👑 spaCy building blocks and visualizers for Streamlit apps

Explosion

Last update: Dec 29, 2022

Related tags

Text Data & NLP nlp machine-learning natural-language-processing text-classification spacy visualizer named-entity-recognition ner dependency-parsing tokenization word-vectors visualizers streamlit part-of-speech-tagging

Overview

spacy-streamlit: spaCy building blocks for Streamlit apps

This package contains utilities for visualizing spaCy models and building interactive spaCy-powered apps with Streamlit. It includes various building blocks you can use in your own Streamlit app, like visualizers for syntactic dependencies, named entities, text classification, semantic similarity via word vectors, token attributes, and more.

🚀 Quickstart

You can install spacy-streamlit from pip:

pip install spacy-streamlit

The package includes building blocks that call into Streamlit and set up all the required elements for you. You can either use the individual components directly and combine them with other elements in your app, or call the visualize function to embed the whole visualizer.

Download the English model from spaCy to get started.

python -m spacy download en_core_web_sm

Then put the following example code in a file.

# streamlit_app.py
import spacy_streamlit

models = ["en_core_web_sm", "en_core_web_md"]
default_text = "Sundar Pichai is the CEO of Google."
spacy_streamlit.visualize(models, default_text)

You can then run your app with streamlit run streamlit_app.py. The app should pop up in your web browser. 😀

📦 Example: `01_out-of-the-box.py`

Use the embedded visualizer with custom settings out-of-the-box.

streamlit run https://raw.githubusercontent.com/explosion/spacy-streamlit/master/examples/01_out-of-the-box.py

👑 Example: `02_custom.py`

Use individual components in your existing app.

streamlit run https://raw.githubusercontent.com/explosion/spacy-streamlit/master/examples/02_custom.py

🎛 API

Visualizer components

These functions can be used in your Streamlit app. They call into streamlit under the hood and set up the required elements.

`function` `visualize`

Embed the full visualizer with selected components.

import spacy_streamlit

models = ["en_core_web_sm", "/path/to/model"]
default_text = "Sundar Pichai is the CEO of Google."
visualizers = ["ner", "textcat"]
spacy_streamlit.visualize(models, default_text, visualizers)

Argument	Type	Description
`models`	List[str] / Dict[str, str]	Names of loadable spaCy models (paths or package names). The models become selectable via a dropdown. Can either be a list of names or the names mapped to descriptions to display in the dropdown.
`default_text`	str	Default text to analyze on load. Defaults to `""`.
`default_model`	Optional[str]	Optional name of default model. If not set, the first model in the list of `models` is used.
`visualizers`	List[str]	Names of visualizers to show. Defaults to `["parser", "ner", "textcat", "similarity", "tokens"]`.
`ner_labels`	Optional[List[str]]	NER labels to include. If not set, all labels present in the `"ner"` pipeline component will be used.
`ner_attrs`	List[str]	Span attributes shown in table of named entities. See `visualizer.py` for defaults.
`token_attrs`	List[str]	Token attributes to show in token visualizer. See `visualizer.py` for defaults.
`similarity_texts`	Tuple[str, str]	The default texts to compare in the similarity visualizer. Defaults to `("apple", "orange")`.
`show_json_doc`	bool	Show button to toggle JSON representation of the `Doc`. Defaults to `True`.
`show_meta`	bool	Show button to toggle `meta.json` of the current pipeline. Defaults to `True`.
`show_config`	bool	Show button to toggle `config.cfg` of the current pipeline. Defaults to `True`.
`show_visualizer_select`	bool	Show sidebar dropdown to select visualizers to display (based on enabled visualizers). Defaults to `False`.
`sidebar_title`	Optional[str]	Title shown in the sidebar. Defaults to `None`.
`sidebar_description`	Optional[str]	Description shown in the sidebar. Accepts Markdown-formatted text.
`show_logo`	bool	Show the spaCy logo in the sidebar. Defaults to `True`.
`color`	Optional[str]	Experimental: Primary color to use for some of the main UI elements (`None` to disable hack). Defaults to `"#09A3D5"`.
`get_default_text`	Callable[[Language], str]	Optional callable that takes the currently loaded `nlp` object and returns the default text. Can be used to provide language-specific default texts. If the function returns `None`, the value of `default_text` is used, if available. Defaults to `None`.

