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‎.gitignore

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.vscode
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.prettierrc
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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pip-wheel-metadata/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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.python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# celery beat schedule file
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celerybeat-schedule
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/

‎LICENSE

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MIT License
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Copyright (c) 2020 ExplosionAI GmbH
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in all
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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SOFTWARE.

‎README.md

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<a href="https://explosion.ai"><img src="https://explosion.ai/assets/img/logo.svg" width="125" height="125" align="right" /></a>
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# spacy-streamlit: spaCy comoponents for Streamlit
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This package contains utilities for visualizing [spaCy](https://spacy.io) models
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and building interactive spaCy-powered apps with
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[Streamlit](https://streamlit.io). It includes various building blocks you can
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use in your own Streamlit app, like visualizers for **syntactic dependencies**,
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**named entities**, **text classification**, **semantic similarity** via word
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vectors, token attributes, and more.
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[![Current Release Version](https://img.shields.io/github/release/explosion/spacy-streamlit.svg?style=flat-square&logo=github)](https://github.com/explosion/spacy-streamlit/releases)
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[![pypi Version](https://img.shields.io/pypi/v/spacy-streamlit.svg?style=flat-square&logo=pypi&logoColor=white)](https://pypi.org/project/spacy-streamlit/)
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<img width="50%" align="right" src="https://user-images.githubusercontent.com/13643239/85388081-f2da8700-b545-11ea-9bd4-e303d3c5763c.png">
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## 🚀 Quickstart
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You can install `spacy-streamlit` from pip:
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```bash
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pip install spacy-streamlit
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```
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The package includes **building blocks** that call into Streamlit and set up all
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the required elements for you. You can either use the individual components
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directly and combine them with other elements in your app, or call the
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`visualizer` function to embed the whole visualizer.
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```python
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# streamlit_app.py
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import spacy_streamlit
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models = ["en_core_web_sm", "en_core_web_md"]
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default_text = "Sundar Pichai is the CEO of Google."
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spacy_streamlit.visualizer(models, default_text))
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```
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You can then run your app with `streamlit run streamlit_app.py`.
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### 📦 Example: [`01_out-of-the-box.py`](examples/01_out-of-the-box.py)
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Use the embedded visualizer with custom settings out-of-the-box.
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```bash
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streamlit run https://raw.githubusercontent.com/explosion/spacy-streamlit/master/examples/01_out-of-the-box.py
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```
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### 👑 Example: [`02_custom.py`](examples/02_custom.py)
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Use individual components in your existing app.
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```bash
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streamlit run https://raw.githubusercontent.com/explosion/spacy-streamlit/master/examples/02_custom.py
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```
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## 🎛 API
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### Visualizer components
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These functions can be used in your Streamlit app. They call into `streamlit`
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under the hood and set up the required elements.
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#### <kbd>function</kbd> `visualizer`
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Embed the full visualizer with selected components.
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```python
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import spacy_streamlit
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models = ["en_core_web_sm", "/path/to/model"]
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default_text = "Sundar Pichai is the CEO of Google."
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visualizers = ["ner", "textcat"]
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spacy_streamlit.visualizer(models, default_text, visualizers)
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```
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| Argument | Type | Description |
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| --------------------- | ------------------- | ---------------------------------------------------------------------------------------------------------------------- |
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| `models` | List[str] | Names of loadable spaCy models (paths or package names). The models become selectable via a dropdown. |
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| `default_text` | str | Default text to analyze on load. Defaults to `""`. |
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| `visualizers` | List[str] | Names of visualizers to show. Defaults to `["parser", "ner", "textcat", "similarity", "tokens"]`. |
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| `ner_labels` | Optional[List[str]] | NER labels to include. If not set, all labels present in the `"ner"` pipeline component will be used. |
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| `ner_attrs` | List[str] | Span attributes shown in table of named entities. See [`visualizer.py`](spacy_streamlit/visualizer.py) for defaults. |
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| `token_attrs` | List[str] | Token attributes to show in token visualizer. See [`visualizer.py`](spacy_streamlit/visualizer.py) for defaults. |
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| `similarity_texts` | Tuple[str, str] | The default texts to compare in the similarity visualizer. Defaults to `("apple", "orange")`. |
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| `show_json_doc` | bool | Show button to toggle JSON representation of the `Doc`. Defaults to `True`. |
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| `show_model_meta` | bool | Show button to toggle model `meta.json`. Defaults to `True`. |
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| `sidebar_title` | Optional[str] | Title shown in the sidebar. Defaults to `None`. |
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| `sidebar_description` | Optional[str] | Description shown in the sidebar. Accepts Markdown-formatted text. |
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| `show_logo` | bool | Show the spaCy logo in the sidebar. Defaults to `True`. |
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| `color` | Optional[str] | Experimental: Primary color to use for some of the main UI elements (`None` to disable hack). Defaults to `"#09A3D5"`. |
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#### <kbd>function</kbd> `visualize_parser`
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Visualize the dependency parse and part-of-speech tags using spaCy's
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[`displacy` visualizer](https://spacy.io/usage/visualizers).
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```python
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import spacy
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from spacy_streamlit import visualize_parser
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nlp = spacy.load("en_core_web_sm")
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doc = nlp("This is a text")
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visualize_parser(doc)
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```
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| Argument | Type | Description |
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| --------------- | ------------- | -------------------------------------------- |
