text is empty. Hence we currently use $ftext everywhere - # and ignore $text, even though we note it when seeing the text attribute. - # $text .= ' ' unless($chinese); - # Empty line between documents and paragraphs. - $buffer = print_new_paragraph_if_needed($start, $newdoc, $newpar, $buffer); - $buffer .= $ftext; - # Line breaks at word boundaries after at most 80 characters. - $buffer = print_lines_from_buffer($buffer, 80, $chinese); - # Start is only true until we write the first sentence of the input stream. - $start = 0; - $newdoc = 0; - $newpar = 0; - $text = ''; - $ftext = ''; - $mwtlast = undef; - } -} -# There may be unflushed buffer contents after the last sentence, less than 80 characters -# (otherwise we would have already dealt with it), so just flush it. -if($buffer ne '') -{ - print("$buffer\n"); -} - - - -#------------------------------------------------------------------------------ -# Checks whether we have to print an extra line to separate paragraphs. Does it -# if necessary. Returns the updated buffer. -#------------------------------------------------------------------------------ -sub print_new_paragraph_if_needed -{ - my $start = shift; - my $newdoc = shift; - my $newpar = shift; - my $buffer = shift; - if(!$start && ($newdoc || $newpar)) - { - if($buffer ne '') - { - print("$buffer\n"); - $buffer = ''; - } - print("\n"); - } - return $buffer; -} - - - -#------------------------------------------------------------------------------ -# Prints as many complete lines of text as there are in the buffer. Returns the -# remaining contents of the buffer. -#------------------------------------------------------------------------------ -sub print_lines_from_buffer -{ - my $buffer = shift; - # Maximum number of characters allowed on one line, not counting the line - # break character(s), which also replace any number of trailing spaces. - # Exception: If there is a word longer than the limit, it will be printed - # on one line. - # Note that this algorithm is not suitable for Chinese and Japanese. - my $limit = shift; - # We need a different algorithm for Chinese and Japanese. - my $chinese = shift; - if($chinese) - { - return print_chinese_lines_from_buffer($buffer, $limit); - } - if(length($buffer) >= $limit) - { - my @cbuffer = split(//, $buffer); - # There may be more than one new line waiting in the buffer. - while(scalar(@cbuffer) >= $limit) - { - ###!!! We could make it simpler if we ignored multi-space sequences - ###!!! between words. It sounds OK to ignore them because at the - ###!!! line break we do not respect original spacing anyway. - my $i; - my $ilastspace; - for($i = 0; $i<=$#cbuffer; $i++) - { - if($i>$limit && defined($ilastspace)) - { - last; - } - if($cbuffer[$i] =~ m/\s/) - { - $ilastspace = $i; - } - } - if(defined($ilastspace) && $ilastspace>0) - { - my @out = @cbuffer[0..($ilastspace-1)]; - splice(@cbuffer, 0, $ilastspace+1); - print(join('', @out), "\n"); - } - else - { - print(join('', @cbuffer), "\n"); - splice(@cbuffer); - } - } - $buffer = join('', @cbuffer); - } - return $buffer; -} - - - -#------------------------------------------------------------------------------ -# Prints as many complete lines of text as there are in the buffer. Returns the -# remaining contents of the buffer. Assumes that there are no spaces between -# words and lines can be broken between any two characters, as is the custom in -# Chinese and Japanese. -#------------------------------------------------------------------------------ -sub print_chinese_lines_from_buffer -{ - my $buffer = shift; - # Maximum number of characters allowed on one line, not counting the line - # break character(s). - my $limit = shift; - # We cannot simply print the first $limit characters from the buffer, - # followed by a line break. There could be embedded Latin words or - # numbers and we do not want to insert a line break in the middle of - # a foreign word. - my @cbuffer = split(//, $buffer); - while(scalar(@cbuffer) >= $limit) - { - my $nprint = 0; - for(my $i = 0; $i <= $#cbuffer; $i++) - { - if($i > $limit && $nprint > 0) - { - last; - } - unless($i < $#cbuffer && $cbuffer[$i] =~ m/[\p{Latin}0-9]/ && $cbuffer[$i+1] =~ m/[\p{Latin}0-9]/) - { - $nprint = $i+1; - } - } - my @out = @cbuffer[0..($nprint-1)]; - splice(@cbuffer, 0, $nprint); - print(join('', @out), "\n"); - } - $buffer = join('', @cbuffer); - return $buffer; -} diff --git a/udpipe2/ud-2.6/get.sh b/udpipe2/ud-2.6/get.sh deleted file mode 100644 index 5f76bccd202a4c1f27cecd2b67f052e6cf9ca0da..0000000000000000000000000000000000000000 --- a/udpipe2/ud-2.6/get.sh +++ /dev/null @@ -1,41 +0,0 @@ -#!/bin/sh - -wget https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/1-3226/ud-treebanks-v2.6.tgz -tar xf ud-treebanks-v2.6.tgz -rm ud-treebanks-v2.6.tgz -for tb in ud-treebanks-v2.6/*/*-ud-train.conllu; do - dir=`dirname $tb` - long_name=`basename $dir` - code=`basename $tb` - code=${code%%-*} - - case $long_name in - UD_Arabic-NYUAD|UD_English-ESL|UD_English-GUMReddit|UD_French-FTB|UD_Hindi_English-HIENCS|UD_Japanese-BCCWJ) echo Skipping treebank $long_name without forms; continue;; - esac - - mkdir -p "$code" - cp "$dir"/* "$code" - if [ -f "$dir"/"$code"-ud-dev.conllu ]; then - cp "$dir"/"$code"-ud-train.conllu "$dir"/"$code"-ud-dev.conllu "$code" - else - perl conllu_split.pl "$code" "$code" <"$dir"/"$code"-ud-train.conllu - fi - - train_words=$(grep -c '^[0-9]' $code/*-ud-train.conllu) - [ "$train_words" -lt 1000 ] && { echo Skipping treebank $code with $train_words training words. >&2; rm -r "$code/"; continue; } - echo $code $long_name | tee -a iso_names.txt - - for conllu in "$code"/*.conllu; do - sed "1i# variant = $code" -i "$conllu" - case "$conllu" in - *train.conllu) rm "${conllu%.conllu}.txt";; - *) perl conllu_to_text.pl --language="$code" <"$conllu" >"${conllu%.conllu}.txt";; - esac - done -done -rm -r ud-treebanks-v2.6 - -for code in $(for d in */; do echo ${d%%[_/]*}; done | sort | uniq -d); do - mkdir "${code}_all" - cat "$code"*/*train.conllu >"${code}_all/${code}_all-ud-train.conllu" -done diff --git a/udpipe2/ud-2.6/iso_names.txt b/udpipe2/ud-2.6/iso_names.txt deleted file mode 100644 index 05438935869da6916fa2d76afa1d43701dfee827..0000000000000000000000000000000000000000 --- a/udpipe2/ud-2.6/iso_names.txt +++ /dev/null @@ -1,99 +0,0 @@ -af_afribooms UD_Afrikaans-AfriBooms -grc_proiel UD_Ancient_Greek-PROIEL -grc_perseus UD_Ancient_Greek-Perseus -ar_padt UD_Arabic-PADT -hy_armtdp UD_Armenian-ArmTDP -eu_bdt UD_Basque-BDT -be_hse UD_Belarusian-HSE -bg_btb UD_Bulgarian-BTB -ca_ancora UD_Catalan-AnCora -zh_gsd UD_Chinese-GSD -zh_gsdsimp UD_Chinese-GSDSimp -lzh_kyoto UD_Classical_Chinese-Kyoto -cop_scriptorium UD_Coptic-Scriptorium -hr_set UD_Croatian-SET -cs_cac UD_Czech-CAC -cs_cltt UD_Czech-CLTT -cs_fictree UD_Czech-FicTree -cs_pdt UD_Czech-PDT -da_ddt UD_Danish-DDT -nl_alpino UD_Dutch-Alpino -nl_lassysmall UD_Dutch-LassySmall -en_ewt UD_English-EWT -en_gum UD_English-GUM -en_lines UD_English-LinES -en_partut UD_English-ParTUT -et_edt UD_Estonian-EDT -et_ewt UD_Estonian-EWT -fi_ftb UD_Finnish-FTB -fi_tdt UD_Finnish-TDT -fr_gsd UD_French-GSD -fr_partut UD_French-ParTUT -fr_sequoia UD_French-Sequoia -fr_spoken UD_French-Spoken -gl_ctg UD_Galician-CTG -gl_treegal UD_Galician-TreeGal -de_gsd UD_German-GSD -de_hdt UD_German-HDT -got_proiel UD_Gothic-PROIEL -el_gdt UD_Greek-GDT -he_htb UD_Hebrew-HTB -hi_hdtb UD_Hindi-HDTB -hu_szeged UD_Hungarian-Szeged -id_gsd UD_Indonesian-GSD -ga_idt UD_Irish-IDT -it_isdt UD_Italian-ISDT -it_partut UD_Italian-ParTUT -it_postwita UD_Italian-PoSTWITA -it_twittiro UD_Italian-TWITTIRO -it_vit UD_Italian-VIT -ja_gsd UD_Japanese-GSD -ko_gsd UD_Korean-GSD -ko_kaist UD_Korean-Kaist -la_ittb UD_Latin-ITTB -la_llct UD_Latin-LLCT -la_proiel UD_Latin-PROIEL -la_perseus UD_Latin-Perseus -lv_lvtb UD_Latvian-LVTB -lt_alksnis UD_Lithuanian-ALKSNIS -lt_hse UD_Lithuanian-HSE -mt_mudt UD_Maltese-MUDT -mr_ufal UD_Marathi-UFAL -pcm_nsc UD_Naija-NSC -sme_giella UD_North_Sami-Giella -no_bokmaal UD_Norwegian-Bokmaal -no_nynorsk UD_Norwegian-Nynorsk -no_nynorsklia UD_Norwegian-NynorskLIA -cu_proiel UD_Old_Church_Slavonic-PROIEL -fro_srcmf UD_Old_French-SRCMF -orv_rnc UD_Old_Russian-RNC -orv_torot UD_Old_Russian-TOROT -fa_seraji UD_Persian-Seraji -pl_lfg UD_Polish-LFG -pl_pdb UD_Polish-PDB -pt_bosque UD_Portuguese-Bosque -pt_gsd UD_Portuguese-GSD -ro_nonstandard UD_Romanian-Nonstandard -ro_rrt UD_Romanian-RRT -ru_gsd UD_Russian-GSD -ru_syntagrus UD_Russian-SynTagRus -ru_taiga UD_Russian-Taiga -sa_vedic UD_Sanskrit-Vedic -gd_arcosg UD_Scottish_Gaelic-ARCOSG -sr_set UD_Serbian-SET -sk_snk UD_Slovak-SNK -sl_ssj UD_Slovenian-SSJ -sl_sst UD_Slovenian-SST -es_ancora UD_Spanish-AnCora -es_gsd UD_Spanish-GSD -sv_lines UD_Swedish-LinES -sv_talbanken UD_Swedish-Talbanken -ta_ttb UD_Tamil-TTB -te_mtg UD_Telugu-MTG -tr_imst UD_Turkish-IMST -uk_iu UD_Ukrainian-IU -ur_udtb UD_Urdu-UDTB -ug_udt UD_Uyghur-UDT -vi_vtb UD_Vietnamese-VTB -cy_ccg UD_Welsh-CCG -wo_wtb UD_Wolof-WTB diff --git a/udpipe2/ud-2.6/langs_sizes b/udpipe2/ud-2.6/langs_sizes deleted file mode 100644 index 48e34458d86d03caaa113cb858598ab55f8af25e..0000000000000000000000000000000000000000 --- a/udpipe2/ud-2.6/langs_sizes +++ /dev/null @@ -1,123 +0,0 @@ -de_all 3017431 -de_hdt 2753627 -cs_all 1806270 -cs_pdt 1173282 -ru_all 988942 -ru_syntagrus 870479 -es_all 827052 -la_all 675968 -it_all 672929 -la_evalatin20 607158 -ro_all 595517 -fr_all 580582 -no_all 524423 -cs_cac 472609 -pt_all 462907 -es_ancora 444616 -ca_ancora 417587 -ro_nonstandard 410404 -en_all 387318 -pl_all 386486 -es_gsd 382436 -et_all 366441 -fr_gsd 354659 -ko_all 353133 -grc_all 346928 -et_edt 344646 -ko_kaist 296446 -la_ittb 293305 -fi_all 290418 -pl_pdb 281736 -hi_hdtb 281057 -it_isdt 276018 -de_gsd 263804 -nl_all 260963 -pt_gsd 256135 -no_nynorsk 245330 -no_bokmaal 243886 -it_vit 224901 -ar_padt 223881 -pt_bosque 206772 -en_ewt 204585 -zh_all 197232 -la_llct 194143 -grc_proiel 187033 -nl_alpino 185883 -ro_rrt 185113 -la_proiel 172133 -lv_lvtb 167594 -ja_gsd 167482 -fi_tdt 162816 -grc_perseus 159895 -hr_set 152857 -he_htb 137721 -fro_srcmf 136020 -cs_fictree 133637 -sl_all 129817 -orv_all 128181 -fi_ftb 127602 -bg_btb 124336 -sv_all 122096 -fa_seraji 121064 -orv_torot 118630 -sl_ssj 112530 -lzh_kyoto 110058 -ur_udtb 108690 -pl_lfg 104750 -pcm_nsc 101351 -it_postwita 99442 -zh_gsdsimp 98616 -zh_gsd 98616 -id_gsd 97531 -gl_all 93273 -uk_iu 92401 -en_gum 81857 -sk_snk 80575 -da_ddt 80378 -gl_ctg 79327 -nl_lassysmall 75080 -ru_gsd 74906 -sr_set 74259 -eu_bdt 72974 -sv_talbanken 66645 -en_lines 57372 -ko_gsd 56687 -sv_lines 55451 -lt_all 50851 -fr_sequoia 50517 -it_partut 48934 -lt_alksnis 47641 -ga_idt 44636 -ru_taiga 43557 -en_partut 43504 -el_gdt 42326 -hy_armtdp 42105 -tr_imst 37784 -cu_proiel 37432 -no_nynorsklia 35207 -got_proiel 35024 -af_afribooms 33894 -cs_cltt 26742 -fr_partut 24122 -it_twittiro 23634 -wo_wtb 23561 -gd_arcosg 22954 -mt_mudt 22880 -et_ewt 21795 -cop_scriptorium 21484 -vi_vtb 20285 -hu_szeged 20166 -ug_udt 19262 -sl_sst 17287 -la_perseus 16387 -sa_vedic 15671 -fr_spoken 15264 -sme_giella 14254 -gl_treegal 13946 -cy_ccg 12919 -orv_rnc 9551 -be_hse 6383 -ta_ttb 6329 -te_mtg 5082 -lt_hse 3210 -mr_ufal 2997 diff --git a/udpipe2/ud-2.6/langs_sizes.sh b/udpipe2/ud-2.6/langs_sizes.sh deleted file mode 100644 index 5ee811aa7d06f75bc74c374307e4dfd4b2baaa89..0000000000000000000000000000000000000000 --- a/udpipe2/ud-2.6/langs_sizes.sh +++ /dev/null @@ -1,6 +0,0 @@ -#!/bin/sh - -for d in */; do - echo $d >&2 - echo ${d%/} $(grep -cP "^\d+\t" $d*train.conllu) -done | sort -rnk2 >langs_sizes diff --git a/udpipe2/ud-2.9/.gitignore b/udpipe2/ud-2.9/.gitignore deleted file mode 100644 index 4cb222367d70dc2bb002676ded89d70f7784f836..0000000000000000000000000000000000000000 --- a/udpipe2/ud-2.9/.gitignore +++ /dev/null @@ -1,145 +0,0 @@ -af_afribooms/ -ar_padt/ -be_hse/ -bg_btb/ -ca_ancora/ -cop_scriptorium/ -cs_all/ -cs_cac/ -cs_cltt/ -cs_fictree/ -cs_pdt/ -cu_proiel/ -cy_ccg/ -da_ddt/ -de_all/ -de_gsd/ -de_hdt/ -el_gdt/ -en_all/ -en_atis/ -en_ewt/ -en_gum/ -en_lines/ -en_partut/ -es_all/ -es_ancora/ -es_gsd/ -et_all/ -et_edt/ -et_ewt/ -eu_bdt/ -fa_all/ -fa_perdt/ -fa_seraji/ -fi_all/ -fi_ftb/ -fi_tdt/ -fo_farpahc/ -fr_all/ -fr_gsd/ -fr_parisstories/ -fr_partut/ -fr_rhapsodie/ -fr_sequoia/ -fro_srcmf/ -ga_idt/ -gd_arcosg/ -gl_all/ -gl_ctg/ -gl_treegal/ -got_proiel/ -grc_all/ -grc_perseus/ -grc_proiel/ -he_htb/ -hi_hdtb/ -hr_set/ -hu_szeged/ -hy_armtdp/ -hyw_armtdp/ -id_all/ -id_csui/ -id_gsd/ -is_all/ -is_icepahc/ -is_modern/ -it_all/ -it_isdt/ -it_partut/ -it_postwita/ -it_twittiro/ -it_vit/ -ja_all/ -ja_gsd/ -ja_gsdluw/ -ko_all/ -ko_gsd/ -ko_kaist/ -la_all/ -la_ittb/ -la_llct/ -la_perseus/ -la_proiel/ -la_udante/ -lt_alksnis/ -lt_all/ -lt_hse/ -lv_lvtb/ -lzh_kyoto/ -mr_ufal/ -mt_mudt/ -nl_all/ -nl_alpino/ -nl_lassysmall/ -no_all/ -no_bokmaal/ -no_nynorsk/ -no_nynorsklia/ -orv_all/ -orv_rnc/ -orv_torot/ -pcm_nsc/ -pl_all/ -pl_lfg/ -pl_pdb/ -pt_all/ -pt_bosque/ -pt_gsd/ -qtd_sagt/ -ro_all/ -ro_nonstandard/ -ro_rrt/ -ro_simonero/ -ru_all/ -ru_gsd/ -ru_syntagrus/ -ru_taiga/ -sa_vedic/ -sk_snk/ -sl_all/ -sl_ssj/ -sl_sst/ -sme_giella/ -sr_set/ -sv_all/ -sv_lines/ -sv_talbanken/ -ta_ttb/ -te_mtg/ -tr_all/ -tr_atis/ -tr_boun/ -tr_framenet/ -tr_imst/ -tr_kenet/ -tr_penn/ -tr_tourism/ -ug_udt/ -uk_iu/ -ur_udtb/ -vi_vtb/ -wo_wtb/ -zh_all/ -zh_gsd/ -zh_gsdsimp/ diff --git a/udpipe2/ud-2.9/conllu_split.pl b/udpipe2/ud-2.9/conllu_split.pl deleted file mode 100644 index 75746712235a4a7aecef6606681582a02f50bc18..0000000000000000000000000000000000000000 --- a/udpipe2/ud-2.9/conllu_split.pl +++ /dev/null @@ -1,42 +0,0 @@ -#!/usr/bin/perl -use warnings; -use strict; -use utf8; -use open qw(:std :utf8); - -use File::Copy; - -sub write_sentences { - my ($target_file, @sentences) = @_; - - open (my $f, ">", $target_file) or die "Cannot open file $target_file: $!"; - foreach my $sentence (@sentences) { - print $f "$sentence\n"; - } - close $f; -} - - -@ARGV >= 2 or die "Usage: $0 target_directory lang ) { - chomp; - if (/^$/) { - push @sentences, $sentence; - $sentence = ""; - } else { - $sentence .= $_ . "\n"; - } -} -die "Unfinished sentence" if $sentence; - -# Generate everything from train_file -my $train_end = int(@sentences * 90 / 100); -die "Zero sentences for dev_file" if $train_end == int(@sentences); - -write_sentences("$target/$lang-ud-train.conllu", @sentences[0..$train_end-1]); -write_sentences("$target/$lang-ud-dev.conllu", @sentences[$train_end..$#sentences]); diff --git a/udpipe2/ud-2.9/conllu_to_text.pl b/udpipe2/ud-2.9/conllu_to_text.pl deleted file mode 100644 index e6df8824ddca3744769cbbcd78a70c3797c0abb3..0000000000000000000000000000000000000000 --- a/udpipe2/ud-2.9/conllu_to_text.pl +++ /dev/null @@ -1,250 +0,0 @@ -#!/usr/bin/env perl -# Extracts raw text from CoNLL-U file. Uses newdoc and newpar tags when available. -# Copyright © 2017 Dan Zeman -# License: GNU GPL - -use utf8; -use open ':utf8'; -binmode(STDIN, ':utf8'); -binmode(STDOUT, ':utf8'); -binmode(STDERR, ':utf8'); -use Getopt::Long; - -# Language code 'zh' or 'ja' will trigger Chinese-like text formatting. -my $language = 'en'; -GetOptions -( - 'language=s' => \$language -); -my $chinese = $language =~ m/^(zh|ja)(_|$)/; - -my @sentence = (); -my $text = ''; # from the text attribute of the sentence -my $ftext = ''; # from the word forms of the tokens -my $newpar = 0; -my $newdoc = 0; -my $buffer = ''; -my $start = 1; -my $mwtlast; -while(<>) -{ - push(@sentence, $_); - if(m/^\#\s*text\s*=\s*(.+)/) - { - $text = $1; - } - elsif(m/^\#\s*newpar(\s|$)/i) - { - $newpar = 1; - } - elsif(m/^\#\s*newdoc(\s|$)/i) - { - $newdoc = 1; - } - elsif(m/^\d+-(\d+)\t/) - { - $mwtlast = $1; - my @f = split(/\t/, $_); - # Paragraphs may start in the middle of a sentence (bulleted lists, verse etc.) - # The first token of the new paragraph has "NewPar=Yes" in the MISC column. - # Multi-word tokens have this in the token-introducing line. - if($f[9] =~ m/NewPar=Yes/i) - { - # Empty line between documents and paragraphs. (There may have been - # a paragraph break before the first part of this sentence as well!) - $buffer = print_new_paragraph_if_needed($start, $newdoc, $newpar, $buffer); - $buffer .