v2.0.0: Neural networks, 13 new models for 7+ languages, better training, custom pipelines, Pickle & lots of API improvements

We're very excited to finally introduce spaCy v2.0. The new version gets spaCy up to date with the latest deep learning technologies and makes it much easier to run spaCy in scalable cloud computing workflows. We've fixed over 60 bugs (every open bug!), including several long-standing issues, trained 13 neural network models for 7+ languages and added alpha tokenization support for 8 new languages. We also re-wrote almost all of the usage guides, API docs and code examples.

pip install -U spacy

conda install -c conda-forge spacy

✨ Major features and improvements

• NEW: Convolutional neural network models for English, German, Spanish, Portuguese, French, Italian, Dutch and multi-language NER. Substantial improvements in accuracy over the v1.x models.
• NEW: Vectors class for managing word vectors, plus trainable document vectors and contextual similarity via convolutional neural networks.
• NEW: Custom processing pipeline components and extension attributes on the Doc, Token and Span via Doc._, Token._ and Span._.
• NEW: Built-in, trainable text classification pipeline component.
• NEW: Built-in displaCy visualizers for dependencies and entities, with Jupyter notebook support.
• NEW: Alpha tokenization for Danish, Polish, Indonesian, Thai, Hindi, Irish, Turkish, Croatian and Romanian.
• Improved language data, support for lazy loading and simple, lookup-based lemmatization for English, German, French, Spanish, Italian, Hungarian, Portuguese and Swedish.
• Support for multi-language models and new MultiLanguage class (xx).
• Strings are now resolved to hash values, instead of mapped to integer IDs. This means that the string-to-int mapping no longer depends on the vocabulary state.
• Improved and consistent saving, loading and serialization across objects, plus Pickle support.
• PhraseMatcher for matching large terminology lists as Doc objects, plus revised Matcher API.
• New CLI commands validate, vocab and evaluate, plus entry point for spacy command to use instead of python -m spacy.
• Experimental GPU support via Chainer's CuPy module.

🔮 Models

spaCy v2.0 comes with 13 new convolutional neural network models for 7+ languages. The models have been designed and implemented from scratch specifically for spaCy. A novel bloom embedding strategy with subword features is used to support huge vocabularies in tiny tables.

All core models include part-of-speech tags, dependency labels and named entities. Small models include only context-specific token vectors, while medium-sized and large models ship with word vectors. For more details, see the models directory or try our new model comparison tool.

Name	Language	Features	Size
en_core_web_sm	English	Tagger, parser, entities	35 MB
en_core_web_md	English	Tagger, parser, entities, vectors	115 MB
en_core_web_lg	English	Tagger, parser, entities, vectors	812 MB
en_vectors_web_lg	English	Vectors	627 MB
de_core_news_sm	German	Tagger, parser, entities	36 MB
es_core_news_sm	Spanish	Tagger, parser, entities	35 MB
es_core_news_md	Spanish	Tagger, parser, entities, vectors	93 MB
pt_core_news_sm	Portuguese	Tagger, parser, entities	36 MB
fr_core_news_sm	French	Tagger, parser, entities	37 MB
fr_core_news_md	French	Tagger, parser, entities, vectors	106 MB
it_core_news_sm	Italian	Tagger, parser, entities	34 MB
nl_core_news_sm	Dutch	Tagger, parser, entities	34 MB
xx_ent_wiki_sm	Multi-language	Entities	33MB

You can download a model by using its name or shortcut. To load a model, use spacy.load(), or import it as a module and call its load() method:

spacy download en_core_web_sm

import spacy
nlp = spacy.load('en_core_web_sm')

import en_core_web_sm
nlp = en_core_web_sm.load()

📈 Benchmarks

spaCy v2.0's new neural network models bring significant improvements in accuracy, especially for English Named Entity Recognition. The new en_core_web_lg model makes about 25% fewer mistakes than the corresponding v1.x model and is within 1% of the current state-of-the-art (Strubell et al., 2017). The v2.0 models are also cheaper to run at scale, as they require under 1 GB of memory per process.

