# Introducing spaCy v3.5

- Jan 30, 2023
- 3 minute read

We’re excited to release v3.5 of the [spaCy](https://spacy.io/) Natural Language Processing library. spaCy v3.5 introduces three new CLI commands, adds fuzzy matching, provides improvements to our entity linking functionality, and includes a range of language updates and bug fixes.

### [New CLI commands](/content/blog/spacy-v3-5#cli/index.html)

- [`apply`](https://spacy.io/api/cli#apply) applies a pipeline to one or more `.txt`, `.jsonl` or `.spacy` files
- [`benchmark speed`](https://spacy.io/api/cli#benchmark) profiles a pipeline’s speed with a warmup and a confidence interval
- [`find-threshold`](https://spacy.io/api/cli#find-threshold) tests a range of threshold values for `spancat`, `textcat_multilabel`, etc, to identify the most optimal one.

Examples on how to run these commands can be found in our [CLI documentation](https://spacy.io/api/cli) as well as in our [v3.5 usage notes](https://spacy.io/usage/v3-5#cli).

### [Fuzzy matching](/content/blog/spacy-v3-5#fuzzy/index.html)

The new `FUZZY` operator allows [fuzzy matches](https://spacy.io/usage/rule-based-matching#fuzzy) based on Levenshtein edit distance:

```python
pattern = [{"LOWER": {"FUZZY": "definitely"}}]
```

The `FUZZY` and `REGEX` operators are now also supported for lists with `IN` and `NOT_IN`:

```python
pattern = [{"TEXT": {"REGEX": {"NOT_IN": ["^awe(some)?$", "^wonder(ful)?"]}}}]
```

### [Entity linking](/content/blog/spacy-v3-5#el/index.html)

The entity linker’s knowledge base has been refactored for easier customization. [`KnowledgeBase`](https://spacy.io/api/kb) is now an abstract class and the default implementation is the new class [`InMemoryLookupKB`](https://spacy.io/api/kb_in_memory).

Read more about all the improvements, updates and bug fixes:

- [v3.5 usage notes](https://spacy.io/usage/v3-5)
- [v3.5.0 release notes](https://github.com/explosion/spaCy/releases/v3.5.0)

## [New additions to spaCy universe and projects](/content/blog/spacy-v3-5#universe/index.html)

Many cool new plugins, extensions, pipelines and tutorials have been added to the [spaCy universe](https://spacy.io/universe) and [spaCy projects](https://github.com/explosion/projects) since v3.4:

|  |  |
| --- | --- |
| [BERTopic](https://spacy.io/universe/project/bertopic) | Leveraging BERT and c-TF-IDF to create easily interpretable topics. |
| [concepCy](https://spacy.io/universe/project/concepcy) | A multilingual knowledge graph in spaCy. |
| [greCy](https://spacy.io/universe/project/grecy) | Trained Ancient Greek models for use in spaCy. |
| [English Interpretation Sentence Pattern](https://spacy.io/universe/project/sent-pattern) | English interpretation for accurate translation from English to Japanese. |
| [spaCy - Partial Tagger](https://spacy.io/universe/project/spacy-partial-tagger) | Sequence tagger for partially annotated datasets in spaCy. |
| [spacy-cleaner](https://spacy.io/universe/project/spacy-cleaner) | Easily clean text with spaCy. |
| [spaCy-PyThaiNLP](https://spacy.io/universe/project/spacy-pythainlp) | Add Thai support for spaCy. |
| [Speedster pipeline acceleration](https://github.com/explosion/projects/tree/v3/experimental/ner_wikiner_speedster) | Named Entity Recognition (WikiNER) accelerated using Speedster. |
| [Zshot](https://spacy.io/universe/project/Zshot) | Zero and Few shot named entity & relationships recognition. |

[View the spaCy universe](https://spacy.io/universe)

Additionally, the spaCy team has added demo projects for two newer components:

|  |  |
| --- | --- |
| [`experimental/coref`](https://github.com/explosion/projects/tree/v3/experimental/coref) | Use the new experimental [`coref` component](/content/blog/coref/index.html) to train a coreference model using OntoNotes. |
| [`pipelines/spancat_demo`](https://github.com/explosion/projects/tree/v3/pipelines/spancat_demo) | A minimal demo spancat project. |

### Resources

- [spaCy v3.5](https://spacy.io/usage/v3-5): What’s new in v3.5
- [Release notes](https://github.com/explosion/spaCy/releases/tag/v3.5.0): Detailed overview
- [spaCy models directory](https://spacy.io/models): Download trained pipelines
- [spaCy universe](https://spacy.io/universe): Projects, plugins and extensions
- [spaCy project templates](https://github.com/explosion/projects): End-to-end NLP workflows
- [Video tutorials](https://youtube.com/c/ExplosionAI): More in-depth spaCy content on YouTube