--- title: What's New in v2.1 teaser: New features, backwards incompatibilities and migration guide menu: - ['New Features', 'features'] - ['Backwards Incompatibilities', 'incompat'] --- ## New Features {#features hidden="true"} spaCy v2.1 has focussed primarily on stability and performance, solidifying the design changes introduced in [v2.0](/usage/v2). As well as smaller models, faster runtime, and many bug fixes, v2.1 also introduces experimental support for some exciting new NLP innovations. For the full changelog, see the [release notes on GitHub](https://github.com/explosion/spaCy/releases/tag/v2.1.0). For more details and a behind-the-scenes look at the new release, [see our blog post](https://explosion.ai/blog/spacy-v2-1). ### BERT/ULMFit/Elmo-style pre-training {#pretraining tag="experimental"} > #### Example > > ```bash > $ python -m spacy pretrain ./raw_text.jsonl > en_vectors_web_lg ./pretrained-model > ``` spaCy v2.1 introduces a new CLI command, `spacy pretrain`, that can make your models much more accurate. It's especially useful when you have **limited training data**. The `spacy pretrain` command lets you use transfer learning to initialize your models with information from raw text, using a language model objective similar to the one used in Google's BERT system. We've taken particular care to ensure that pretraining works well even with spaCy's small default architecture sizes, so you don't have to compromise on efficiency to use it. **API:** [`spacy pretrain`](/api/cli#pretrain) **Usage: ** [Improving accuracy with transfer learning](/usage/training#transfer-learning) ### Extended match pattern API {#matcher-api} > #### Example > > ```python > # Matches "love cats" or "likes flowers" > pattern1 = [{"LEMMA": {"IN": ["like", "love"]}}, {"POS": "NOUN"}] > # Matches tokens of length >= 10 > pattern2 = [{"LENGTH": {">=": 10}}] > # Matches custom attribute with regex > pattern3 = [{"_": {"country": {"REGEX": "^([Uu](\\.?|nited) ?[Ss](\\.?|tates)"}}}] > ``` Instead of mapping to a single value, token patterns can now also map to a **dictionary of properties**. For example, to specify that the value of a lemma should be part of a list of values, or to set a minimum character length. It now also supports a `REGEX` property, as well as set membership via `IN` and `NOT_IN`, custom extension attributes via `_` and rich comparison for numeric values. **API:** [`Matcher`](/api/matcher) **Usage: ** [Extended pattern syntax and attributes](/usage/rule-based-matching#adding-patterns-attributes-extended), [Regular expressions](/usage/rule-based-matching#regex) ### Easy rule-based entity recognition {#entity-ruler} > #### Example > > ```python > from spacy.pipeline import EntityRuler > ruler = EntityRuler(nlp) > ruler.add_patterns([{"label": "ORG", "pattern": "Apple"}]) > nlp.add_pipe(ruler, before="ner") > ``` The `EntityRuler` is an exciting new component that lets you add named entities based on pattern dictionaries, and makes it easy to combine rule-based and statistical named entity recognition for even more powerful models. Entity rules can be phrase patterns for exact string matches, or token patterns for full flexibility. **API:** [`EntityRuler`](/api/entityruler) **Usage: ** [Rule-based entity recognition](/usage/rule-based-matching#entityruler) ### Phrase matching with other attributes {#phrasematcher} > #### Example > > ```python > matcher = PhraseMatcher(nlp.vocab, attr="POS") > matcher.add("PATTERN", None, nlp("I love cats")) > doc = nlp("You like dogs") > matches = matcher(doc) > ``` By default, the `PhraseMatcher` will match on the verbatim token text, e.g. `Token.text`. By setting the `attr` argument on initialization, you can change **which token attribute the matcher should use** when comparing the phrase pattern to the matched `Doc`. For example, `LOWER` for case-insensitive matches or `POS` for finding sequences of the same part-of-speech tags. **API:** [`PhraseMatcher`](/api/phrasematcher) **Usage: ** [Matching on other token attributes](/usage/rule-based-matching#phrasematcher-attrs) ### Retokenizer for merging and splitting {#retokenizer} > #### Example > > ```python > doc = nlp("I like David Bowie") > with doc.retokenize() as retokenizer: > attrs = {"LEMMA": "David Bowie"} > retokenizer.merge(doc[2:4], attrs=attrs) > ``` The new `Doc.retokenize` context manager allows merging spans of multiple tokens into one single token, and splitting single tokens into multiple tokens. Modifications to the `Doc`'s tokenization are stored, and then made all at once when the context manager exits. This is much more efficient, and less error-prone. `Doc.merge` and `Span.merge` still work, but they're considered deprecated. **API:** [`Doc.retokenize`](/api/doc#retokenize), [`Retokenizer.merge`](/api/doc#retokenizer.merge), [`Retokenizer.split`](/api/doc#retokenizer.split)
