explosion/spaCy: v2.1.0a3: 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.

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

• 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.
• 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: 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.

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: Doc.retokenize context manager for merging 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.
• 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.

🚧 Under construction

This section includes new features and improvements that are planned for the stable v2.1.x release, but aren't included in the nightly yet.

• Enhanced pattern API for rule-based Matcher (see #1971).
• Improve tokenizer performance (see #1642).
• Allow retokenizer to update Lexeme attributes on merge (see #2390).
• md and lg models and new, pre-trained word vectors for German, French, Spanish, Italian, Portuguese and Dutch.
• Improved JSON(L) format for training (see #2928, #2932).
• 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).
• Refactor CLI and add debug-data command to validate training data (see #2932).

🔴 Bug fixes

• Fix issue #1487: Add Doc.retokenize() context manager.
• Fix issue #1574: Make sure stop words are available in medium and large English models.
• Fix issue #1665: Correct typos in symbol Animacy_inan and add Animacy_nhum.
• 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 #2014: Make Token.pos_ writeable.
• Fix issue #2369: Respect pre-defined warning filters.
• Fix issue #2482: Fix serialization when parser model is empty.
• Fix issue #2671, #2675: Fix incorrect match ID on some patterns.
• Fix issue #2772: Fix bug in sentence starts for non-projective parses.
• Fix issue #2782: Make like_num work with prefixed numbers.
• Fix serialization of custom tokenizer if not all functions are defined.
• 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.
• 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 2.1.0a4 91.7 89.8 96.8 85.7 𐄂 12 MB en_core_web_md English 2.1.0a4 92.0 90.1 97.0 86.2 ✓ 93 MB en_core_web_lg English 2.1.0a4 92.1 90.3 97.0 86.5 ✓ 780 MB de_core_news_sm German 2.1.0a4 91.9 89.8 97.2 83.4 𐄂 12 MB de_core_news_md German 2.1.0a4 91.3 90.5 97.4 83.6 ✓ 212 MB es_core_news_sm Spanish 2.1.0a4 90.1 87.1 96.8 89.3 𐄂 12 MB es_core_news_md Spanish 2.1.0a4 90.7 87.8 97.1 89.4 ✓ 72 MB pt_core_news_sm Portuguese 2.1.0a4 89.2 85.8 79.8 82.4 𐄂 14 MB fr_core_news_sm French 2.1.0a4 87.2 84.0 94.4 67.0 ¹ 𐄂 16 MB fr_core_news_md French 2.1.0a4 88.8 86.0 94.9 70.0 ¹ ✓ 84 MB it_core_news_sm Italian 2.1.0a4 90.6 87.0 96.0 81.7 𐄂 12 MB nl_core_news_sm Dutch 2.1.0a4 83.1 77.2 91.3 87.3 𐄂 12 MB el_core_news_sm Greek 2.1.0a4 84.2 80.4 94.6 71.5 𐄂 12 MB el_core_news_md Greek 2.1.0a4 87.5 84.1 96.4 78.3 ✓ 128 MB xx_ent_wiki_sm Multi 2.1.0a4 - - - 83.2 𐄂 4 MB

1) We're currently investigating this, as the results are anomalously low.

💬 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 and @skrcode for the pull requests and contributions.