spaCy/website/docs/usage/v2-2.md

---
title: What's New in v2.2
teaser: New features, backwards incompatibilities and migration guide
menu:
  - ['New Features', 'features']
  - ['Backwards Incompatibilities', 'incompat']
---

## New Features {#features hidden="true"}

spaCy v2.2 features improved statistical models, new pretrained models for
Norwegian and Lithuanian, better Dutch NER, as well as a new mechanism for
storing language data that makes the installation about **15× smaller** on
disk. We've also added a new API for **entity linking**, a new class to
efficiently **serialize annotations**, built-in scoring and **CLI training for
text classification** and a new command to analyze and **debug training data**.
For the full changelog, see the
[release notes on GitHub](https://github.com/explosion/spaCy/releases/tag/v2.2.0).

<!-- For more details and a behind-the-scenes look at the new release,
[see our blog post](https://explosion.ai/blog/spacy-v2-2). -->

### Better pretrained models and more languages {#models}

> #### Example
>
> ```bash
> python -m spacy download nl_core_news_sm
> python -m spacy download nb_core_news_sm
> python -m spacy download nb_core_news_md
> python -m spacy download lt_core_news_sm
> ```

The new version also features new and re-trained models for all languages and
resolves a number of data bugs. The [Dutch model](/models/nl) has been retrained
with a new and custom-labelled NER corpus using the same extended label scheme
as the English models. It should now produce significantly better NER results
overall. We've also added new core models for [Norwegian](/models/nb) (MIT) and
[Lithuanian](/models/lt) (CC BY-SA).

<Infobox>

**Usage:** [Models directory](/models) **Benchmarks: **
[Release notes](https://github.com/explosion/spaCy/releases/tag/v2.2.0)

</Infobox>

### Entity linking API {#entity-linking}

> #### Example
>
> ```python
> nlp = spacy.load("my_custom_wikidata_model")
> doc = nlp("Ada Lovelace was born in London")
> print([(e.text, e.label_, e.kb_id_) for e in doc.ents])
> # [('Ada Lovelace', 'PERSON', 'Q7259'), ('London', 'GPE', 'Q84')]
> ```

Entity linking lets you ground named entities into the "real world". We're
excited to now provide a built-in API for training entity linking models and
resolving textual entities to unique identifiers from a knowledge base. The
annotated KB identifier is accessible as either a hash value or as a string from
a `Span` or `Token` object. For more details on entity linking in spaCy, check
out
[Sofie's talk](https://www.youtube.com/watch?v=PW3RJM8tDGo&list=PLBmcuObd5An4UC6jvK_-eSl6jCvP1gwXc&index=6)
at spaCy IRL 2019.

<Infobox>

**API:** [`EntityLinker`](/api/entitylinker),
[`KnowledgeBase`](/api/knowledgebase) **Code: **
[`bin/wiki_entity_linking`](https://github.com/explosion/spaCy/tree/master/bin/wiki_entity_linking)
**Usage: ** [Entity linking](/usage/linguistic-features#entity-linking),
[Training an entity linking model](/usage/training#entity-linker)

</Infobox>

### Serializable lookup table and dictionary API {#lookups}

> #### Example
>
> ```python
> data = {"foo": "bar"}
> nlp.vocab.lookups.add_table("my_dict", data)
>
> def custom_component(doc):
>    table = doc.vocab.lookups.get_table("my_dict")
>    print(table.get("foo"))  # look something up
>    return doc
> ```

The new `Lookups` API lets you add large dictionaries and lookup tables to the
`Vocab` and access them from the tokenizer or custom components and extension
attributes. Internally, the tables use Bloom filters for efficient lookup
checks. They're also fully serializable out-of-the-box. All large data resources
included with spaCy now use this API and are additionally compressed at build
time. This allowed us to make the installed library roughly **15 times smaller
on disk**.

<Infobox>

**API:** [`Lookups`](/api/lookups) **Usage: **
[Adding languages: Lemmatizer](/usage/adding-languages#lemmatizer)

</Infobox>

### Text classification scores and CLI training {#train-textcat-cli}

> #### Example
>
> ```bash
> $ python -m spacy train en /output /train /dev \\
> --pipeline textcat --textcat-arch simple_cnn \\
> --textcat-multilabel
> ```

When training your models using the `spacy train` command, you can now also
include text categories in the JSON-formatted training data. The `Scorer` and
`nlp.evaluate` now report the text classification scores, calculated as the
F-score on positive label for binary exclusive tasks, the macro-averaged F-score
for 3+ exclusive labels or the macro-averaged AUC ROC score for multilabel
classification.