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| `doc` | `Doc` | The spaCy `Doc` object to visualize. |
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| _keyword-only_ | | |
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| `title` | Optional[str] | Title of the visualizer block. |
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| `sidebar_title` | Optional[str] | Title of the config settings in the sidebar. |
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#### <kbd>function</kbd> `visualize_ner`
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Visualize the named entities in a `Doc` using spaCy's
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[`displacy` visualizer](https://spacy.io/usage/visualizers).
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```python
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import spacy
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from spacy_streamlit import visualize_ner
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nlp = spacy.load("en_core_web_sm")
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doc = nlp("Sundar Pichai is the CEO of Google.")
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visualize_ner(doc, labels=nlp.get_pipe("ner").labels)
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```
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| Argument | Type | Description |
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| --------------- | ------------- | ----------------------------------------------------------------------------- |
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| `doc` | `Doc` | The spaCy `Doc` object to visualize. |
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| _keyword-only_ | | |
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| `labels` | Sequence[str] | The labels to show in the labels dropdown. |
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| `attrs` | List[str] | The span attributes to show in entity table. |
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| `show_table` | bool | Whether to show a table of entities and their attributes. Defaults to `True`. |
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| `title` | Optional[str] | Title of the visualizer block. |
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| `sidebar_title` | Optional[str] | Title of the config settings in the sidebar. |
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#### <kbd>function</kbd> `visualize_textcat`
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Visualize text categories predicted by a trained text classifier.
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```python
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import spacy
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from spacy_streamlit import visualize_textcat
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nlp = spacy.load("./my_textcat_model")
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doc = nlp("This is a text about a topic")
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visualize_textcat(doc)
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```
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| Argument | Type | Description |
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| -------------- | ------------- | ------------------------------------ |
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| `doc` | `Doc` | The spaCy `Doc` object to visualize. |
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| _keyword-only_ | | |
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| `title` | Optional[str] | Title of the visualizer block. |
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#### `visualize_similarity`
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Visualize semantic similarity using the model's word vectors. Will show a
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warning if no vectors are present in the model.
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```python
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import spacy
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from spacy_streamlit import visualize_similarity
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nlp = spacy.load("en_core_web_lg")
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visualize_similarity(nlp, ("pizza", "fries"))
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```
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| Argument | Type | Description |
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| --------------- | --------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------- |
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| `nlp` | `Language` | The loaded `nlp` object with vectors. |
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| `default_texts` | Tuple[str, str] | The default texts to compare on load. Defaults to `("apple", "orange")`. |
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| _keyword-only_ | | |
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| `threshold` | float | Threshold for what's considered "similar". If the similarity score is greater than the threshold, the result is shown as similar. Defaults to `0.5`. |
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| `title` | Optional[str] | Title of the visualizer block. |
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#### <kbd>function</kbd> `visualize_tokens`
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Visualize the tokens in a `Doc` and their attributes.
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```python
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import spacy
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from spacy_streamlit import visualize_tokens
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nlp = spacy.load("en_core_web_sm")
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doc = nlp("This is a text")
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visualize_tokens(doc, atrrs=["text", "pos_", "dep_", "ent_type_"])
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```
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| Argument | Type | Description |
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| -------------- | ------------- | -------------------------------------------------------------------------------------------------------- |
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| `doc` | `Doc` | The spaCy `Doc` object to visualize. |
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| _keyword-only_ | | |
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| `attrs` | List[str] | The names of token attributes to use. See [`visualizer.py`](spacy_streamlit/visualizer.py) for defaults. |
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| `title` | Optional[str] | Title of the visualizer block. |
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### Cached helpers
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These helpers attempt to cache loaded models and created `Doc` objects.
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#### <kbd>function</kbd> `process_text`
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Process a text with a model of a given name and create a `Doc` object. Calls
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into the `load_model` helper to load the model.
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```python
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import streamlit as st
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from spacy_streamlit import process_text
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spacy_model = st.sidebar.selectbox("Model name", ["en_core_web_sm", "en_core_web_md"])
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text = st.text_area("Text to analyze", "This is a text")
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doc = process_text(spacy_model, text)
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```
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| Argument | Type | Description |
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| ------------ | ----- | ------------------------------------------------------- |
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| `model_name` | str | Loadable spaCy model name. Can be path or package name. |
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| `text` | str | The text to process. |
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| **RETURNS** | `Doc` | The processed document. |
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#### <kbd>function</kbd> `load_model`
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Load a spaCy model from a path or installed package and return a loaded `nlp`
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object.
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```python
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import streamlit as st
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from spacy_streamlit import load_model
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spacy_model = st.sidebar.selectbox("Model name", ["en_core_web_sm", "en_core_web_md"])
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nlp = load_model(spacy_model)
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```
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| Argument | Type | Description |
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| ----------- | ---------- | -------------------------------------------------------- |
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| `name` | str |  Loadable spaCy model name. Can be path or package name. |
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| **RETURNS** | `Language` | The loaded `nlp` object. |

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