`function` `visualize_parser`

Visualize the dependency parse and part-of-speech tags using spaCy's displacy visualizer.

import spacy
from spacy_streamlit import visualize_parser

nlp = spacy.load("en_core_web_sm")
doc = nlp("This is a text")
visualize_parser(doc)

Argument	Type	Description
`doc`	`Doc`	The spaCy `Doc` object to visualize.
keyword-only
`title`	Optional[str]	Title of the visualizer block.
`sidebar_title`	Optional[str]	Title of the config settings in the sidebar.

`function` `visualize_ner`

Visualize the named entities in a Doc using spaCy's displacy visualizer.

import spacy
from spacy_streamlit import visualize_ner

nlp = spacy.load("en_core_web_sm")
doc = nlp("Sundar Pichai is the CEO of Google.")
visualize_ner(doc, labels=nlp.get_pipe("ner").labels)

Argument	Type	Description
`doc`	`Doc`	The spaCy `Doc` object to visualize.
keyword-only
`labels`	Sequence[str]	The labels to show in the labels dropdown.
`attrs`	List[str]	The span attributes to show in entity table.
`show_table`	bool	Whether to show a table of entities and their attributes. Defaults to `True`.
`title`	Optional[str]	Title of the visualizer block.
`sidebar_title`	Optional[str]	Title of the config settings in the sidebar.
`colors`	Dict[str,str]	A dictionary mapping labels to display colors ({"LABEL": "COLOR"})

`function` `visualize_textcat`

Visualize text categories predicted by a trained text classifier.

import spacy
from spacy_streamlit import visualize_textcat

nlp = spacy.load("./my_textcat_model")
doc = nlp("This is a text about a topic")
visualize_textcat(doc)

Argument	Type	Description
`doc`	`Doc`	The spaCy `Doc` object to visualize.
keyword-only
`title`	Optional[str]	Title of the visualizer block.

`visualize_similarity`

Visualize semantic similarity using the model's word vectors. Will show a warning if no vectors are present in the model.

import spacy
from spacy_streamlit import visualize_similarity

nlp = spacy.load("en_core_web_lg")
visualize_similarity(nlp, ("pizza", "fries"))

Argument	Type	Description
`nlp`	`Language`	The loaded `nlp` object with vectors.
`default_texts`	Tuple[str, str]	The default texts to compare on load. Defaults to `("apple", "orange")`.
keyword-only
`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`.
`title`	Optional[str]	Title of the visualizer block.

`function` `visualize_tokens`

Visualize the tokens in a Doc and their attributes.

import spacy
from spacy_streamlit import visualize_tokens

nlp = spacy.load("en_core_web_sm")
doc = nlp("This is a text")
visualize_tokens(doc, attrs=["text", "pos_", "dep_", "ent_type_"])

Argument	Type	Description
`doc`	`Doc`	The spaCy `Doc` object to visualize.
keyword-only
`attrs`	List[str]	The names of token attributes to use. See `visualizer.py` for defaults.
`title`	Optional[str]	Title of the visualizer block.

Cached helpers

These helpers attempt to cache loaded models and created Doc objects.

`function` `process_text`

Process a text with a model of a given name and create a Doc object. Calls into the load_model helper to load the model.

import streamlit as st
from spacy_streamlit import process_text

spacy_model = st.sidebar.selectbox("Model name", ["en_core_web_sm", "en_core_web_md"])
text = st.text_area("Text to analyze", "This is a text")
doc = process_text(spacy_model, text)

Argument	Type	Description
`model_name`	str	Loadable spaCy model name. Can be path or package name.
`text`	str	The text to process.
RETURNS	`Doc`	The processed document.

`function` `load_model`

Load a spaCy model from a path or installed package and return a loaded nlp object.

import streamlit as st
from spacy_streamlit import load_model

spacy_model = st.sidebar.selectbox("Model name", ["en_core_web_sm", "en_core_web_md"])
nlp = load_model(spacy_model)

Argument	Type	Description
`name`	str	Loadable spaCy model name. Can be path or package name.
RETURNS	`Language`	The loaded `nlp` object.

Comments

Support for span categorizer?

Is it possible to visualize spancat predictions via spacy-streamlit yet? Either directly or by customizing one of the existing visualizers? I haven't been able to find any examples of this.
enhancement

opened by jonmcalder 5
Pass manual=True to visualize_ner

Adjusted visualize_ner to include manual as an argument that is passed into displacy_render, and added a new file 03_visualize-ner-manual.py to show how it works.