<Infobox>

**API:** [`spacy train`](/api/cli#train), [`Scorer`](/api/scorer),
[`Language.evaluate`](/api/language#evaluate)

</Infobox>

### New DocBin class to efficiently serialize Doc collections

> #### Example
>
> ```python
> from spacy.tokens import DocBin
> doc_bin = DocBin(attrs=["LEMMA", "ENT_IOB", "ENT_TYPE"], store_user_data=True)
> for doc in nlp.pipe(texts):
>     doc_bin.add(doc)
> bytes_data = doc_bin.to_bytes()
> # Deserialize later, e.g. in a new process
> nlp = spacy.blank("en")
> doc_bin = DocBin().from_bytes(bytes_data)
> docs = list(doc_bin.get_docs(nlp.vocab))
> ```

If you're working with lots of data, you'll probably need to pass analyses
between machines, either to use something like [Dask](https://dask.org) or
[Spark](https://spark.apache.org), or even just to save out work to disk. Often
it's sufficient to use the `Doc.to_array` functionality for this, and just
serialize the numpy arrays – but other times you want a more general way to save
and restore `Doc` objects.

The new `DocBin` class makes it easy to serialize and deserialize a collection
of `Doc` objects together, and is much more efficient than calling
`Doc.to_bytes` on each individual `Doc` object. You can also control what data
gets saved, and you can merge pallets together for easy map/reduce-style
processing.

<Infobox>

**API:** [`DocBin`](/api/docbin) **Usage: **
[Serializing Doc objects](/usage/saving-loading#docs)

</Infobox>

### CLI command to debug and validate training data {#debug-data}

> #### Example
>
> ```bash
> $ python -m spacy debug-data en train.json dev.json
> ```

The new `debug-data` command lets you analyze and validate your training and
development data, get useful stats, and find problems like invalid entity
annotations, cyclic dependencies, low data labels and more. If you're training a
model with `spacy train` and the results seem surprising or confusing,
`debug-data` may help you track down the problems and improve your training
data.

