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497a708c71
* Temporarily disable CI tests
* Start v3.3 website updates
* Add trainable lemmatizer to pipeline design
* Fix Vectors.most_similar
* Add floret vector info to pipeline design
* Add Lower and Upper Sorbian
* Add span to sidebar
* Work on release notes
* Copy from release notes
* Update pipeline design graphic
* Upgrading note about Doc.from_docs
* Add tables and details
* Update website/docs/models/index.md
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
* Fix da lemma acc
* Add minimal intro, various updates
* Round lemma acc
* Add section on floret / word lists
* Add new pipelines table, minor edits
* Fix displacy spans example title
* Clarify adding non-trainable lemmatizer
* Update adding-languages URLs
* Revert "Temporarily disable CI tests"
This reverts commit 1dee505920.
* Spell out words/sec
Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
248 lines
12 KiB
Markdown
248 lines
12 KiB
Markdown
---
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title: What's New in v3.3
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teaser: New features and how to upgrade
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menu:
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- ['New Features', 'features']
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- ['Upgrading Notes', 'upgrading']
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---
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## New features {#features hidden="true"}
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spaCy v3.3 improves the speed of core pipeline components, adds a new trainable
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lemmatizer, and introduces trained pipelines for Finnish, Korean and Swedish.
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### Speed improvements {#speed}
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v3.3 includes a slew of speed improvements:
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- Speed up parser and NER by using constant-time head lookups.
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- Support unnormalized softmax probabilities in `spacy.Tagger.v2` to speed up
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inference for tagger, morphologizer, senter and trainable lemmatizer.
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- Speed up parser projectivization functions.
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- Replace `Ragged` with faster `AlignmentArray` in `Example` for training.
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- Improve `Matcher` speed.
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- Improve serialization speed for empty `Doc.spans`.
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For longer texts, the trained pipeline speeds improve **15%** or more in
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prediction. We benchmarked `en_core_web_md` (same components as in v3.2) and
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`de_core_news_md` (with the new trainable lemmatizer) across a range of text
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sizes on Linux (Intel Xeon W-2265) and OS X (M1) to compare spaCy v3.2 vs. v3.3:
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**Intel Xeon W-2265**
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| Model | Avg. Words/Doc | v3.2 Words/Sec | v3.3 Words/Sec | Diff |
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| :----------------------------------------------- | -------------: | -------------: | -------------: | -----: |
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| [`en_core_web_md`](/models/en#en_core_web_md) | 100 | 17292 | 17441 | 0.86% |
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| (=same components) | 1000 | 15408 | 16024 | 4.00% |
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| | 10000 | 12798 | 15346 | 19.91% |
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| [`de_core_news_md`](/models/de/#de_core_news_md) | 100 | 20221 | 19321 | -4.45% |
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| (+v3.3 trainable lemmatizer) | 1000 | 17480 | 17345 | -0.77% |
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| | 10000 | 14513 | 17036 | 17.38% |
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**Apple M1**
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| Model | Avg. Words/Doc | v3.2 Words/Sec | v3.3 Words/Sec | Diff |
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| ------------------------------------------------ | -------------: | -------------: | -------------: | -----: |
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| [`en_core_web_md`](/models/en#en_core_web_md) | 100 | 18272 | 18408 | 0.74% |
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| (=same components) | 1000 | 18794 | 19248 | 2.42% |
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| | 10000 | 15144 | 17513 | 15.64% |
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| [`de_core_news_md`](/models/de/#de_core_news_md) | 100 | 19227 | 19591 | 1.89% |
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| (+v3.3 trainable lemmatizer) | 1000 | 20047 | 20628 | 2.90% |
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| | 10000 | 15921 | 18546 | 16.49% |
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### Trainable lemmatizer {#trainable-lemmatizer}
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The new [trainable lemmatizer](/api/edittreelemmatizer) component uses
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[edit trees](https://explosion.ai/blog/edit-tree-lemmatizer) to transform tokens
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into lemmas. Try out the trainable lemmatizer with the
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[training quickstart](/usage/training#quickstart)!
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### displaCy support for overlapping spans and arcs {#displacy}
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displaCy now supports overlapping spans with a new
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[`span`](/usage/visualizers#span) style and multiple arcs with different labels
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between the same tokens for [`dep`](/usage/visualizers#dep) visualizations.
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Overlapping spans can be visualized for any spans key in `doc.spans`:
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```python
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import spacy
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from spacy import displacy
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from spacy.tokens import Span
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nlp = spacy.blank("en")
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text = "Welcome to the Bank of China."