Let me know if this PR can be improved, as this is one of my first open-source contributions, and I am still learning :)

Closes #14

opened by callistachang 4
Be able to specify port & host

I'm currently running the spacy-streamlit app on a server. I prefer to have this on my server on my local network because otherwise my laptop gets very hot during training. In order to properly host it though I need to be able to customise the port-number as well as the host. It seems like these settings are currently missing. Would @ines you be open to these commands? I wouldn't mind picking this up.
enhancement

opened by koaning 4
'NoneType' object has no attribute 'replace'

When I ran the example:

import spacy_streamlit models = ["en_core_web_sm", "en_core_web_md"] default_text = "Sundar Pichai is the CEO of Google." spacy_streamlit.visualize(models, default_text)

Have this problem

AttributeError Traceback (most recent call last) in 3 models = ["en_core_web_sm", "en_core_web_md"] 4 default_text = "Sundar Pichai is the CEO of Google." ----> 5 spacy_streamlit.visualize(models, default_text)

~/anaconda3/lib/python3.7/site-packages/spacy_streamlit/visualizer.py in visualize(models, default_text, visualizers, ner_labels, ner_attrs, similarity_texts, token_attrs, show_json_doc, show_model_meta, sidebar_title, sidebar_description, show_logo, color) 51 52 if "parser" in visualizers: ---> 53 visualize_parser(doc) 54 if "ner" in visualizers: 55 ner_labels = ner_labels or nlp.get_pipe("ner").labels

~/anaconda3/lib/python3.7/site-packages/spacy_streamlit/visualizer.py in visualize_parser(doc, title, sidebar_title) 98 html = displacy.render(sent, options=options, style="dep") 99 # Double newlines seem to mess with the rendering --> 100 html = html.replace("\n\n", "\n") 101 if split_sents and len(docs) > 1: 102 st.markdown(f"> {sent.text}")

AttributeError: 'NoneType' object has no attribute 'replace'

opened by PilarHidalgo 4
`visualize_spans` function + `manual` argument + docstrings
Adds a visualize_spans function for using the span visualization in displaCy.

I made a few choices that may need justification:

Even though spans_key is part of the options passed to displaCy, it feels critical enough to pass as an argument to visualize_spans rather than as a key/value in a dict to displacy_options - but we can obviously undo this.

I didn't include the span viz in the "display everything" visualize function because none of the base models will have labeled spans in them, so it wouldn't display anything useful.

Compared to visualize_ner:

I removed the manual argument because there's no such option for that with displaCy for spans

There's no label filtering/selection with spans, so all labels will be displayed for spans within a span_key. This removes the keys and labels args.

Extra:

Also added a missing docstring for the show_table argument on visualize_ner (as well as visualize_spans)

Open questions:

Is there a script to generate the nice readme.md documentation so we can include the new function?

I'm not totally sure what needs to happen for a release, but I'm happy to help do whatever needs to be done!
opened by pmbaumgartner 2

NotImplementedError: [E894] The 'noun_chunks' syntax iterator is not implemented for language 'it'.

I'm trying to use the italian model and getting this error why usine visualize_parser.

File "/home/irfan/environments/The-Entities-Swissknife/lib/python3.7/site-packages/streamlit/scriptrunner/script_runner.py", line 443, in _run_script
    exec(code, module.__dict__)
  File "/home/irfan/PycharmProjects/TES-Clone/main.py", line 385, in <module>
    visualize_parser(doc)
  File "/home/irfan/environments/The-Entities-Swissknife/lib/python3.7/site-packages/spacy_streamlit/visualizer.py", line 156, in visualize_parser
    html = displacy.render(sent, options=options, style="dep")
  File "/home/irfan/environments/The-Entities-Swissknife/lib/python3.7/site-packages/spacy/displacy/__init__.py", line 57, in render
    parsed = [converter(doc, options) for doc in docs] if not manual else docs  # type: ignore
  File "/home/irfan/environments/The-Entities-Swissknife/lib/python3.7/site-packages/spacy/displacy/__init__.py", line 57, in <listcomp>
    parsed = [converter(doc, options) for doc in docs] if not manual else docs  # type: ignore
  File "/home/irfan/environments/The-Entities-Swissknife/lib/python3.7/site-packages/spacy/displacy/__init__.py", line 131, in parse_deps
    for np in list(doc.noun_chunks):
  File "spacy/tokens/doc.pyx", line 852, in noun_chunks
NotImplementedError: [E894] The 'noun_chunks' syntax iterator is not implemented for language 'it'.