<Accordion title="Example output">

```
=========================== Data format validation ===========================
✔ Corpus is loadable

=============================== Training stats ===============================
Training pipeline: tagger, parser, ner
Starting with blank model 'en'
18127 training docs
2939 evaluation docs
⚠ 34 training examples also in evaluation data

============================== Vocab & Vectors ==============================
ℹ 2083156 total words in the data (56962 unique)
⚠ 13020 misaligned tokens in the training data
⚠ 2423 misaligned tokens in the dev data
10 most common words: 'the' (98429), ',' (91756), '.' (87073), 'to' (50058),
'of' (49559), 'and' (44416), 'a' (34010), 'in' (31424), 'that' (22792), 'is'
(18952)
ℹ No word vectors present in the model

========================== Named Entity Recognition ==========================
ℹ 18 new labels, 0 existing labels
528978 missing values (tokens with '-' label)
New: 'ORG' (23860), 'PERSON' (21395), 'GPE' (21193), 'DATE' (18080), 'CARDINAL'
(10490), 'NORP' (9033), 'MONEY' (5164), 'PERCENT' (3761), 'ORDINAL' (2122),
'LOC' (2113), 'TIME' (1616), 'WORK_OF_ART' (1229), 'QUANTITY' (1150), 'FAC'
(1134), 'EVENT' (974), 'PRODUCT' (935), 'LAW' (444), 'LANGUAGE' (338)
✔ Good amount of examples for all labels
✔ Examples without occurences available for all labels
✔ No entities consisting of or starting/ending with whitespace

=========================== Part-of-speech Tagging ===========================
ℹ 49 labels in data (57 labels in tag map)
'NN' (266331), 'IN' (227365), 'DT' (185600), 'NNP' (164404), 'JJ' (119830),
'NNS' (110957), '.' (101482), ',' (92476), 'RB' (90090), 'PRP' (90081), 'VB'
(74538), 'VBD' (68199), 'CC' (62862), 'VBZ' (50712), 'VBP' (43420), 'VBN'
(42193), 'CD' (40326), 'VBG' (34764), 'TO' (31085), 'MD' (25863), 'PRP$'
(23335), 'HYPH' (13833), 'POS' (13427), 'UH' (13322), 'WP' (10423), 'WDT'
(9850), 'RP' (8230), 'WRB' (8201), ':' (8168), '''' (7392), '``' (6984), 'NNPS'
(5817), 'JJR' (5689), '$' (3710), 'EX' (3465), 'JJS' (3118), 'RBR' (2872),
'-RRB-' (2825), '-LRB-' (2788), 'PDT' (2078), 'XX' (1316), 'RBS' (1142), 'FW'
(794), 'NFP' (557), 'SYM' (440), 'WP$' (294), 'LS' (293), 'ADD' (191), 'AFX'
(24)
✔ All labels present in tag map for language 'en'

============================= Dependency Parsing =============================
ℹ Found 111703 sentences with an average length of 18.6 words.
ℹ Found 2251 nonprojective train sentences
ℹ Found 303 nonprojective dev sentences
ℹ 47 labels in train data
ℹ 211 labels in projectivized train data
'punct' (236796), 'prep' (188853), 'pobj' (182533), 'det' (172674), 'nsubj'
(169481), 'compound' (116142), 'ROOT' (111697), 'amod' (107945), 'dobj' (93540),
'aux' (86802), 'advmod' (86197), 'cc' (62679), 'conj' (59575), 'poss' (36449),
'ccomp' (36343), 'advcl' (29017), 'mark' (27990), 'nummod' (24582), 'relcl'
(21359), 'xcomp' (21081), 'attr' (18347), 'npadvmod' (17740), 'acomp' (17204),
'auxpass' (15639), 'appos' (15368), 'neg' (15266), 'nsubjpass' (13922), 'case'
(13408), 'acl' (12574), 'pcomp' (10340), 'nmod' (9736), 'intj' (9285), 'prt'
(8196), 'quantmod' (7403), 'dep' (4300), 'dative' (4091), 'agent' (3908), 'expl'
(3456), 'parataxis' (3099), 'oprd' (2326), 'predet' (1946), 'csubj' (1494),
'subtok' (1147), 'preconj' (692), 'meta' (469), 'csubjpass' (64), 'iobj' (1)
⚠ Low number of examples for label 'iobj' (1)
⚠ Low number of examples for 130 labels in the projectivized dependency
trees used for training. You may want to projectivize labels such as punct
before training in order to improve parser performance.
⚠ Projectivized labels with low numbers of examples: appos||attr: 12
advmod||dobj: 13 prep||ccomp: 12 nsubjpass||ccomp: 15 pcomp||prep: 14
amod||dobj: 9 attr||xcomp: 14 nmod||nsubj: 17 prep||advcl: 2 prep||prep: 5
nsubj||conj: 12 advcl||advmod: 18 ccomp||advmod: 11 ccomp||pcomp: 5 acl||pobj:
10 npadvmod||acomp: 7 dobj||pcomp: 14 nsubjpass||pcomp: 1 nmod||pobj: 8
amod||attr: 6 nmod||dobj: 12 aux||conj: 1 neg||conj: 1 dative||xcomp: 11
pobj||dative: 3 xcomp||acomp: 19 advcl||pobj: 2 nsubj||advcl: 2 csubj||ccomp: 1
advcl||acl: 1 relcl||nmod: 2 dobj||advcl: 10 advmod||advcl: 3 nmod||nsubjpass: 6
