Repository Analysis

stanfordnlp/stanza

Stanford NLP Python library for tokenization, sentence segmentation, NER, and parsing of many human languages

27.8 Moderate AI signal View on GitHub

Analysis Overview

This report presents the forensic synthetic code analysis of stanfordnlp/stanza, a Python project with 7,858 GitHub stars. SynthScan v2.0 examined 114,226 lines of code across 631 source files, recording 2231 pattern matches distributed across 20 syntactic categories. The overall adjusted score of 27.8 places this repository in the Moderate AI signal band.

The scanner applied 160+ deterministic lexical heuristics, multi-line block detectors, abstract syntax tree depth profilers, and a cross-file Jaccard similarity matrix to construct a statistically normalised synthetic code estimate. All matches are individually weighted by severity coefficient and contextual multiplier before summation, and the resulting headline score is temporally discounted to account for the repository's development history relative to the commercial emergence of large language model coding tooling (November 2022 onward).

27.8
Adjusted Score
27.8
Raw Score
100%
Time Factor
2026-08-08
Last Push
7.9K
Stars
Python
Language
114.2K
Lines of Code
631
Files
2.2K
Pattern Hits
2026-08-09
Scan Date
0.09
HC Hit Rate

What These Metrics Mean

Adjusted Score
Primary synthetic code indicator. Raw score normalised per 1,000 lines of code and multiplied by the temporal discount factor. This is the definitive comparative metric — use it to rank repositories by AI authorship density.
Raw Score
The unmodified sum of all severity-weighted, context-multiplied pattern match scores before temporal discounting. Reflects the absolute signal strength independent of when the repository was last active.
Time Factor
The temporal discount multiplier (0–100%) applied to the raw score. Repositories last updated before ChatGPT's launch (Nov 2022) receive a 5% factor. Full signal is only assigned to repositories active in the post-adoption era (Jan 2024+).
Pattern Hits
Total count of individual pattern matches across all files and categories. A high hit count with a low score may indicate a very large codebase with isolated AI snippets; a low count with a high score indicates dense, concentrated AI signatures.
HC Hit Rate
High+Critical pattern hits per file, averaged across the repository. This orthogonal signal catches repositories where a few files are densely packed with high-severity AI tells — a strong indicator even when the normalised score appears moderate due to codebase size.
Lines of Code / Files
Total lines and files analysed. The scanner examines 94 file extensions. These denominators are used to normalise the score, enabling fair comparison between repositories of vastly different sizes.

Score History

Longitudinal tracking requires multiple scan runs. Once this repository is re-scanned after new commits land, this chart will visualise how the synthetic code signal evolves over time — enabling you to detect whether AI authorship is growing, stabilising, or being actively corrected by human engineers.

No multi-scan history yet — run the scanner again to build trend data.

Severity Breakdown

Classifies detected patterns by their diagnostic confidence and structural impact. CRITICAL patterns (coefficient 10) represent definitive synthetic signatures — hallucinated imports, explicit LLM attribution metadata — virtually never produced by human authors. HIGH (5) indicates strong structural tells such as cross-file repetition or cross-linguistic idioms. MEDIUM (2) covers recognisable conversational padding and AI-specific vocabulary. LOW (1) captures subtle indicators like tautological comments and generic boilerplate that require density to carry independent signal.

CRITICAL 1HIGH 56MEDIUM 316LOW 1858

Directory Score Breakdown

This horizontal bar chart decomposes the repository's raw synthetic code score by top-level directory, allowing you to pinpoint precisely which modules or components carry the highest AI authorship density. Directories with disproportionately high scores relative to their size warrant targeted manual review: concentrated AI signatures often trace back to mass-generated configuration layers, auto-ported test suites, LLM-scaffolded boilerplate classes, or entire subsystems authored under heavy copilot assistance. Use this view to prioritise your human code-review effort.

Pattern Findings

The scanner identified 2231 distinct pattern matches across 20 syntactic categories. Each entry below represents a discrete location in the source code where the engine recorded a statistically significant AI authorship indicator. Expand any category row to inspect the individual file paths, line numbers, code snippets, and the lexical context (CODE, COMMENT, or STRING) in which each match was detected.

Reading the findings table: The Severity column indicates the diagnostic confidence level (CRITICAL / HIGH / MEDIUM / LOW). The Context column identifies whether the match occurred inside executable code, an inline comment, or a string literal — comment-context matches receive a ×1.5 weight because LLMs systematically over-annotate. The ⚡ bolt icon marks clustered matches: three or more patterns within a 10-line window, each receiving an additional ×1.5 density multiplier as dense clusters constitute far stronger evidence of synthetic authorship than isolated hits.