English

Model	spaCy	Type	UAS	LAS	NER F	POS	Size
en_core_web_sm-2.0.0	v2.x	neural	91.7	89.8	85.3	97.0	35MB
en_core_web_md-2.0.0	v2.x	neural	91.7	89.8	85.9	97.1	115MB
en_core_web_lg-2.0.0	v2.x	neural	91.9	90.1	85.9	97.2	812MB
en_core_web_sm-1.1.0	v1.x	linear	86.6	83.8	78.5	96.6	50MB
en_core_web_md-1.2.1	v1.x	linear	90.6	88.5	81.4	96.7	1GB

Spanish

Model	spaCy	Type	UAS	LAS	NER F	POS	Size
es_core_news_sm-2.0.0	v2.x	neural	89.8	86.8	88.7	96.9	35MB
es_core_news_md-2.0.0	v2.x	neural	90.2	87.2	89.0	97.8	93MB
es_core_web_md-1.1.0	v1.x	linear	87.5	n/a	94.2	96.7	377MB

For more details of the other models, see the models directory and model comparison tool.

🔴 Bug fixes

• Fix issue #125, #228, #299, #377, #460, #606, #930: Add full Pickle support.
• Fix issue #152, #264, #322, #343, #437, #514, #636, #785, #927, #985, #992, #1011: Fix and improve serialization and deserialization of Doc objects.
• Fix issue #285, #1225: Fix memory growth problem when streaming data.
• Fix issue #512: Improve parser to prevent it from returning two ROOT objects.
• Fix issue #519, #611, #725: Retrain German model with better tokenized input.
• Fix issue #524: Improve parser and handling of noun chunks.
• Fix issue #621: Prevent double spaces from changing the parser result.
• Fix issue #664, #999, #1026: Fix bugs that would prevent loading trained NER models.
• Fix issue #671, #809, #856: Fix importing and loading of word vectors.
• Fix issue #683, #1052, #1442: Don't require tag maps to provide SP tag.
• Fix issue #753: Resolve bug that would tag OOV items as personal pronouns.
• Fix issue #860, #956, #1085, #1381: Allow custom attribute extensions on Doc, Token and Span.
• Fix issue #905, #954, #1021, #1040, #1042: Improve parsing model and allow faster accuracy updates.
• Fix issue #933, #977, #1406: Update online demos.
• Fix issue #995: Improve punctuation rules for Hebrew and other non-latin languages.
• Fix issue #1008: train command finally works correctly if used without dev_data.
• Fix issue #1012: Improve word vectors documentation.
• Fix issue #1043: Improve NER models and allow faster accuracy updates.
• Fix issue #1044: Fix bugs in French model and improve performance.
• Fix issue #1051: Improve error messages if functionality needs a model to be installed.
• Fix issue #1071: Correct typo of "whereve" in English tokenizer exceptions.
• Fix issue #1088: Emoji are now split into separate tokens wherever possible.
• Fix issue #1240: Allow merging Spans without keyword arguments.
• Fix issue #1243: Resolve undefined names in deprecated functions.
• Fix issue #1250: Fix caching bug that would cause tokenizer to ignore special case rules after first parse.
• Fix issue #1257: Ensure the compare operator == works as expected on tokens.
• Fix issue #1291: Improve documentation of training format.
• Fix issue #1336: Fix bug that caused inconsistencies in NER results.
• Fix issue #1375: Make sure Token.nbor raises IndexError correctly.
• Fix issue #1450: Fix error when OP quantifier "*" ends the match pattern.
• Fix issue #1452: Fix bug that would mutate the original text.

📖 Documentation and examples

• NEW: Completely rewritten, reorganised and redesigned [usage](http...

v1.10.0: Alpha support for Thai & Russian, plus improvements and bug fixes

⚠️ Important note: This is a bridge release that gets the current state of the v1.x branch published. Stay tuned for v2.0.

✨ Major features and improvements

• NEW: Alpha tokenization support for Thai and Russian.
• NEW: Alpha support for Japanese part-of-speech tagging.
• NEW: Dependency pattern-matching algorithm (see #1120).
• Add support for getting a lowest common ancestor matrix via Doc.get_lca_matrix().
• Improve capturing of English noun chunks.

🔴 Bug fixes

• Fix issue #1078: Simplify URL pattern.
• Fix issue #1174: Fix NER model loading bug and make sure JSON keys are loaded as strings.
• Fix issue #1291: Document correct JSON format for training.
• Fix issue #1292: Fix error when adding custom infix rules.
• Fix issue #1387: Ensure that lemmatizer respects exception rules.
• Fix issue #1410: Support single value for attribute list in Doc.to_scalar and Doc.to_array.

📖 Documentation and examples

• Document correct JSON format for training.
• Fix various typos and inconsistencies.