= $ftext; - # Line breaks at word boundaries after at most 80 characters. - $buffer = print_lines_from_buffer($buffer, 80, $chinese); - print("$buffer\n\n"); - $buffer = ''; - # Start is only true until we write the first sentence of the input stream. - $start = 0; - $newdoc = 0; - $newpar = 0; - $text = ''; - $ftext = ''; - } - $ftext .= $f[1]; - $ftext .= ' ' unless($f[9] =~ m/SpaceAfter=No/); - } - elsif(m/^(\d+)\t/ && !(defined($mwtlast) && $1<=$mwtlast)) - { - $mwtlast = undef; - my @f = split(/\t/, $_); - # Paragraphs may start in the middle of a sentence (bulleted lists, verse etc.) - # The first token of the new paragraph has "NewPar=Yes" in the MISC column. - # Multi-word tokens have this in the token-introducing line. - if($f[9] =~ m/NewPar=Yes/i) - { - # Empty line between documents and paragraphs. (There may have been - # a paragraph break before the first part of this sentence as well!) - $buffer = print_new_paragraph_if_needed($start, $newdoc, $newpar, $buffer); - $buffer .= $ftext; - # Line breaks at word boundaries after at most 80 characters. - $buffer = print_lines_from_buffer($buffer, 80, $chinese); - print("$buffer\n\n"); - $buffer = ''; - # Start is only true until we write the first sentence of the input stream. - $start = 0; - $newdoc = 0; - $newpar = 0; - $text = ''; - $ftext = ''; - } - $ftext .= $f[1]; - $ftext .= ' ' unless($f[9] =~ m/SpaceAfter=No/); - } - elsif(m/^\s*$/) - { - # In a valid CoNLL-U file, $text should be equal to $ftext except for the - # space after the last token. However, if there have been intra-sentential - # paragraph breaks, $ftext contains only the part after the last such - # break, and $text is empty. Hence we currently use $ftext everywhere - # and ignore $text, even though we note it when seeing the text attribute. - # $text .= ' ' unless($chinese); - # Empty line between documents and paragraphs. - $buffer = print_new_paragraph_if_needed($start, $newdoc, $newpar, $buffer); - $buffer .= $ftext; - # Line breaks at word boundaries after at most 80 characters. - $buffer = print_lines_from_buffer($buffer, 80, $chinese); - # Start is only true until we write the first sentence of the input stream. - $start = 0; - $newdoc = 0; - $newpar = 0; - $text = ''; - $ftext = ''; - $mwtlast = undef; - } -} -# There may be unflushed buffer contents after the last sentence, less than 80 characters -# (otherwise we would have already dealt with it), so just flush it. -if($buffer ne '') -{ - print("$buffer\n"); -} - - - -#------------------------------------------------------------------------------ -# Checks whether we have to print an extra line to separate paragraphs. Does it -# if necessary. Returns the updated buffer. -#------------------------------------------------------------------------------ -sub print_new_paragraph_if_needed -{ - my $start = shift; - my $newdoc = shift; - my $newpar = shift; - my $buffer = shift; - if(!$start && ($newdoc || $newpar)) - { - if($buffer ne '') - { - print("$buffer\n"); - $buffer = ''; - } - print("\n"); - } - return $buffer; -} - - - -#------------------------------------------------------------------------------ -# Prints as many complete lines of text as there are in the buffer. Returns the -# remaining contents of the buffer. -#------------------------------------------------------------------------------ -sub print_lines_from_buffer -{ - my $buffer = shift; - # Maximum number of characters allowed on one line, not counting the line - # break character(s), which also replace any number of trailing spaces. - # Exception: If there is a word longer than the limit, it will be printed - # on one line. - # Note that this algorithm is not suitable for Chinese and Japanese. - my $limit = shift; - # We need a different algorithm for Chinese and Japanese. - my $chinese = shift; - if($chinese) - { - return print_chinese_lines_from_buffer($buffer, $limit); - } - if(length($buffer) >= $limit) - { - my @cbuffer = split(//, $buffer); - # There may be more than one new line waiting in the buffer. - while(scalar(@cbuffer) >= $limit) - { - ###!!! We could make it simpler if we ignored multi-space sequences - ###!!! between words. It sounds OK to ignore them because at the - ###!!! line break we do not respect original spacing anyway. - my $i; - my $ilastspace; - for($i = 0; $i<=$#cbuffer; $i++) - { - if($i>$limit && defined($ilastspace)) - { - last; - } - if($cbuffer[$i] =~ m/\s/) - { - $ilastspace = $i; - } - } - if(defined($ilastspace) && $ilastspace>0) - { - my @out = @cbuffer[0..($ilastspace-1)]; - splice(@cbuffer, 0, $ilastspace+1); - print(join('', @out), "\n"); - } - else - { - print(join('', @cbuffer), "\n"); - splice(@cbuffer); - } - } - $buffer = join('', @cbuffer); - } - return $buffer; -} - - - -#------------------------------------------------------------------------------ -# Prints as many complete lines of text as there are in the buffer. Returns the -# remaining contents of the buffer. Assumes that there are no spaces between -# words and lines can be broken between any two characters, as is the custom in -# Chinese and Japanese. -#------------------------------------------------------------------------------ -sub print_chinese_lines_from_buffer -{ - my $buffer = shift; - # Maximum number of characters allowed on one line, not counting the line - # break character(s). - my $limit = shift; - # We cannot simply print the first $limit characters from the buffer, - # followed by a line break. There could be embedded Latin words or - # numbers and we do not want to insert a line break in the middle of - # a foreign word. - my @cbuffer = split(//, $buffer); - while(scalar(@cbuffer) >= $limit) - { - my $nprint = 0; - for(my $i = 0; $i <= $#cbuffer; $i++) - { - if($i > $limit && $nprint > 0) - { - last; - } - unless($i < $#cbuffer && $cbuffer[$i] =~ m/[\p{Latin}0-9]/ && $cbuffer[$i+1] =~ m/[\p{Latin}0-9]/) - { - $nprint = $i+1; - } - } - my @out = @cbuffer[0..($nprint-1)]; - splice(@cbuffer, 0, $nprint); - print(join('', @out), "\n"); - } - $buffer = join('', @cbuffer); - return $buffer; -} diff --git a/udpipe2/ud-2.9/get.sh b/udpipe2/ud-2.9/get.sh deleted file mode 100644 index 47193626ee83be3f30f9db2585a7988e3bb1aca5..0000000000000000000000000000000000000000 --- a/udpipe2/ud-2.9/get.sh +++ /dev/null @@ -1,41 +0,0 @@ -#!/bin/sh - -wget https://lindat.mff.cuni.cz/repository/xmlui/bitstream/handle/11234/1-4611/ud-treebanks-v2.9.tgz -tar xf ud-treebanks-v2.9.tgz -rm ud-treebanks-v2.9.tgz -for tb in ud-treebanks-v2.9/*/*-ud-train.conllu; do - dir=`dirname $tb` - long_name=`basename $dir` - code=`basename $tb` - code=${code%%-*} - - case $long_name in - UD_Arabic-NYUAD|UD_English-ESL|UD_English-GUMReddit|UD_French-FTB|UD_Hindi_English-HIENCS|UD_Japanese-BCCWJ|UD_Japanese-BCCWJLUW) echo Skipping treebank $long_name without forms; continue;; - esac - - mkdir -p "$code" - cp "$dir"/* "$code" - if [ -f "$dir"/"$code"-ud-dev.conllu ]; then - cp "$dir"/"$code"-ud-train.conllu "$dir"/"$code"-ud-dev.conllu "$code" - else - perl conllu_split.pl "$code" "$code" <"$dir"/"$code"-ud-train.conllu - fi - - train_words=$(grep -c '^[0-9]' $code/*-ud-train.conllu) - [ "$train_words" -lt 1000 ] && { echo Skipping treebank $code with $train_words training words. >&2; rm -r "$code/"; continue; } - echo $code $long_name | tee -a iso_names.txt - - for conllu in "$code"/*.conllu; do - sed "1i# variant = $code" -i "$conllu" - case "$conllu" in - *train.conllu) rm "${conllu%.conllu}.txt";; - *) perl conllu_to_text.pl --language="$code" <"$conllu" >"${conllu%.conllu}.txt";; - esac - done -done -rm -r ud-treebanks-v2.9 - -for code in $(for d in */; do echo ${d%%[_/]*}; done | sort | uniq -d); do - mkdir "${code}_all" - cat "$code"*/*train.conllu >"${code}_all/${code}_all-ud-train.conllu" -done diff --git a/udpipe2/ud-2.9/iso_names.txt b/udpipe2/ud-2.9/iso_names.txt deleted file mode 100644 index b9e2df072e560c154b83edf67676bcfc16c66fc5..0000000000000000000000000000000000000000 --- a/udpipe2/ud-2.9/iso_names.txt +++ /dev/null @@ -1,117 +0,0 @@ -af_afribooms UD_Afrikaans-AfriBooms -grc_proiel UD_Ancient_Greek-PROIEL -grc_perseus UD_Ancient_Greek-Perseus -ar_padt UD_Arabic-PADT -hy_armtdp UD_Armenian-ArmTDP -eu_bdt UD_Basque-BDT -be_hse UD_Belarusian-HSE -bg_btb UD_Bulgarian-BTB -ca_ancora UD_Catalan-AnCora -zh_gsd UD_Chinese-GSD -zh_gsdsimp UD_Chinese-GSDSimp -lzh_kyoto UD_Classical_Chinese-Kyoto -cop_scriptorium UD_Coptic-Scriptorium -hr_set UD_Croatian-SET -cs_cac UD_Czech-CAC -cs_cltt UD_Czech-CLTT -cs_fictree UD_Czech-FicTree -cs_pdt UD_Czech-PDT -da_ddt UD_Danish-DDT -nl_alpino UD_Dutch-Alpino -nl_lassysmall UD_Dutch-LassySmall -en_atis UD_English-Atis -en_ewt UD_English-EWT -en_gum UD_English-GUM -en_lines UD_English-LinES -en_partut UD_English-ParTUT -et_edt UD_Estonian-EDT -et_ewt UD_Estonian-EWT -fo_farpahc UD_Faroese-FarPaHC -fi_ftb UD_Finnish-FTB -fi_tdt UD_Finnish-TDT -fr_gsd UD_French-GSD -fr_partut UD_French-ParTUT -fr_parisstories UD_French-ParisStories -fr_rhapsodie UD_French-Rhapsodie -fr_sequoia UD_French-Sequoia -gl_ctg UD_Galician-CTG -gl_treegal UD_Galician-TreeGal -de_gsd UD_German-GSD -de_hdt UD_German-HDT -got_proiel UD_Gothic-PROIEL -el_gdt UD_Greek-GDT -he_htb UD_Hebrew-HTB -hi_hdtb UD_Hindi-HDTB -hu_szeged UD_Hungarian-Szeged -is_icepahc UD_Icelandic-IcePaHC -is_modern UD_Icelandic-Modern -id_csui UD_Indonesian-CSUI -id_gsd UD_Indonesian-GSD -ga_idt UD_Irish-IDT -it_isdt UD_Italian-ISDT -it_partut UD_Italian-ParTUT -it_postwita UD_Italian-PoSTWITA -it_twittiro UD_Italian-TWITTIRO -it_vit UD_Italian-VIT -ja_gsd UD_Japanese-GSD -ja_gsdluw UD_Japanese-GSDLUW -ko_gsd UD_Korean-GSD -ko_kaist UD_Korean-Kaist -la_ittb UD_Latin-ITTB -la_llct UD_Latin-LLCT -la_proiel UD_Latin-PROIEL -la_perseus UD_Latin-Perseus -la_udante UD_Latin-UDante -lv_lvtb UD_Latvian-LVTB -lt_alksnis UD_Lithuanian-ALKSNIS -lt_hse UD_Lithuanian-HSE -mt_mudt UD_Maltese-MUDT -mr_ufal UD_Marathi-UFAL -pcm_nsc UD_Naija-NSC -sme_giella UD_North_Sami-Giella -no_bokmaal UD_Norwegian-Bokmaal -no_nynorsk UD_Norwegian-Nynorsk -no_nynorsklia UD_Norwegian-NynorskLIA -cu_proiel UD_Old_Church_Slavonic-PROIEL -orv_rnc UD_Old_East_Slavic-RNC -orv_torot UD_Old_East_Slavic-TOROT -fro_srcmf UD_Old_French-SRCMF -fa_perdt UD_Persian-PerDT -fa_seraji UD_Persian-Seraji -pl_lfg UD_Polish-LFG -pl_pdb UD_Polish-PDB -pt_bosque UD_Portuguese-Bosque -pt_gsd UD_Portuguese-GSD -ro_nonstandard UD_Romanian-Nonstandard -ro_rrt UD_Romanian-RRT -ro_simonero UD_Romanian-SiMoNERo -ru_gsd UD_Russian-GSD -ru_syntagrus UD_Russian-SynTagRus -ru_taiga UD_Russian-Taiga -sa_vedic UD_Sanskrit-Vedic -gd_arcosg UD_Scottish_Gaelic-ARCOSG -sr_set UD_Serbian-SET -sk_snk UD_Slovak-SNK -sl_ssj UD_Slovenian-SSJ -sl_sst UD_Slovenian-SST -es_ancora UD_Spanish-AnCora -es_gsd UD_Spanish-GSD -sv_lines UD_Swedish-LinES -sv_talbanken UD_Swedish-Talbanken -ta_ttb UD_Tamil-TTB -te_mtg UD_Telugu-MTG -tr_atis UD_Turkish-Atis -tr_boun UD_Turkish-BOUN -tr_framenet UD_Turkish-FrameNet -tr_imst UD_Turkish-IMST -tr_kenet UD_Turkish-Kenet -tr_penn UD_Turkish-Penn -tr_tourism UD_Turkish-Tourism -qtd_sagt UD_Turkish_German-SAGT -uk_iu UD_Ukrainian-IU -ur_udtb UD_Urdu-UDTB -ug_udt UD_Uyghur-UDT -vi_vtb UD_Vietnamese-VTB -cy_ccg UD_Welsh-CCG -hyw_armtdp UD_Western_Armenian-ArmTDP -wo_wtb UD_Wolof-WTB diff --git a/udpipe2/ud-2.9/langs_sizes b/udpipe2/ud-2.9/langs_sizes deleted file mode 100644 index 368f5e0a4951ec2b7d415793d543fe17a43eb3c4..0000000000000000000000000000000000000000 --- a/udpipe2/ud-2.9/langs_sizes +++ /dev/null @@ -1,145 +0,0 @@ -de_all 3062873 -de_hdt 2799069 -cs_all 1807289 -ru_all 1456177 -ru_syntagrus 1204640 -cs_pdt 1173282 -es_all 835174 -ro_all 834851 -is_all 829620 -la_all 803884 -is_icepahc 705754 -it_all 673206 -fr_all 624578 -fa_all 573560 -tr_all 569569 -ro_nonstandard 532881 -no_all 524423 -cs_cac 472609 -en_all 457510 -es_ancora 452738 -fa_perdt 452496 -ca_ancora 429141 -pt_all 427089 -et_all 394249 -la_ittb 390785 -pl_all 386486 -es_gsd 382436 -fr_gsd 354505 -ko_all 353133 -grc_all 346928 -et_edt 344646 -ja_all 298631 -ko_kaist 296446 -fi_all 290418 -pl_pdb 281736 -hi_hdtb 281057 -it_isdt 276018 -be_hse 273171 -de_gsd 263804 -nl_all 261160 -pt_gsd 255312 -no_nynorsk 245330 -no_bokmaal 243886 -lzh_kyoto 231701 -it_vit 225083 -ar_padt 223881 -en_ewt 204579 -lv_lvtb 201596 -zh_all 197232 -la_llct 194143 -grc_proiel 187033 -nl_alpino 186026 -ro_rrt 185113 -ru_taiga 176631 -la_proiel 172133 -pt_bosque 171777 -ja_gsd 168333 -tr_penn 166513 -fi_tdt 162816 -grc_perseus 159895 -fro_srcmf 158620 -hr_set 152857 -tr_kenet 143287 -he_htb 137717 -cs_fictree 133637 -orv_all 131692 -ja_gsdluw 130298 -sl_all 129817 -fi_ftb 127602 -bg_btb 124336 -is_modern 123866 -sv_all 122097 -fa_seraji 121064 -orv_torot 118630 -ro_simonero 116857 -id_all 113959 -sl_ssj 112530 -pcm_nsc 111878 -ur_udtb 108690 -pl_lfg 104750 -en_gum 103400 -it_postwita 99537 -zh_gsdsimp 98616 -zh_gsd 98616 -tr_boun 98214 -id_gsd 97604 -ga_idt 95881 -gl_all 93273 -uk_iu 92401 -sk_snk 80628 -da_ddt 80378 -gl_ctg 79327 -nl_lassysmall 75134 -ru_gsd 74906 -sr_set 74259 -eu_bdt 72974 -hyw_armtdp 72089 -tr_tourism 71238 -sv_talbanken 66646 -gd_arcosg 63702 -en_lines 57372 -ko_gsd 56687 -sv_lines 55451 -lt_all 50851 -fr_sequoia 50505 -et_ewt 49603 -it_partut 48934 -en_atis 48655 -lt_alksnis 47641 -en_partut 43504 -el_gdt 42326 -hy_armtdp 42105 -tr_imst 37784 -cu_proiel 37432 -tr_atis 36200 -no_nynorsklia 35207 -got_proiel 35024 -af_afribooms 33880 -cop_scriptorium 30836 -la_udante 30436 -cs_cltt 27761 -fr_partut 24122 -it_twittiro 23634 -wo_wtb 23561 -fo_farpahc 23094 -mt_mudt 22880 -vi_vtb 20285 -hu_szeged 20166 -ug_udt 19262 -fr_rhapsodie 19144 -fr_parisstories 17682 -sl_sst 17287 -la_perseus 16387 -id_csui 16355 -tr_framenet 16333 -sa_vedic 15671 -cy_ccg 15007 -sme_giella 14254 -gl_treegal 13946 -orv_rnc 13062 -qtd_sagt 10081 -ta_ttb 6329 -te_mtg 5082 -lt_hse 3210 -mr_ufal 2997 diff --git a/udpipe2/ud-2.9/langs_sizes.sh b/udpipe2/ud-2.9/langs_sizes.sh deleted file mode 100644 index 5dfcd9a049bb589d5e647cf719701c894e556142..0000000000000000000000000000000000000000 --- a/udpipe2/ud-2.9/langs_sizes.sh +++ /dev/null @@ -1,6 +0,0 @@ -#!/bin/sh - -for d in */; do - echo $d >&2 - echo "${d%/} $(grep -cP "^\d+\t" $d*train.conllu)" -done | sort -rnk2 >langs_sizes diff --git a/udpipe2/ud-evalatin20/.gitignore b/udpipe2/ud-evalatin20/.gitignore deleted file mode 100644 index e1d893e2b0697ad6efce248be8431757150744ed..0000000000000000000000000000000000000000 --- a/udpipe2/ud-evalatin20/.gitignore +++ /dev/null @@ -1 +0,0 @@ -la_evalatin20/ diff --git a/udpipe2/ud-evalatin20/get_evalatin20.sh b/udpipe2/ud-evalatin20/get_evalatin20.sh deleted file mode 100644 index 264a4c5b8fa092621cab27243a24ee442abf80df..0000000000000000000000000000000000000000 --- a/udpipe2/ud-evalatin20/get_evalatin20.sh +++ /dev/null @@ -1,44 +0,0 @@ -#!