mirror of https://github.com/explosion/spaCy.git
Add pkuseg warnings and auto-format [ci skip]
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Ines Montani
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@ -117,6 +117,18 @@ The Chinese language class supports three word segmentation options:
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better segmentation for Chinese OntoNotes and the new
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[Chinese models](/models/zh).
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<Infobox variant="warning">
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Note that [`pkuseg`](https://github.com/lancopku/pkuseg-python) doesn't yet ship
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with pre-compiled wheels for Python 3.8. If you're running Python 3.8, you can
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install it from our fork and compile it locally:
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```bash
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$ pip install https://github.com/honnibal/pkuseg-python/archive/master.zip
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```
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</Infobox>
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<Accordion title="Details on spaCy's PKUSeg API">
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The `meta` argument of the `Chinese` language class supports the following
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@ -196,8 +208,8 @@ nlp = Chinese(meta={"tokenizer": {"config": {"pkuseg_model": "/path/to/pkuseg_mo
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The Japanese language class uses
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[SudachiPy](https://github.com/WorksApplications/SudachiPy) for word
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segmentation and part-of-speech tagging. The default Japanese language class
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and the provided Japanese models use SudachiPy split mode `A`.
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segmentation and part-of-speech tagging. The default Japanese language class and
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the provided Japanese models use SudachiPy split mode `A`.
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The `meta` argument of the `Japanese` language class can be used to configure
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the split mode to `A`, `B` or `C`.
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@ -14,10 +14,10 @@ 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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**2-4×** faster. For the full changelog, see the
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[release notes on GitHub](https://github.com/explosion/spaCy/releases/tag/v2.3.0).
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For more details and a behind-the-scenes look at the new release,
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[see our blog post](https://explosion.ai/blog/spacy-v2-3).
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### Expanded model families with vectors {#models}
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@ -33,10 +33,10 @@ post](https://explosion.ai/blog/spacy-v2-3).
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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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a total of 46 model packages. For models trained using
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[Universal Dependencies](https://universaldependencies.org) corpora, the
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training data has been updated to UD v2.5 (v2.6 for Japanese, v2.3 for Polish)
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and Dutch has been extended to include both UD Dutch Alpino and LassySmall.
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<Infobox>
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@ -48,6 +48,7 @@ extended to include both UD Dutch Alpino and LassySmall.
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### Chinese {#chinese}
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> #### Example
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>
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> ```python
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> from spacy.lang.zh import Chinese
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>
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@ -57,41 +58,49 @@ extended to include both UD Dutch Alpino and LassySmall.
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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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> ```
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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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[`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. The
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Chinese tokenizer can be initialized with both `pkuseg` and custom models and
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the `pkuseg` user dictionary is easy to customize. Note that
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[`pkuseg`](https://github.com/lancopku/pkuseg-python) doesn't yet ship with
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pre-compiled wheels for Python 3.8. See the
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[usage documentation](/usage/models#chinese) for details on how to install it on
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Python 3.8.
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<Infobox>
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**Chinese:** [Chinese tokenizer usage](/usage/models#chinese)
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**Models:** [Chinese models](/models/zh) **Usage: **
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[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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[`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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**Models:** [Japanese models](/models/ja) **Usage:**
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[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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- [`spacy debug-data`](/api/cli#debug-data) provides the coverage of the vectors
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in a base model with `spacy debug-data lang train dev -b base_model`
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- [`spacy evaluate`](/api/cli#evaluate) supports `blank:lg` (e.g.
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`spacy evaluate blank:en dev.json`) to evaluate the tokenization accuracy
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without loading a model
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- [`spacy train`](/api/cli#train) on GPU restricts the CPU timing evaluation to
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the first iteration
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## Backwards incompatibilities {#incompat}
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@ -100,8 +109,8 @@ installing spaCy for Japanese, which is now possible with a single command:
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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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with models for v2.3. To check if all of your models are up to date, you can run
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the [`spacy validate`](/api/cli#validate) command.
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</Infobox>
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@ -116,21 +125,20 @@ run the [`spacy validate`](/api/cli#validate) command.
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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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[`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data), which
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now includes both the lemmatization tables (as in v2.2) and the normalization
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tables (new in v2.3). If you're using pretrained models, **nothing changes**,
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because the relevant tables are included in the model 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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tagsets contain new merged tags related to contracted forms, such as `ADP_DET`
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for French `"au"`, which maps to UPOS `ADP` based on the head `"à"`. This
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increases the accuracy of the models by improving the alignment between
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spaCy's tokenization and Universal Dependencies multi-word tokens used for
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contractions.
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### Migrating from spaCy 2.2 {#migrating}
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@ -143,29 +151,28 @@ 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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cases like URLs where the tokenizer should remove prefixes and suffixes (e.g., a
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comma at the end of a URL) before applying the match. See the full
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[tokenizer 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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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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setting `SPACY_WARNING_IGNORE`, use the
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[`warnings` 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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[`spacy/lang`](https://github.com/explosion/spaCy/tree/master/spacy/lang) to the
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package [`spacy-lookups-data`](https://github.com/explosion/spacy-lookups-data).
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If you're adding data for a new language, the normalization table should be
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added to `spacy-lookups-data`. See
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[adding norm exceptions](/usage/adding-languages#norm-exceptions).
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#### Probability and cluster features
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@ -181,28 +188,28 @@ exceptions](/usage/adding-languages#norm-exceptions).
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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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pretrained models to reduce the model size. To keep these features available for
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users relying on them, the `prob` and `cluster` features for the most frequent
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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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installed and your code accesses `Token.prob`, the full table is loaded into the
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model vocab, which will take a few seconds on initial loading. When you save
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this model after loading the `prob` table, the full `prob` table will be saved
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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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If you'd like to include custom `cluster`, `prob`, or `sentiment` tables as part
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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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currently only used to provide a custom `oov_prob`. See examples in the
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[`data` 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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