👥 Contributors

Thanks to @raphael0202, @gideonite, @delirious-lettuce, @polm, @kevinmarsh, @IamJeffG, @Vimos, @ericzhao28, @galaxyh, @hscspring, @wannaphongcom, @Wellan89, @kokes, @mdcclv, @ameyuuno, @ramananbalakrishnan, @Demfier, @johnhaley81, @mayukh18 and @jnothman for the pull requests and contributions.

v1.9.0: Spanish model, alpha support for Norwegian & Japanese, and bug fixes

Thanks to all of you for 5,000 stars on GitHub, the valuable feedback in the user survey and testing spaCy v2.0 alpha. We're working hard on getting the new version ready and can't wait to release it. In the meantime, here's a new release for the 1.x branch that fixes a variety of outstanding bugs and adds capabilities for new languages.

💌 P.S.: If you haven't gotten your hands on a set of spaCy stickers yet, you can still do so – send us a DM with your address on Twitter or Gitter, and we'll mail you some!

---

✨ Major features and improvements

• NEW: The first official Spanish model (377 MB) including vocab, syntax, entities and word vectors. Thanks to the amazing folks at recogn.ai for the collaboration!

python -m spacy download es

nlp = spacy.load('es')
doc = nlp(u'Esto es una frase.')

• NEW: Alpha tokenization for Norwegian Bokmål and Japanese (via Janome).
• NEW: Allow dropout training for Parser and EntityRecognizer, using the drop keyword argument to the update() method.
• NEW: Glossary for POS, dependency and NER annotation scheme via spacy.explain(). For example, spacy.explain('NORP') will return "Nationalities or religious or political groups".
• Improve language data for Dutch, French and Spanish.
• Add Language.parse_tree method to generate POS tree for all sentences in a Doc.

🔴 Bug fixes

• Fix issue #1031: Close gaps in Lexeme API.
• Fix issue #1034: Add annotation scheme glossary and spacy.explain().
• Fix issue #1051: Improved error messaging when trying to load non-existing model.
• Fix issue #1052: Add missing SP symbol to tag map.
• Fix issue #1061: Add flush_cache method to tokenizer.
• Fix issue #1069: Fix Doc.sents iterator when customised with generator.
• Fix issue ##1099, #1143: Improve documentation on models in requirements.txt.
• Fix issue #1137: Use lower min version for requests dependency.
• Fix issue #1207: Fix Span.noun_chunks.
• Fix issue with six and its dependencies that occasionally caused spaCy to fail.
• Fix typo in package command that caused error when printing error messages.

📖 Documentation and examples

• Fix various typos and inconsistencies.
• NEW: spaCy 101 guide for v2.0: all important concepts, explained with examples and illustrations. Note that some of the behaviour and examples are specific to v2.0+ – but the NLP basics are relevant independent of the spaCy version you're using.

👥 Contributors

Thanks to @kengz, @luvogels, @ferdous-al-imran, @uetchy, @akYoung, @pasupulaphani, @dvsrepo, @raphael0202, @yuvalpinter, @frascuchon, @kootenpv, @oroszgy, @bartbroere, @ianmobbs, @garfieldnate, @polm, @callumkift, @swierh, @val314159, @lgenerknol and @jsparedes for the contributions!

v2.0.0 alpha: Neural network models, Pickle, better training & lots of API improvements

Last update: 2.0.0rc2, 2017-11-07

This is an alpha pre-release of spaCy v2.0.0 and available on pip as spacy-nightly. It's not intended for production use. The alpha documentation is available at alpha.spacy.io. Please note that the docs reflect the library's intended state on release, not the current state of the implementation. For bug reports, feedback and questions, see the spaCy v2.0.0 alpha thread.

Before installing v2.0.0 alpha, we recommend setting up a clean environment.

pip install spacy-nightly

The models are still under development and will keep improving. For more details, see the benchmarks below. There will also be additional models for German, French and Spanish.

Name	Lang	Capabilities	Size	spaCy	Info
en_core_web_sm-2.0.0a4	en	Parser, Tagger, NER	42MB	>=2.0.0a14	ℹ️
en_vectors_web_lg-2.0.0a0	en	Vectors (GloVe)	627MB	>=2.0.0a10	ℹ️
xx_ent_wiki_sm-2.0.0a0	multi	NER	12MB	<=2.0.0a9	ℹ️

You can download a model by using its name or shortcut. To load a model, use spaCy's loader, e.g. nlp = spacy.load('en_core_web_sm') , or import it as a module (import en_core_web_sm) and call its load() method, e.g nlp = en_core_web_sm.load().

python -m spacy download en_core_web_sm

📈 Benchmarks

The evaluation was conducted on raw text with no gold standard information. Speed and accuracy are currently comparable to the v1.x models: speed on CPU is slightly lower, while accuracy is slightly higher. We expect performance to improve quickly between now and the release date, as we run more experiments and optimise the implementation.