Decorative Section Separators302 hits · 1038 pts
SeverityFileLineSnippetContext
MEDIUMstanza/pipeline/demo/stanza-parseviewer.js47// ── Layout constants ───────────────────────────────────────────────COMMENT
MEDIUMstanza/pipeline/demo/stanza-parseviewer.js55// ── Tree layout helpers ────────────────────────────────────────────COMMENT
MEDIUMstanza/pipeline/demo/stanza-parseviewer.js125// ── Drawing ────────────────────────────────────────────────────────COMMENT
MEDIUMstanza/pipeline/demo/stanza-parseviewer.js207// ── Public API (identical to original) ────────────────────────────COMMENT
MEDIUMstanza/tests/pipeline/test_tokenize_speaker.py87# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/pipeline/test_tokenize_speaker.py89# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/pipeline/test_tokenize_speaker.py174# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/pipeline/test_tokenize_speaker.py176# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/pipeline/test_tokenize_speaker.py220# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/pipeline/test_tokenize_speaker.py222# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/pipeline/test_tokenize_speaker.py301# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/pipeline/test_tokenize_speaker.py303# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/pipeline/test_tokenize_speaker.py26# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/pipeline/test_tokenize_speaker.py29# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/pipeline/test_tokenize_speaker.py341# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/pipeline/test_tokenize_speaker.py343# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/resources/test_list_installed.py81# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/resources/test_list_installed.py83# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/resources/test_list_installed.py101# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/resources/test_list_installed.py103# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/resources/test_list_installed.py121# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/resources/test_list_installed.py123# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/resources/test_list_installed.py228# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/resources/test_list_installed.py230# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/resources/test_list_installed.py257# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/resources/test_list_installed.py259# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/resources/test_list_installed.py294# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/resources/test_list_installed.py296# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/resources/test_list_installed.py361# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/resources/test_list_installed.py363# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/resources/test_list_installed.py32# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/resources/test_list_installed.py34# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/mwt/test_mwt_training.py32# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/mwt/test_mwt_training.py42# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/mwt/test_mwt_training.py115# ---------------------------------------------------------------------------STRING
MEDIUMstanza/tests/mwt/test_mwt_training.py117# ---------------------------------------------------------------------------STRING
MEDIUMstanza/tests/mwt/test_mwt_training.py159# ---------------------------------------------------------------------------STRING
MEDIUMstanza/tests/mwt/test_mwt_training.py161# ---------------------------------------------------------------------------STRING
MEDIUMstanza/tests/mwt/test_mwt_training.py345# ---------------------------------------------------------------------------STRING
MEDIUMstanza/tests/mwt/test_mwt_training.py352# ---------------------------------------------------------------------------STRING
MEDIUM…a/tests/lemma_classifier/test_lemma_classifier_base.py18# ---------------------------------------------------------------------------COMMENT
MEDIUM…a/tests/lemma_classifier/test_lemma_classifier_base.py20# ---------------------------------------------------------------------------COMMENT
MEDIUM…a/tests/lemma_classifier/test_lemma_classifier_base.py44# ---------------------------------------------------------------------------COMMENT
MEDIUM…a/tests/lemma_classifier/test_lemma_classifier_base.py46# ---------------------------------------------------------------------------COMMENT
MEDIUM…a/tests/lemma_classifier/test_lemma_classifier_base.py79# ---------------------------------------------------------------------------COMMENT
MEDIUM…a/tests/lemma_classifier/test_lemma_classifier_base.py81# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/lemma_classifier/test_contextual_preds.py38# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/lemma_classifier/test_contextual_preds.py42# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/lemma_classifier/test_contextual_preds.py74# ---------------------------------------------------------------------------STRING
MEDIUMstanza/tests/lemma_classifier/test_contextual_preds.py76# ---------------------------------------------------------------------------STRING
MEDIUMstanza/tests/lemma_classifier/test_contextual_preds.py116# ---------------------------------------------------------------------------STRING
MEDIUMstanza/tests/lemma_classifier/test_contextual_preds.py118# ---------------------------------------------------------------------------STRING
MEDIUMstanza/tests/lemma_classifier/test_contextual_preds.py143# ---------------------------------------------------------------------------STRING