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doc = nlp(text)
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doc.spans["custom"] = [Span(doc, 3, 6, "ORG"), Span(doc, 5, 6, "GPE")]
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displacy.serve(doc, style="span", options={"spans_key": "custom"})
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```
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import DisplacySpanHtml from 'images/displacy-span.html'
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<Iframe title="displaCy visualizer for overlapping spans" html={DisplacySpanHtml} height={180} />
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## Additional features and improvements
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- Config comparisons with [`spacy debug diff-config`](/api/cli#debug-diff).
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- Span suggester debugging with
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[`SpanCategorizer.set_candidates`](/api/spancategorizer#set_candidates).
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- Big endian support with
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[`thinc-bigendian-ops`](https://github.com/andrewsi-z/thinc-bigendian-ops) and
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updates to make `floret`, `murmurhash`, Thinc and spaCy endian neutral.
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- Initial support for Lower Sorbian and Upper Sorbian.
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- Language updates for English, French, Italian, Japanese, Korean, Norwegian,
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Russian, Slovenian, Spanish, Turkish, Ukrainian and Vietnamese.
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- New noun chunks for Finnish.
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## Trained pipelines {#pipelines}
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### New trained pipelines {#new-pipelines}
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v3.3 introduces new CPU/CNN pipelines for Finnish, Korean and Swedish, which use
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the new trainable lemmatizer and
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[floret vectors](https://github.com/explosion/floret). Due to the use
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[Bloom embeddings](https://explosion.ai/blog/bloom-embeddings) and subwords, the
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pipelines have compact vectors with no out-of-vocabulary words.
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| Package | Language | UPOS | Parser LAS | NER F |
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| ----------------------------------------------- | -------- | ---: | ---------: | ----: |
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| [`fi_core_news_sm`](/models/fi#fi_core_news_sm) | Finnish | 92.5 | 71.9 | 75.9 |
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| [`fi_core_news_md`](/models/fi#fi_core_news_md) | Finnish | 95.9 | 78.6 | 80.6 |
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| [`fi_core_news_lg`](/models/fi#fi_core_news_lg) | Finnish | 96.2 | 79.4 | 82.4 |
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| [`ko_core_news_sm`](/models/ko#ko_core_news_sm) | Korean | 86.1 | 65.6 | 71.3 |
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| [`ko_core_news_md`](/models/ko#ko_core_news_md) | Korean | 94.7 | 80.9 | 83.1 |
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| [`ko_core_news_lg`](/models/ko#ko_core_news_lg) | Korean | 94.7 | 81.3 | 85.3 |
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| [`sv_core_news_sm`](/models/sv#sv_core_news_sm) | Swedish | 95.0 | 75.9 | 74.7 |
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| [`sv_core_news_md`](/models/sv#sv_core_news_md) | Swedish | 96.3 | 78.5 | 79.3 |
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| [`sv_core_news_lg`](/models/sv#sv_core_news_lg) | Swedish | 96.3 | 79.1 | 81.1 |
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### Pipeline updates {#pipeline-updates}
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The following languages switch from lookup or rule-based lemmatizers to the new
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trainable lemmatizer: Danish, Dutch, German, Greek, Italian, Lithuanian,
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Norwegian, Polish, Portuguese and Romanian. The overall lemmatizer accuracy
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improves for all of these pipelines, but be aware that the types of errors may
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look quite different from the lookup-based lemmatizers. If you'd prefer to
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continue using the previous lemmatizer, you can
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[switch from the trainable lemmatizer to a non-trainable lemmatizer](/models#design-modify).
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<figure>
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| Model | v3.2 Lemma Acc | v3.3 Lemma Acc |
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| ----------------------------------------------- | -------------: | -------------: |
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| [`da_core_news_md`](/models/da#da_core_news_md) | 84.9 | 94.8 |
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| [`de_core_news_md`](/models/de#de_core_news_md) | 73.4 | 97.7 |
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| [`el_core_news_md`](/models/el#el_core_news_md) | 56.5 | 88.9 |
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| [`fi_core_news_md`](/models/fi#fi_core_news_md) | - | 86.2 |
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| [`it_core_news_md`](/models/it#it_core_news_md) | 86.6 | 97.2 |
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| [`ko_core_news_md`](/models/ko#ko_core_news_md) | - | 90.0 |
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| [`lt_core_news_md`](/models/lt#lt_core_news_md) | 71.1 | 84.8 |
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| [`nb_core_news_md`](/models/nb#nb_core_news_md) | 76.7 | 97.1 |
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| [`nl_core_news_md`](/models/nl#nl_core_news_md) | 81.5 | 94.0 |
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| [`pl_core_news_md`](/models/pl#pl_core_news_md) | 87.1 | 93.7 |
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| [`pt_core_news_md`](/models/pt#pt_core_news_md) | 76.7 | 96.9 |
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| [`ro_core_news_md`](/models/ro#ro_core_news_md) | 81.8 | 95.5 |
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| [`sv_core_news_md`](/models/sv#sv_core_news_md) | - | 95.5 |
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</figure>
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In addition, the vectors in the English pipelines are deduplicated to improve
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the pruned vectors in the `md` models and reduce the `lg` model size.