Model	spaCy	Type	UAS	LAS	NER F	POS	Words/s
en_core_web_sm-2.0.0a4	v2.x	neural	91.9	90.0	85.0	97.1	10,000
en_core_web_sm-2.0.0a3	v2.x	neural	91.2	89.2	85.3	96.9	10,000
en_core_web_sm-2.0.0a2	v2.x	neural	91.5	89.5	84.7	96.9	10,000
en_core_web_sm-1.1.0	v1.x	linear	86.6	83.8	78.5	96.6	25,700
en_core_web_md-1.2.1	v1.x	linear	90.6	88.5	81.4	96.7	18,800

✨ Major features and improvements

• NEW: Neural network model for English (comparable performance to the >1GB v1.x models) and multi-language NER (still experimental).
• NEW: GPU support via Chainer's CuPy module.
• NEW: Strings are now resolved to hash values, instead of mapped to integer IDs. This means that the string-to-int mapping no longer depends on the vocabulary state.
• NEW: Trainable document vectors and contextual similarity via convolutional neural networks.
• NEW: Built-in text classification component.
• NEW: Built-in displaCy visualizers with Jupyter notebook support.
• NEW: Alpha tokenization for Danish, Polish and Indonesian.
• Improved language data, support for lazy loading and simple, lookup-based lemmatization for English, German, French, Spanish, Italian, Hungarian, Portuguese and Swedish.
• Improved language processing pipelines and support for custom, model-specific components.
• Improved and consistent saving, loading and serialization across objects, plus Pickle support.
• Revised matcher API to make it easier to add and manage patterns and callbacks in one step.
• Support for multi-language models and new MultiLanguage class (xx).
• Entry point for spacy command to use instead of python -m spacy.

🚧 Work in progress (not yet implemented)

• NEW: Neural network models for German, French and Spanish.
• NEW: Binder, a container class for serializing collections of Doc objects.

🔴 Bug fixes

• Fix issue #125, #228, #299, #377, #460, #606, #930: Add full Pickle support.
• Fix issue #152, #264, #322, #343, #437, #514, #636, #785, #927, #985, #992, #1011: Fix and improve serialization and deserialization of Doc objects.
• Fix issue #512: Improve parser to prevent it from returning two ROOT objects.
• Fix issue #524: Improve parser and handling of noun chunks.
• Fix issue #621: Prevent double spaces from changing the parser result.
• Fix issue #664, #999, #1026: Fix bugs that would prevent loading trained NER models.
• Fix issue #671, #809, #856: Fix importing and loading of word vectors.
• Fix issue #753: Resolve bug that would tag OOV items as personal pronouns.
• Fix issue #905, #1021, #1042: Improve parsing model and allow faster accuracy updates.
• Fix issue #995: Improve punctuation rules for Hebrew and other non-latin languages.
• Fix issue #1008: train command finally works correctly if used without dev_data.
• Fix issue #1012: Improve documentation on model saving and loading.
• Fix issue #1043: Improve NER models and allow faster accuracy updates.
• Fix issue #1051: Improve error messages if functionality needs a model to be installed.
• Fix issue #1071: Correct typo of "whereve" in English tokenizer exceptions.
• Fix issue #1088: Emoji are now split into separate tokens wherever possible.

🚧 Work in progress (not yet implemented)

• Fix issue #519, #611, #725: Retrain German model with better tokenized input.
• Fix issue #933, #954, #977, #1040: Improve accuracy and update online demos.
• Fix issue #1044: Fix bugs in French model and improve performance.

📖 Documentation and examples

• NEW: spacy 101 guide with simple explanations and illustrations of the most important concepts and an overview of spaCy's features and capabilities.
• NEW: Visualizing spaCy guide on how to use the built-in displacy module.
• NEW: API docs for top-level functions, spacy.displacy, spacy.util, spacy.gold.GoldCorpus.
• NEW: Full code example for text classification (sentiment analysis).
• Improved rule-based matching guide with examples for matching entities and phone numbers, and social media analysis.
• Improved processing pipelines guide with examples for custom sentence segmentation logic and hooking in a sentiment analysis model.
• Re-wrote all API and usage docs and added more examples.