MEDIUMstanza/tests/lemma_classifier/test_contextual_preds.py145# ---------------------------------------------------------------------------STRING
MEDIUMstanza/tests/utils/test_confusion.py60# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/utils/test_confusion.py62# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/utils/test_confusion.py92# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/utils/test_confusion.py94# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/utils/test_confusion.py21# ---------------------------------------------------------------------------COMMENT
MEDIUMstanza/tests/utils/test_confusion.py23# ---------------------------------------------------------------------------COMMENT
242 more matches not shown…
Hyper-Verbose Identifiers779 hits · 718 pts
SeverityFileLineSnippetContext
LOWstanza/pipeline/tokenize_processor.py119def _apply_speaker_to_sentences(result_doc, segments, original_text):CODE
LOWstanza/pipeline/tokenize_processor.py256 def process_pre_tokenized_text(self, input_src):CODE
LOWstanza/pipeline/tokenize_processor.py326 def _process_speaker_tagged_text(self, original_text):CODE
LOWstanza/pipeline/core.py90def build_default_config_option(model_specs):CODE
LOWstanza/pipeline/core.py163def normalize_download_method(download_method):CODE
LOWstanza/pipeline/processor.py286def register_processor_variant(name, variant):CODE
LOWstanza/pipeline/demo/stanza-brat.js211function ConstituencyParseProcessor() {CODE
LOWstanza/resources/prepare_resources.py181def get_lemma_pretrain_package(lang, package):CODE
LOWstanza/resources/prepare_resources.py211def get_tokenizer_charlm_package(lang, package):CODE
LOWstanza/resources/prepare_resources.py214def get_tokenizer_dependencies(lang, package):CODE
LOWstanza/resources/prepare_resources.py221def get_depparse_charlm_package(lang, package):CODE
LOWstanza/resources/prepare_resources.py224def get_depparse_dependencies(lang, package):CODE
LOWstanza/resources/prepare_resources.py258def get_sentiment_dependencies(lang, package):CODE
LOWstanza/resources/prepare_resources.py361def get_default_depparse_package(lang, ud_package, known_resources):CODE
LOWstanza/resources/prepare_resources.py508def get_default_optional_processors(resources, lang):CODE
LOWstanza/resources/prepare_resources.py518def update_processor_add_transformer(resources, lang, current_processors, processor, transformer):CODE
LOWstanza/resources/common.py468def process_pipeline_parameters(lang, model_dir, package, processors):CODE
LOWstanza/tests/__init__.py109def compare_ignoring_whitespace(predicted, expected):CODE
LOWstanza/tests/pipeline/test_decorators.py79def test_register_nonprocessor():CODE
LOWstanza/tests/pipeline/test_decorators.py96def test_register_processor_variant():CODE
LOWstanza/tests/pipeline/test_decorators.py117def test_register_processor_variant_with_override():CODE
LOWstanza/tests/pipeline/test_decorators.py123def test_register_nonprocessor_variant():CODE
LOWstanza/tests/pipeline/test_tokenizer.py337def test_tokenize_ssplit_robustness(basic_pipeline):CODE
LOWstanza/tests/pipeline/test_tokenizer.py350def test_pretokenized_multidoc(pretokenized_pipeline):CODE
LOWstanza/tests/pipeline/test_tokenizer.py391def test_postprocessor_typeerror():CODE
LOWstanza/tests/pipeline/test_tokenizer.py406def test_zh_tokenizer_skip_newline(zh_pipeline):CODE
LOWstanza/tests/pipeline/test_tokenizer.py412def test_zh_tokenizer_skip_newline_offsets(zh_pipeline):CODE
LOWstanza/tests/pipeline/test_english_pipeline.py327 def test_sentence_indices_multidoc(self, processed_multidoc):CODE
LOWstanza/tests/pipeline/test_english_pipeline.py332 def test_dependency_parse_multidoc(self, processed_multidoc):CODE
LOWstanza/tests/pipeline/test_english_pipeline.py338 def processed_multidoc_variant(self):CODE
LOWstanza/tests/pipeline/test_english_pipeline.py344 def test_dependency_parse_multidoc_variant(self, processed_multidoc_variant):CODE
LOWstanza/tests/pipeline/test_core.py66def test_download_missing_ner_model():CODE
LOWstanza/tests/pipeline/test_core.py91 def fake_request_file_with_restore(url, path, *args, **kwargs):CODE
LOWstanza/tests/pipeline/test_core.py117def test_download_missing_resources():CODE
LOWstanza/tests/pipeline/test_core.py140 def fake_request_file_with_restore(url, path, *args, **kwargs):CODE
LOWstanza/tests/pipeline/test_core.py164def test_download_resources_overwrites():CODE
LOWstanza/tests/pipeline/test_core.py177 def fake_request_file_with_restore(url, path, *args, **kwargs):CODE
LOWstanza/tests/pipeline/test_core.py202def test_reuse_resources_overwrites():CODE
LOWstanza/tests/pipeline/test_core.py226def test_download_not_repeated():CODE
LOWstanza/tests/pipeline/test_core.py273def check_download_method_updates(download_method):CODE
LOWstanza/tests/pipeline/test_core.py294 def fake_request_file_with_restore(url, path, *args, **kwargs):CODE
LOWstanza/tests/pipeline/test_core.py318def test_download_fixed_reuse_resources():CODE
LOWstanza/tests/pipeline/test_core.py324def test_download_fixed_download_resources():CODE
LOWstanza/tests/pipeline/test_core.py371def test_empty_unknown_language(unknown_language_name):CODE
LOWstanza/tests/pipeline/test_core.py378def test_unknown_language_tokenizer(unknown_language_name):CODE
LOWstanza/tests/pipeline/test_core.py397def test_unknown_language_mwt(unknown_language_name):CODE
LOWstanza/tests/pipeline/test_pipeline_multi_doc.py10def test_process_many_strings():CODE
LOWstanza/tests/pipeline/test_pipeline_multi_doc.py33def test_process_many_documents():CODE
LOWstanza/tests/pipeline/test_requirements.py27def test_missing_requirements():CODE
LOWstanza/tests/pipeline/test_lemmatizer.py124def test_latin_caseless_lemmatizer():STRING
LOWstanza/tests/pipeline/test_lemmatizer.py139def test_contextual_lemmatizer():STRING