Here are the models and library info

en-core-web-sm @ https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.2.0/en_core_web_sm-3.2.0-py3-none-any.whl
it-core-news-sm @ https://github.com/explosion/spacy-models/releases/download/it_core_news_sm-3.2.0/it_core_news_sm-3.2.0-py3-none-any.whl
spacy==3.2.4
spacy-legacy==3.0.9
spacy-loggers==1.0.2
spacy-streamlit==1.0.3

opened by mirfan899 2

OSError: [E050] Can't find model 'en_core_web_sm'. It doesn't seem to be a Python package or a valid path to a data directory.
Hi. 👋 This is my first time ever submitting an issue on Github. I resolved the issue somehow myself but I thought it's important to report and share the possible bug anyway. I'll try my best to follow the github issue submit best practices, I hope I'm doing it correctly.

My code

import spacy nlp = spacy.load('en')

Errors I ran into

Error 1

OSError: [E050] Can't find model 'en_core_web_sm'. It doesn't seem to be a Python package or a valid path to a data directory.

Error 2

[E053] Could not read config.cfg from c:\users\Pycharmprojects\my_project_foler\venv\lib\site-packages\en_core_web_sm\en_core_web_sm-2.2.0\config.cfg

After Error 1, I tried the following link. https://github.com/explosion/spaCy/issues/4577

Then the first error resolved and pycharm threw the error 2 right after. I tried all the pip install github_links but didn't work. https://github.com/explosion/spaCy/issues/7453

Unsuccessful attempts in order

download python3 -m spacy download en_core_web_sm

Upgrade pip3 install -U spacy

Github link 1 pip3 install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.0/en_core_web_sm-2.2.0.tar.gz

Github link 2 pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-3.0.0/en_core_web_sm-3.0.0.tar.gz Failed to build spacy

validate python -m spacy validate

install pip install --no-cache-dir spacy

What worked

python -m spacy download en

As of spaCy v3.0, shortcuts like 'en' are deprecated. Please use the full pipeline package name 'en_core_web_sm' instead. Collecting en-core-web-sm==3.2.0

The terminal automatically downloaded en_core_web_sm and it worked. But this was the first thing I had tried. Is it because I used python3 instead of python in the command...?

Thank you!
opened by deep-woods 2
Text from a scanned document.

Many research institutes in humanities scan documents and books and get text via OCR. We can analyze the text, but is there a standard way to connect the scan and the text when you know the coefficient of where the word or phrase is on the image.

opened by ericvanderlinden 2
Getting text or disabling text box from .visualizer
When you use:

spacy_streamlit.visualize()

How do you get the textual data input into the text box? Can we disable that text box and feed directly into visualize instead?
question
opened by ritchieng 2
'labels' parameter of visualize_ner()
Hi! The default labels parameter of visualize_ner() is an empty tuple and I think we can make it more explicit because today I was debugging to figure out why my custom entities were not being displayed :P. Maybe I'm missing something, what does keyword-only mean? (docs table)

Back to the labels parameter, I'm not sure if it's best to:

modify the method to check if the tuple is empty and if so get all labels from the doc

simply add a required label in the docs

I modified the docs because it's more straightforward, let me know if you think it's not a good path. Thanks!
opened by dmesquita 2

AttributeError: module 'spacy_streamlit' has no attribute 'visualizer'

import spacy_streamlit

models = ["en_core_web_sm", "en_core_web_md"]
default_text = "Sundar Pichai is the CEO of Google."
spacy_streamlit.visualize(models, default_text)

gives the error:

AttributeError: module 'spacy_streamlit' has no attribute 'visualizer'
Traceback:

File "e:\wpy-3710\python-3.7.1.amd64\lib\site-packages\streamlit\ScriptRunner.py", line 322, in _run_script
    exec(code, module.__dict__)
File "e:\work\Python\spacy_streamlit.py", line 1, in <module>
    import spacy_streamlit
File "e:\work\Python\spacy_streamlit.py", line 5, in <module>
    spacy_streamlit.visualizer(models, default_text)

