explosion/spaCy: v2.1.0a8: New models, ULMFit/BERT/Elmo-like pretraining, joint word segmentation and parsing, better Matcher, bug fixes & more

This is an alpha pre-release of spaCy v2.1.0 and available on pip as spacy-nightly. It's not intended for production use. See here for the updated nightly docs. pip install -U spacy-nightly If you want to test the new version, we recommend using a new virtual environment. Also make sure to download the new models – see below for details and benchmarks. ⚠️ Due to difficulties linking our new blis for faster platform-independent matrix multiplication, this nightly release currently doesn't work on Python 2.7 on Windows. We expect this problem to be corrected in the future. ✨ New features and improvements Tagger, Parser, NER and Text Categorizer NEW: Experimental ULMFit/BERT/Elmo-like pretraining (see #2931) via the new spacy pretrain command. This pre-trains the CNN using BERT's cloze task. A new trick we're calling Language Modelling with Approximate Outputs is used to apply the pre-training to smaller models. The pre-training outputs CNN and embedding weights that can be used in spacy train, using the new -t2v argument. NEW: Allow parser to do joint word segmentation and parsing. If you pass in data where the tokenizer over-segments, the parser now learns to merge the tokens. Make parser, tagger and NER faster, through better hyperparameters. Make TextCategorizer default to a simpler, GPU-friendly model. Add EntityRecognizer.labels property. Remove document length limit during training, by implementing faster Levenshtein alignment. Use Thinc v7.0, which defaults to single-thread with fast blis kernel for matrix multiplication. Parallelisation should be performed at the task level, e.g. by running more containers. Models & Language Data NEW: 2-3 times faster tokenization across all languages at the same accuracy! NEW: Small accuracy improvements for parsing, tagging and NER for 6+ languages. NEW: The English and German models are now available under the MIT license. NEW: Statistical models for Greek. NEW: Alpha support for Tamil, Ukrainian and Kannada, and base language classes for Afrikaans, Bulgarian, Czech, Icelandic, Lithuanian, Latvian, Slovak, Slovenian and Albanian. Improve loading time of French by ~30%. CLI NEW: pretrain command for ULMFit/BERT/Elmo-like pretraining (see #2931). NEW: New ud-train command, to train and evaluate using the CoNLL 2017 shared task data. Check if model is already installed before downloading it via spacy download. Pass additional arguments of download command to pip to customise installation. Improve train command by letting GoldCorpus stream data, instead of loading into memory. Improve init-model command, including support for lexical attributes and word-vectors, using a variety of formats. This replaces the spacy vocab command, which is now deprecated. Add support for multi-task objectives to train command. Add support for data-augmentation to train command. Other NEW: Enhanced pattern API for rule-based Matcher (see #1971). NEW: Doc.retokenize context manager for merging and splitting tokens more efficiently. NEW: Add support for custom pipeline component factories via entry points (#2348). NEW: Implement fastText vectors with subword features. NEW: Built-in rule-based NER component to add entities based on match patterns (see #2513). NEW: Allow PhraseMatcher to match on token attributes other than ORTH, e.g. LOWER (for case-insensitive matching) or even POS or TAG. NEW: Replace ujson, msgpack, msgpack-numpy, pickle, cloudpickle and dill with our own package srsly to centralise dependencies and allow binary wheels. NEW: Doc.to_json() method which outputs data in spaCy's training format. This will be the only place where the format is hard-coded (see #2932). NEW: Built-in EntityRuler component to make it easier to build rule-based NER and combinations of statistical and rule-based systems. NEW: gold.spans_from_biluo_tags helper that returns Span objects, e.g. to overwrite the doc.ents. Add warnings if .similarity method is called with empty vectors or without word