LOWstanza/tests/pipeline/test_depparse.py77def test_depparse_with_pretagged_doc():STRING
LOWstanza/tests/pipeline/test_depparse.py88def test_raises_requirements_exception_if_pretagged_not_passed():STRING
LOWstanza/tests/pipeline/test_tokenize_speaker.py91def test_parse_no_tags_returns_single_unlabeled_segment():CODE
LOWstanza/tests/pipeline/test_tokenize_speaker.py95def test_parse_single_tag_at_start():CODE
LOWstanza/tests/pipeline/test_tokenize_speaker.py110def test_parse_unlabeled_prefix():CODE
LOWstanza/tests/pipeline/test_tokenize_speaker.py117def test_parse_multi_char_label():CODE
LOWstanza/tests/pipeline/test_tokenize_speaker.py125def test_parse_curly_brace_delimiter():CODE
LOWstanza/tests/pipeline/test_tokenize_speaker.py133def test_parse_same_opener_closer_raises():CODE
LOWstanza/tests/pipeline/test_tokenize_speaker.py137def test_parse_consecutive_tags_empty_middle():CODE
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Unused Imports574 hits · 558 pts
SeverityFileLineSnippetContext
LOWdemo/pipeline_demo.py7CODE
LOWstanza/__init__.py1CODE
LOWstanza/__init__.py1CODE
LOWstanza/__init__.py2CODE
LOWstanza/__init__.py3CODE
LOWstanza/__init__.py4CODE
LOWstanza/__init__.py5CODE
LOWstanza/__init__.py5CODE
LOWstanza/__init__.py6CODE
LOWstanza/__init__.py6CODE
LOWstanza/__init__.py7CODE
LOWstanza/pipeline/tokenize_processor.py6CODE
LOWstanza/pipeline/tokenize_processor.py15CODE
LOWstanza/pipeline/tokenize_processor.py17CODE
LOWstanza/pipeline/tokenize_processor.py21CODE
LOWstanza/pipeline/tokenize_processor.py22CODE
LOWstanza/pipeline/tokenize_processor.py23CODE
LOWstanza/pipeline/tokenize_processor.py24CODE
LOWstanza/pipeline/langid_processor.py7CODE
LOWstanza/pipeline/langid_processor.py12CODE
LOWstanza/pipeline/constituency_processor.py7CODE
LOWstanza/pipeline/constituency_processor.py10CODE
LOWstanza/pipeline/pos_processor.py12CODE
LOWstanza/pipeline/depparse_processor.py12CODE
LOWstanza/pipeline/depparse_processor.py16CODE
LOWstanza/pipeline/sentiment_processor.py19CODE
LOWstanza/pipeline/coref_processor.py9CODE
LOWstanza/pipeline/coref_processor.py12CODE
LOWstanza/pipeline/core.py9CODE
LOWstanza/pipeline/core.py10CODE
LOWstanza/pipeline/core.py12CODE
LOWstanza/pipeline/core.py15CODE
LOWstanza/pipeline/core.py20CODE
LOWstanza/pipeline/core.py22CODE
LOWstanza/pipeline/core.py23CODE
LOWstanza/pipeline/core.py24CODE
LOWstanza/pipeline/core.py25CODE
LOWstanza/pipeline/core.py26CODE
LOWstanza/pipeline/core.py27CODE
LOWstanza/pipeline/core.py28CODE
LOWstanza/pipeline/core.py29CODE
LOWstanza/pipeline/core.py30CODE
LOWstanza/pipeline/core.py31CODE
LOWstanza/pipeline/core.py32CODE
LOWstanza/pipeline/multilingual.py13CODE
LOWstanza/pipeline/ner_processor.py11CODE
LOWstanza/pipeline/ner_processor.py15CODE
LOWstanza/pipeline/lemma_processor.py12CODE
LOWstanza/pipeline/mwt_processor.py5CODE
LOWstanza/pipeline/mwt_processor.py11CODE
LOWstanza/pipeline/external/sudachipy.py7CODE
LOWstanza/pipeline/external/sudachipy.py18CODE
LOWstanza/pipeline/external/sudachipy.py19CODE
LOWstanza/pipeline/external/pythainlp.py16CODE
LOWstanza/resources/prepare_resources.py24CODE
LOWstanza/resources/installation.py7CODE
LOWstanza/resources/common.py25CODE
LOWstanza/resources/common.py25CODE
LOWstanza/resources/common.py25CODE
LOWstanza/resources/common.py25CODE
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Deep Nesting299 hits · 294 pts
SeverityFileLineSnippetContext
LOWstanza/pipeline/tokenize_processor.py94CODE
LOWstanza/pipeline/tokenize_processor.py119CODE
LOWstanza/pipeline/tokenize_processor.py378CODE
LOWstanza/pipeline/core.py115CODE
LOWstanza/pipeline/core.py178CODE
LOWstanza/pipeline/ner_processor.py37CODE
LOWstanza/pipeline/lemma_processor.py69CODE
LOWstanza/pipeline/mwt_processor.py28CODE
LOWstanza/resources/prepare_resources.py277CODE
LOWstanza/resources/prepare_resources.py383CODE
LOWstanza/resources/prepare_resources.py608CODE
LOWstanza/resources/common.py147CODE
LOWstanza/resources/common.py243CODE
LOWstanza/resources/common.py267CODE
LOWstanza/resources/common.py468CODE
LOWstanza/resources/list_installed.py57CODE
LOWstanza/tests/constituency/test_trainer.py610CODE
LOWstanza/tests/constituency/test_trainer.py647CODE
LOWstanza/tests/tokenization/test_tokenizer_training.py46CODE
LOW…/tests/tokenization/test_tokenize_data_augmentation.py739CODE
LOWstanza/tests/pos/test_data.py101CODE
LOWstanza/tests/classifiers/test_classifier.py267CODE
LOWstanza/server/java_protobuf_requests.py206CODE
LOWstanza/server/java_protobuf_requests.py284CODE
LOWstanza/server/ssurgeon.py54CODE
LOWstanza/server/ssurgeon.py129CODE
LOWstanza/server/ssurgeon.py250CODE
LOWstanza/server/client.py129CODE
LOWstanza/server/client.py257CODE
LOWstanza/server/ud_enhancer.py9CODE
LOWstanza/server/main.py33CODE
LOWstanza/server/semgrex.py53CODE
LOWstanza/server/semgrex.py104CODE
LOWstanza/utils/conll.py19CODE
LOWstanza/utils/conll.py59CODE
LOWstanza/utils/conll.py105CODE
LOWstanza/utils/conll.py127CODE
LOWstanza/utils/conll.py153CODE
LOW…anza/utils/visualization/conll_deprel_visualization.py10CODE
LOWstanza/utils/visualization/semgrex_visualizer.py21CODE
LOWstanza/utils/visualization/semgrex_visualizer.py97CODE
LOWstanza/utils/visualization/semgrex_visualizer.py245CODE
LOWstanza/utils/visualization/dependency_visualization.py12CODE
LOWstanza/utils/visualization/ner_visualization.py14CODE
LOWstanza/utils/visualization/semgrex_app.py107CODE
LOWstanza/utils/visualization/semgrex_app.py255CODE
LOWstanza/utils/ner/flair_ner_tag_dataset.py14CODE
LOWstanza/utils/ner/spacy_ner_tag_dataset.py55CODE
LOWstanza/utils/training/separate_ner_pretrain.py37CODE
LOWstanza/utils/training/common.py81CODE
LOWstanza/utils/training/common.py309CODE
LOWstanza/utils/training/common.py344CODE
LOWstanza/utils/training/common.py40CODE
LOWstanza/utils/training/run_pos.py40CODE
LOWstanza/utils/training/run_lemma.py107CODE