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## Notes about upgrading from v3.2 {#upgrading}
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### Span comparisons
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Span comparisons involving ordering (`<`, `<=`, `>`, `>=`) now take all span
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attributes into account (start, end, label, and KB ID) so spans may be sorted in
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a slightly different order.
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### Whitespace annotation
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During training, annotation on whitespace tokens is handled in the same way as
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annotation on non-whitespace tokens in order to allow custom whitespace
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annotation.
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### Doc.from_docs
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[`Doc.from_docs`](/api/doc#from_docs) now includes `Doc.tensor` by default and
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supports excludes with an `exclude` argument in the same format as
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`Doc.to_bytes`. The supported exclude fields are `spans`, `tensor` and
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`user_data`.
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Docs including `Doc.tensor` may be quite a bit larger in RAM, so to exclude
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`Doc.tensor` as in v3.2:
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```diff
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-merged_doc = Doc.from_docs(docs)
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+merged_doc = Doc.from_docs(docs, exclude=["tensor"])
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```
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### Using trained pipelines with floret vectors
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If you're running a new trained pipeline for Finnish, Korean or Swedish on new
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texts and working with `Doc` objects, you shouldn't notice any difference with
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floret vectors vs. default vectors.
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If you use vectors for similarity comparisons, there are a few differences,
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mainly because a floret pipeline doesn't include any kind of frequency-based
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word list similar to the list of in-vocabulary vector keys with default vectors.
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- If your workflow iterates over the vector keys, you should use an external
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word list instead:
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```diff
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- lexemes = [nlp.vocab[orth] for orth in nlp.vocab.vectors]
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+ lexemes = [nlp.vocab[word] for word in external_word_list]
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```
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- `Vectors.most_similar` is not supported because there's no fixed list of
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vectors to compare your vectors to.
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### Pipeline package version compatibility {#version-compat}
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> #### Using legacy implementations
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>
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> In spaCy v3, you'll still be able to load and reference legacy implementations
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> via [`spacy-legacy`](https://github.com/explosion/spacy-legacy), even if the
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> components or architectures change and newer versions are available in the
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> core library.
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When you're loading a pipeline package trained with an earlier version of spaCy
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v3, you will see a warning telling you that the pipeline may be incompatible.
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This doesn't necessarily have to be true, but we recommend running your
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pipelines against your test suite or evaluation data to make sure there are no
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unexpected results.
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If you're using one of the [trained pipelines](/models) we provide, you should
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run [`spacy download`](/api/cli#download) to update to the latest version. To
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see an overview of all installed packages and their compatibility, you can run
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[`spacy validate`](/api/cli#validate).
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If you've trained your own custom pipeline and you've confirmed that it's still
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working as expected, you can update the spaCy version requirements in the
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[`meta.json`](/api/data-formats#meta):
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```diff
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- "spacy_version": ">=3.2.0,<3.3.0",
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+ "spacy_version": ">=3.2.0,<3.4.0",
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```
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### Updating v3.2 configs
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To update a config from spaCy v3.2 with the new v3.3 settings, run
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[`init fill-config`](/api/cli#init-fill-config):
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```cli
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$ python -m spacy init fill-config config-v3.2.cfg config-v3.3.cfg
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```
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In many cases ([`spacy train`](/api/cli#train),
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[`spacy.load`](/api/top-level#spacy.load)), the new defaults will be filled in
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automatically, but you'll need to fill in the new settings to run
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[`debug config`](/api/cli#debug) and [`debug data`](/api/cli#debug-data).
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To see the speed improvements for the
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[`Tagger` architecture](/api/architectures#Tagger), edit your config to switch
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from `spacy.Tagger.v1` to `spacy.Tagger.v2` and then run `init fill-config`.
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