amod||pobj: 5 cc||neg: 1 attr||ccomp: 16 advcl||xcomp: 3 nmod||attr: 4
advcl||nsubjpass: 5 advcl||ccomp: 4 ccomp||conj: 1 punct||acl: 1 meta||acl: 1
parataxis||acl: 1 prep||acl: 1 amod||nsubj: 7 ccomp||ccomp: 3 acomp||xcomp: 5
dobj||acl: 5 prep||oprd: 6 advmod||acl: 2 dative||advcl: 1 pobj||agent: 5
xcomp||amod: 1 dep||advcl: 1 prep||amod: 8 relcl||compound: 1 advcl||csubj: 3
npadvmod||conj: 2 npadvmod||xcomp: 4 advmod||nsubj: 3 ccomp||amod: 7
advcl||conj: 1 nmod||conj: 2 advmod||nsubjpass: 2 dep||xcomp: 2 appos||ccomp: 1
advmod||dep: 1 advmod||advmod: 5 aux||xcomp: 8 dep||advmod: 1 dative||ccomp: 2
prep||dep: 1 conj||conj: 1 dep||ccomp: 4 cc||ROOT: 1 prep||ROOT: 1 nsubj||pcomp:
3 advmod||prep: 2 relcl||dative: 1 acl||conj: 1 advcl||attr: 4 prep||npadvmod: 1
nsubjpass||xcomp: 1 neg||advmod: 1 xcomp||oprd: 1 advcl||advcl: 1 dobj||dep: 3
nsubjpass||parataxis: 1 attr||pcomp: 1 ccomp||parataxis: 1 advmod||attr: 1
nmod||oprd: 1 appos||nmod: 2 advmod||relcl: 1 appos||npadvmod: 1 appos||conj: 1
prep||expl: 1 nsubjpass||conj: 1 punct||pobj: 1 cc||pobj: 1 conj||pobj: 1
punct||conj: 1 ccomp||dep: 1 oprd||xcomp: 3 ccomp||xcomp: 1 ccomp||nsubj: 1
nmod||dep: 1 xcomp||ccomp: 1 acomp||advcl: 1 intj||advmod: 1 advmod||acomp: 2
relcl||oprd: 1 advmod||prt: 1 advmod||pobj: 1 appos||nummod: 1 relcl||npadvmod:
3 mark||advcl: 1 aux||ccomp: 1 amod||nsubjpass: 1 npadvmod||advmod: 1 conj||dep:
1 nummod||pobj: 1 amod||npadvmod: 1 intj||pobj: 1 nummod||npadvmod: 1
xcomp||xcomp: 1 aux||dep: 1 advcl||relcl: 1
⚠ The following labels were found only in the train data: xcomp||amod,
advcl||relcl, prep||nsubjpass, acl||nsubj, nsubjpass||conj, xcomp||oprd,
advmod||conj, advmod||advmod, iobj, advmod||nsubjpass, dobj||conj, ccomp||amod,
meta||acl, xcomp||xcomp, prep||attr, prep||ccomp, advcl||acomp, acl||dobj,
advcl||advcl, pobj||agent, prep||advcl, nsubjpass||xcomp, prep||dep,
acomp||xcomp, aux||ccomp, ccomp||dep, conj||dep, relcl||compound,
nsubjpass||ccomp, nmod||dobj, advmod||advcl, advmod||acl, dobj||advcl,
dative||xcomp, prep||nsubj, ccomp||ccomp, nsubj||ccomp, xcomp||acomp,
prep||acomp, dep||advmod, acl||pobj, appos||dobj, npadvmod||acomp, cc||ROOT,
relcl||nsubj, nmod||pobj, acl||nsubjpass, ccomp||advmod, pcomp||prep,
amod||dobj, advmod||attr, advcl||csubj, appos||attr, dobj||pcomp, prep||ROOT,
relcl||pobj, advmod||pobj, amod||nsubj, ccomp||xcomp, prep||oprd,
npadvmod||advmod, appos||nummod, advcl||pobj, neg||advmod, acl||attr,
appos||nsubjpass, csubj||ccomp, amod||nsubjpass, intj||pobj, dep||advcl,
cc||neg, xcomp||ccomp, dative||ccomp, nmod||oprd, pobj||dative, prep||dobj,
dep||ccomp, relcl||attr, ccomp||nsubj, advcl||xcomp, nmod||dep, advcl||advmod,
ccomp||conj, pobj||prep, advmod||acomp, advmod||relcl, attr||pcomp,
ccomp||parataxis, oprd||xcomp, intj||advmod, nmod||nsubjpass, prep||npadvmod,
parataxis||acl, prep||pobj, advcl||dobj, amod||pobj, prep||acl, conj||pobj,
advmod||dep, punct||pobj, ccomp||acomp, acomp||advcl, nummod||npadvmod,
dobj||dep, npadvmod||xcomp, advcl||conj, relcl||npadvmod, punct||acl,
relcl||dobj, dobj||xcomp, nsubjpass||parataxis, dative||advcl, relcl||nmod,
advcl||ccomp, appos||npadvmod, ccomp||pcomp, prep||amod, mark||advcl,
prep||advmod, prep||xcomp, appos||nsubj, attr||ccomp, advmod||prt, dobj||ccomp,
aux||conj, advcl||nsubj, conj||conj, advmod||ccomp, advcl||nsubjpass,
attr||xcomp, nmod||conj, npadvmod||conj, relcl||dative, prep||expl,
nsubjpass||pcomp, advmod||xcomp, advmod||dobj, appos||pobj, nsubj||conj,
relcl||nsubjpass, advcl||attr, appos||ccomp, advmod||prep, prep||conj,
nmod||attr, punct||conj, neg||conj, dep||xcomp, aux||xcomp, dobj||acl,
nummod||pobj, amod||npadvmod, nsubj||pcomp, advcl||acl, appos||nmod,
relcl||oprd, prep||prep, cc||pobj, nmod||nsubj, amod||attr, aux||dep,
appos||conj, advmod||nsubj, nsubj||advcl, acl||conj
To train a parser, your data should include at least 20 instances of each label.
⚠ Multiple root labels (ROOT, nsubj, aux, npadvmod, prep) found in
training data. spaCy's parser uses a single root label ROOT so this distinction
will not be available.

================================== Summary ==================================
✔ 5 checks passed
⚠ 8 warnings
```