LOWstanza/utils/datasets/prepare_mwt_treebank.py34CODE
LOWstanza/utils/datasets/prepare_tokenizer_data.py60CODE
LOWstanza/utils/datasets/prepare_tokenizer_treebank.py152CODE
LOWstanza/utils/datasets/prepare_tokenizer_treebank.py178CODE
LOWstanza/utils/datasets/prepare_tokenizer_treebank.py223CODE
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Cross-File Repetition43 hits · 215 pts
SeverityFileLineSnippetContext
HIGHstanza/pipeline/external/sudachipy.py0tokenize a document with the spacy tokenizer and wrap the results into a doc object.STRING
HIGHstanza/pipeline/external/pythainlp.py0tokenize a document with the spacy tokenizer and wrap the results into a doc object.STRING
HIGHstanza/pipeline/external/jieba.py0tokenize a document with the spacy tokenizer and wrap the results into a doc object.STRING
HIGHstanza/pipeline/external/spacy.py0tokenize a document with the spacy tokenizer and wrap the results into a doc object.STRING
HIGHstanza/tests/server/test_ssurgeon.py0# sent_id = 271 # text = hers is easy to clean. # previous = what did the dealer like about alex's car? # comment = extrSTRING
HIGHstanza/server/ssurgeon.py0# sent_id = 271 # text = hers is easy to clean. # previous = what did the dealer like about alex's car? # comment = extrSTRING
HIGHstanza/utils/visualization/constants.py0# sent_id = 271 # text = hers is easy to clean. # previous = what did the dealer like about alex's car? # comment = extrSTRING
HIGHstanza/tests/server/test_server_start.py0sentence #1 (6 tokens): joe smith lives in california. tokens: [text=joe characteroffsetbegin=0 characteroffsetend=3 parSTRING
HIGHstanza/tests/server/test_server_request.py0sentence #1 (6 tokens): joe smith lives in california. tokens: [text=joe characteroffsetbegin=0 characteroffsetend=3 parSTRING
HIGHstanza/tests/server/test_server_misc.py0sentence #1 (6 tokens): joe smith lives in california. tokens: [text=joe characteroffsetbegin=0 characteroffsetend=3 parSTRING
HIGHstanza/tests/tokenization/test_tokenize_utils.py0# sent_id = weblog-juancole.com_juancole_20051126063000_eng_20051126_063000-0003 # text = dpa: iraqi authorities announcSTRING
HIGHstanza/tests/depparse/test_parser.py0# sent_id = weblog-juancole.com_juancole_20051126063000_eng_20051126_063000-0003 # text = dpa: iraqi authorities announcSTRING
HIGHstanza/tests/pos/test_tagger.py0# sent_id = weblog-juancole.com_juancole_20051126063000_eng_20051126_063000-0003 # text = dpa: iraqi authorities announcSTRING
HIGHstanza/tests/lemma/test_lemma_trainer.py0# sent_id = weblog-juancole.com_juancole_20051126063000_eng_20051126_063000-0003 # text = dpa: iraqi authorities announcSTRING
HIGHstanza/tests/lemma/test_data.py0# sent_id = weblog-juancole.com_juancole_20051126063000_eng_20051126_063000-0003 # text = dpa: iraqi authorities announcSTRING
HIGHstanza/tests/depparse/test_parser.py01 from from adp in _ 3 case 3:case _ 2 the the det dt definite=def|prontype=art 3 det 3:det _ 3 ap ap propn nnp number=sSTRING
HIGHstanza/tests/pos/test_tagger.py01 from from adp in _ 3 case 3:case _ 2 the the det dt definite=def|prontype=art 3 det 3:det _ 3 ap ap propn nnp number=sSTRING
HIGHstanza/tests/lemma/test_lemma_trainer.py01 from from adp in _ 3 case 3:case _ 2 the the det dt definite=def|prontype=art 3 det 3:det _ 3 ap ap propn nnp number=sSTRING
HIGHstanza/tests/depparse/parser_training.py0run the training for a few iterations, load & return the modelSTRING
HIGHstanza/tests/pos/test_tagger.py0run the training for a few iterations, load & return the modelSTRING
HIGHstanza/tests/lemma/test_lemma_trainer.py0run the training for a few iterations, load & return the modelSTRING
HIGHstanza/tests/depparse/test_depparse_data.py0test a punctuation simplification that should make it so unexpected question/exclamation marks types are processed into STRING
HIGHstanza/tests/common/test_utils.py0test a punctuation simplification that should make it so unexpected question/exclamation marks types are processed into STRING
HIGHstanza/tests/pos/test_data.py0test a punctuation simplification that should make it so unexpected question/exclamation marks types are processed into STRING
HIGHstanza/server/ssurgeon.py0semgrex context window this is a context window which keeps a process open. should allow for multiple requests without lSTRING
HIGHstanza/server/tsurgeon.py0semgrex context window this is a context window which keeps a process open. should allow for multiple requests without lSTRING
HIGHstanza/server/ud_enhancer.py0semgrex context window this is a context window which keeps a process open. should allow for multiple requests without lSTRING
HIGHstanza/server/parser_eval.py0semgrex context window this is a context window which keeps a process open. should allow for multiple requests without lSTRING
HIGHstanza/server/semgrex.py0semgrex context window this is a context window which keeps a process open. should allow for multiple requests without lSTRING
HIGHstanza/utils/datasets/prepare_pos_treebank.py0a script to prepare all mwt datasets. for example, do python -m stanza.utils.datasets.prepare_mwt_treebank treebank suchSTRING
HIGHstanza/utils/datasets/prepare_lemma_treebank.py0a script to prepare all mwt datasets. for example, do python -m stanza.utils.datasets.prepare_mwt_treebank treebank suchSTRING
HIGHstanza/utils/datasets/prepare_mwt_treebank.py0a script to prepare all mwt datasets. for example, do python -m stanza.utils.datasets.prepare_mwt_treebank treebank suchSTRING
HIGHstanza/models/ner/trainer.py0a trainer class to handle training and testing of models.STRING
HIGHstanza/models/mwt/trainer.py0a trainer class to handle training and testing of models.STRING
HIGHstanza/models/depparse/trainer.py0a trainer class to handle training and testing of models.STRING
HIGHstanza/models/pos/trainer.py0a trainer class to handle training and testing of models.STRING