/bin/sh - -# Add EvaLatin tagger-only data -mkdir -p la_evalatin20 -git -C la_evalatin20 clone --depth=1 https://github.com/CIRCSE/LT4HALA - -echo "# variant = la_evalatin20" >la_evalatin20/la_evalatin20-train.conllu -echo "# variant = la_evalatin20" >la_evalatin20/la_evalatin20-dev.conllu - -python3 get_evalatin20_split.py la_evalatin20/LT4HALA/data_and_doc/training_data_10-12-2019/*.conllu \ - --train=la_evalatin20/la_evalatin20-train.conllu --dev=la_evalatin20/la_evalatin20-dev.conllu - -echo "# variant = la_ittb" >>la_evalatin20/la_evalatin20-train.conllu -python3 get_evalatin20_split.py ../ud-2.6/la_ittb/la_ittb-ud-train.conllu --ratio 0.6 --train=la_evalatin20/la_evalatin20-train.conllu --dev=/dev/null - -echo "# variant = la_perseus" >>la_evalatin20/la_evalatin20-train.conllu -python3 get_evalatin20_split.py ../ud-2.6/la_perseus/la_perseus-ud-train.conllu --ratio 1 --train=la_evalatin20/la_evalatin20-train.conllu --dev=/dev/null - -echo "# variant = la_proiel" >>la_evalatin20/la_evalatin20-train.conllu -python3 get_evalatin20_split.py ../ud-2.6/la_proiel/la_proiel-ud-train.conllu --ratio 1 --train=la_evalatin20/la_evalatin20-train.conllu --dev=/dev/null - -sed ' - s#^14\tnescio\tnescio\tnescio\tVERB#14\tnescio\tnescio\tVERB# - s#^9\tne\tPART#9\tne\tne\tPART# - s#\(\t[^\t]*\)\{6\}$#\t_\t_\t_\t_\t_\t_# -' -i la_evalatin20/la_evalatin20-train.conllu - -echo "# variant = la_evalatin20" >la_evalatin20/la_evalatin20-test.conllu -echo "# variant = la_evalatin20" >la_evalatin20/la_evalatin20-test-crossgenre.conllu -echo "# variant = la_evalatin20" >la_evalatin20/la_evalatin20-test-crosstime.conllu -for f in la_evalatin20/LT4HALA/data_and_doc/gold_EvaLatin/*.conllu; do - case "$(basename $f)" in - Horatius-Carmina_GOLD.conllu) name=test-crossgenre;; - SummaContraGentiles_IV_GOLD.conllu) name=test-crosstime;; - *) name=test;; - esac - (echo "# newdoc id = $(basename $f)"; cat $f) >>la_evalatin20/la_evalatin20-$name.conllu -done - -for conllu in la_evalatin20/la_evalatin20-dev.conllu la_evalatin20/la_evalatin20-test*.conllu; do - perl ../ud-2.6/conllu_to_text.pl --language=la <$conllu >${conllu%.conllu}.txt -done - -rm -rf la_evalatin20/LT4HALA diff --git a/udpipe2/ud-evalatin20/get_evalatin20_split.py b/udpipe2/ud-evalatin20/get_evalatin20_split.py deleted file mode 100644 index 97e59969f275fedaeb3af3f7e7647f435289e2cd..0000000000000000000000000000000000000000 --- a/udpipe2/ud-evalatin20/get_evalatin20_split.py +++ /dev/null @@ -1,44 +0,0 @@ -#!/usr/bin/env python3 -import argparse -import os -import random -import sys - - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("paths", nargs="+", type=str, help="Input CoNLL-U files") - parser.add_argument("--ratio", default=0.95, type=float, help="Train/dev ratio") - parser.add_argument("--train", default=None, type=str, help="Train file to append to") - parser.add_argument("--dev", default=None, type=str, help="Dev file to append to") - parser.add_argument("--seed", default=42, type=int, help="Random seed") - args = parser.parse_args() - - train, dev = [], [] - - for path in args.paths: - print("Loading {}".format(path), file=sys.stderr) - - with open(path, "r", encoding="utf-8") as input_file: - blocks, block = [], [] - header = "# newdoc id = {}\n".format(os.path.basename(path)) - for line in input_file: - if line.startswith("# variant "): continue - block.append(line) - if not line.rstrip("\n"): - block = "".join(block) - if "greek.expression" not in block: - blocks.append(block) - block = [] - assert not block - - random.Random(args.seed).shuffle(blocks) - train.extend([header] + blocks[:int(args.ratio * len(blocks))]) - if args.ratio < 1: - dev.extend([header] + blocks[int(args.ratio * len(blocks)):]) - - for path, blocks in [(args.train, train), (args.dev, dev)]: - if not blocks: continue - - with open(path, "a", encoding="utf-8") as output_file: - print("".join(blocks), end="", file=output_file) diff --git a/udpipe2/ud-evalatin20/langs_sizes b/udpipe2/ud-evalatin20/langs_sizes deleted file mode 100644 index b6df2ac67c3153c07d023508969889af529e1363..0000000000000000000000000000000000000000 --- a/udpipe2/ud-evalatin20/langs_sizes +++ /dev/null @@ -1 +0,0 @@ -la_evalatin20 607158 diff --git a/udpipe2/ud-evalatin20/langs_sizes.sh b/udpipe2/ud-evalatin20/langs_sizes.sh deleted file mode 100644 index 5ee811aa7d06f75bc74c374307e4dfd4b2baaa89..0000000000000000000000000000000000000000 --- a/udpipe2/ud-evalatin20/langs_sizes.sh +++ /dev/null @@ -1,6 +0,0 @@ -#!/bin/sh - -for d in */; do - echo $d >&2 - echo ${d%/} $(grep -cP "^\d+\t" $d*train.conllu) -done | sort -rnk2 >langs_sizes diff --git a/udpipe2/udpipe2.py b/udpipe2/udpipe2.py deleted file mode 100644 index ac904e8eaf981ddf019683ca51ec097538db9313..0000000000000000000000000000000000000000 --- a/udpipe2/udpipe2.py +++ /dev/null @@ -1,601 +0,0 @@ -#!/usr/bin/env python3 - -# This file is part of UDPipe 2 . -# -# Copyright 2020 Institute of Formal and Applied Linguistics, Faculty of -# Mathematics and Physics, Charles University in Prague, Czech Republic. -# -# This Source Code Form is subject to the terms of the Mozilla Public -# License, v. 2.0. If a copy of the MPL was not distributed with this -# file, You can obtain one at http://mozilla.org/MPL/2.0/. - -import argparse -import os -import sys -import time -import warnings - -import numpy as np -import tensorflow as tf -import ufal.chu_liu_edmonds - -import udpipe2_dataset -import udpipe2_eval - -__version__ = "2.1.1-dev" - - -# Use tf.compat.v1 if running with TF2. Only prediction is supported -# in this case, because we use tf.compat.opt.LazyAdamOptimizer, which -# is not available in TF2. -if not tf.__version__.startswith("1"): - tf = tf.compat.v1 - -# Disable TF warnings -tf.logging.set_verbosity(tf.logging.ERROR) - -# Ignore warnings containing "is deprecated" -warnings.filterwarnings("ignore", message=".*is deprecated") - -class UDPipe2: - METRICS = ["UPOS", "XPOS", "UFeats", "AllTags", "Lemmas", "UAS", "LAS", "CLAS", "MLAS", "BLEX"] - - def __init__(self, threads, seed=42): - self.morphodita = None - - # Create an empty graph and a session - graph = tf.Graph() - graph.seed = seed - self.session = tf.Session(graph=graph, config=tf.ConfigProto(inter_op_parallelism_threads=threads, - intra_op_parallelism_threads=threads, - allow_soft_placement=True)) - - def construct(self, args, train, devs, tests, predict_only): - num_words = len(train.factors[train.FORMS].words) - num_chars = len(train.factors[train.FORMS].alphabet) - num_tags = {tag: len(train.factors[train.FACTORS_MAP[tag]].words) for tag in args.tags} - num_deprels = len(train.factors[train.DEPREL].words) - - with self.session.graph.as_default(): - # Inputs - self.sentence_lens = tf.placeholder(tf.int32, [None]) - self.word_ids = tf.placeholder(tf.int32, [None, None]) - self.charseqs = tf.placeholder(tf.int32, [None, None]) - self.charseq_lens = tf.placeholder(tf.int32, [None]) - self.charseq_ids = tf.placeholder(tf.int32, [None, None]) - if train.variants > 1: self.variants = tf.placeholder(tf.int32, [None]) - if train.embeddings_size: self.embeddings = tf.placeholder(tf.float32, [None, None, train.embeddings_size]) - self.tags = dict((tag, tf.placeholder(tf.int32, [None, None])) for tag in args.tags) - self.heads = tf.placeholder(tf.int32, [None, None]) - self.deprels = tf.placeholder(tf.int32, [None, None]) - self.is_training = tf.placeholder(tf.bool, []) - self.learning_rate = tf.placeholder(tf.float32, []) - - # RNN Cell - if args.rnn_cell == "LSTM": - rnn_cell = tf.nn.rnn_cell.LSTMCell - elif args.rnn_cell == "GRU": - rnn_cell = tf.nn.rnn_cell.GRUCell - else: - raise ValueError("Unknown rnn_cell {}".format(args.rnn_cell)) - - # Word embeddings - inputs = [] - if args.we_dim: - word_embeddings = tf.get_variable("word_embeddings", shape=[num_words, args.we_dim], dtype=tf.float32) - inputs.append(tf.nn.embedding_lookup(word_embeddings, self.word_ids)) - - # Character-level embeddings - character_embeddings = tf.get_variable("character_embeddings", shape=[num_chars, args.cle_dim], dtype=tf.float32) - characters_embedded = tf.nn.embedding_lookup(character_embeddings, self.charseqs) - characters_embedded = tf.layers.dropout(characters_embedded, rate=args.dropout, training=self.is_training) - _, (state_fwd, state_bwd) = tf.nn.bidirectional_dynamic_rnn( - tf.nn.rnn_cell.GRUCell(args.cle_dim), tf.nn.rnn_cell.GRUCell(args.cle_dim), - characters_embedded, sequence_length=self.charseq_lens, dtype=tf.float32) - cle = tf.concat([state_fwd, state_bwd], axis=1) - cle_inputs = tf.nn.embedding_lookup(cle, self.charseq_ids) - # If CLE dim is half WE dim, we add them together, which gives - # better results; otherwise we concatenate CLE and WE. - if 2 * args.cle_dim == args.we_dim: - inputs[-1] += cle_inputs - else: - inputs.append(cle_inputs) - - # Variants - if train.variants > 1: - variant_embeddings = tf.get_variable("variant_embeddings", shape=[train.variants, args.variant_dim], dtype=tf.float32) - variant_embeddings = tf.nn.embedding_lookup(variant_embeddings, self.variants) - variant_embeddings = tf.expand_dims(variant_embeddings, axis=1) - variant_embeddings = tf.tile(variant_embeddings, [1, tf.shape(self.word_ids)[1], 1]) - inputs.append(variant_embeddings) - - # Contextualized embeddings - if train.embeddings_size: - inputs.append(self.embeddings) - - # All inputs done - inputs = tf.concat(inputs, axis=2) - - # Shared RNN layers - hidden_layer = tf.layers.dropout(inputs, rate=args.dropout, training=self.is_training) - for i in range(args.rnn_layers): - (hidden_layer_fwd, hidden_layer_bwd), _ = tf.nn.bidirectional_dynamic_rnn( - rnn_cell(args.rnn_cell_dim), rnn_cell(args.rnn_cell_dim), - hidden_layer, sequence_length=self.sentence_lens + 1, dtype=tf.float32, - scope="word-level-rnn-{}".format(i)) - previous = hidden_layer - hidden_layer = tf.layers.dropout(hidden_layer_fwd + hidden_layer_bwd, rate=args.dropout, training=self.is_training) - if i: hidden_layer += previous - - # Tagger - loss = 0 - weights = tf.sequence_mask(self.sentence_lens, dtype=tf.float32) - weights_sum = tf.reduce_sum(weights) - self.predictions, self.predictions_logits = {}, {} - tag_hidden_layer = hidden_layer[:, 1:] - for i in range(args.rnn_layers_tagger): - (hidden_layer_fwd, hidden_layer_bwd), _ = tf.nn.bidirectional_dynamic_rnn( - rnn_cell(args.rnn_cell_dim), rnn_cell(args.rnn_cell_dim), - tag_hidden_layer, sequence_length=self.sentence_lens, dtype=tf.float32, - scope="word-level-rnn-tag-{}".format(i)) - previous = tag_hidden_layer - tag_hidden_layer = tf.layers.dropout(hidden_layer_fwd + hidden_layer_bwd, rate=args.dropout, training=self.is_training) - if i: tag_hidden_layer += previous - for tag in args.tags: - tag_layer = tag_hidden_layer - for _ in range(args.tag_layers): - tag_layer += tf.layers.dropout(tf.layers.dense(tag_layer, args.rnn_cell_dim, activation=tf.nn.tanh), rate=args.dropout, training=self.is_training) - if tag == "LEMMAS": tag_layer = tf.concat([tag_layer, cle_inputs[:, 1:]], axis=2) - output_layer = tf.layers.dense(tag_layer, num_tags[tag]) - self.predictions[tag] = tf.argmax(output_layer, axis=2, output_type=tf.int32) - self.predictions_logits[tag] = output_layer - - if args.label_smoothing: - gold_labels = tf.one_hot(self.tags[tag], num_tags[tag]) * (1 - args.label_smoothing) + args.label_smoothing / num_tags[tag] - loss += tf.losses.softmax_cross_entropy(gold_labels, output_layer, weights=weights) - else: - loss += tf.losses.sparse_softmax_cross_entropy(self.tags[tag], output_layer, weights=weights) - - # Parsing - if args.parse: - max_words = tf.reduce_max(self.sentence_lens) - - if args.rnn_layers == 0: - parser_inputs = [inputs] - for tag in ["UPOS", "XPOS", "FEATS"]: - parser_inputs.append(tf.nn.embedding_lookup(tf.get_variable(tag + "_embeddings", shape=[num_tags[tag], 128], dtype=tf.float32), - tf.pad(self.predictions[tag], ((0, 0),(1, 0)), constant_values=2))) - parser_inputs = tf.concat(parser_inputs, axis=2) - hidden_layer = tf.layers.dropout(parser_inputs, rate=args.dropout, training=self.is_training) - - for i in range(args.rnn_layers_parser): - (hidden_layer_fwd, hidden_layer_bwd), _ = tf.nn.bidirectional_dynamic_rnn( - rnn_cell(args.rnn_cell_dim), rnn_cell(args.rnn_cell_dim), - hidden_layer, sequence_length=self.sentence_lens + 1, dtype=tf.float32, - scope="word-level-rnn-parser-{}".format(i)) - previous = hidden_layer - hidden_layer = tf.layers.dropout(hidden_layer_fwd + hidden_layer_bwd, rate=args.dropout, training=self.is_training) - if i: hidden_layer += previous - - # Heads - head_deps = hidden_layer[:, 1:] - for _ in range(args.parser_layers): - head_deps += tf.layers.dropout(tf.layers.dense(head_deps, args.rnn_cell_dim, activation=tf.nn.tanh), rate=args.dropout, training=self.is_training) - head_roots = hidden_layer - for _ in range(args.parser_layers): - head_roots += tf.layers.dropout(tf.layers.dense(head_roots, args.rnn_cell_dim, activation=tf.nn.tanh), rate=args.dropout, training=self.is_training) - - head_deps_bias = tf.get_variable("head_deps_bias", [args.rnn_cell_dim], dtype=tf.float32, initializer=tf.zeros_initializer) - head_roots_bias = tf.get_variable("head_roots_bias", [args.rnn_cell_dim], dtype=tf.float32, initializer=tf.zeros_initializer) - head_biaffine = tf.get_variable("head_biaffine", [args.rnn_cell_dim, args.rnn_cell_dim], dtype=tf.float32, initializer=tf.zeros_initializer) - - heads = tf.reshape(tf.matmul(tf.reshape(head_deps, [-1, args.rnn_cell_dim]) + head_deps_bias, head_biaffine), - [tf.shape(hidden_layer)[0], -1, args.rnn_cell_dim]) - heads = tf.matmul(heads, head_roots + head_roots_bias, transpose_b=True) - self.heads_logs = tf.nn.log_softmax(heads) - if args.label_smoothing: - gold_labels = tf.one_hot(self.heads, max_words + 1) * (1 - args.label_smoothing) - gold_labels += args.label_smoothing / tf.to_float(max_words + 1) - loss += tf.losses.softmax_cross_entropy(gold_labels, heads, weights=weights) - else: - loss += tf.losses.sparse_softmax_cross_entropy(self.heads, heads, weights=weights) - - # Deprels - self.deprel_hidden_layer = tf.identity(hidden_layer) - self.deprel_heads = tf.identity(self.heads) - - deprel_deps = tf.layers.dropout(tf.layers.dense(self.deprel_hidden_layer[:, 1:], args.parser_deprel_dim, activation=tf.nn.tanh), rate=args.dropout, training=self.is_training) - for _ in range(args.parser_layers - 1): - deprel_deps += tf.layers.dropout(tf.layers.dense(deprel_deps, args.parser_deprel_dim, activation=tf.nn.tanh), rate=args.dropout, training=self.is_training) - - deprel_indices = tf.stack([ - tf.tile(tf.expand_dims(tf.range(tf.shape(self.deprel_heads)[0]), axis=1), multiples=[1, tf.shape(self.deprel_heads)[1]]), - self.deprel_heads], axis=2) - deprel_roots = tf.gather_nd(self.deprel_hidden_layer, deprel_indices, ) - deprel_roots = tf.layers.dropout(tf.layers.dense(deprel_roots, args.parser_deprel_dim, activation=tf.nn.tanh), rate=args.dropout, training=self.is_training) - for _ in range(args.parser_layers - 1): - deprel_roots += tf.layers.dropout(tf.layers.dense(deprel_roots, args.parser_deprel_dim, activation=tf.nn.tanh), rate=args.dropout, training=self.is_training) - - deprel_deps_bias = tf.get_variable("deprel_deps_bias", [args.parser_deprel_dim], dtype=tf.float32, initializer=tf.zeros_initializer) - deprel_roots_bias = tf.get_variable("deprel_roots_bias", [args.parser_deprel_dim], dtype=tf.float32, initializer=tf.zeros_initializer) - deprel_biaffine = tf.get_variable("deprel_biaffine", [args.parser_deprel_dim, num_deprels * args.parser_deprel_dim], dtype=tf.float32, initializer=tf.zeros_initializer) - - deprels = tf.reshape(tf.matmul(tf.reshape(deprel_deps, [-1, args.parser_deprel_dim]) + deprel_deps_bias, deprel_biaffine), - [tf.shape(self.deprel_hidden_layer)[0], -1, num_deprels, args.parser_deprel_dim]) - deprels = tf.squeeze(tf.matmul(deprels, tf.expand_dims(deprel_roots + deprel_roots_bias, axis=3)), axis=3) - self.predictions_deprel = tf.argmax(deprels, axis=2, output_type=tf.int32) - if args.label_smoothing: - gold_labels = tf.one_hot(self.deprels, num_deprels) * (1 - args.label_smoothing) - gold_labels += args.label_smoothing / num_deprels - loss += tf.losses.softmax_cross_entropy(gold_labels, deprels, weights=weights) - else: - loss += tf.losses.sparse_softmax_cross_entropy(self.deprels, deprels, weights=weights) - - # Pretrain saver - self.saver = tf.train.Saver(max_to_keep=1) - if predict_only: return - - # Training - self.global_step = tf.train.create_global_step() - if args.clip_gradient: - optimizer = tf.contrib.opt.LazyAdamOptimizer(learning_rate=self.learning_rate, beta2=args.beta_2) - gradients, variables = zip(*optimizer.compute_gradients(loss)) - gradients, gradient_norm = tf.clip_by_global_norm(gradients, args.clip_gradient) - self.training = optimizer.apply_gradients(zip(gradients, variables), global_step=self.global_step) - else: - self.training = tf.contrib.opt.LazyAdamOptimizer(learning_rate=self.learning_rate, beta2=args.beta_2).minimize(loss, global_step=self.global_step) - - # Summaries - summary_writer = tf.contrib.summary.create_file_writer(args.model, flush_millis=10 * 1000) - with summary_writer.as_default(), tf.contrib.summary.record_summaries_every_n_global_steps(100): - self.training_summaries = [ - tf.contrib.summary.scalar("train/loss", loss), - tf.contrib.summary.scalar("train/lr", self.learning_rate)] - if args.clip_gradient: - self.training_summaries.append(tf.contrib.summary.scalar("train/gradient_norm", gradient_norm)) - for tag in args.tags: - self.training_summaries.append(tf.contrib.summary.scalar( - "train/{}".format(tag), - tf.reduce_sum(tf.cast(tf.equal(self.tags[tag], self.predictions[tag]), tf.float32) * weights) / - weights_sum)) - if args.parse: - heads_acc = tf.reduce_sum(tf.cast(tf.equal(self.heads, tf.argmax(heads, axis=-1, output_type=tf.int32)), - tf.float32) * weights) / weights_sum - self.training_summaries.extend([tf.contrib.summary.scalar("train/heads_acc", heads_acc)]) - deprels_acc = tf.reduce_sum(tf.cast(tf.equal(self.deprels, tf.argmax(deprels, axis=-1, output_type=tf.int32)), - tf.float32) * weights) / weights_sum - self.training_summaries.extend([tf.contrib.summary.scalar("train/deprels_acc", deprels_acc)]) - - # Evaluation summaries - self.summary_writers = {"": summary_writer} - for dataset in devs + tests: - if dataset.label not in self.summary_writers: - self.summary_writers[dataset.label] = tf.contrib.summary.create_file_writer( - os.path.join(args.model, dataset.label), flush_millis=10 * 1000) - - self.event = tf.placeholder(tf.string, []) - self.event_summaries = {} - for name, writer in self.summary_writers.items(): - with writer.as_default(): - self.event_summaries[name] = tf.contrib.summary.import_event(self.event) - self.summary_writers_close = [writer.close() for writer in self.summary_writers.values()] - - # Initialize variables - self.session.run(tf.global_variables_initializer()) - for summary_writer in self.summary_writers.values(): - with summary_writer.as_default(): - tf.contrib.summary.initialize(session=self.session) - - def load(self, path, morphodita_dictionary=None): - # We use the following version instead of calling `self.saver.restore`, - # because it works even TF 2 is in Eager mode. - self.session.run(self.saver.saver_def.restore_op_name, - {self.saver.saver_def.filename_tensor_name: os.path.join(path, "weights")}) - - # Try loading also consistent feats table. - consistent_feats_table = os.path.join(path, "consistent_feats.table") - if os.path.exists(consistent_feats_table): - import gzip - with gzip.open(consistent_feats_table, "rb") as consistent_feats_table_file: - consistent_feats_table = np.load(consistent_feats_table_file) - - with self.session.graph.as_default(): - consistent_feats_table = tf.convert_to_tensor(consistent_feats_table, dtype=tf.float32) - self.predictions["FEATS"] = tf.argmax( - tf.nn.softmax(self.predictions_logits["FEATS"], axis=2) * tf.gather(consistent_feats_table, self.predictions["UPOS"]), - axis=2, output_type=tf.int32) - - # Load MorphoDiTa if requested - if morphodita_dictionary: - import ufal.morphodita - self.morphodita = ufal.morphodita.Morpho.load(os.path.join(path, morphodita_dictionary)) - assert "XPOS" in self.tags and "LEMMAS" in self.tags, "MorphoDiTa dictionary operates on XPOS and LEMMAS, which are not present." - - def close_writers(self): - self.session.run(self.summary_writers_close) - - def train_epoch(self, train, learning_rate, args): - batches, at_least_one_epoch = 0, False - while batches < args.min_epoch_batches: - while not train.epoch_finished(): - sentence_lens, word_ids, charseq_ids, charseqs, charseq_lens = train.next_batch(args.batch_size) - if args.word_dropout: - mask = np.random.binomial(n=1, p=args.word_dropout, size=word_ids[train.FORMS].shape) - word_ids[train.FORMS] = (1 - mask) * word_ids[train.FORMS] + mask * train.factors[train.FORMS].words_map[""] - if args.char_dropout: - mask = np.random.binomial(n=1, p=args.char_dropout, size=charseqs[train.FORMS].shape) - charseqs[train.FORMS] = (1 - mask) * charseqs[train.FORMS] + mask * train.factors[train.FORMS].alphabet_map[""] - - feeds = {self.is_training: True, self.learning_rate: learning_rate, self.sentence_lens: sentence_lens, - self.charseqs: charseqs[train.FORMS], self.charseq_lens: charseq_lens[train.FORMS], - self.word_ids: word_ids[train.FORMS], self.charseq_ids: charseq_ids[train.FORMS]} - if train.variants > 1: - feeds[self.variants] = word_ids[train.VARIANT] - if train.embeddings_size: - if args.word_dropout: - mask = np.random.binomial(n=1, p=args.word_dropout, size=[*word_ids[train.EMBEDDINGS].shape[:2], 1]) - word_ids[train.EMBEDDINGS] *= (1 - mask) - feeds[self.embeddings] = word_ids[train.EMBEDDINGS] - for tag in args.tags: feeds[self.tags[tag]] = word_ids[train.FACTORS_MAP[tag]] - if args.parse: - feeds[self.heads] = word_ids[train.HEAD] - feeds[self.deprels] = word_ids[train.DEPREL] - self.session.run([self.training, self.training_summaries], feeds) - batches += 1 - if at_least_one_epoch: break - at_least_one_epoch = True - - def predict(self, dataset, evaluating, args): - import io - conllu, sentences = io.StringIO(), 0 - - while not dataset.epoch_finished(): - sentence_lens, word_ids, charseq_ids, charseqs, charseq_lens = dataset.next_batch(args.batch_size) - - feeds = {self.is_training: False, self.sentence_lens: sentence_lens, - self.charseqs: charseqs[dataset.FORMS], self.charseq_lens: charseq_lens[dataset.FORMS], - self.word_ids: word_ids[dataset.FORMS], self.charseq_ids: charseq_ids[dataset.FORMS]} - if dataset.variants > 1: - feeds[self.variants] = word_ids[dataset.VARIANT] - if dataset.embeddings_size: - feeds[self.embeddings] = word_ids[dataset.EMBEDDINGS] - if evaluating: - for tag in args.tags: feeds[self.tags[tag]] = word_ids[dataset.FACTORS_MAP[tag]] - if args.parse: - feeds[self.heads] = word_ids[dataset.HEAD] - feeds[self.deprels] = word_ids[dataset.DEPREL] - - targets = [self.predictions] - if self.morphodita: targets.extend([self.predictions_logits["XPOS"], self.predictions_logits["LEMMAS"]]) - if args.parse: targets.extend([self.heads_logs, self.deprel_hidden_layer]) - predictions, *other_values = self.session.run(targets, feeds) - if self.morphodita: xpos_logits, lemma_logits, *other_values = other_values - if args.parse: prior_heads, deprel_hidden_layer, *other_values = other_values - - if args.parse: - heads = np.zeros(prior_heads.shape[:2], dtype=np.int32) - for i in range(len(sentence_lens)): - padded_heads = np.pad(prior_heads[i][:sentence_lens[i], :sentence_lens[i] + 1].astype(np.float64), - ((1, 0), (0, 0)), mode="constant") - if args.single_root: - padded_heads[:, 0] = np.nan - padded_heads[1 + np.argmax(prior_heads[i][:sentence_lens[i], 0]), 0] = 0 - chosen_heads, _ = ufal.chu_liu_edmonds.chu_liu_edmonds(padded_heads) - heads[i, :sentence_lens[i]] = chosen_heads[1:] - deprels = self.session.run(self.predictions_deprel, - {self.is_training: False, self.deprel_hidden_layer: deprel_hidden_layer, self.deprel_heads: heads}) - - for i in range(len(sentence_lens)): - overrides = [None] * dataset.FACTORS - for tag in args.tags: overrides[dataset.FACTORS_MAP[tag]] = predictions[tag][i] - if self.morphodita: - self.disambiguate_with_morphodita( - dataset.factors[dataset.FORMS].strings[sentences][1:], dataset, xpos_logits[i], lemma_logits[i], overrides) - if args.parse: - overrides[dataset.HEAD] = heads[i] - overrides[dataset.DEPREL] = deprels[i] - dataset.write_sentence(conllu, sentences, overrides) - sentences += 1 - - return conllu.getvalue() - - def disambiguate_with_morphodita(self, forms, dataset, tag_logits, lemma_logits, overrides): - import ufal.morphodita - tags_map = dataset.factors[dataset.XPOS].words_map - lemma_rules_map = dataset.factors[dataset.LEMMAS].words_map - overrides[dataset.XPOS] = overrides[dataset.XPOS].tolist() - overrides[dataset.LEMMAS] = overrides[dataset.LEMMAS].tolist() - - analyses = ufal.morphodita.TaggedLemmas() - for i in range(len(forms)): - if self.morphodita.analyze(forms[i], self.morphodita.NO_GUESSER, analyses) < 0: - continue - - if len(analyses) == 1: - overrides[dataset.XPOS][i] = analyses[0].tag - overrides[dataset.LEMMAS][i] = analyses[0].lemma - continue - - lemmas = {} - for analysis in analyses: - tag_id = tags_map.get(analysis.tag, None) - if tag_id is None: - continue - stripped_lemma = self.morphodita.lemmaId(analysis.lemma) - stripped_lemma_info = lemmas.get(stripped_lemma, None) - if stripped_lemma_info is None: - lemmas[stripped_lemma] = analysis.lemma, tag_id - else: - full_lemma, best_tag_id = stripped_lemma_info - if tag_logits[i, tag_id] > tag_logits[i, best_tag_id]: - lemmas[stripped_lemma] = full_lemma, tag_id - - if len(lemmas) == 1: - lemma, best_tag_id = next(iter(lemmas.values())) - overrides[dataset.XPOS][i] = best_tag_id - overrides[dataset.LEMMAS][i] = lemma - elif len(lemmas) > 1: - best_tag_id, best_unknownlemma_tag_id = None, None - for stripped_lemma, (full_lemma, tag_id) in lemmas.items(): - lemma_rule = dataset._gen_lemma_rule(forms[i], stripped_lemma, dataset._lr_allow_copy) - lemma_rule_id = lemma_rules_map.get(lemma_rule, None) - if lemma_rule_id is None: - if best_unknownlemma_tag_id is None or tag_logits[i, tag_id] > tag_logits[i, best_unknownlemma_tag_id]: - best_unknownlemma_tag_id, best_unknownlemma = tag_id, full_lemma - else: - lemmatag_logits = lemma_logits[i, lemma_rule_id] + tag_logits[i, tag_id] - if best_tag_id is None or lemmatag_logits > best_lemmatag_logits: - best_tag_id, best_lemmatag_logits, best_lemma = tag_id, lemmatag_logits, full_lemma - if best_tag_id is None or (best_unknownlemma_tag_id is not None and tag_logits[i, best_unknownlemma_tag_id] > tag_logits[i, best_tag_id] + 0.5): - best_tag_id, best_lemma = best_unknownlemma_tag_id, best_unknownlemma - overrides[dataset.XPOS][i] = best_tag_id - overrides[dataset.LEMMAS][i] = best_lemma - - def evaluate(self, dataset_name, dataset, args): - import io - - conllu = self.predict(dataset.data, True, args) - metrics = udpipe2_eval.evaluate(dataset.gold, udpipe2_eval.load_conllu(io.StringIO(conllu), args.single_root)) - event = [tf.Summary.Value(tag="{}/{}".format(dataset_name, metric), simple_value=metrics[metric].f1) for metric in self.METRICS] - event = tf.Event(summary=tf.Summary(value=event), step=self.session.run(self.global_step), wall_time=time.time()).SerializeToString() - self.session.run(self.event_summaries[dataset.label], {self.event: event}) - - if args.parse: - return (metrics["LAS"].f1 + metrics["MLAS"].f1 + metrics["BLEX"].f1) / 3., metrics - else: - return metrics["AllTags"].f1, metrics - - @staticmethod - def argument_parser(): - parser = argparse.ArgumentParser() - parser.add_argument("model", type=str, help="Model path") - parser.add_argument("--batch_size", default=32, type=int, help="Batch size.") - parser.add_argument("--beta_2", default=0.99, type=float, help="Adam beta 2") - parser.add_argument("--char_dropout", default=0, type=float, help="Character dropout") - parser.add_argument("--cle_dim", default=256, type=int, help="Character-level embedding dimension.") - parser.add_argument("--clip_gradient", default=2.0, type=float, help="Gradient clipping.") - parser.add_argument("--dev", default=[], nargs="+", type=str, help="Dev files.") - parser.add_argument("--dropout", default=0.5, type=float, help="Dropout") - parser.add_argument("--epochs", default="40:1e-3,20:1e-4", type=str, help="Epochs and learning rates.") - parser.add_argument("--exp", default=None, type=str, help="Experiment name.") - parser.add_argument("--label_smoothing", default=0.03, type=float, help="Label smoothing.") - parser.add_argument("--max_sentence_len", default=120, type=int, help="Max sentence length.") - parser.add_argument("--morphodita", default=None, type=str, help="MorphoDiTa dictionary used for PDT-C prediction.") - parser.add_argument("--min_epoch_batches", default=300, type=int, help="Minimum number of batches per epoch.") - parser.add_argument("--parse", default=1, type=int, help="Parse.") - parser.add_argument("--parser_layers", default=1, type=int, help="Parser layers.") - parser.add_argument("--parser_deprel_dim", default=128, type=int, help="Parser deprel dim.") - parser.add_argument("--predict", default=False, action="store_true", help="Only predict.") - parser.add_argument("--predict_input", default=None, type=str, help="Input to prediction.") - parser.add_argument("--predict_output", default=None, type=str, help="Output to prediction.") - parser.add_argument("--rnn_cell", default="LSTM", type=str, help="RNN cell type.") - parser.add_argument("--rnn_cell_dim", default=512, type=int, help="RNN cell dimension.") - parser.add_argument("--rnn_layers", default=2, type=int, help="RNN layers.") - parser.add_argument("--rnn_layers_parser", default=1, type=int, help="Parser RNN layers.") - parser.add_argument("--rnn_layers_tagger", default=0, type=int, help="Tagger RNN layers.") - parser.add_argument("--seed", default=42, type=int, help="Initial random seed.") - parser.add_argument("--single_root", default=1, type=int, help="Single root allowed only.") - parser.add_argument("--tags", default="UPOS,XPOS,FEATS,LEMMAS", type=str, help="Tags.") - parser.add_argument("--tag_layers", default=1, type=int, help="Additional tag layers.") - parser.add_argument("--test", default=[], nargs="+", type=str, help="Test files.") - parser.add_argument("--train", default=None, type=str, help="Trainig file.") - parser.add_argument("--threads", default=4, type=int, help="Maximum number of threads to use.") - parser.add_argument("--variant_dim", default=128, type=int, help="Variant embedding dimension.") - parser.add_argument("--we_dim", default=512, type=int, help="Word embedding dimension.") - parser.add_argument("--wembedding_model", default="bert-base-multilingual-uncased-last4", type=str, help="WEmbedding model.") - parser.add_argument("--word_dropout", default=0.2, type=float, help="Word dropout") - return parser - - @staticmethod - def postprocess_arguments(args): - # Add option defaults if missing in `args` - args = UDPipe2.argument_parser().parse_args([args.model], namespace=args) - - # Manual args post-processing - args.tags = args.tags.split(",") - args.epochs = [(int(epochs), float(lr)) for epochs, lr in (epochs_lr.split(":") for epochs_lr in args.epochs.split(","))] - - -if __name__ == "__main__": - import collections - import glob - import json - - # Parse arguments - parser = UDPipe2.argument_parser() - args = parser.parse_args() - - # Fix random seed - np.random.seed(args.seed) - - if not args.predict: - # Create model dir and save the passed options - os.makedirs(args.model, exist_ok=True) - with open(os.path.join(args.model, "options.json"), mode="w") as options_file: - json.dump(vars(args), options_file, sort_keys=True) - else: - # Load saved options from the model - with open(os.path.join(args.model, "options.json"), mode="r") as options_file: - args = argparse.Namespace(**json.load(options_file)) - parser.parse_args(namespace=args) - - # Postprocess args - UDPipe2.postprocess_arguments(args) - - # Load the data - devs, tests = [], [] - EvaluationDataset = collections.namedtuple("EvaluationDataset", ["label", "data", "gold"]) - if not args.predict: - train = udpipe2_dataset.UDPipe2Dataset(path=args.train, max_sentence_len=args.max_sentence_len, shuffle_batches=True, - embeddings=glob.glob("{}*.npz".format(args.train))) - train.save_mappings(os.path.join(args.model, "mappings.pickle")) - for sources, target in [(args.dev, devs), (args.test, tests)]: - for source in sources: - label, path = ("", source) if ":" not in source else source.split(":", maxsplit=1) - target.append(EvaluationDataset( - label, - udpipe2_dataset.UDPipe2Dataset(path=path, train=train, shuffle_batches=False, - embeddings=glob.glob("{}*.npz".format(path))), - udpipe2_eval.load_conllu_file(path, args.single_root) - )) - else: - train = udpipe2_dataset.UDPipe2Dataset.load_mappings(os.path.join(args.model, "mappings.pickle")) - test = udpipe2_dataset.UDPipe2Dataset(path=args.predict_input, train=train, shuffle_batches=False, - embeddings=glob.glob("{}*.npz".format(args.predict_input))) - - # Construct the network - network = UDPipe2(threads=args.threads, seed=args.seed) - network.construct(args, train, devs, tests, predict_only=args.predict) - - if args.predict: - network.load(args.model, args.morphodita) - conllu = network.predict(test, False, args) - with open(args.predict_output, "w", encoding="utf-8") as output_file: - print(conllu, end="", file=output_file) - else: - log_files = [open(os.path.join(args.model, "log"), "w", encoding="utf-8"), sys.stderr] - for log_file in log_files: - for tag in args.tags + ["DEPREL"]: - print("{}: {}".format(tag, len(train.factors[train.FACTORS_MAP[tag]].words)), file=log_file, flush=True) - print("VARIANT: {}".format(train.variants), file=log_file, flush=True) - print("Parsing with args:", *["{}: {}".format(key, value) for key, value in sorted(vars(args).items())], - sep="\n", file=log_file, flush=True) - - for i, (epochs, learning_rate) in enumerate(args.epochs): - for epoch in range(epochs): - network.train_epoch(train, learning_rate, args) - - for dev in devs: - dev_accuracy, metrics = network.evaluate("dev", dev, args) - metrics_log = ", ".join(("{}: {:.2f}".format(metric, 100 * metrics[metric].f1) for metric in UDPipe2.METRICS)) - for log_file in log_files: - print("Dev {} epoch {}, lr {}, {}".format(dev.label, epoch + 1, learning_rate, metrics_log), file=log_file, flush=True) - - for test in tests: - test_accuracy, metrics = network.evaluate("test", test, args) - metrics_log = ", ".join(("{}: {:.2f}".format(metric, 100 * metrics[metric].f1) for metric in UDPipe2.METRICS)) - for log_file in log_files: - print("Test {} epoch {}, lr {}, {}".format(test.label, epoch + 1, learning_rate, metrics_log), file=log_file, flush=True) - network.close_writers() - - network.saver.save(network.session, os.path.join(args.model, "weights"), write_meta_graph=False) diff --git a/udpipe2/udpipe2_client.py b/udpipe2/udpipe2_client.py deleted file mode 100644 index 74d53588d42d1356d4cf9e6522033203cb1ed637..0000000000000000000000000000000000000000 --- a/udpipe2/udpipe2_client.py +++ /dev/null @@ -1,129 +0,0 @@ -#!