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243 lines
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243 lines
10 KiB
Plaintext
---
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title: What's New in v3.2
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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 {id="features",hidden="true"}
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spaCy v3.2 adds support for [`floret`](https://github.com/explosion/floret)
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vectors, makes custom `Doc` creation and scoring easier, and includes many bug
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fixes and improvements. For the trained pipelines, there's a new transformer
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pipeline for Japanese and the Universal Dependencies training data has been
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updated across the board to the most recent release.
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<Infobox title="Improve performance for spaCy on Apple M1 with AppleOps" variant="warning" emoji="📣">
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spaCy is now up to **8 × faster on M1 Macs** by calling into Apple's
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native Accelerate library for matrix multiplication. For more details, see
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[`thinc-apple-ops`](https://github.com/explosion/thinc-apple-ops).
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```bash
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$ pip install spacy[apple]
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```
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</Infobox>
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### Registered scoring functions {id="registered-scoring-functions"}
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To customize the scoring, you can specify a scoring function for each component
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in your config from the new [`scorers` registry](/api/top-level#registry):
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```ini {title="config.cfg (excerpt)",highlight="3"}
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[components.tagger]
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factory = "tagger"
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scorer = {"@scorers":"spacy.tagger_scorer.v1"}
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```
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### Overwrite settings {id="overwrite"}
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Most pipeline components now include an `overwrite` setting in the config that
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determines whether existing annotation in the `Doc` is preserved or overwritten:
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```ini {title="config.cfg (excerpt)",highlight="3"}
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[components.tagger]
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factory = "tagger"
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overwrite = false
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```
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### Doc input for pipelines {id="doc-input"}
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[`nlp`](/api/language#call) and [`nlp.pipe`](/api/language#pipe) accept
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[`Doc`](/api/doc) input, skipping the tokenizer if a `Doc` is provided instead
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of a string. This makes it easier to create a `Doc` with custom tokenization or
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to set custom extensions before processing:
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```python
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doc = nlp.make_doc("This is text 500.")
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doc._.text_id = 500
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doc = nlp(doc)
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```
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### Support for floret vectors {id="vectors"}
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We recently published [`floret`](https://github.com/explosion/floret), an
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extended version of [fastText](https://fasttext.cc) that combines fastText's
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subwords with Bloom embeddings for compact, full-coverage vectors. The use of
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subwords means that there are no OOV words and due to Bloom embeddings, the
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vector table can be kept very small at \<100K entries. Bloom embeddings are
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already used by [HashEmbed](https://thinc.ai/docs/api-layers#hashembed) in
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[tok2vec](/api/architectures#tok2vec-arch) for compact spaCy models.
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For easy integration, floret includes a
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[Python wrapper](https://github.com/explosion/floret/blob/main/python/README.md):
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```bash
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$ pip install floret
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```
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A demo project shows how to train and import floret vectors:
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<Project id="pipelines/floret_vectors_demo">
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Train toy English floret vectors and import them into a spaCy pipeline.
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</Project>
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Two additional demo projects compare standard fastText vectors with floret
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vectors for full spaCy pipelines. For agglutinative languages like Finnish or
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Korean, there are large improvements in performance due to the use of subwords
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(no OOV words!), with a vector table containing merely 50K entries.
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<Project id="pipelines/floret_fi_core_demo">
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Finnish UD+NER vector and pipeline training, comparing standard fasttext vs.
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floret vectors.
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For the default project settings with 1M (2.6G) tokenized training texts and 50K
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300-dim vectors, ~300K keys for the standard vectors:
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| Vectors | TAG | POS | DEP UAS | DEP LAS | NER F |
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| -------------------------------------------- | -------: | -------: | -------: | -------: | -------: |
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| none | 93.3 | 92.3 | 79.7 | 72.8 | 61.0 |
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| standard (pruned: 50K vectors for 300K keys) | 95.9 | 94.7 | 83.3 | 77.9 | 68.5 |
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| standard (unpruned: 300K vectors/keys) | 96.0 | 95.0 | **83.8** | 78.4 | 69.1 |
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| floret (minn 4, maxn 5; 50K vectors, no OOV) | **96.6** | **95.5** | 83.5 | **78.5** | **70.9** |
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</Project>
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<Project id="pipelines/floret_ko_ud_demo">
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Korean UD vector and pipeline training, comparing standard fasttext vs. floret
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vectors.
