mirror of https://github.com/explosion/spaCy.git
214 lines
8.7 KiB
Markdown
214 lines
8.7 KiB
Markdown
---
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title: What's New in v2.3
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teaser: New features, backwards incompatibilities and migration guide
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menu:
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- ['New Features', 'features']
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- ['Backwards Incompatibilities', 'incompat']
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- ['Migrating from v2.2', 'migrating']
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---
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## New Features {#features hidden="true"}
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spaCy v2.3 features new pretrained models for five languages, word vectors for
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all language models, and decreased model size and loading times for models with
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vectors. We've added pretrained models for **Chinese, Danish, Japanese, Polish
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and Romanian** and updated the training data and vectors for most languages.
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Model packages with vectors are about **2×** smaller on disk and load
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**2-4×** faster. For the full changelog, see the [release notes on
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GitHub](https://github.com/explosion/spaCy/releases/tag/v2.3.0). For more
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details and a behind-the-scenes look at the new release, [see our blog
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post](https://explosion.ai/blog/spacy-v2-3).
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### Expanded model families with vectors {#models}
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> #### Example
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>
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> ```bash
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> python -m spacy download da_core_news_sm
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> python -m spacy download ja_core_news_sm
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> python -m spacy download pl_core_news_sm
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> python -m spacy download ro_core_news_sm
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> python -m spacy download zh_core_web_sm
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> ```
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With new model families for Chinese, Danish, Polish, Romanian and Chinese plus
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`md` and `lg` models with word vectors for all languages, this release provides
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a total of 46 model packages. For models trained using [Universal
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Dependencies](https://universaldependencies.org) corpora, the training data has
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been updated to UD v2.5 (v2.6 for Japanese, v2.3 for Polish) and Dutch has been
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extended to include both UD Dutch Alpino and LassySmall.
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<Infobox>
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**Models:** [Models directory](/models) **Benchmarks: **
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[Release notes](https://github.com/explosion/spaCy/releases/tag/v2.3.0)
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</Infobox>
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### Chinese {#chinese}
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> #### Example
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> ```python
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> from spacy.lang.zh import Chinese
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>
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> # Load with "default" model provided by pkuseg
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> cfg = {"pkuseg_model": "default", "require_pkuseg": True}
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> nlp = Chinese(meta={"tokenizer": {"config": cfg}})
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>
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> # Append words to user dict
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> nlp.tokenizer.pkuseg_update_user_dict(["中国", "ABC"])
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This release adds support for
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[pkuseg](https://github.com/lancopku/pkuseg-python) for word segmentation and
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the new Chinese models ship with a custom pkuseg model trained on OntoNotes.
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The Chinese tokenizer can be initialized with both `pkuseg` and custom models
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and the `pkuseg` user dictionary is easy to customize.
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<Infobox>
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**Chinese:** [Chinese tokenizer usage](/usage/models#chinese)
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</Infobox>
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### Japanese {#japanese}
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The updated Japanese language class switches to
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[SudachiPy](https://github.com/WorksApplications/SudachiPy) for word
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segmentation and part-of-speech tagging. Using `sudachipy` greatly simplifies
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installing spaCy for Japanese, which is now possible with a single command:
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`pip install spacy[ja]`.
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<Infobox>
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**Japanese:** [Japanese tokenizer usage](/usage/models#japanese)
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</Infobox>
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### Small CLI updates
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- `spacy debug-data` provides the coverage of the vectors in a base model with
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`spacy debug-data lang train dev -b base_model`
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- `spacy evaluate` supports `blank:lg` (e.g. `spacy evaluate blank:en
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dev.json`) to evaluate the tokenization accuracy without loading a model
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- `spacy train` on GPU restricts the CPU timing evaluation to the first
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iteration
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## Backwards incompatibilities {#incompat}
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<Infobox title="Important note on models" variant="warning">
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If you've been training **your own models**, you'll need to **retrain** them
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with the new version. Also don't forget to upgrade all models to the latest
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versions. Models for earlier v2 releases (v2.0, v2.1, v2.2) aren't compatible
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with models for v2.3. To check if all of your models are up to date, you can
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run the [`spacy validate`](/api/cli#validate) command.
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</Infobox>
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> #### Install with lookups data
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>
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> ```bash
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> $ pip install spacy[lookups]
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> ```
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>
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> You can also install
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> [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data)
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> directly.