🚧 Work in progress (not yet implemented)

• NEW: Usage guide on scaling spaCy for production.
• NEW: Usage guide on text classification.
• NEW: API docs for spacy.pipeline.TextCategorizer, spacy.pipeline.Tensorizer, spacy.tokens.binder.Binder and spacy.vectors.Vectors.
• Improved training, NER training and deep learning usage guides with examples.

⚠️ Backwards incompatibilities

Note that the old v1.x models are not compatible with spaCy v2.0.0. If you've trained your own models, you'll have to re-train them to be able to use them with the new version. For a full overview of changes in v2.0, see the alpha documentation and guide on migrating from spaCy 1.x.

Loading models

spacy.load() is now only intended for loading models – if you need an empty language class, import it directly instead, e.g. from spacy.lang.en import English. If the model you're loading is a shortcut link or package name, spaCy will expect it to be a model package, import it and call its load() method. If you supply a path, spaCy will expect it to be a model data directory and use the meta.json to initialise a language class and call nlp.from_disk() with the data path.

nlp = spacy.load('en')
nlp = spacy.load('en_core_web_sm')
nlp = spacy.load('/model-data')
nlp = English().from.disk('/model-data')
# OLD: nlp = spacy.load('en', path='/model-data')

Hash values instead of integer IDs

The StringStore now resolves all strings to hash values instead of integer IDs. This means that the string-to-int mapping no longer depends on the vocabulary state, making a lot of workflows much simpler, especially during training. However, you still need to make sure all objects have access to the same Vocab. Otherwise, spaCy won't be able to resolve hashes back to their string values.

nlp.vocab.strings[u'coffee']       # 3197928453018144401
other_nlp.vocab.strings[u'coffee'] # 3197928453018144401

Serialization

spaCy's [serializ...

v1.8.2: French model and small improvements

We've been delighted to see spaCy growing so much over the last few months. Before the v1.0 release, we asked for your feedback, which has been incredibly helpful in improving the library. As we're getting closer to v2.0 we hope you'll take a few minutes to fill out the survey, to help us understand how you're using the library, and how it can be better.

📊 Take the survey!

---

✨ Major features and improvements

• Move model shortcuts to shortcuts.json to allow adding new ones without updating spaCy.
• NEW: The first official French model (~1.3 GB) including vocab, syntax and word vectors.

python -m spacy download fr_depvec_web_lg

import fr_depvec_web_lg

nlp = fr_depvec_web_lg.load()
doc = nlp(u'Parlez-vous Français?')

🔴 Bug fixes

• Fix reporting if train command is used without dev_data.
• Fix issue #1019: Make Span hashable.

📖 Documentation and examples

• Update list of available models with more information (capabilities, license).
• Update adding languages workflow with data examples and details on training.
• Fix various typos and inconsistencies.

👥 Contributors

Thanks to @raphael0202 and @julien-c for the contributions!

v1.8.1: Saving, loading and training bug fixes

We've been delighted to see spaCy growing so much over the last few months. Before the v1.0 release, we asked for your feedback, which has been incredibly helpful in improving the library. As we're getting closer to v2.0 we hope you'll take a few minutes to fill out the survey, to help us understand how you're using the library, and how it can be better.

📊 Take the survey!

---

🔴 Bug fixes

• Fix issue #988: Ensure noun chunks can't be nested.
• Fix issue #991: convert command now uses Python 2/3 compatible json.dumps.
• Fix issue #995: Use regex library for non-latin characters to simplify punctuation rules.
• Fix issue #999: Fix parser and NER model saving and loading.
• Fix issue #1001: Add SPACE to Spanish tag map.
• Fix issue #1008: train command now works correctly if used without dev_data.
• Fix issue #1009: Language.save_to_directory() now converts strings to pathlib paths.

📖 Documentation and examples

• Fix issue #889, #967: Correct typos in lightning tour and pos_tag.py examples.
• Add Language.save_to_directory() method to API docs.
• Fix various typos and inconsistencies.

👥 Contributors

Thanks to @dvsrepo, @beneyal and @oroszgy for the pull requests!

v1.8.0: Better NER training, saving and loading

We've been delighted to see spaCy growing so much over the last few months. Before the v1.0 release, we asked for your feedback, which has been incredibly helpful in improving the library. As we're getting closer to v2.0 we hope you'll take a few minutes to fill out the survey, to help us understand how you're using the library, and how it can be better.

📊 Take the survey!