/usr/bin/env python3 - -# This file is part of UDPipe 2 . -# -# Copyright 2022 Institute of Formal and Applied Linguistics, Faculty of -# Mathematics and Physics, Charles University in Prague, Czech Republic. -# -# This Source Code Form is subject to the terms of the Mozilla Public -# License, v. 2.0. If a copy of the MPL was not distributed with this -# file, You can obtain one at http://mozilla.org/MPL/2.0/. - -import argparse -import email.mime.multipart -import email.mime.nonmultipart -import email.policy -import json -import os -import sys -import urllib.error -import urllib.request - -__version__ = "2.1.1-dev" - - -def perform_request(server, method, params={}): - if not params: - request_headers, request_data = {}, None - else: - message = email.mime.multipart.MIMEMultipart("form-data", policy=email.policy.HTTP) - - for name, value in params.items(): - payload = email.mime.nonmultipart.MIMENonMultipart("text", "plain") - payload.add_header("Content-Disposition", "form-data; name=\"{}\"".format(name)) - payload.add_header("Content-Transfer-Encoding", "8bit") - payload.set_payload(value, charset="utf-8") - message.attach(payload) - - request_data = message.as_bytes().split(b"\r\n\r\n", maxsplit=1)[1] - request_headers = {"Content-Type": message["Content-Type"]} - - try: - with urllib.request.urlopen(urllib.request.Request( - url="{}/{}".format(server, method), headers=request_headers, data=request_data - )) as request: - return json.loads(request.read()) - except urllib.error.HTTPError as e: - print("An exception was raised during UDPipe 'process' REST request.\n" - "The service returned the following error:\n" - " {}".format(e.fp.read().decode("utf-8")), file=sys.stderr) - raise - except json.JSONDecodeError as e: - print("Cannot parse the JSON response of UDPipe 'process' REST request.\n" - " {}".format(e.msg), file=sys.stderr) - raise - - -def list_models(args): - response = perform_request(args.service, "models") - if "models" in response: - for model in response["models"]: - print(model) - if "default_model" in response: - print("Default model:", response["default_model"]) - - -def process(args, data): - data = { - "input": args.input, - "output": args.output, - "data": data, - } - for option in ["model", "tokenizer", "parser", "tagger"]: - value = getattr(args, option) - if value is not None: - data[option] = value - - response = perform_request(args.service, "process", data) - if "model" not in response or "result" not in response: - raise ValueError("Cannot parse the UDPipe 'process' REST request response.") - - print("UDPipe generated an output using the model '{}'.".format(response["model"]), file=sys.stderr) - print("Please respect the model licence (CC BY-NC-SA unless stated otherwise).", file=sys.stderr) - - return response["result"] - - -if __name__ == "__main__": - # Parse the client arguments. - parser = argparse.ArgumentParser(description=( - "Most of the options are passed directly to the service. For documentation, " - "see https://lindat.mff.cuni.cz/services/udpipe/api-reference.php .")) - parser.add_argument("inputs", nargs="*", type=str, help="Optional input files; stdin if not specified.") - parser.add_argument("--list_models", default=False, action="store_true", help="List available models") - parser.add_argument("--input", default="conllu", type=str, help="Input format") - parser.add_argument("--model", default=None, type=str, help="Model to use") - parser.add_argument("--output", default="conllu", type=str, help="Output format") - parser.add_argument("--parser", default=None, type=str, help="Parser options") - parser.add_argument("--tagger", default=None, type=str, help="Tagger options") - parser.add_argument("--tokenizer", default=None, type=str, help="Tokenizer options") - parser.add_argument("--outfile", default=None, type=str, help="Output path template; use {} as basename") - parser.add_argument("--service", default="https://lindat.mff.cuni.cz/services/udpipe/api", type=str, help="Service URL") - args = parser.parse_args() - - if args.list_models: - list_models(args) - else: - outfile = None # No output file opened. - - for input_path in (args.inputs or [sys.stdin]): - # Use stdin if no inputs are specified - if input_path != sys.stdin: - with open(input_path, "r", encoding="utf-8-sig") as input_file: - data = input_file.read() - else: - data = sys.stdin.read() - - if args.outfile and not outfile: - outfile = args.outfile.replace("{}", ( - os.path.splitext(os.path.basename(input_path))[0] if input_path != sys.stdin else "{}")) - outfile = open(outfile, "w", encoding="utf-8") - - (outfile or sys.stdout).write(process(args, data)) - - if args.outfile and "{}" in args.outfile: - outfile.close() - outfile = None - - if outfile: - outfile.close() diff --git a/udpipe2/udpipe2_dataset.py b/udpipe2/udpipe2_dataset.py deleted file mode 100644 index 11159552c254536140e130f7aa75476a3d702b92..0000000000000000000000000000000000000000 --- a/udpipe2/udpipe2_dataset.py +++ /dev/null @@ -1,442 +0,0 @@ -# This file is part of UDPipe 2 . -# -# Copyright 2020 Institute of Formal and Applied Linguistics, Faculty of -# Mathematics and Physics, Charles University in Prague, Czech Republic. -# -# This Source Code Form is subject to the terms of the Mozilla Public -# License, v. 2.0. If a copy of the MPL was not distributed with this -# file, You can obtain one at http://mozilla.org/MPL/2.0/. - -import io -import pickle -import re -import sys - -import numpy as np - -__version__ = "2.1.1-dev" - - -class UDPipe2Dataset: - FORMS = 0 - LEMMAS = 1 - UPOS = 2 - XPOS = 3 - FEATS = 4 - HEAD = 5 - DEPREL = 6 - DEPS = 7 - MISC = 8 - FACTORS = 9 - VARIANT = 9 - EMBEDDINGS = 10 - - FACTORS_MAP = {"FORMS": FORMS, "LEMMAS": LEMMAS, "UPOS": UPOS, "XPOS": XPOS, "FEATS": FEATS, - "HEAD": HEAD, "DEPREL": DEPREL, "DEPS": DEPS, "MISC": MISC} - - re_extras = re.compile(r"^#|^\d+-|^\d+\.") - re_variant = re.compile(r"^#\s*variant\s*=\s*(\S+)") - - class _Factor: - ROOT = 2 - def __init__(self, with_root, characters, train=None): - self.words_map = train.words_map if train else {'': 0, '': 1, '': 2} - self.words = train.words if train else ['', '', ''] - self.word_ids = [] - self.strings = [] - self.with_root = with_root - self.characters = characters - if characters: - self.alphabet_map = train.alphabet_map if train else {'': 0, '': 1, '': 2} - self.alphabet = train.alphabet if train else ['', '', ''] - self.charseqs_map = {'': 0, '': 1, '': 2} - self.charseqs = [[0], [1], [2]] - self.charseq_ids = [] - - def __init__(self, path=None, text=None, embeddings=[], train=None, shuffle_batches=True, - override_variant=None, max_sentence_len=None, max_sentences=None): - # Create factors and other variables - self._factors = [] - for f in range(self.FACTORS): - self._factors.append(self._Factor(f == self.FORMS, f == self.FORMS, train._factors[f] if train else None)) - self._extras = [] - - form_dict = {} - - self._lr_allow_copy = train._lr_allow_copy if train else None - lemma_dict_with_copy, lemma_dict_no_copy = {}, {} - - self._variant_map = train._variant_map if train else {} - self._variants = [] - - # Load contextualized embeddings - if isinstance(embeddings, list) and all(isinstance(embedding, np.ndarray) for embedding in embeddings): - self._embeddings = embeddings - else: - self._embeddings = [] - for embeddings_path in embeddings: - with np.load(embeddings_path, allow_pickle=True) as embeddings_file: - for i, (_, value) in enumerate(embeddings_file.items()): - if max_sentence_len: value = value[:max_sentence_len] - if i >= len(self._embeddings): self._embeddings.append(value) - else: self._embeddings[i] = np.concatenate([self._embeddings[i], value], axis=1) - assert i + 1 == len(self._embeddings) - self._embeddings_size = self._embeddings[0].shape[1] if self._embeddings else 0 - - # Load the sentences - with open(path, "r", encoding="utf-8") if path is not None else io.StringIO(text) as file: - in_sentence = False - variant = "" - for line in file: - line = line.rstrip("\r\n") - - if line: - if self.re_extras.match(line): - variant_match = self.re_variant.match(line) - if variant_match: - variant = variant_match.group(1) - if in_sentence: - while len(self._extras) < len(self._factors[0].word_ids): self._extras.append([]) - while len(self._extras[-1]) <= len(self._factors[0].word_ids[-1]) - self._factors[0].with_root: - self._extras[-1].append("") - else: - while len(self._extras) <= len(self._factors[0].word_ids): self._extras.append([]) - if not len(self._extras[-1]): self._extras[-1].append("") - self._extras[-1][-1] += ("\n" if self._extras[-1][-1] else "") + line - continue - - if max_sentence_len and in_sentence and len(self._factors[0].strings[-1]) - self._factors[0].with_root >= max_sentence_len: - continue - - columns = line.split("\t")[1:] - for f in range(self.FACTORS): - factor = self._factors[f] - if not in_sentence: - if len(factor.word_ids): factor.word_ids[-1] = np.array(factor.word_ids[-1], np.int32) - factor.word_ids.append([]) - factor.strings.append([]) - if factor.characters: factor.charseq_ids.append([]) - if factor.with_root: - factor.word_ids[-1].append(factor.ROOT) - factor.strings[-1].append(factor.words[factor.ROOT]) - if factor.characters: factor.charseq_ids[-1].append(factor.ROOT) - - word = columns[f] - factor.strings[-1].append(word) - - # Preprocess word - if f == self.LEMMAS and self._lr_allow_copy is not None: - word = self._gen_lemma_rule(columns[self.FORMS], columns[self.LEMMAS], self._lr_allow_copy) - - # Character-level information - if factor.characters: - if word not in factor.charseqs_map: - factor.charseqs_map[word] = len(factor.charseqs) - factor.charseqs.append([]) - for c in word: - if c not in factor.alphabet_map: - if train: - c = '' - else: - factor.alphabet_map[c] = len(factor.alphabet) - factor.alphabet.append(c) - factor.charseqs[-1].append(factor.alphabet_map[c]) - factor.charseq_ids[-1].append(factor.charseqs_map[word]) - - # Word-level information - if f == self.HEAD: - factor.word_ids[-1].append(int(word) if word != "_" else -1) - elif f == self.FORMS and not train: - factor.word_ids[-1].append(0) - form_dict[word] = form_dict.get(word, 0) + 1 - elif f == self.LEMMAS and self._lr_allow_copy is None: - factor.word_ids[-1].append(0) - lemma_dict_with_copy[self._gen_lemma_rule(columns[self.FORMS], word, True)] = 1 - lemma_dict_no_copy[self._gen_lemma_rule(columns[self.FORMS], word, False)] = 1 - else: - if word not in factor.words_map: - if train: - word = '' - else: - factor.words_map[word] = len(factor.words) - factor.words.append(word) - factor.word_ids[-1].append(factor.words_map[word]) - if not in_sentence: - if override_variant is not None: variant = override_variant - if (variant not in self._variant_map) and (not train): - self._variant_map[variant] = len(self._variant_map) - self._variants.append(self._variant_map.get(variant, 0)) - in_sentence = True - else: - in_sentence = False - if max_sentences is not None and len(self._factors[self.FORMS].word_ids) >= max_sentences: - break - - # Finalize forms if needed - if not train: - forms = self._factors[self.FORMS] - for i in range(len(forms.word_ids)): - for j in range(forms.with_root, len(forms.word_ids[i])): - word = "" if form_dict[forms.strings[i][j]] < 2 else forms.strings[i][j] - if word not in forms.words_map: - forms.words_map[word] = len(forms.words) - forms.words.append(word) - forms.word_ids[i][j] = forms.words_map[word] - - - # Finalize lemmas if needed - if self._lr_allow_copy is None: - self._lr_allow_copy = True if len(lemma_dict_with_copy) < len(lemma_dict_no_copy) else False - lemmas = self._factors[self.LEMMAS] - for i in range(len(lemmas.word_ids)): - for j in range(lemmas.with_root, len(lemmas.word_ids[i])): - word = self._gen_lemma_rule(self._factors[self.FORMS].strings[i][j - lemmas.with_root + self._factors[self.FORMS].with_root], - lemmas.strings[i][j], self._lr_allow_copy) - if word not in lemmas.words_map: - lemmas.words_map[word] = len(lemmas.words) - lemmas.words.append(word) - lemmas.word_ids[i][j] = lemmas.words_map[word] - - # Compute sentence lengths - sentences = len(self._factors[self.FORMS].word_ids) - self._sentence_lens = np.zeros([sentences], np.int32) - for i in range(len(self._factors[self.FORMS].word_ids)): - self._sentence_lens[i] = len(self._factors[self.FORMS].word_ids[i]) - self._factors[self.FORMS].with_root - - self._shuffle_batches = shuffle_batches - self._permutation = np.random.permutation(len(self._sentence_lens)) if self._shuffle_batches else np.arange(len(self._sentence_lens)) - - if self._embeddings: - assert sentences == len(self._embeddings) - for i in range(sentences): - assert self._sentence_lens[i] == len(self._embeddings[i]), "{} {} {}".format(i, self._sentence_lens[i], len(self._embeddings[i])) - - @property - def sentence_lens(self): - return self._sentence_lens - - @property - def factors(self): - return self._factors - - @property - def variants(self): - return len(self._variant_map) - - @property - def embeddings_size(self): - return self._embeddings_size - - def save_mappings(self, path): - mappings = UDPipe2Dataset.