HIGHstanza/models/lemma/trainer.py0a trainer class to handle training and testing of models.STRING
HIGHstanza/models/constituency/in_order_compound_oracle.py0enumerate values as normal, but also keep a pointer to a function which repairs that kind of errorSTRING
HIGHstanza/models/constituency/top_down_oracle.py0enumerate values as normal, but also keep a pointer to a function which repairs that kind of errorSTRING
HIGHstanza/models/depparse/transition/dynamic_oracle.py0enumerate values as normal, but also keep a pointer to a function which repairs that kind of errorSTRING
HIGHstanza/models/coref/pairwise_encoder.py0a workaround to get current device (which is assumed to be the device of the first parameter of one of the submodules)STRING
HIGHstanza/models/coref/word_encoder.py0a workaround to get current device (which is assumed to be the device of the first parameter of one of the submodules)STRING
HIGHstanza/models/coref/span_predictor.py0a workaround to get current device (which is assumed to be the device of the first parameter of one of the submodules)STRING
Over-Commented Block123 hits · 113 pts
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LOWstanza/__init__.py21COMMENT
LOWstanza/pipeline/demo/stanza-brat.js481 //COMMENT
LOWstanza/resources/default_packages.py461"""COMMENT
LOWstanza/resources/default_packages.py481 # bg_btb LAS dev testCOMMENT
LOWstanza/resources/default_packages.py501 # no bert 0.8245 0.8230COMMENT
LOWstanza/resources/default_packages.py521 # NER scoresCOMMENT
LOWstanza/resources/default_packages.py541 # dbmdz/bert 89.67 88.54COMMENT
LOWstanza/resources/default_packages.py561 "el": "nlpaueb/bert-base-greek-uncased-v1",COMMENT
LOWstanza/resources/default_packages.py581 # roberta-large: 93.47 91.56 90.13 89.71 90.13COMMENT
LOWstanza/resources/default_packages.py601COMMENT
LOWstanza/resources/default_packages.py621 "fa": 'HooshvareLab/bert-base-parsbert-uncased',COMMENT
LOWstanza/resources/default_packages.py641 # 98.70 100.00 98.69 98.24COMMENT
LOWstanza/resources/default_packages.py661 # Microbert MX 0.8910 0.7755COMMENT
LOWstanza/resources/default_packages.py681 # UPOS XPOS UFeats AllTagsCOMMENT
LOWstanza/resources/default_packages.py701 # crosloeng 96.64 96.48 91.31 91.51COMMENT
LOWstanza/resources/default_packages.py721 # flax-community/indonesian-roberta-base 90.14 xxxCOMMENT
LOWstanza/resources/default_packages.py741 # in-order constituency parser on the VIT dev set:COMMENT
LOWstanza/resources/default_packages.py761 # there are others that would also work,COMMENT
LOWstanza/resources/default_packages.py781 # l3cube-pune/marathi-bert-v2COMMENT
LOWstanza/resources/default_packages.py801 # DTAI-KULeuven/robbert-2023-dutch-large 94.57 93.80COMMENT
LOWstanza/resources/default_packages.py821 # xlm-roberta-large: 0.9254COMMENT
LOWstanza/resources/default_packages.py841 # no transformer 90.37 90.70COMMENT
LOWstanza/resources/default_packages.py861 # jerteh/Jerteh-355 90.89 91.98COMMENT
LOWstanza/resources/default_packages.py881COMMENT
LOWstanza/resources/default_packages.py901 # https://huggingface.co/airesearch/wangchanberta-base-att-spm-uncasedCOMMENT
LOWstanza/resources/default_packages.py921 # does not help the scoresCOMMENT
LOWstanza/tests/constituency/test_top_down_oracle.py81 # and then the rest:COMMENT
LOW…/tests/tokenization/test_tokenize_data_augmentation.py661# Every test above calls an augmentation method directly on a `sentence`COMMENT
LOWstanza/tests/tokenization/test_tokenization_lst20.py81COMMENT
LOWstanza/tests/tokenization/test_tokenization_lst20.py101# รายงาน 6COMMENT
LOWstanza/utils/datasets/constituency/convert_icepahc.py21 (NP (D-D þessu-þessi)))COMMENT
LOWstanza/utils/datasets/constituency/convert_icepahc.py41# (P í)COMMENT
LOWstanza/utils/datasets/constituency/convert_it_vit.py501 # but the corresponding ' is skipped in the con version of VIT,COMMENT
LOWstanza/utils/datasets/constituency/convert_it_vit.py521 # and before you ask, there are also clitics which areCOMMENT
LOWstanza/utils/datasets/coref/convert_hebrew_iahlt.py101 # expanded_end = min(len(text), mention_end + 10)COMMENT
LOWstanza/utils/constituency/condense_output_layers.py341#COMMENT
LOWstanza/utils/constituency/condense_output_layers.py361# middle_output_sizes = self.args.get('output_layer_sizes', [self.hidden_size] * middle_layers)COMMENT
LOWstanza/utils/charlm/make_lm_data.py361 buffers[bucket_idx] = []COMMENT
LOWstanza/utils/charlm/community_oscar_inventory.py101# so it's in a predictable, already-known location, but it's a separateCOMMENT
LOWstanza/utils/charlm/community_oscar_inventory.py121# Stanza lookup.COMMENT
LOWstanza/models/classifier.py121# For the most part, these values are for the constituency parser.COMMENT
LOWstanza/models/classifier.py241 # dev set macro f1 scores on 3 class problemsCOMMENT
LOWstanza/models/parser.py61 # multiple UD datasetsCOMMENT
LOWstanza/models/parser.py161 # Later experiments found the current defaults helped the resultsCOMMENT
LOWstanza/models/parser.py181 # In addition to these experiments, we ran multiple alternate optimizer combinations, none of whichCOMMENT
LOWstanza/models/constituency_parser.py201 parser.add_argument('--charlm_backward_file', type=str, default=None, help="Exact path to use for backward charlm")COMMENT
LOWstanza/models/constituency_parser.py221 # 5 layers 0.892064 (299)COMMENT
LOWstanza/models/constituency_parser.py241 # 150 with no finetuningCOMMENT
LOWstanza/models/constituency_parser.py261 # observation that at the 150 iteration mark, 0.09 was winning:COMMENT
LOWstanza/models/constituency_parser.py281 # 1: 0.900594 (226)COMMENT
LOWstanza/models/constituency_parser.py301 # 0.004: 0.899566 (290)COMMENT
LOWstanza/models/constituency_parser.py381 #COMMENT
