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144 lines
5.8 KiB
Markdown
144 lines
5.8 KiB
Markdown
---
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title: What's New in v3.4
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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 v3.4 brings typing and speed improvements along with new vectors for
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English CNN pipelines and new trained pipelines for Croatian. This release also
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includes prebuilt linux aarch64 wheels for all spaCy dependencies distributed by
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Explosion.
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### Typing improvements {#typing}
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spaCy v3.4 supports pydantic v1.9 and mypy 0.950+ through extensive updates to
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types in Thinc v8.1.
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### Speed improvements {#speed}
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- For the parser, use C `saxpy`/`sgemm` provided by the `Ops` implementation in
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order to use Accelerate through `thinc-apple-ops`.
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- Improved speed of vector lookups.
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- Improved speed for `Example.get_aligned_parse` and `Example.get_aligned`.
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## Additional features and improvements
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- Min/max `{n,m}` operator for `Matcher` patterns.
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- Language updates:
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- Improve tokenization for Cyrillic combining diacritics.
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- Improve English tokenizer exceptions for contractions with
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this/that/these/those.
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- Updated `spacy project clone` to try both `main` and `master` branches by
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default.
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- Added confidence threshold for named entity linker.
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- Improved handling of Typer optional default values for `init_config_cli`.
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- Added cycle detection in parser projectivization methods.
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- Added counts for NER labels in `debug data`.
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- Support for adding NVTX ranges to `TrainablePipe` components.
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- Support env variable `SPACY_NUM_BUILD_JOBS` to specify the number of build
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jobs to run in parallel with `pip`.
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## Trained pipelines {#pipelines}
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### New trained pipelines {#new-pipelines}
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v3.4 introduces new CPU/CNN pipelines for Croatian, which use the trainable
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lemmatizer and [floret vectors](https://github.com/explosion/floret). Due to the
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use of [Bloom embeddings](https://explosion.ai/blog/bloom-embeddings) and
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subwords, the pipelines have compact vectors with no out-of-vocabulary words.
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| Package | UPOS | Parser LAS | NER F |
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| ----------------------------------------------- | ---: | ---------: | ----: |
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| [`hr_core_news_sm`](/models/hr#hr_core_news_sm) | 96.6 | 77.5 | 76.1 |
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| [`hr_core_news_md`](/models/hr#hr_core_news_md) | 97.3 | 80.1 | 81.8 |
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| [`hr_core_news_lg`](/models/hr#hr_core_news_lg) | 97.5 | 80.4 | 83.0 |
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### Pipeline updates {#pipeline-updates}
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All CNN pipelines have been extended with whitespace augmentation.
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The English CNN pipelines have new word vectors:
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| Package | Model Version | TAG | Parser LAS | NER F |
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| ----------------------------------------------- | ------------- | ---: | ---------: | ----: |
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| [`en_core_web_md`](/models/en#en_core_web_md) | v3.3.0 | 97.3 | 90.1 | 84.6 |
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| [`en_core_web_md`](/models/en#en_core_web_md) | v3.4.0 | 97.2 | 90.3 | 85.5 |
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| [`en_core_web_lg`](/models/en#en_core_web_lg) | v3.3.0 | 97.4 | 90.1 | 85.3 |
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| [`en_core_web_lg`](/models/en#en_core_web_lg) | v3.4.0 | 97.3 | 90.2 | 85.6 |
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## Notes about upgrading from v3.3 {#upgrading}
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### Doc.has_vector
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`Doc.has_vector` now matches `Token.has_vector` and `Span.has_vector`: it
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returns `True` if at least one token in the doc has a vector rather than
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checking only whether the vocab contains vectors.
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### Using trained pipelines with floret vectors
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If you're using a trained pipeline for Croatian, Finnish, Korean or Swedish with
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new texts and working with `Doc` objects, you shouldn't notice any difference
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between floret vectors and 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": ">=3.3.0,<3.4.0",
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+ "spacy_version": ">=3.3.0,<3.5.0",
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```
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### Updating v3.3 configs
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To update a config from spaCy v3.3 with the new v3.4 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-v3.3.cfg config-v3.4.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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