__new__(UDPipe2Dataset) - for field in ["_lr_allow_copy", "_variant_map", "_embeddings_size"]: - setattr(mappings, field, getattr(self, field)) - mappings._factors = [] - for factor in self._factors: - mappings._factors.append(mappings._Factor(factor.with_root, factor.characters, factor)) - - with open(path, "wb") as mappings_file: - pickle.dump(mappings, mappings_file, protocol=3) - - @staticmethod - def load_mappings(path): - with open(path, "rb") as mappings_file: - return pickle.load(mappings_file) - - def epoch_finished(self): - if len(self._permutation) == 0: - self._permutation = np.random.permutation(len(self._sentence_lens)) if self._shuffle_batches else np.arange(len(self._sentence_lens)) - return True - return False - - def next_batch(self, batch_size, max_form_length=64): - batch_size = min(batch_size, len(self._permutation)) - batch_perm = self._permutation[:batch_size] - self._permutation = self._permutation[batch_size:] - - # General data - batch_sentence_lens = self._sentence_lens[batch_perm] - max_sentence_len = np.max(batch_sentence_lens) - - # Word-level data - batch_word_ids = [] - for factor in self._factors: - batch_word_ids.append(np.zeros([batch_size, max_sentence_len + factor.with_root], np.int32)) - for i in range(batch_size): - batch_word_ids[-1][i, 0:batch_sentence_lens[i] + factor.with_root] = factor.word_ids[batch_perm[i]] - - # Variants - batch_word_ids.append(np.zeros([batch_size], np.int32)) - for i in range(batch_size): - batch_word_ids[-1][i] = self._variants[batch_perm[i]] - - # Contextualized embeddings - if self._embeddings: - forms = self._factors[self.FORMS] - batch_word_ids.append(np.zeros([batch_size, max_sentence_len + forms.with_root, self.embeddings_size], np.float16)) - for i in range(batch_size): - batch_word_ids[-1][i, forms.with_root:forms.with_root + len(self._embeddings[batch_perm[i]])] = \ - self._embeddings[batch_perm[i]] - - # Character-level data - batch_charseq_ids, batch_charseqs, batch_charseq_lens = [], [], [] - for factor in self._factors: - if not factor.characters: - batch_charseq_ids.append([]) - batch_charseqs.append([]) - batch_charseq_lens.append([]) - continue - - batch_charseq_ids.append(np.zeros([batch_size, max_sentence_len + factor.with_root], np.int32)) - charseqs_map = {} - charseqs = [] - charseq_lens = [] - for i in range(batch_size): - for j, charseq_id in enumerate(factor.charseq_ids[batch_perm[i]]): - if charseq_id not in charseqs_map: - charseqs_map[charseq_id] = len(charseqs) - charseqs.append(factor.charseqs[charseq_id][:max_form_length]) - batch_charseq_ids[-1][i, j] = charseqs_map[charseq_id] - - batch_charseq_lens.append(np.array([len(charseq) for charseq in charseqs], np.int32)) - batch_charseqs.append(np.zeros([len(charseqs), np.max(batch_charseq_lens[-1])], np.int32)) - for i in range(len(charseqs)): - batch_charseqs[-1][i, 0:len(charseqs[i])] = charseqs[i] - - return self._sentence_lens[batch_perm], batch_word_ids, batch_charseq_ids, batch_charseqs, batch_charseq_lens - - def write_sentence(self, output, index, overrides): - for i in range(self._sentence_lens[index] + 1): - # Start by writing extras - if index < len(self._extras) and i < len(self._extras[index]) and self._extras[index][i]: - print(self._extras[index][i], file=output) - if i == self._sentence_lens[index]: break - - fields = [] - fields.append(str(i + 1)) - for f in range(self.FACTORS): - factor = self._factors[f] - offset = i + factor.with_root - - field = factor.strings[index][offset] - - # Overrides - if overrides is not None and f < len(overrides) and overrides[f] is not None: - override = overrides[f][offset] - if f == self.HEAD: - field = str(override) if override >= 0 else "_" - elif (f == self.LEMMAS or f == self.XPOS) and isinstance(override, str): - field = override - else: - field = factor.words[override] - if f == self.LEMMAS: - try: - field = self._apply_lemma_rule(fields[-1], field) - except: - print("Applying lemma rule failed for form '{}' and rule '{}', using the form as lemma".format( - fields[-1], field), file=sys.stderr) - field = fields[-1] - # Do not generate empty lemmas - field = field or fields[-1] - - fields.append(field) - - print("\t".join(fields), file=output) - print(file=output) - - @staticmethod - def _min_edit_script(source, target, allow_copy): - a = [[(len(source) + len(target) + 1, None)] * (len(target) + 1) for _ in range(len(source) + 1)] - for i in range(0, len(source) + 1): - for j in range(0, len(target) + 1): - if i == 0 and j == 0: - a[i][j] = (0, "") - else: - if allow_copy and i and j and source[i - 1] == target[j - 1] and a[i-1][j-1][0] < a[i][j][0]: - a[i][j] = (a[i-1][j-1][0], a[i-1][j-1][1] + "→") - if i and a[i-1][j][0] < a[i][j][0]: - a[i][j] = (a[i-1][j][0] + 1, a[i-1][j][1] + "-") - if j and a[i][j-1][0] < a[i][j][0]: - a[i][j] = (a[i][j-1][0] + 1, a[i][j-1][1] + "+" + target[j - 1]) - return a[-1][-1][1] - - @staticmethod - def _gen_lemma_rule(form, lemma, allow_copy): - form = form.lower() - - previous_case = -1 - lemma_casing = "" - for i, c in enumerate(lemma): - case = "↑" if c.lower() != c else "↓" - if case != previous_case: - lemma_casing += "{}{}{}".format("¦" if lemma_casing else "", case, i if i <= len(lemma) // 2 else i - len(lemma)) - previous_case = case - lemma = lemma.lower() - - best, best_form, best_lemma = 0, 0, 0 - for l in range(len(lemma)): - for f in range(len(form)): - cpl = 0 - while f + cpl < len(form) and l + cpl < len(lemma) and form[f + cpl] == lemma[l + cpl]: cpl += 1 - if cpl > best: - best = cpl - best_form = f - best_lemma = l - - rule = lemma_casing + ";" - if not best: - rule += "a" + lemma - else: - rule += "d{}¦{}".format( - UDPipe2Dataset._min_edit_script(form[:best_form], lemma[:best_lemma], allow_copy), - UDPipe2Dataset._min_edit_script(form[best_form + best:], lemma[best_lemma + best:], allow_copy), - ) - return rule - - @staticmethod - def _apply_lemma_rule(form, lemma_rule): - casing, rule = lemma_rule.split(";", 1) - if rule.startswith("a"): - lemma = rule[1:] - else: - form = form.lower() - rules, rule_sources = rule[1:].split("¦"), [] - assert len(rules) == 2 - for rule in rules: - source, i = 0, 0 - while i < len(rule): - if rule[i] == "→" or rule[i] == "-": - source += 1 - else: - assert rule[i] == "+" - i += 1 - i += 1 - rule_sources.append(source) - - try: - lemma, form_offset = "", 0 - for i in range(2): - j, offset = 0, (0 if i == 0 else len(form) - rule_sources[1]) - while j < len(rules[i]): - if rules[i][j] == "→": - lemma += form[offset] - offset += 1 - elif rules[i][j] == "-": - offset += 1 - else: - assert(rules[i][j] == "+") - lemma += rules[i][j + 1] - j += 1 - j += 1 - if i == 0: - lemma += form[rule_sources[0] : len(form) - rule_sources[1]] - except: - lemma = form - - for rule in casing.split("¦"): - if rule == "↓0": continue # The lemma is lowercased initially - if not rule: continue # Empty lemma might generate empty casing rule - case, offset = rule[0], int(rule[1:]) - lemma = lemma[:offset] + (lemma[offset:].upper() if case == "↑" else lemma[offset:].lower()) - - return lemma diff --git a/udpipe2/udpipe2_eval.py b/udpipe2/udpipe2_eval.py deleted file mode 100644 index dcc89b04e5b6dcf1f50bd22d5921e7a772444ac3..0000000000000000000000000000000000000000 --- a/udpipe2/udpipe2_eval.py +++ /dev/null @@ -1,594 +0,0 @@ -#!/usr/bin/env python - -# Compatible with Python 2.7 and 3.2+, can be used either as a module -# or a standalone executable. -# -# Copyright 2017, 2018 Institute of Formal and Applied Linguistics (UFAL), -# Faculty of Mathematics and Physics, Charles University, Czech Republic. -# -# This Source Code Form is subject to the terms of the Mozilla Public -# License, v. 2.0. If a copy of the MPL was not distributed with this -# file, You can obtain one at http://mozilla.org/MPL/2.0/. -# -# Authors: Milan Straka, Martin Popel -# -# Changelog: -# - [12 Apr 2018] Version 0.9: Initial release. -# - [19 Apr 2018] Version 1.0: Fix bug in MLAS (duplicate entries in functional_children). -# Add --counts option. -# - [02 May 2018] Version 1.1: When removing spaces to match gold and system characters, -# consider all Unicode characters of category Zs instead of -# just ASCII space. -# - [12 Sep 2020] Version 1.2: Renamed to udpipe2_eval when releasing with UDPipe 2 -# Allow evaluation with underscores in HEAD. -# - [26 Jul 2022] Version 1.3: Allow evaluation with multiple roots, if requested. - -# Command line usage -# ------------------ -# udpipe2_eval.py [-v] gold_conllu_file system_conllu_file -# -# - if no -v is given, only the official CoNLL18 UD Shared Task evaluation metrics -# are printed -# - if -v is given, more metrics are printed (as precision, recall, F1 score, -# and in case the metric is computed on aligned words also accuracy on these): -# - Tokens: how well do the gold tokens match system tokens -# - Sentences: how well do the gold sentences match system sentences -# - Words: how well can the gold words be aligned to system words -# - UPOS: using aligned words, how well does UPOS match -# - XPOS: using aligned words, how well does XPOS match -# - UFeats: using aligned words, how well does universal FEATS match -# - AllTags: using aligned words, how well does UPOS+XPOS+FEATS match -# - Lemmas: using aligned words, how well does LEMMA match -# - UAS: using aligned words, how well does HEAD match -# - LAS: using aligned words, how well does HEAD+DEPREL(ignoring subtypes) match -# - CLAS: using aligned words with content DEPREL, how well does -# HEAD+DEPREL(ignoring subtypes) match -# - MLAS: using aligned words with content DEPREL, how well does -# HEAD+DEPREL(ignoring subtypes)+UPOS+UFEATS+FunctionalChildren(DEPREL+UPOS+UFEATS) match -# - BLEX: using aligned words with content DEPREL, how well does -# HEAD+DEPREL(ignoring subtypes)+LEMMAS match -# - if -c is given, raw counts of correct/gold_total/system_total/aligned words are printed -# instead of precision/recall/F1/AlignedAccuracy for all metrics. - -# API usage -# --------- -# - load_conllu(file) -# - loads CoNLL-U file from given file object to an internal representation -# - the file object should return str in both Python 2 and Python 3 -# - raises UDError exception if the given file cannot be loaded -# - evaluate(gold_ud, system_ud) -# - evaluate the given gold and system CoNLL-U files (loaded with load_conllu) -# - raises UDError if the concatenated tokens of gold and system file do not match -# - returns a dictionary with the metrics described above, each metric having -# three fields: precision, recall and f1 - -# Description of token matching -# ----------------------------- -# In order to match tokens of gold file and system file, we consider the text -# resulting from concatenation of gold tokens and text resulting from -# concatenation of system tokens. These texts should match -- if they do not, -# the evaluation fails. -# -# If the texts do match, every token is represented as a range in this original -# text, and tokens are equal only if their range is the same. - -# Description of word matching -# ---------------------------- -# When matching words of gold file and system file, we first match the tokens. -# The words which are also tokens are matched as tokens, but words in multi-word -# tokens have to be handled differently. -# -# To handle multi-word tokens, we start by finding "multi-word spans". -# Multi-word span is a span in the original text such that -# - it contains at least one multi-word token -# - all multi-word tokens in the span (considering both gold and system ones) -# are completely inside the span (i.e., they do not "stick out") -# - the multi-word span is as small as possible -# -# For every multi-word span, we align the gold and system words completely -# inside this span using LCS on their FORMs. The words not intersecting -# (even partially) any multi-word span are then aligned as tokens. - - -from __future__ import division -from __future__ import print_function - -import argparse -import io -import sys -import unicodedata -import unittest - -__version__ = "2.1.1-dev" - - -# CoNLL-U column names -ID, FORM, LEMMA, UPOS, XPOS, FEATS, HEAD, DEPREL, DEPS, MISC = range(10) - -# Content and functional relations -CONTENT_DEPRELS = { - "nsubj", "obj", "iobj", "csubj", "ccomp", "xcomp", "obl", "vocative", - "expl", "dislocated", "advcl", "advmod", "discourse", "nmod", "appos", - "nummod", "acl", "amod", "conj", "fixed", "flat", "compound", "list", - "parataxis", "orphan", "goeswith", "reparandum", "root", "dep" -} - -FUNCTIONAL_DEPRELS = { - "aux", "cop", "mark", "det", "clf", "case", "cc" -} - -UNIVERSAL_FEATURES = { - "PronType", "NumType", "Poss", "Reflex", "Foreign", "Abbr", "Gender", - "Animacy", "Number", "Case", "Definite", "Degree", "VerbForm", "Mood", - "Tense", "Aspect", "Voice", "Evident", "Polarity", "Person", "Polite" -} - -# UD Error is used when raising exceptions in this module -class UDError(Exception): - pass - -# Load given CoNLL-U file into internal representation -def load_conllu(file, single_root=1): - # Internal representation classes - class UDRepresentation: - def __init__(self): - # Characters of all the tokens in the whole file. - # Whitespace between tokens is not included. - self.characters = [] - # List of UDSpan instances with start&end indices into `characters`. - self.tokens = [] - # List of UDWord instances. - self.words = [] - # List of UDSpan instances with start&end indices into `characters`. - self.sentences = [] - class UDSpan: - def __init__(self, start, end): - self.start = start - # Note that self.end marks the first position **after the end** of span, - # so we can use characters[start:end] or range(start, end). - self.end = end - class UDWord: - def __init__(self, span, columns, is_multiword): - # Span of this word (or MWT, see below) within ud_representation.characters. - self.span = span - # 10 columns of the CoNLL-U file: ID, FORM, LEMMA,... - self.columns = columns - # is_multiword==True means that this word is part of a multi-word token. - # In that case, self.span marks the span of the whole multi-word token. - self.is_multiword = is_multiword - # Reference to the UDWord instance representing the HEAD (or None if root). - self.parent = None - # List of references to UDWord instances representing functional-deprel children. - self.functional_children = [] - # Only consider universal FEATS. - self.columns[FEATS] = "|".join(sorted(feat for feat in columns[FEATS].split("|") - if feat.split("=", 1)[0] in UNIVERSAL_FEATURES)) - # Let's ignore language-specific deprel subtypes. - self.columns[DEPREL] = columns[DEPREL].split(":")[0] - # Precompute which deprels are CONTENT_DEPRELS and which FUNCTIONAL_DEPRELS - self.is_content_deprel = self.columns[DEPREL] in CONTENT_DEPRELS - self.is_functional_deprel = self.columns[DEPREL] in FUNCTIONAL_DEPRELS - - ud = UDRepresentation() - - # Load the CoNLL-U file - index, sentence_start = 0, None - while True: - line = file.readline() - if not line: - break - line = line.rstrip("\r\n") - - # Handle sentence start boundaries - if sentence_start is None: - # Skip comments - if line.startswith("#"): - continue - # Start a new sentence - ud.sentences.append(UDSpan(index, 0)) - sentence_start = len(ud.words) - if not line: - # Add parent and children UDWord links and check there are no cycles - def process_word(word): - if word.parent == "remapping": - raise UDError("There is a cycle in a sentence") - if word.parent is None: - if word.columns[HEAD] == "_": - word.parent = "missing" - else: - head = int(word.columns[HEAD]) - if head < 0 or head > len(ud.words) - sentence_start: - raise UDError("HEAD '{}' points outside of the sentence".format(word.columns[HEAD])) - if head: - parent = ud.words[sentence_start + head - 1] - word.parent = "remapping" - process_word(parent) - word.parent = parent - - for word in ud.words[sentence_start:]: - process_word(word) - # func_children cannot be assigned within process_word - # because it is called recursively and may result in adding one child twice. - for word in ud.words[sentence_start:]: - if word.parent and word.is_functional_deprel: - word.parent.functional_children.append(word) - - # Check there is a single root node - if single_root: - if len([word for word in ud.words[sentence_start:] if word.parent is None]) > 1: - raise UDError("There are multiple roots in a sentence") - - # End the sentence - ud.sentences[-1].end = index - sentence_start = None - continue - - # Read next token/word - columns = line.split("\t") - if len(columns) != 10: - raise UDError("The CoNLL-U line does not contain 10 tab-separated columns: '{}'".format(line)) - - # Skip empty nodes - if "." in columns[ID]: - continue - - # Delete spaces from FORM, so gold.characters == system.characters - # even if one of them tokenizes the space. Use any Unicode character - # with category Zs. - if sys.version_info < (3, 0) and isinstance(line, str): - columns[FORM] = columns[FORM].decode("utf-8") - columns[FORM] = "".join(filter(lambda c: unicodedata.category(c) != "Zs", columns[FORM])) - if