LOWstanza/models/constituency_parser.py401 parser.add_argument('--deactivated_oracle_levels', type=str, default=None, help='Temporarily turn off a default oracCOMMENT
LOWstanza/models/constituency_parser.py441 # baseline is an 80.6 model that occurs when trained using adadelta, lr 1.0COMMENT
LOWstanza/models/constituency_parser.py461 # 0.00002: 80.15COMMENT
LOWstanza/models/constituency_parser.py481 # Hardshrink: 0.3COMMENT
LOWstanza/models/constituency_parser.py501 # 0.0001 0.818566COMMENT
LOWstanza/models/constituency_parser.py521 # 0.00005 0.819871COMMENT
LOWstanza/models/constituency_parser.py561COMMENT
LOWstanza/models/constituency_parser.py581 # 0.05 : 0.929669COMMENT
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Cross-Language Confusion13 hits · 88 pts
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HIGHstanza/tests/constituency/test_tree_stack.py11 stack = stack.push(3)CODE
HIGHstanza/tests/constituency/test_tree_stack.py12 stack = stack.push(1)CODE
HIGHstanza/tests/constituency/test_tree_stack.py22 stack = stack.push(3)CODE
HIGHstanza/tests/constituency/test_tree_stack.py23 stack = stack.push(1)CODE
HIGHstanza/tests/constituency/test_tree_stack.py30 stack = stack.push(3)CODE
HIGHstanza/tests/constituency/test_tree_stack.py31 stack = stack.push(1)CODE
HIGHstanza/tests/constituency/test_tree_stack.py39 stack = stack.push(3)CODE
HIGHstanza/tests/constituency/test_tree_stack.py40 stack = stack.push(1)CODE
HIGHstanza/tests/constituency/test_tree_stack.py49 stack = stack.push(i)CODE
HIGHstanza/models/constituency/transformer_tree_stack.py189 new_stacks.append(stack.push(value=Node(node_value, new_key_stack, new_value_stack, output)))CODE
HIGHstanza/models/constituency/base_model.py503 return [stack.push(constituent) for stack, constituent in zip(constituent_stacks, constituents)]CODE
HIGHstanza/models/constituency/base_model.py509 return [stack.push(transition) for stack, transition in zip(transition_stacks, transitions)]CODE
HIGHstanza/models/constituency/lstm_tree_stack.py94 new_stacks = [stack.push(Node(transition, hx[:, i, :], cx[:, i, :], last_layer[i]))CODE
AI Slop Vocabulary14 hits · 30 pts
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MEDIUMstanza/resources/default_packages.py868 # Certainly it looks like the original bert-base is best, but weCOMMENT
LOWstanza/tests/__init__.py89 # just return if path doesn't existCOMMENT
LOWstanza/utils/conll.py180 else: # no comments defined a new doc_id, so just add it to the current documentCODE
LOWstanza/utils/datasets/dataset_retagging.py67 # TODO: just create a Pipeline for the retagging instead?COMMENT
MEDIUMstanza/utils/datasets/slovenian/sloleks_lemmas.py63 # We want unique forms only (some paradigm slots share surface forms)COMMENT
MEDIUMstanza/models/constituency_parser.py625 # When using word_dropout and predict_dropout in conjunction with relu, one particular experiment produced the folloCOMMENT
LOWstanza/models/lemma_classifier/train_lstm_model.py57 # TODO: just pass around the args insteadCOMMENT
LOWstanza/models/constituency/in_order_compound_oracle.py78 # no fix is possible, so we just return hereCOMMENT
MEDIUMstanza/models/constituency/lstm_model.py169# Essentially C Linears stacked on top of each other,COMMENT
MEDIUMstanza/models/tokenization/data.py468# colon -- "Fax:", "Cell:", "Job Group:", "Notice Regarding:". EssentiallyCOMMENT
LOWstanza/models/common/pretrain.py273 # if we can't find it anywhere, just return the first location searched...COMMENT
LOWstanza/models/common/bert_embedding.py504 # after all, we just set the first one to be 0COMMENT
MEDIUMstanza/models/pos/vocab.py38 # when using the __len__ / __getitem__ paradigm,COMMENT
LOWstanza/models/coref/pairwise_encoder.py78 # if speaker is not found in the doc, simply return "speaker#1" for all the speakersCOMMENT
AI Structural Patterns28 hits · 28 pts
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LOWstanza/pipeline/core.py178CODE
LOWstanza/pipeline/multilingual.py40CODE
LOWstanza/resources/common.py648CODE
LOWstanza/tests/pos/test_xpos_vocab_factory.py56CODE
LOWstanza/server/client.py257CODE
LOWstanza/models/charlm.py40CODE
LOWstanza/models/ner/trainer.py65CODE
LOWstanza/models/mwt/data.py121CODE
LOWstanza/models/mwt/data.py156CODE
LOWstanza/models/lemma_classifier/transformer_model.py88CODE
LOWstanza/models/lemma_classifier/lstm_model.py214CODE
LOWstanza/models/constituency/transformer_tree_stack.py103CODE
LOWstanza/models/constituency/label_attention.py625CODE
LOWstanza/models/constituency/parser_training.py299CODE
LOWstanza/models/constituency/positional_encoding.py89CODE
LOWstanza/models/constituency/partitioned_transformer.py227CODE
LOWstanza/models/common/warmup_plateau_scheduler.py119CODE
LOWstanza/models/common/biaffine.py28CODE
LOWstanza/models/common/utils.py271CODE
LOWstanza/models/common/utils.py322CODE
LOWstanza/models/common/bert_embedding.py286CODE
LOWstanza/models/common/dropout.py71CODE
LOWstanza/models/common/hlstm.py60CODE
LOWstanza/models/pos/data.py284CODE
LOWstanza/models/lemma/data.py210CODE
LOWstanza/models/classifiers/cnn_classifier.py508CODE
LOWstanza/models/classifiers/constituency_classifier.py72CODE
LOWstanza/models/coref/anaphoricity_scorer.py122CODE
Excessive Try-Catch Wrapping21 hits · 24 pts
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LOWstanza/resources/installation.py80 except Exception as e:CODE
LOWstanza/resources/installation.py117 except Exception as e:CODE
LOWstanza/tests/server/test_server_start.py211 except Exception as e:CODE
LOWstanza/tests/common/test_foundation_cache.py178 except Exception as e:CODE
MEDIUMstanza/tests/common/test_foundation_cache.py174def load():CODE
LOWstanza/tests/morphseg/test_stanza_integration.py175 except Exception as e:CODE
LOWstanza/tests/morphseg/test_stanza_integration.py215 except Exception as e:CODE