---

✨ Major features and improvements

• NEW: Add experimental Language.save_to_directory() method to make it easier to save user-trained models.
• Add spacy.compat module to handle platform and Python version compatibility.
• Update package command to read from existing meta.json and supply custom location to meta file.
• Fix various compatibility issues and improve error messages in spacy.cli.

🔴 Bug fixes

• Fix issue #701, #822, #937, #959: Updated docs for NER training and saving/loading.
• Fix issue #968: spacy.load() now prints warning if no model is found.
• Fix issue #970, #978: Use correct unicode paths for symlinks on Python 2 / Windows.
• Fix issue #973: Make token.lemma and token.lemma_ attributes writeable.
• Fix issue #983: Add spacy.compat to handle compatibility.

📖 Documentation and examples

• NEW: Example for training a new entity type.
• NEW: Workflow for training the Named Entity Recognizer.
• NEW: Workflow for saving and loading models.
• Update Contributing Guidelines with code conventions for Python and Cython.
• Fix various typos and inconsistencies.

👥 Contributors

Thanks to @tsohil and @oroszgy for the pull requests!

v1.7.5: Bug fixes and new CLI commands

We've been delighted to see spaCy growing so much over the last few months. Before the v1.0 release, we asked for your feedback, which has been incredibly helpful in improving the library. As we're getting closer to v2.0 we hope you'll take a few minutes to fill out the survey, to help us understand how you're using the library, and how it can be better.

📊 Take the survey!

---

✨ Major features and improvements

• NEW: Experimental convert and model commands to convert files to spaCy's JSON format for training, and initialise a new model and its data directory.
• Updated language data for Spanish and Portuguese.

🔴 Bug fixes

• Error messages now show the new download commands if no model is loaded.
• The package command now works correctly and doesn't fail when creating files.
• Fix issue #693: Improve rules for detecting noun chunks.
• Fix issue #758: Adding labels now doesn't cause EntityRecognizer transition bug.
• Fix issue #862: label keyword argument is now handled correctly in doc.merge().
• Fix issue #891: Tokens containing / infixes are now split by the tokenizer.
• Fix issue #898: Dependencies are now deprojectivized correctly.
• Fix issue #910: NER models with new labels now saved correctly, preventing memory errors.
• Fix issue #934, #946: Symlink paths are now handled correctly on Windows, preventing invalid switch error.
• Fix issue #947: Hebrew module is now added to setup.py and __init__.py.
• Fix issue #948: Contractions are now lemmatized correctly.
• Fix issue #957: Use regex module to avoid back-tracking on URL regex.

📖 Documentation and examples

• Documentation for new convert and model commands.
• Update troubleshooting guide with --no-cache-dir error resulting from outdated pip version and file name shadowing model problem.
• Fix various typos and inconsistencies.

👥 Contributors

Thanks to @ericzhao28, @Gregory-Howard, @kinow, @jreeter, @mamoit, @kumaranvpl and @dvsrepo for the pull requests!

v1.7.3: Alpha support for Hebrew, new CLI commands and bug fixes

✨ Major features and improvements

• NEW: Alpha tokenization for Hebrew.
• NEW: Experimental train and package commands to train a model and convert it to a Python package.
• Enable experimental support for L1-regularized regression loss in dependency parser and named entity recognizer. Should improve fine-tuning of existing models.
• Fix high memory usage in download command.

🔴 Bug fixes

• Fix issue #903, #912: Base forms are now correctly protected from lemmatization.
• Fix issue #909, #925: Use mlink to create symlinks in Python 2 on Windows.
• Fix issue #910: Update config when adding label to pre-trained model.
• Fix issue #911: Delete old training scripts.
• Fix issue #918: Use --no-cache-dir when downloading models via pip.
• Fixed infinite recursion in spacy.info.
• Fix initialisation of languages when no model is available.

📖 Documentation and examples

• Troubleshooting guide for most common issues and usage problems.
• Documentation for new package and train commands.
• Documentation for spaCy's JSON format for training data.
• Fix various typos and inconsistencies.

👥 Contributors

Thanks to @raphael0202, @pavlin99th, @iddoberger and @solresol for the pull requests!

v1.7.2: Small fixes to beam parser and model linking

🔴 Bug fixes

• Success message in link is now displayed correctly when using local paths.
• Decrease beam density and fix Python 3 problem in beam_parser.
• Fix issue #894: Model packages now install and compile paths correctly on Windows.

📖 Documentation and examples

• Standalone NER training example.
• Fix various typos and inconsistencies.