sys.version_info < (3, 0) and isinstance(line, str): - columns[FORM] = columns[FORM].encode("utf-8") - if not columns[FORM]: - raise UDError("There is an empty FORM in the CoNLL-U file") - - # Save token - ud.characters.extend(columns[FORM]) - ud.tokens.append(UDSpan(index, index + len(columns[FORM]))) - index += len(columns[FORM]) - - # Handle multi-word tokens to save word(s) - if "-" in columns[ID]: - try: - start, end = map(int, columns[ID].split("-")) - except: - raise UDError("Cannot parse multi-word token ID '{}'".format(columns[ID])) - - for _ in range(start, end + 1): - word_line = file.readline().rstrip("\r\n") - word_columns = word_line.split("\t") - if len(word_columns) != 10: - raise UDError("The CoNLL-U line does not contain 10 tab-separated columns: '{}'".format(word_line)) - ud.words.append(UDWord(ud.tokens[-1], word_columns, is_multiword=True)) - # Basic tokens/words - else: - try: - word_id = int(columns[ID]) - except: - raise UDError("Cannot parse word ID '{}'".format(columns[ID])) - if word_id != len(ud.words) - sentence_start + 1: - raise UDError("Incorrect word ID '{}' for word '{}', expected '{}'".format(columns[ID], columns[FORM], len(ud.words) - sentence_start + 1)) - - if columns[HEAD] != "_": - try: - head_id = int(columns[HEAD]) - except: - raise UDError("Cannot parse HEAD '{}'".format(columns[HEAD])) - if head_id < 0: - raise UDError("HEAD cannot be negative") - - ud.words.append(UDWord(ud.tokens[-1], columns, is_multiword=False)) - - if sentence_start is not None: - raise UDError("The CoNLL-U file does not end with empty line") - - return ud - -# Evaluate the gold and system treebanks (loaded using load_conllu). -def evaluate(gold_ud, system_ud): - class Score: - def __init__(self, gold_total, system_total, correct, aligned_total=None): - self.correct = correct - self.gold_total = gold_total - self.system_total = system_total - self.aligned_total = aligned_total - self.precision = correct / system_total if system_total else 0.0 - self.recall = correct / gold_total if gold_total else 0.0 - self.f1 = 2 * correct / (system_total + gold_total) if system_total + gold_total else 0.0 - self.aligned_accuracy = correct / aligned_total if aligned_total else aligned_total - class AlignmentWord: - def __init__(self, gold_word, system_word): - self.gold_word = gold_word - self.system_word = system_word - class Alignment: - def __init__(self, gold_words, system_words): - self.gold_words = gold_words - self.system_words = system_words - self.matched_words = [] - self.matched_words_map = {} - def append_aligned_words(self, gold_word, system_word): - self.matched_words.append(AlignmentWord(gold_word, system_word)) - self.matched_words_map[system_word] = gold_word - - def lower(text): - if sys.version_info < (3, 0) and isinstance(text, str): - return text.decode("utf-8").lower() - return text.lower() - - def spans_score(gold_spans, system_spans): - correct, gi, si = 0, 0, 0 - while gi < len(gold_spans) and si < len(system_spans): - if system_spans[si].start < gold_spans[gi].start: - si += 1 - elif gold_spans[gi].start < system_spans[si].start: - gi += 1 - else: - correct += gold_spans[gi].end == system_spans[si].end - si += 1 - gi += 1 - - return Score(len(gold_spans), len(system_spans), correct) - - def alignment_score(alignment, key_fn=None, filter_fn=None): - if filter_fn is not None: - gold = sum(1 for gold in alignment.gold_words if filter_fn(gold)) - system = sum(1 for system in alignment.system_words if filter_fn(system)) - aligned = sum(1 for word in alignment.matched_words if filter_fn(word.gold_word)) - else: - gold = len(alignment.gold_words) - system = len(alignment.system_words) - aligned = len(alignment.matched_words) - - if key_fn is None: - # Return score for whole aligned words - return Score(gold, system, aligned) - - def gold_aligned_gold(word): - return word - def gold_aligned_system(word): - return alignment.matched_words_map.get(word, "NotAligned") if word is not None else None - correct = 0 - for words in alignment.matched_words: - if filter_fn is None or filter_fn(words.gold_word): - if key_fn(words.gold_word, gold_aligned_gold) == key_fn(words.system_word, gold_aligned_system): - correct += 1 - - return Score(gold, system, correct, aligned) - - def beyond_end(words, i, multiword_span_end): - if i >= len(words): - return True - if words[i].is_multiword: - return words[i].span.start >= multiword_span_end - return words[i].span.end > multiword_span_end - - def extend_end(word, multiword_span_end): - if word.is_multiword and word.span.end > multiword_span_end: - return word.span.end - return multiword_span_end - - def find_multiword_span(gold_words, system_words, gi, si): - # We know gold_words[gi].is_multiword or system_words[si].is_multiword. - # Find the start of the multiword span (gs, ss), so the multiword span is minimal. - # Initialize multiword_span_end characters index. - if gold_words[gi].is_multiword: - multiword_span_end = gold_words[gi].span.end - if not system_words[si].is_multiword and system_words[si].span.start < gold_words[gi].span.start: - si += 1 - else: # if system_words[si].is_multiword - multiword_span_end = system_words[si].span.end - if not gold_words[gi].is_multiword and gold_words[gi].span.start < system_words[si].span.start: - gi += 1 - gs, ss = gi, si - - # Find the end of the multiword span - # (so both gi and si are pointing to the word following the multiword span end). - while not beyond_end(gold_words, gi, multiword_span_end) or \ - not beyond_end(system_words, si, multiword_span_end): - if gi < len(gold_words) and (si >= len(system_words) or - gold_words[gi].span.start <= system_words[si].span.start): - multiword_span_end = extend_end(gold_words[gi], multiword_span_end) - gi += 1 - else: - multiword_span_end = extend_end(system_words[si], multiword_span_end) - si += 1 - return gs, ss, gi, si - - def compute_lcs(gold_words, system_words, gi, si, gs, ss): - lcs = [[0] * (si - ss) for i in range(gi - gs)] - for g in reversed(range(gi - gs)): - for s in reversed(range(si - ss)): - if lower(gold_words[gs + g].columns[FORM]) == lower(system_words[ss + s].columns[FORM]): - lcs[g][s] = 1 + (lcs[g+1][s+1] if g+1 < gi-gs and s+1 < si-ss else 0) - lcs[g][s] = max(lcs[g][s], lcs[g+1][s] if g+1 < gi-gs else 0) - lcs[g][s] = max(lcs[g][s], lcs[g][s+1] if s+1 < si-ss else 0) - return lcs - - def align_words(gold_words, system_words): - alignment = Alignment(gold_words, system_words) - - gi, si = 0, 0 - while gi < len(gold_words) and si < len(system_words): - if gold_words[gi].is_multiword or system_words[si].is_multiword: - # A: Multi-word tokens => align via LCS within the whole "multiword span". - gs, ss, gi, si = find_multiword_span(gold_words, system_words, gi, si) - - if si > ss and gi > gs: - lcs = compute_lcs(gold_words, system_words, gi, si, gs, ss) - - # Store aligned words - s, g = 0, 0 - while g < gi - gs and s < si - ss: - if lower(gold_words[gs + g].columns[FORM]) == lower(system_words[ss + s].columns[FORM]): - alignment.append_aligned_words(gold_words[gs+g], system_words[ss+s]) - g += 1 - s += 1 - elif lcs[g][s] == (lcs[g+1][s] if g+1 < gi-gs else 0): - g += 1 - else: - s += 1 - else: - # B: No multi-word token => align according to spans. - if (gold_words[gi].span.start, gold_words[gi].span.end) == (system_words[si].span.start, system_words[si].span.end): - alignment.append_aligned_words(gold_words[gi], system_words[si]) - gi += 1 - si += 1 - elif gold_words[gi].span.start <= system_words[si].span.start: - gi += 1 - else: - si += 1 - - return alignment - - # Check that the underlying character sequences do match. - if gold_ud.characters != system_ud.characters: - index = 0 - while index < len(gold_ud.characters) and index < len(system_ud.characters) and \ - gold_ud.characters[index] == system_ud.characters[index]: - index += 1 - - raise UDError( - "The concatenation of tokens in gold file and in system file differ!\n" + - "First 20 differing characters in gold file: '{}' and system file: '{}'".format( - "".join(gold_ud.characters[index:index + 20]), - "".join(system_ud.characters[index:index + 20]) - ) - ) - - # Align words - alignment = align_words(gold_ud.words, system_ud.words) - - # Compute the F1-scores - return { - "Tokens": spans_score(gold_ud.tokens, system_ud.tokens), - "Sentences": spans_score(gold_ud.sentences, system_ud.sentences), - "Words": alignment_score(alignment), - "UPOS": alignment_score(alignment, lambda w, _: w.columns[UPOS]), - "XPOS": alignment_score(alignment, lambda w, _: w.columns[XPOS]), - "UFeats": alignment_score(alignment, lambda w, _: w.columns[FEATS]), - "AllTags": alignment_score(alignment, lambda w, _: (w.columns[UPOS], w.columns[XPOS], w.columns[FEATS])), - "Lemmas": alignment_score(alignment, lambda w, ga: w.columns[LEMMA] if ga(w).columns[LEMMA] != "_" else "_"), - "UAS": alignment_score(alignment, lambda w, ga: ga(w.parent)), - "LAS": alignment_score(alignment, lambda w, ga: (ga(w.parent), w.columns[DEPREL])), - "CLAS": alignment_score(alignment, lambda w, ga: (ga(w.parent), w.columns[DEPREL]), - filter_fn=lambda w: w.is_content_deprel), - "MLAS": alignment_score(alignment, lambda w, ga: (ga(w.parent), w.columns[DEPREL], w.columns[UPOS], w.columns[FEATS], - [(ga(c), c.columns[DEPREL], c.columns[UPOS], c.columns[FEATS]) - for c in w.functional_children]), - filter_fn=lambda w: w.is_content_deprel), - "BLEX": alignment_score(alignment, lambda w, ga: (ga(w.parent), w.columns[DEPREL], - w.columns[LEMMA] if ga(w).columns[LEMMA] != "_" else "_"), - filter_fn=lambda w: w.is_content_deprel), - } - - -def load_conllu_file(path, single_root=1): - _file = open(path, mode="r", **({"encoding": "utf-8"} if sys.version_info >= (3, 0) else {})) - return load_conllu(_file, single_root) - -def evaluate_wrapper(args): - # Load CoNLL-U files - gold_ud = load_conllu_file(args.gold_file, args.single_root) - system_ud = load_conllu_file(args.system_file, args.single_root) - return evaluate(gold_ud, system_ud) - -def main(): - # Parse arguments - parser = argparse.ArgumentParser() - parser.add_argument("gold_file", type=str, - help="Name of the CoNLL-U file with the gold data.") - parser.add_argument("system_file", type=str, - help="Name of the CoNLL-U file with the predicted data.") - parser.add_argument("--verbose", "-v", default=False, action="store_true", - help="Print all metrics.") - parser.add_argument("--counts", "-c", default=False, action="store_true", - help="Print raw counts of correct/gold/system/aligned words instead of prec/rec/F1 for all metrics.") - parser.add_argument("--no_single_root", dest="single_root", default=True, action="store_false", - help="Allow multiple roots in a sentence.") - args = parser.parse_args() - - # Evaluate - evaluation = evaluate_wrapper(args) - - # Print the evaluation - if not args.verbose and not args.counts: - print("LAS F1 Score: {:.2f}".format(100 * evaluation["LAS"].f1)) - print("MLAS Score: {:.2f}".format(100 * evaluation["MLAS"].f1)) - print("BLEX Score: {:.2f}".format(100 * evaluation["BLEX"].f1)) - else: - if args.counts: - print("Metric | Correct | Gold | Predicted | Aligned") - else: - print("Metric | Precision | Recall | F1 Score | AligndAcc") - print("-----------+-----------+-----------+-----------+-----------") - for metric in["Tokens", "Sentences", "Words", "UPOS", "XPOS", "UFeats", "AllTags", "Lemmas", "UAS", "LAS", "CLAS", "MLAS", "BLEX"]: - if args.counts: - print("{:11}|{:10} |{:10} |{:10} |{:10}".format( - metric, - evaluation[metric].correct, - evaluation[metric].gold_total, - evaluation[metric].system_total, - evaluation[metric].aligned_total or (evaluation[metric].correct if metric == "Words" else "") - )) - else: - print("{:11}|{:10.2f} |{:10.2f} |{:10.2f} |{}".format( - metric, - 100 * evaluation[metric].precision, - 100 * evaluation[metric].recall, - 100 * evaluation[metric].f1, - "{:10.2f}".format(100 * evaluation[metric].aligned_accuracy) if evaluation[metric].aligned_accuracy is not None else "" - )) - -if __name__ == "__main__": - main() - -# Tests, which can be executed with `python -m unittest udpipe2_eval`. -class TestAlignment(unittest.TestCase): - @staticmethod - def _load_words(words): - """Prepare fake CoNLL-U files with fake HEAD to prevent multiple roots errors.""" - lines, num_words = [], 0 - for w in words: - parts = w.split(" ") - if len(parts) == 1: - num_words += 1 - lines.append("{}\t{}\t_\t_\t_\t_\t{}\t_\t_\t_".format(num_words, parts[0], int(num_words>1))) - else: - lines.append("{}-{}\t{}\t_\t_\t_\t_\t_\t_\t_\t_".format(num_words + 1, num_words + len(parts) - 1, parts[0])) - for part in parts[1:]: - num_words += 1 - lines.append("{}\t{}\t_\t_\t_\t_\t{}\t_\t_\t_".format(num_words, part, int(num_words>1))) - return load_conllu((io.StringIO if sys.version_info >= (3, 0) else io.BytesIO)("\n".join(lines+["\n"]))) - - def _test_exception(self, gold, system): - self.assertRaises(UDError, evaluate, self._load_words(gold), self._load_words(system)) - - def _test_ok(self, gold, system, correct): - metrics = evaluate(self._load_words(gold), self._load_words(system)) - gold_words = sum((max(1, len(word.split(" ")) - 1) for word in gold)) - system_words = sum((max(1, len(word.split(" ")) - 1) for word in system)) - self.assertEqual((metrics["Words"].precision, metrics["Words"].recall, metrics["Words"].f1), - (correct / system_words, correct / gold_words, 2 * correct / (gold_words + system_words))) - - def test_exception(self): - self._test_exception(["a"], ["b"]) - - def test_equal(self): - self._test_ok(["a"], ["a"], 1) - self._test_ok(["a", "b", "c"], ["a", "b", "c"], 3) - - def test_equal_with_multiword(self): - self._test_ok(["abc a b c"], ["a", "b", "c"], 3) - self._test_ok(["a", "bc b c", "d"], ["a", "b", "c", "d"], 4) - self._test_ok(["abcd a b c d"], ["ab a b", "cd c d"], 4) - self._test_ok(["abc a b c", "de d e"], ["a", "bcd b c d", "e"], 5) - - def test_alignment(self): - self._test_ok(["abcd"], ["a", "b", "c", "d"], 0) - self._test_ok(["abc", "d"], ["a", "b", "c", "d"], 1) - self._test_ok(["a", "bc", "d"], ["a", "b", "c", "d"], 2) - self._test_ok(["a", "bc b c", "d"], ["a", "b", "cd"], 2) - self._test_ok(["abc a BX c", "def d EX f"], ["ab a b", "cd c d", "ef e f"], 4) - self._test_ok(["ab a b", "cd bc d"], ["a", "bc", "d"], 2) - self._test_ok(["a", "bc b c", "d"], ["ab AX BX", "cd CX a"], 1) diff --git a/udpipe2/udpipe2_server.py b/udpipe2/udpipe2_server.py deleted file mode 100644 index ad2b03cc38198f69c91763a113e47ac369b48c3a..0000000000000000000000000000000000000000 --- a/udpipe2/udpipe2_server.py +++ /dev/null @@ -1,447 +0,0 @@ -#!