Spacy - v2.1.0-a0
🌙 This is an alpha pre-release of spaCy v2.1.0 and available on pip as
spacy-nightly. It's not intended for production use.
pip install -U spacy-nightly
If you want to test the new version, we recommend using a new virtual environment. Also make sure to download the new models – see below for details and benchmarks.
✨ New features and improvements
Tagger, Parser & NER
- NEW: Allow parser to do joint word segmentation and parsing. If you pass in data where the tokenizer over-segments, the parser now learns to merge the tokens.
- Make parser, tagger and NER faster, through better hyperparameters.
- Fix bugs in beam-search training objective.
- Remove document length limit during training, by implementing faster Levenshtein alignment.
- Use Thinc v6.11, which defaults to single-thread with fast OpenBLAS kernel. Parallelisation should be performed at the task level, e.g. by running more containers.
Models & Language Data
- NEW: Small accuracy improvements for parsing, tagging and NER for 6+ languages.
- NEW: The English and German models are now available under the MIT license.
- NEW: New
ud-traincommand, to train and evaluate using the CoNLL 2017 shared task data.
- Check if model is already installed before downloading it via
- Pass additional arguments of
pipto customise installation.
traincommand by letting
GoldCorpusstream data, instead of loading into memory.
init-modelcommand, including support for lexical attributes and word-vectors, using a variety of formats. This replaces the
spacy vocabcommand, which is now deprecated.
Doc.retokenizecontext manager for merging tokens more efficiently.
- NEW: Add support for custom pipeline component factories via entry points (#2348).
- NEW: Implement fastText vectors with subword features.
- Add warnings if
.similaritymethod is called with empty vectors or without word vectors.
- Improve rule-based
return_matcheskeyword argument to
(doc, matches)tuples instead of only
as_tuplesto add context to the
- Make stop words via
🚧 Under construction
This section includes new features and improvements that are planned for the stable
v2.1.xrelease, but aren't included in the nightly yet.
- Enhanced pattern API for rule-based
- Built-in rule-based NER component to add entities based on match patterns (see #2513).
- Improve tokenizer performance (see #1642).
- Allow retokenizer to update
Lexemeattributes on merge (see #2390).
lgmodels and new, pre-trained word vectors for German, French, Spanish, Italian, Portuguese and Dutch.
🔴 Bug fixes
- Fix issue #1487: Add
- Fix issue #1574: Make sure stop words are available in medium and large English models.
- Fix issue #1665: Correct typos in symbol
- Fix issue #1865: Correct licensing of
- Fix issue #1889: Make stop words case-insensitive.
- Fix issue #1903: Add
relcldependency label to symbols.
- Fix issue #2014: Make
- Fix issue #2369: Respect pre-defined warning filters.
- Fix serialization of custom tokenizer if not all functions are defined.
⚠️ Backwards incompatibilities
- This version of spaCy requires downloading new models. You can use the
spacy validatecommand to find out which models need updating, and print update instructions.
- If you've been training your own models, you'll need to retrain them with the new version.
- While the
MatcherAPI is fully backwards compatible, its algorithm has changed to fix a number of bugs and performance issues. This means that the
v2.1.xmay produce different results compared to the
- Also note that some of the model licenses have changed:
it_core_news_smis now correctly licensed under CC BY-NC-SA 3.0, and all English and German models are now published under the MIT license.
| Model | Version | UAS | LAS | POS | NER F | Vec | Size |
| --- | ---: | ---: | ---: | ---: | ---: | :---: | ---: |
en_core_web_sm | 2.1.0a0 | 91.8 | 90.0 | 96.8 | 85.6 | 𐄂 | 28 MB |
en_core_web_md | 2.1.0a0 | 92.0 | 90.2 | 97.0 | 86.2 | ✓ | 107 MB |
en_core_web_lg | 2.1.0a0 | 92.1 | 90.3 | 97.0 | 86.2 | ✓ | 805 MB |
de_core_news_sm | 2.1.0a0 | 92.0 | 90.1 | 97.2 | 83.8 | 𐄂 | 26 MB |
de_core_news_md | 2.1.0a0 | 92.4 | 90.7 | 97.4 | 84.2 | ✓ | 228 MB |
es_core_news_sm | 2.1.0a0 | 90.1 | 87.2 | 96.9 | 89.4 | 𐄂 | 28 MB |
es_core_news_md | 2.1.0a0 | 90.7 | 88.0 | 97.2 | 89.5 | ✓ | 88 MB |
pt_core_news_sm | 2.1.0a0 | 89.4 | 86.3 | 80.1 | 82.7 | 𐄂 | 29 MB |
fr_core_news_sm | 2.1.0a0 | 88.8 | 85.7 | 94.4 | 67.3 1 | 𐄂 | 32 MB |
fr_core_news_md | 2.1.0a0 | 88.7 | 86.0 | 95.0 | 70.4 1 | ✓ | 100 MB |
it_core_news_sm | 2.1.0a0 | 90.7 | 87.1 | 96.1 | 81.3 | 𐄂 | 27 MB |
nl_core_news_sm | 2.1.0a0 | 83.5 | 77.6 | 91.5 | 87.3 | 𐄂 | 27 MB |
xx_ent_wiki_sm | 2.1.0a0 | - | - | - | 83.8 | 𐄂 | 9 MB |
1) We're currently investigating this, as the results are anomalously low.
💬 UAS: Unlabelled dependencies (parser). LAS: Labelled dependencies (parser). POS: Part-of-speech tags (fine-grained tags, i.e.
Token.tag_). NER F: Named entities (F-score). Vec: Model contains word vectors. Size: Model file size (zipped archive).
📖 Documentation and examples
- Fix various typos and inconsistencies.
Thanks to @DuyguA for the pull requests and contributions.
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- 🚀Much more coming soon!