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For the default project settings with 1M (3.3G) tokenized training texts and 50K
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300-dim vectors, ~800K keys for the standard vectors:
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| Vectors | TAG | POS | DEP UAS | DEP LAS |
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| -------------------------------------------- | -------: | -------: | -------: | -------: |
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| none | 72.5 | 85.0 | 73.2 | 64.3 |
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| standard (pruned: 50K vectors for 800K keys) | 77.9 | 89.4 | 78.8 | 72.8 |
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| standard (unpruned: 800K vectors/keys) | 79.0 | 90.2 | 79.2 | 73.9 |
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| floret (minn 2, maxn 3; 50K vectors, no OOV) | **82.5** | **93.8** | **83.0** | **80.1** |
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</Project>
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### Updates for spacy-transformers v1.1 {id="spacy-transformers"}
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[`spacy-transformers`](https://github.com/explosion/spacy-transformers) v1.1 has
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been refactored to improve serialization and support of inline transformer
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components and replacing listeners. In addition, the transformer model output is
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provided as
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[`ModelOutput`](https://huggingface.co/transformers/main_classes/output.html?highlight=modeloutput#transformers.file_utils.ModelOutput)
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instead of tuples in
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`TransformerData.model_output and FullTransformerBatch.model_output.` For
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backwards compatibility, the tuple format remains available under
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`TransformerData.tensors` and `FullTransformerBatch.tensors`. See more details
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in the [transformer API docs](/api/architectures#TransformerModel).
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`spacy-transfomers` v1.1 also adds support for `transformer_config` settings
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such as `output_attentions`. Additional output is stored under
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`TransformerData.model_output`. More details are in the
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[TransformerModel docs](/api/architectures#TransformerModel). The training speed
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has been improved by streamlining allocations for tokenizer output and there is
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new support for [mixed-precision training](/api/architectures#TransformerModel).
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### New transformer package for Japanese {id="pipeline-packages"}
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spaCy v3.2 adds a new transformer pipeline package for Japanese
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[`ja_core_news_trf`](/models/ja#ja_core_news_trf), which uses the `basic`
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pretokenizer instead of `mecab` to limit the number of dependencies required for
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the pipeline. Thanks to Hiroshi Matsuda and the spaCy Japanese community for
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their contributions!
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### Pipeline and language updates {id="pipeline-updates"}
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- All Universal Dependencies training data has been updated to v2.8.
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- The Catalan data, tokenizer and lemmatizer have been updated, thanks to Carlos
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Rodriguez, Carme Armentano and the Barcelona Supercomputing Center!
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- The transformer pipelines are trained using spacy-transformers v1.1, with
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improved IO and more options for
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[model config and output](/api/architectures#TransformerModel).
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- Trailing whitespace has been added as a `tok2vec` feature, improving the
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performance for many components, especially fine-grained tagging and sentence
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segmentation.
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- The English attribute ruler patterns have been overhauled to improve
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`Token.pos` and `Token.morph`.
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spaCy v3.2 also features a new Irish lemmatizer, support for `noun_chunks` in
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Portuguese, improved `noun_chunks` for Spanish and additional updates for
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Bulgarian, Catalan, Sinhala, Tagalog, Tigrinya and Vietnamese.
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## Notes about upgrading from v3.1 {id="upgrading"}
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### Pipeline package version compatibility {id="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 spaCy v3.0 or v3.1, you will
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see a warning telling you that the pipeline may be incompatible. This doesn't
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necessarily have to be true, but we recommend running your pipelines against
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your test suite or evaluation data to make sure there are no 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": ">=3.1.0,<3.2.0",
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+ "spacy_version": ">=3.2.0,<3.3.0",
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```
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### Updating v3.1 configs
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To update a config from spaCy v3.1 with the new v3.2 settings, run
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[`init fill-config`](/api/cli#init-fill-config):
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```bash
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$ python -m spacy init fill-config config-v3.1.cfg config-v3.2.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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## Notes about upgrading from spacy-transformers v1.0 {id="upgrading-transformers"}
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When you're loading a transformer pipeline package trained with
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[`spacy-transformers`](https://github.com/explosion/spacy-transformers) v1.0
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after upgrading to `spacy-transformers` v1.1, you'll see a warning telling you
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that the pipeline may be incompatible. `spacy-transformers` v1.1 should be able
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to import v1.0 `transformer` components into the new internal format with no
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change in performance, but here we'd also recommend running your test suite to
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verify that the pipeline still performs as expected.
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If you save your pipeline with [`nlp.to_disk`](/api/language#to_disk), it will
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be saved in the new v1.1 format and should be fully compatible with
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`spacy-transformers` v1.1. Once you've confirmed the performance, you can update
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the requirements in [`meta.json`](/api/data-formats#meta):
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```diff
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"requirements": [
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- "spacy-transformers>=1.0.3,<1.1.0"
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+ "spacy-transformers>=1.1.2,<1.2.0"
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]
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```
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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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