/usr/bin/env python3 - -# This file is part of UDPipe 2.0 . -# -# Copyright 2020 Institute of Formal and Applied Linguistics, Faculty of -# Mathematics and Physics, Charles University in Prague, Czech Republic. -# -# This Source Code Form is subject to the terms of the Mozilla Public -# License, v. 2.0. If a copy of the MPL was not distributed with this -# file, You can obtain one at http://mozilla.org/MPL/2.0/. - -import argparse -import contextlib -import email.parser -import http.server -import itertools -import json -import os -import socketserver -import sys -import threading -import time -import unicodedata -import urllib.parse - -import udpipe2 -import udpipe2_dataset -import ufal.udpipe -import wembedding_service.wembeddings.wembeddings as wembeddings - -__version__ = "2.1.1-dev" - - -class TooLongError(Exception): - pass - -class Models: - class Model: - class Network: - _mutex = threading.Lock() - - def __init__(self, path, server_args): - self._path = path - self._server_args = server_args - self.network, self.args, self.train = None, None, None - - def load(self): - if self.network is not None: - return - with self._mutex: - if self.network is not None: - return - - with open(os.path.join(self._path, "options.json"), mode="r") as options_file: - self.args = argparse.Namespace(**json.load(options_file)) - udpipe2.UDPipe2.postprocess_arguments(self.args) - self.args.batch_size = self._server_args.batch_size - - self.train = udpipe2_dataset.UDPipe2Dataset.load_mappings(os.path.join(self._path, "mappings.pickle")) - self.network = udpipe2.UDPipe2(threads=self._server_args.threads) - self.network.construct(self.args, self.train, [], [], predict_only=True) - self.network.load(self._path, self.args.morphodita) - - print("Loaded model {}".format(os.path.basename(self._path)), file=sys.stderr, flush=True) - - - def __init__(self, names, path, network, variant, acknowledgements, server_args): - self.names = names - self.acknowledgements = acknowledgements - self._network = network - self._variant = variant - self._server_args = server_args - - # Load the tokenizer - tokenizer_path = os.path.join(path, "{}.tokenizer".format(variant)) - self._tokenizer = ufal.udpipe.Model.load(tokenizer_path) - if self._tokenizer is None: - raise RuntimeError("Cannot load tokenizer from {}".format(tokenizer_path)) - - self._conllu_input = ufal.udpipe.InputFormat.newConlluInputFormat() - if self._conllu_input is None: - raise RuntimeError("Cannot create CoNLL-U input format") - - self._conllu_output = ufal.udpipe.OutputFormat.newConlluOutputFormat() - if self._conllu_output is None: - raise RuntimeError("Cannot create CoNLL-U output format") - - # Load the network if requested - if names[0] in server_args.preload_models or "all" in server_args.preload_models: - self._network.load() - - def read(self, text, input_format): - reader = ufal.udpipe.InputFormat.newInputFormat(input_format) - if reader is None: - raise RuntimeError("Unknown input format '{}'".format(input_format)) - # Do not return a generator, but a list to raise exceptions early - return list(self._read(text, reader)) - - def tokenize(self, text, tokenizer_options): - tokenizer = self._tokenizer.newTokenizer(tokenizer_options) - if tokenizer is None: - raise RuntimeError("Cannot create tokenizer.") - # Do not return a generator, but a list to raise exceptions early - return list(self._read(text, tokenizer)) - - def _read(self, text, reader): - sentence = ufal.udpipe.Sentence() - processing_error = ufal.udpipe.ProcessingError() - reader.setText(text) - while reader.nextSentence(sentence, processing_error): - if len(sentence.words) > 1001: - raise TooLongError() - yield sentence - sentence = ufal.udpipe.Sentence() - if processing_error.occurred(): - raise RuntimeError("Cannot read input data: '{}'".format(processing_error.message)) - - def create_writer(self, output_format): - writer = ufal.udpipe.OutputFormat.newOutputFormat(output_format) - if writer is None: - raise RuntimeError("Unknown output format '{}'".format(output_format)) - return writer - - def predict(self, sentences, tag, parse, writer): - # Run the model - if tag or parse: - # Load the network if it has not been loaded already - self._network.load() - - wembedding_input, conllu_input = [], [] - for sentence in sentences: - wembedding_input.append([word.form for word in sentence.words[1:]]) - conllu_input.append(self._conllu_output.writeSentence(sentence)) - - time_wes = time.time() - # Compute the WEmbeddings - with self._server_args.optional_semaphore: - time_we = time.time() - if self._network.args.wembedding_model: - wembeddings = self._server_args.wembedding_server.compute_embeddings(self._network.args.wembedding_model, wembedding_input) - else: - wembeddings = [] - - time_ds = time.time() - # Create UDPipe2Dataset - dataset = udpipe2_dataset.UDPipe2Dataset(text="".join(conllu_input), train=self._network.train, shuffle_batches=False, - embeddings=wembeddings, override_variant=self._variant) - - # Prepare network arguments - network_args = argparse.Namespace(**vars(self._network.args)) - if not tag: network_args.tags = [] - if not parse: network_args.parse = 0 - - # Perform the prediction - time_nws = time.time() - with self._server_args.optional_semaphore: - time_nw = time.time() - predicted = self._network.network.predict(dataset, evaluating=False, args=network_args) - time_rd = time.time() - - # Load the predicted CoNLL-U to ufal.udpipe sentences - sentences = self._read(predicted, self._conllu_input) - - print("Request, WE {:.2f}+{:.2f}ms,".format(1000 * (time_ds - time_we), 1000 * (time_we - time_wes)), - "DS {:.2f}ms,".format(1000 * (time_nws - time_ds)), - "NW {:.2f}+{:.2f}ms,".format(1000 * (time_rd - time_nw), 1000 * (time_nw - time_nws)), - "RD {:.2f}ms.".format(1000 * (time.time() - time_rd)), - file=sys.stderr, flush=True) - - # Generate output - output = [] - for sentence in sentences: - output.append(writer.writeSentence(sentence)) - output.append(writer.finishDocument()) - return "".join(output) - - def __init__(self, server_args): - self.default_model = server_args.default_model - self.models_list = [] - self.models_by_names = {} - networks_by_path = {} - - for i in range(0, len(server_args.models), 4): - names, path, variant, acknowledgements = server_args.models[i:i+4] - names = names.split(":") - names = [name.split("-") for name in names] - names = ["-".join(parts[:None if not i else -i]) for parts in names for i in range(len(parts))] - - if not path in networks_by_path: - networks_by_path[path] = self.Model.Network(path, server_args) - self.models_list.append(self.Model(names, path, networks_by_path[path], variant, acknowledgements, server_args)) - for name in names: - self.models_by_names.setdefault(name, self.models_list[-1]) - - # Check the default model exists - assert self.default_model in self.models_by_names - - -class UDServer(socketserver.ThreadingTCPServer): - class UDServerRequestHandler(http.server.BaseHTTPRequestHandler): - protocol_version = "HTTP/1.1" - - def respond(request, content_type, code=200, additional_headers={}): - request.close_connection = True - request.send_response(code) - request.send_header("Connection", "close") - request.send_header("Content-Type", content_type) - request.send_header("Access-Control-Allow-Origin", "*") - for key, value in additional_headers.items(): - request.send_header(key, value) - request.end_headers() - - def respond_error(request, message, code=400): - request.respond("text/plain", code) - request.wfile.write(message.encode("utf-8")) - - def do_GET(request): - # Parse the URL - params = {} - try: - request.path = request.path.encode("iso-8859-1").decode("utf-8") - url = urllib.parse.urlparse(request.path) - for name, value in urllib.parse.parse_qsl(url.query, encoding="utf-8", keep_blank_values=True, errors="strict"): - params[name] = value - except: - return request.respond_error("Cannot parse request URL.") - - # Parse the body of a POST request - if request.command == "POST": - if request.headers.get("Transfer-Encoding", "identity").lower() != "identity": - return request.respond_error("Only 'identity' Transfer-Encoding of payload is supported for now.") - - try: - content_length = int(request.headers["Content-Length"]) - except: - return request.respond_error("The Content-Length of payload is required.") - - if content_length > request.server._server_args.max_request_size: - return request.respond_error("The payload size is too large.") - - # Raw text on input for weblicht - if url.path.startswith("/weblicht/"): - # Ignore all but `model` GET param - params = {"model": params["model"]} if "model" in params else {} - - try: - params["data"] = request.rfile.read(content_length).decode("utf-8") - except: - return request.respond_error("The payload is not in UTF-8 encoding.") - - if url.path == "/weblicht/tokenize": params["tokenizer"] = "" - else: params["input"] = "conllu" - params["output"] = "conllu" - if url.path == "/weblicht/tag": params["tagger"] = "" - if url.path == "/weblicht/parse": params["parser"] = "" - # multipart/form-data - elif request.headers.get("Content-Type", "").startswith("multipart/form-data"): - try: - parser = email.parser.BytesFeedParser() - parser.feed(b"Content-Type: " + request.headers["Content-Type"].encode("ascii") + b"\r\n\r\n") - while content_length: - parser.feed(request.rfile.read(min(content_length, 4096))) - content_length -= min(content_length, 4096) - for part in parser.close().get_payload(): - name = part.get_param("name", header="Content-Disposition") - if name: - params[name] = part.get_payload(decode=True).decode("utf-8") - except: - return request.respond_error("Cannot parse the multipart/form-data payload.") - # application/x-www-form-urlencoded - elif request.headers.get("Content-Type", "").startswith("application/x-www-form-urlencoded"): - try: - for name, value in urllib.parse.parse_qsl( - request.rfile.read(content_length).decode("utf-8"), encoding="utf-8", keep_blank_values=True, errors="strict"): - params[name] = value - except: - return request.respond_error("Cannot parse the application/x-www-form-urlencoded payload.") - else: - return request.respond_error("Unsupported payload Content-Type '{}'.".format(request.headers.get("Content-Type", ""))) - - # Handle /models - if url.path == "/models": - response = { - "models": {model.names[0]: ["tokenizer", "tagger", "parser"] for model in request.server._models.models_list}, - "default_model": request.server._models.default_model, - } - request.respond("application/json") - request.wfile.write(json.dumps(response, indent=1).encode("utf-8")) - # Handle /process - elif url.path in ["/process", "/weblicht/tokenize", "/weblicht/tag", "/weblicht/parse"]: - weblicht = url.path.startswith("/weblicht") - - if "data" not in params: - return request.respond_error("The parameter 'data' is required.") - params["data"] = unicodedata.normalize("NFC", params["data"]) - - model = params.get("model", request.server._models.default_model) - if model not in request.server._models.models_by_names: - return request.respond_error("The requested model '{}' does not exist.".format(model)) - model = request.server._models.models_by_names[model] - - # Start by reading and optionally tokenizing the input data. - if "tokenizer" in params: - try: - sentences = model.tokenize(params["data"], params["tokenizer"]) - except TooLongError: - return request.respond_error("During tokenization, sentence longer than 1000 words was found, aborting.\nThat should only happen with presegmented input.\nPlease make sure you do not generate such long sentences.\n") - except: - return request.respond_error("An error occured during tokenization of the input.") - else: - try: - sentences = model.read(params["data"], params.get("input", "conllu")) - except TooLongError: - return request.respond_error("Sentence longer than 1000 words was found on input, aborting.\nPlease make sure the input sentences have at most 1000 words.\n") - except: - return request.respond_error("Cannot parse the input in '{}' format.".format(params.get("input", "conllu"))) - infclen = sum(sum(len(word.form) for word in sentence.words[1:]) for sentence in sentences) - - # Create the writer - output_format = params.get("output", "conllu") - try: - writer = model.create_writer(output_format) - except: - return request.respond_error("Unknown output format '{}'.".format(output_format)) - - # Process the data - tag, parse, output_format = "tagger" in params, "parser" in params, params.get("output", "conllu") - batch, started_responding = [], False - try: - for sentence in itertools.chain(sentences, ["EOF"]): - if sentence == "EOF" or len(batch) == request.server._server_args.batch_size: - output = model.predict(batch, tag, parse, writer) - if not started_responding: - # The first batch is ready, we commit to generate output. - started_responding=True - if weblicht: - request.respond("application/conllu") - else: - request.respond("application/json", additional_headers={"X-Billing-Input-NFC-Len": str(infclen)}) - request.wfile.write(json.dumps({ - "model": model.names[0], - "acknowledgements": ["http://ufal.mff.cuni.cz/udpipe/2#udpipe2_acknowledgements", model.acknowledgements], - "result": "", - }, indent=1)[:-3].encode("utf-8")) - if output_format == "conllu": - request.wfile.write(json.dumps( - "# generator = UDPipe 2, https://lindat.mff.cuni.cz/services/udpipe\n" - "# udpipe_model = {}\n" - "# udpipe_model_licence = CC BY-NC-SA\n".format(model.names[0]))[1:-1].encode("utf-8")) - if weblicht: - request.wfile.write(output.encode("utf-8")) - else: - request.wfile.write(json.dumps(output, ensure_ascii=False)[1:-1].encode("utf-8")) - batch = [] - batch.append(sentence) - if not weblicht: - request.wfile.write(b'"\n}\n') - except: - import traceback - traceback.print_exc(file=sys.stderr) - sys.stderr.flush() - - if not started_responding: - request.respond_error("An internal error occurred during processing.") - else: - if weblicht: - request.wfile.write(b'\n\nAn internal error occurred during processing, producing incorrect CoNLL-U!') - else: - request.wfile.write(b'",\n"An internal error occurred during processing, producing incorrect JSON!"') - # Unknown URL - else: - request.respond_error("No handler for the given URL '{}'".format(url.path), code=404) - - def do_POST(request): - return request.do_GET() - - daemon_threads = False - - def __init__(self, server_args, models): - super().__init__(("", server_args.port), self.UDServerRequestHandler) - - self._server_args = server_args - self._models = models - - def server_bind(self): - import socket - self.socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) - self.socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEPORT, 1) - super().server_bind() - - def service_actions(self): - if isinstance(getattr(self, "_threads", None), list): - if len(self._threads) >= 1024: - self._threads = [thread for thread in self._threads if thread.is_alive()] - - -if __name__ == "__main__": - import signal - import threading - - # Parse server arguments - parser = argparse.ArgumentParser() - parser.add_argument("port", type=int, help="Port to use") - parser.add_argument("default_model", type=str, help="Default model") - parser.add_argument("models", type=str, nargs="+", help="Models to serve") - parser.add_argument("--batch_size", default=32, type=int, help="Batch size") - parser.add_argument("--concurrent", default=None, type=int, help="Concurrent computations of NN") - parser.add_argument("--logfile", default=None, type=str, help="Log path") - parser.add_argument("--max_request_size", default=4096*1024, type=int, help="Maximum request size") - parser.add_argument("--preload_models", default=[], nargs="*", type=str, help="Models to preload, or `all`") - parser.add_argument("--threads", default=0, type=int, help="Threads to use") - parser.add_argument("--wembedding_preload_models", default=[], nargs="*", type=str, help="WEmbedding models to preload") - parser.add_argument("--wembedding_server", default=None, type=str, help="Address of an WEmbedding server") - args = parser.parse_args() - - # Log stderr to logfile if given - if args.logfile is not None: - sys.stderr = open(args.logfile, "a", encoding="utf-8") - - # Load the models - models = Models(args) - - # Create the WEmbeddings client - if args.wembedding_server is not None: - args.wembedding_server = wembeddings.WEmbeddings.ClientNetwork(args.wembedding_server) - else: - args.wembedding_server = wembeddings.WEmbeddings(threads=args.threads, preload_models=args.wembedding_preload_models) - - # Create a semaphore if needed - args.optional_semaphore = threading.Semaphore(args.concurrent) if args.concurrent is not None else contextlib.nullcontext() - - # Create the server - server = UDServer(args, models) - server_thread = threading.Thread(target=server.serve_forever, daemon=True) - server_thread.start() - - print("Started UDPipe 2 server on port {}.".format(args.port), file=sys.stderr) - print("To stop it gracefully, either send SIGINT (Ctrl+C) or SIGUSR1.", file=sys.stderr, flush=True) - - # Wait until the server should be closed - signal.pthread_sigmask(signal.SIG_BLOCK, [signal.SIGINT, signal.SIGUSR1]) - signal.sigwait([signal.SIGINT, signal.SIGUSR1]) - print("Initiating shutdown of the UDPipe 2 server.", file=sys.stderr, flush=True) - server.shutdown() - print("Stopped handling new requests, processing all current ones.", file=sys.stderr, flush=True) - server.server_close() - print("Finished shutdown of the UDPipe 2 server.", file=sys.stderr, flush=True)