</Accordion>

<Infobox>

**API:** [`spacy debug-data`](/api/cli#debug-data)

</Infobox>

## Backwards incompatibilities {#incompat}

<Infobox title="Important note on models" variant="warning">

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 or v2.1 aren't compatible with models for v2.2. To
check if all of your models are up to date, you can run the
[`spacy validate`](/api/cli#validate) command.

</Infobox>

- The [Dutch model](/models/nl) has been trained on a new NER corpus (custom
  labelled UD instead of WikiNER), so their predictions may be very different
  compared to the previous version. The results should be significantly better
  and more generalizable, though.
- The [`spacy download`](/api/cli#download) command does **not** set the
  `--no-deps` pip argument anymore by default, meaning that model package
  dependencies (if available) will now be also downloaded and installed. If
  spaCy (which is also a model dependency) is not installed in the current
  environment, e.g. if a user has built from source, `--no-deps` is added back
  automatically to prevent spaCy from being downloaded and installed again from
  pip.
- The built-in
  [`biluo_tags_from_offsets`](/api/goldparse#biluo_tags_from_offsets) converter
  is now stricter and will raise an error if entities are overlapping (instead
  of silently skipping them). If your data contains invalid entity annotations,
  make sure to clean it and resolve conflicts. You can now also use the new
  `debug-data` command to find problems in your data.
- Pipeline components can now overwrite IOB tags of tokens that are not yet part
  of an entity. Once a token has an `ent_iob` value set, it won't be reset to an
  "unset" state and will always have at least `O` assigned. `list(doc.ents)` now
  actually keeps the annotations on the token level consistent, instead of
  resetting `O` to an empty string.
- The default punctuation in the [`Sentencizer`](/api/sentencizer) has been
  extended and now includes more characters common in various languages. This
  also means that the results it produces may change, depending on your text. If
  you want the previous behaviour with limited characters, set
  `punct_chars=[".", "!", "?"]` on initialization.
- The [`PhraseMatcher`](/api/phrasematcher) algorithm was rewritten from scratch
  and it's now 10&times; faster. The rewrite also resolved a few subtle bugs
  with very large terminology lists. So if you were matching large lists, you
  may see slightly different results – however, the results should now be fully
  correct. See [this PR](https://github.com/explosion/spaCy/pulls/4309) for more
  details.
- Lemmatization tables (rules, exceptions, index and lookups) are now part of
  the `Vocab` and serialized with it. This means that serialized objects (`nlp`,
  pipeline components, vocab) will now include additional data, and models
  written to disk will include additional files.
- The `Serbian` language class (introduced in v2.1.8) incorrectly used the
  language code `rs` instead of `sr`. This has now been fixed, so `Serbian` is
  now available via `spacy.lang.sr`.
- The `"sources"` in the `meta.json` have changed from a list of strings to a
  list of dicts. This is mostly internals, but if your code used
  `nlp.meta["sources"]`, you might have to update it.