LOWstanza/tests/morphseg/test_stanza_integration.py236 except Exception as e:CODE
LOWstanza/server/ssurgeon.py79 except Exception as e:CODE
LOWstanza/server/ssurgeon.py209 except Exception as e:CODE
LOWstanza/server/parser_eval.py40 except Exception as e:CODE
LOWstanza/utils/training/run_ner.py111 except Exception as e:CODE
MEDIUMstanza/utils/datasets/ner/convert_amt.py204 print("Error: no documents found in the input file!")CODE
LOWstanza/utils/datasets/constituency/convert_ctb.py191 except Exception as e:CODE
LOW…nza/utils/datasets/depparse/assemble_silver_dataset.py53 except Exception as e:CODE
LOWstanza/utils/charlm/community_oscar_inventory.py315 except Exception as e:CODE
LOWstanza/utils/charlm/community_oscar_inventory.py338 except Exception as e:CODE
LOWstanza/utils/charlm/community_oscar_dedup.py348 except Exception as e:CODE
LOWstanza/utils/charlm/community_oscar_dedup.py376 except Exception as e:CODE
LOW…nza/utils/charlm/community_oscar_inspect_similarity.py176 except Exception:CODE
LOWstanza/models/pos/trainer.py130 except Exception as e:CODE
Redundant / Tautological Comments8 hits · 14 pts
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LOWstanza/server/client.py33# Check if str is CoreNLP supported languageCOMMENT
LOWstanza/server/client.py194 # Check if the service is active and aliveCOMMENT
LOWstanza/utils/default_paths.py26 # Set directories to store external word vector dataCOMMENT
LOWscripts/config.sh5# Set UDBASE to the location of UD data folderCOMMENT
LOWscripts/config.sh16# Set NERBASE to the location of NER data folderCOMMENT
LOWscripts/config.sh25# Set CONSTITUENCY_BASE to the location of NER data folderCOMMENT
LOWscripts/config.sh34# Set directories to store processed training/evaluation filesCOMMENT
LOWscripts/config.sh50# Set directories to store external word vector dataCOMMENT
Fake / Example Data9 hits · 14 pts
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LOWstanza/pipeline/demo/stanza-brat.js1070 $('#text').attr('placeholder', 'على سبيل المثال، قفز الثعلب البني السريع فوق الكلب الكسول.');CODE
LOWstanza/pipeline/demo/stanza-brat.js1072 $('#text').attr('placeholder', 'e.g., The quick brown fox jumped over the lazy dog.');CODE
LOWstanza/pipeline/demo/stanza-brat.js1074 $('#text').attr('placeholder', '例如,快速的棕色狐狸跳过了懒惰的狗。');CODE
LOWstanza/pipeline/demo/stanza-brat.js1076 $('#text').attr('placeholder', '例如,快速的棕色狐狸跳過了懶惰的狗。');CODE
LOWstanza/pipeline/demo/stanza-brat.js1078 $('#text').attr('placeholder', 'Par exemple, le renard brun rapide a sauté sur le chien paresseux.');CODE
LOWstanza/pipeline/demo/stanza-brat.js1080 $('#text').attr('placeholder', 'Z. B. sprang der schnelle braune Fuchs über den faulen Hund.');CODE
LOWstanza/pipeline/demo/stanza-brat.js1082 $('#text').attr('placeholder', 'Por ejemplo, el rápido zorro marrón saltó sobre el perro perezoso.');CODE
LOWstanza/pipeline/demo/stanza-brat.js1084 $('#text').attr('placeholder', 'میرا نام علی ہے');CODE
LOWstanza/pipeline/demo/stanza-brat.js1086 $('#text').attr('placeholder', 'Unknown language for placeholder query: ' + $('#language').val());CODE
Self-Referential Comments4 hits · 11 pts
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MEDIUMstanza/pipeline/external/spacy.py37 # Create a Tokenizer with the default settings for EnglishCOMMENT
MEDIUMstanza/utils/datasets/constituency/vtb_split.py76 # Create a random shuffle list of the file names in the original directoryCOMMENT
MEDIUMstanza/models/common/build_short_name_to_treebank.py39 fout.write("# This module is autogenerated by build_short_name_to_treebank.py\n")CODE
MEDIUMstanza/models/common/short_name_to_treebank.py1# This module is autogenerated by build_short_name_to_treebank.pyCOMMENT
Hallucination Indicators1 hit · 10 pts
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CRITICALstanza/utils/training/separate_ner_pretrain.py193 trainer.model.delta_emb.weight.data.copy_(delta_vectors)CODE
Verbosity Indicators4 hits · 9 pts
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LOW…a/tests/depparse/test_parser_gradient_checkpointing.py74 # Step 1: load with gradient checkpointingCOMMENT
LOW…a/tests/depparse/test_parser_gradient_checkpointing.py80 # Step 2: wrap with PEFT — model should still have checkpointing onCOMMENT
LOWstanza/tests/common/test_peft_gradient_checkpointing.py59 # Step 1: load with gradient checkpointingCOMMENT
LOWstanza/tests/common/test_peft_gradient_checkpointing.py65 # Step 2: wrap with PEFT — model should still have checkpointing onCOMMENT
Structural Annotation Overuse4 hits · 9 pts
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LOW…a/tests/depparse/test_parser_gradient_checkpointing.py74 # Step 1: load with gradient checkpointingCOMMENT
LOW…a/tests/depparse/test_parser_gradient_checkpointing.py80 # Step 2: wrap with PEFT — model should still have checkpointing onCOMMENT
LOWstanza/tests/common/test_peft_gradient_checkpointing.py59 # Step 1: load with gradient checkpointingCOMMENT
LOWstanza/tests/common/test_peft_gradient_checkpointing.py65 # Step 2: wrap with PEFT — model should still have checkpointing onCOMMENT
Slop Phrases1 hit · 3 pts
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MEDIUMsetup.py74 # simple. Or you can use find_packages().COMMENT
Modern Structural Boilerplate2 hits · 2 pts
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LOWstanza/server/client.py779__all__ = ["CoreNLPClient", "AnnotationException", "TimeoutException", "to_text"]CODE
LOWstanza/utils/lemma/convert_lemma_dict.py29logger = logging.getLogger(__name__)CODE
Dead Code1 hit · 2 pts
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MEDIUMstanza/utils/datasets/sentiment/process_es_tass2020.py157CODE
Overly Generic Function Names1 hit · 1 pts
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LOWstanza/models/constituency/transition_sequence.py84 def helper(tree):CODE