vectors. Improve rule-based Matcher and add return_matches keyword argument to Matcher.pipe to yield (doc, matches) tuples instead of only Doc objects, and as_tuples to add context to the Doc objects. Make stop words via Token.is_stop and Lexeme.is_stop case-insensitive. Accept "TEXT" as an alternative to "ORTH" in Matcher patterns. Refactor CLI and add debug-data command to validate training data (see #2932). Use black for auto-formatting .py source and optimse codebase using flake8. You can now run flake8 spacy and it should return no errors or warnings. See CONTRIBUTING.md for details. Bug fixes Fix issue #1487: Add Doc.retokenize() context manager. Fix issue #1537: Make Span.as_doc return a copy, not a view. Fix issue #1574: Make sure stop words are available in medium and large English models. Fix issue #1585: Prevent parser from predicting unseen classes. Fix issue #1642: Replace regex with re and speed up tokenization. Fix issue #1665: Correct typos in symbol Animacy_inan and add Animacy_nhum. Fix issue #1748, #1798, #2756, #2934: Make TextCategorizer default to a simpler, GPU-friendly model. Fix issue #1773: Prevent tokenizer exceptions from setting POS but not TAG. Fix issue #1782, #2343: Fix training on GPU. Fix issue #1816: Allow custom Language subclasses via entry points. Fix issue #1865: Correct licensing of it_core_news_sm model. Fix issue #1889: Make stop words case-insensitive. Fix issue #1903: Add relcl dependency label to symbols. Fix issue #1963: Resize Doc.tensor when merging spans. Fix issue #1971: Update Matcher engine to support regex, extension attributes and rich comparison. Fix issue #2014: Make Token.pos_ writeable. Fix issue #2369: Respect pre-defined warning filters. Fix issue #2396: Fix Doc.get_lca_matrix. Fix issue #2464, #3009: Fix behaviour of Matcher's ? quantifier. Fix issue #2482: Fix serialization when parser model is empty. Fix issue #2648: Fix KeyError in Vectors.most_similar. Fix issue #2671, #2675: Fix incorrect match ID on some patterns. Fix issue #2693: Only use 'sentencizer' as built-in sentence boundary component name. Fix issue #2754, #3028: Make NORM a Token attribute instead of a Lexeme attribute to allow setting context-specific norms in tokenizer exceptions. Fix issue #2769: Fix issue that'd cause segmentation fault when calling EntityRecognizer.add_label. Fix issue #2772: Fix bug in sentence starts for non-projective parses. Fix issue #2779: Fix handling of pre-set entities. Fix issue #2782: Make like_num work with prefixed numbers. Fix issue #2833: Raise better error if Token or Span are pickled. Fix issue #2838: Add Retokenizer.split method to split one token into several. Fix issue #2870: Make it illegal for the entity recognizer to predict whitespace tokens as B, L or U. Fix issue #2871: Fix vectors for reserved words. Fix issue #2901: Fix issue with first call of nlp in Japanese (MeCab). Fix issue #2924: Make IDs of displaCy arcs more unique to avoid clashes. Fix issue #3012: Fix clobber of Doc.is_tagged in Doc.from_array. Fix issue #3027: Allow Span to take unicode value for label argument. Fix issue #3048: Raise better errors for uninitialized pipeline components. Fix issue #3064: Allow single string attributes in Doc.to_array. Fix issue #3093, #3067: Set vectors.name correctly when exporting model via CLI. Fix serialization of custom tokenizer if not all functions are defined. Fix issue #3122: Correct docs of Token.subtree and Span.subtree. Fix issue #3128: Improve error handling in converters. Fix issue #3248: Fix PhraseMatcher pickling and make __len__ consistent. Fix issue #3277: Add en/em dash to tokenizer prefixes and suffixes. Fix bugs in beam-search training objective. Fix problems with model pickling. ⚠️ Backwards incompatibilities This version of spaCy requires downloading new models. You can use the spacy validate command to find out which models need updating, and print update instructions. If you've been training your own models, you'll need to retrain them with the new version. While the Matcher API is fully backwards compatible, its algorithm has changed to fix a number of