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- If you're training new models, you'll want to install the package
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[`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data),
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which now includes both the lemmatization tables (as in v2.2) and the
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normalization tables (new in v2.3). If you're using pretrained models,
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**nothing changes**, because the relevant tables are included in the model
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packages.
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- Due to the updated Universal Dependencies training data, the fine-grained
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part-of-speech tags will change for many provided language models. The
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coarse-grained part-of-speech tagset remains the same, but the mapping from
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particular fine-grained to coarse-grained tags may show minor differences.
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- For French, Italian, Portuguese and Spanish, the fine-grained part-of-speech
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tagsets contain new merged tags related to contracted forms, such as
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`ADP_DET` for French `"au"`, which maps to UPOS `ADP` based on the head
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`"à"`. This increases the accuracy of the models by improving the alignment
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between spaCy's tokenization and Universal Dependencies multi-word tokens
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used for contractions.
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### Migrating from spaCy 2.2 {#migrating}
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#### Tokenizer settings
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In spaCy v2.2.2-v2.2.4, there was a change to the precedence of `token_match`
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that gave prefixes and suffixes priority over `token_match`, which caused
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problems for many custom tokenizer configurations. This has been reverted in
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v2.3 so that `token_match` has priority over prefixes and suffixes as in v2.2.1
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and earlier versions.
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A new tokenizer setting `url_match` has been introduced in v2.3.0 to handle
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cases like URLs where the tokenizer should remove prefixes and suffixes (e.g.,
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a comma at the end of a URL) before applying the match. See the full [tokenizer
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documentation](/usage/linguistic-features#tokenization) and try out
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[`nlp.tokenizer.explain()`](/usage/linguistic-features#tokenizer-debug) when
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debugging your tokenizer configuration.
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#### Warnings configuration
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spaCy's custom warnings have been replaced with native python
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[`warnings`](https://docs.python.org/3/library/warnings.html). Instead of
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setting `SPACY_WARNING_IGNORE`, use the [warnings
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filters](https://docs.python.org/3/library/warnings.html#the-warnings-filter)
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to manage warnings.
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#### Normalization tables
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The normalization tables have moved from the language data in
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[`spacy/lang`](https://github.com/explosion/spaCy/tree/master/spacy/lang) to
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the package
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[`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data). If
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you're adding data for a new language, the normalization table should be added
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to `spacy-lookups-data`. See [adding norm
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exceptions](/usage/adding-languages#norm-exceptions).
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#### Probability and cluster features
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> #### Load and save extra prob lookups table
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>
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> ```python
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> from spacy.lang.en import English
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> nlp = English()
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> doc = nlp("the")
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> print(doc[0].prob) # lazily loads extra prob table
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> nlp.to_disk("/path/to/model") # includes prob table
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> ```
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The `Token.prob` and `Token.cluster` features, which are no longer used by the
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core pipeline components as of spaCy v2, are no longer provided in the
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pretrained models to reduce the model size. To keep these features available
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for users relying on them, the `prob` and `cluster` features for the most
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frequent 1M tokens have been moved to
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[`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data) as
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`extra` features for the relevant languages (English, German, Greek and
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Spanish).
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The extra tables are loaded lazily, so if you have `spacy-lookups-data`
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installed and your code accesses `Token.prob`, the full table is loaded into
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the model vocab, which will take a few seconds on initial loading. When you
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save this model after loading the `prob` table, the full `prob` table will be
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saved as part of the model vocab.
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If you'd like to include custom `cluster`, `prob`, or `sentiment` tables as
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part of a new model, add the data to
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[`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data) under
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the entry point `lg_extra`, e.g. `en_extra` for English. Alternatively, you can
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initialize your [`Vocab`](/api/vocab) with the `lookups_extra` argument with a
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[`Lookups`](/api/lookups) object that includes the tables `lexeme_cluster`,
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`lexeme_prob`, `lexeme_sentiment` or `lexeme_settings`. `lexeme_settings` is
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currently only used to provide a custom `oov_prob`. See examples in the [`data`
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directory](https://github.com/explosion/spacy-lookups-data/tree/master/spacy_lookups_data/data)
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in `spacy-lookups-data`.
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#### Initializing new models without extra lookups tables
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When you initialize a new model with [`spacy init-model`](/api/cli#init-model),
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the `prob` table from `spacy-lookups-data` may be loaded as part of the
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initialization. If you'd like to omit this extra data as in spaCy's provided
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v2.3 models, use the new flag `--omit-extra-lookups`.
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