--- 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 class to efficiently **serialize annotations**, an improved and **10× faster** phrase matching engine, built-in scoring and **CLI training for text classification**, a new command to analyze and **debug training data**, data augmentation during training and more. For the full changelog, see the [release notes on GitHub](https://github.com/explosion/spaCy/releases/tag/v2.2.0). ### 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 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). **Usage:** [Models directory](/models) **Benchmarks: ** [Release notes](https://github.com/explosion/spaCy/releases/tag/v2.2.0) ### 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**. **API:** [`Lookups`](/api/lookups) **Usage: ** [Adding languages: Lemmatizer](/usage/adding-languages#lemmatizer) ### 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. **API:** [`spacy train`](/api/cli#train), [`Scorer`](/api/scorer), [`Language.evaluate`](/api/language#evaluate) ### 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. **API:** [`DocBin`](/api/docbin) **Usage: ** [Serializing Doc objects](/usage/saving-loading#docs) ### 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. ``` =========================== 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 ``` **API:** [`spacy debug-data`](/api/cli#debug-data) ## 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 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. - 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× 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.