**Usage: **[Merging and splitting](/usage/linguistic-features#retokenization) ### Components and languages via entry points {#entry-points} > #### Example > > ```python > from setuptools import setup > setup( > name="custom_extension_package", > entry_points={ > "spacy_factories": ["your_component = component:ComponentFactory"] > "spacy_languages": ["xyz = language:XYZLanguage"] > } > ) > ``` Using entry points, model packages and extension packages can now define their own `"spacy_factories"` and `"spacy_languages"`, which will be added to the built-in factories and languages. If a package in the same environment exposes spaCy entry points, all of this happens automatically and no further user action is required. **Usage:** [Using entry points](/usage/saving-loading#entry-points) ### Improved documentation {#docs} Although it looks pretty much the same, we've rebuilt the entire documentation using [Gatsby](https://www.gatsbyjs.org/) and [MDX](https://mdxjs.com/). It's now an even faster progressive web app and allows us to write all content entirely **in Markdown**, without having to compromise on easy-to-use custom UI components. We're hoping that the Markdown source will make it even easier to contribute to the documentation. For more details, check out the [styleguide](/styleguide) and [source](https://github.com/explosion/spaCy/tree/master/website). While converting the pages to Markdown, we've also fixed a bunch of typos, improved the existing pages and added some new content: - **Usage Guide:** [Rule-based Matching](/usage/rule-based-matching)
How to use the `Matcher`, `PhraseMatcher` and the new `EntityRuler`, and write powerful components to combine statistical models and rules. - **Usage Guide:** [Saving and Loading](/usage/saving-loading)
Everything you need to know about serialization, and how to save and load pipeline components, package your spaCy models as Python modules and use entry points. - **Usage Guide: ** [Merging and Splitting](/usage/linguistic-features#retokenization)
How to retokenize a `Doc` using the new `retokenize` context manager and merge spans into single tokens and split single tokens into multiple. - **Universe:** [Videos](/universe/category/videos) and [Podcasts](/universe/category/podcasts) - **API:** [`EntityRuler`](/api/entityruler) - **API:** [`Sentencizer`](/api/sentencizer) - **API:** [Pipeline functions](/api/pipeline-functions) ## Backwards incompatibilities {#incompat} If you've been training **your own models**, you'll need to **retrain** them with the new version. Also don't forget to upgrade all models to the latest versions. Models for v2.0.x aren't compatible with models for v2.1.x. To check if all of your models are up to date, you can run the [`spacy validate`](/api/cli#validate) command. - Due to difficulties linking our new [`blis`](https://github.com/explosion/cython-blis) for faster platform-independent matrix multiplication, this release currently **doesn't work on Python 2.7 on Windows**. We expect this to be corrected in the future. - While the [`Matcher`](/api/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`](/api/doc#merge) and [`Span.merge`](/api/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`](/api/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]) ``` - The serialization methods `to_disk`, `from_disk`, `to_bytes` and `from_bytes` now support a single `exclude` argument to provide a list of string names to exclude. The docs have been updated to list the available serialization fields for each class. The `disable` argument on the [`Language`](/api/language) serialization methods has been renamed to `exclude` for consistency. ```diff - nlp.to_disk("/path", disable=["parser", "ner"]) + nlp.to_disk("/path", exclude=["parser", "ner"]) - data = nlp.tokenizer.to_bytes(vocab=False) + data = nlp.tokenizer.to_bytes(exclude=["vocab"]) ``` - The .pos value for several common English words has changed, due to corrections to long-standing mistakes in the English tag map (see [issue #593](https://github.com/explosion/spaCy/issues/593) and [issue #3311](https://github.com/explosion/spaCy/issues/3311) for details). - For better compatibility with the Universal Dependencies data, the lemmatizer now preserves capitalization, e.g. for proper nouns. See [issue #3256](https://github.com/explosion/spaCy/issues/3256) for details. - 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 `is_sent_start` attribute of the first token in a `Doc` now correctly defaults to `True`. It previously defaulted to `None`. - 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 now deprecated. If you need a custom nested JSON representation of a `Doc` object, you might want to write your own helper function. For a simple and consistent JSON representation of the `Doc` object and its annotations, you can now use the [`Doc.to_json`](/api/doc#to_json) method. Going forward, this method will output the same format as the JSON training data expected by [`spacy train`](/api/cli#train). - The [`spacy train`](/api/cli#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`](/api/cli#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`](/models/it#it_core_news_sm) is now correctly licensed under CC BY-NC-SA 3.0, and all [English](/models/en) and [German](/models/de) models are now published under the MIT license.