bugs and performance issues. This means that the Matcher in v2.1.x may produce different results compared to the Matcher in v2.0.x. The deprecated Doc.merge and Span.merge methods still work, but you may notice that they now run slower when merging many objects in a row. That's because the merging engine was rewritten to be more reliable and to support more efficient merging in bulk. To take advantage of this, you should rewrite your logic to use the Doc.retokenize context manager and perform as many merges as possible together in the with block. ```diff doc[1:5].merge() doc[6:8].merge() with doc.retokenize() as retokenizer: retokenizer.merge(doc[1:5]) retokenizer.merge(doc[6:8]) ``` For better compatibility with the Universal Dependencies data, the lemmatizer now preserves capitalization, e.g. for proper nouns (see #3256). The keyword argument n_threads on the .pipe methods is now deprecated, as the v2.x models cannot release the global interpreter lock. (Future versions may introduce a n_process argument for parallel inference via multiprocessing.) The Doc.print_tree method is not deprecated in favour of a unified Doc.to_json method, which outputs data in the same format as the expected JSON training data. The built-in rule-based sentence boundary detector is now only called 'sentencizer' – the name 'sbd' is deprecated. ```diff sentence_splitter = nlp.create_pipe('sbd') sentence_splitter = nlp.create_pipe('sentencizer') ``` The spacy train command now lets you specify a comma-separated list of pipeline component names, instead of separate flags like --no-parser to disable components. This is more flexible and also handles custom components out-of-the-box. ```diff $ spacy train en /output train_data.json dev_data.json --no-parser $ spacy train en /output train_data.json dev_data.json --pipeline tagger,ner ``` The spacy init-model command now uses a --jsonl-loc argument to pass in a a newline-delimited JSON (JSONL) file containing one lexical entry per line instead of a separate --freqs-loc and --clusters-loc. ```diff $ spacy init-model en ./model --freqs-loc ./freqs.txt --clusters-loc ./clusters.txt $ spacy init-model en ./model --jsonl-loc ./vocab.jsonl ``` Also note that some of the model licenses have changed: it_core_news_sm is now correctly licensed under CC BY-NC-SA 3.0, and all English and German models are now published under the MIT license. Benchmarks Model Language Version UAS LAS POS NER F Vec Size en_core_web_sm English 2.1.0a7 91.6 89.7 96.8 85.5 10 MB en_core_web_md English 2.1.0a7 91.8 90.0 96.9 86.3 ✓ 90 MB en_core_web_lg English 2.1.0a7 91.9 90.1 97.0 86.6 ✓ 788 MB de_core_news_sm German 2.1.0a7 91.7 89.5 97.3 83.4 10 MB de_core_news_md German 2.1.0a7 92.3 90.4 97.4 83.8 ✓ 210 MB es_core_news_sm Spanish 2.1.0a7 90.2 87.1 97.0 89.1 10 MB es_core_news_md Spanish 2.1.0a7 91.2 88.4 97.2 89.4 ✓ 69 MB pt_core_news_sm Portuguese 2.1.0a7 89.5 86.2 80.1 89.0 12 MB fr_core_news_sm French 2.1.0a7 87.3 84.4 94.7 83.0 14 MB fr_core_news_md French 2.1.0a7 89.1 86.2 95.3 83.3 ✓ 82 MB it_core_news_sm Italian 2.1.0a7 91.1 87.2 96.0 86.3 10 MB nl_core_news_sm Dutch 2.1.0a7 83.9 77.6 91.5 87.0 10 MB el_core_news_sm Greek 2.1.0a7 85.1 81.5 94.5 73.3 10 MB el_core_news_md Greek 2.1.0a7 88.2 85.1 96.7 78.1 ✓ 126 MB xx_ent_wiki_sm Multi 2.1.0a7 - - - 81.6 3 MB UAS: Unlabelled dependencies (parser). LAS: Labelled dependencies (parser). POS: Part-of-speech tags (fine-grained tags, i.e. Token.tag_). NER F: Named entities (F-score). Vec: Model contains word vectors. Size: Model file size (zipped archive). Documentation and examples Fix various typos and inconsistencies. Contributors Thanks to @DuyguA, @giannisdaras, @mgogoulos, @louridas, @skrcode, @gavrieltal, @svlandeg, @jarib, @alvaroabascar, @kbulygin, @moreymat, @mirfan899, @ozcankasal, @willprice, @alvations, @amperinet, @retnuh, @Loghijiaha, @DeNeutoy, @gavrieltal, @boena, @BramVanroy, @pganssle, @foufaster, @adrianeboyd, @maknotavailable, @pierremonico, @lauraBaakman, @juliamakogon, @Gizzio, @Abhijit-2592, @akki2825, @grivaz, @roshni-b, @mpuig and @mikelibg for the pull requests and contributions