I am using Windows 10 WinPython 3.7.1

Python 3.7.1 (v3.7.1:260ec2c36a, Oct 20 2018, 14:57:15) [MSC v.1915 64 bit (AMD64)] on win32

opened by Code4SAFrankie 2

ModuleNotFoundError in demo

when opening https://ines-spacy-streamlit-demo-app-a6h19v.streamlit.app/ I encounter a ModuleNotFoundError

Traceback: File "/home/appuser/venv/lib/python3.7/site-packages/streamlit/runtime/scriptrunner/script_runner.py", line 565, in _run_script exec(code, module.dict) File "/app/spacy-streamlit-demo/app.py", line 1, in import spacy_streamlit File "/home/appuser/venv/lib/python3.7/site-packages/spacy_streamlit/init.py", line 1, in from .visualizer import visualize, visualize_parser, visualize_ner, visualize_spans File "/home/appuser/venv/lib/python3.7/site-packages/spacy_streamlit/visualizer.py", line 3, in import spacy File "/home/appuser/venv/lib/python3.7/site-packages/spacy/init.py", line 14, in from .cli.info import info # noqa: F401 File "/home/appuser/venv/lib/python3.7/site-packages/spacy/cli/init.py", line 3, in from ._util import app, setup_cli # noqa: F401 File "/home/appuser/venv/lib/python3.7/site-packages/spacy/cli/_util.py", line 8, in import typer File "/home/appuser/venv/lib/python3.7/site-packages/typer/init.py", line 29, in from .main import Typer as Typer File "/home/appuser/venv/lib/python3.7/site-packages/typer/main.py", line 11, in from .completion import get_completion_inspect_parameters File "/home/appuser/venv/lib/python3.7/site-packages/typer/completion.py", line 10, in import click._bashcomplete

opened by bsenst 1

Visualization of nested spans

Dear all,

I'm trying to use the new span visualizer to display some custom, nested spans predicted by spancat. This is my code:

MODEL_PATH = # ...
DEFAULT_TEXT = """Cetuximab ist ein monoklonaler Antikörper, der gegen den epidermalen Wachstumsfaktorrezeptor (EGFR) gerichtet ist und \
    dient zur Therapie des fortgeschrittenen kolorektalen Karzinoms zusammen mit Irinotecan oder in Kombination mit FOLFOX bzw. \
    allein nach Versagen einer Behandlung mit Oxaliplatin und Irinotecan."""

st.set_page_config(layout="wide")

text = st.text_area("Text to analyze", DEFAULT_TEXT)
doc = process_text(MODEL_PATH, text)

visualize_spans(doc, spans_key="snomed", displacy_options=
    {"colors": {
        "Clinical_Drug": "#99FF99", "External_Substance" : "#CCFFCC", "Nutrient_or_Body_Substance" : "#E5FFCC", 
        "Therapeutic" : "#66B2FF", "Diagnostic" : "#CCE5FF",
        "Other_Finding" : "#FFE5CC", "Diagnosis_or_Pathology" : "#FFCCCC"}} )

myapp

The nested spans are all shown on the same line, which makes it very hard to read. I learned from the feature on LinkedIn, where a video was shown with nested spans nicely displayed.

demo

How can I achieve this behavior? Do I need to pass some other options?

opened by phlobo 0

visualize_spans

I have been trying to use this but keep on getting this error ImportError: cannot import name 'visualize_spans' from partially initialized module 'spacy_streamlit' (most likely due to a circular import)

spacy 3.3.1 spacy_streamlit 1.0.4

opened by monWork 2
Unexpected keyword errors

Hello,

I aa looking to use spacy-streamlit to visualise custom sentences and entities output from my own model.

When running some of your example .py files, I have found errors https://github.com/explosion/spacy-streamlit/tree/master/examples

03_visualize-ner-manual.py The error says: TypeError: visualize_ner() got an unexpected keyword argument 'manual'

04_visualize-ner-extra-options.pyThe error says: TypeError: visualize_ner() got an unexpected keyword argument 'displacy_options'

I think this may be a big. Is there a way around this?

opened by rory-hurley-gds 1

Releases(v1.0.4)

v1.0.4(Jun 14, 2022)
✨ New features and improvements

NEW visualize_spans for span support in displaCy.:

PR https://github.com/explosion/spacy-streamlit/pull/37

visualize_{parser,ner,spans} functions now take a manual argument for rendering data manually

Updated docstrings with correct arguments & types

visualize_parser now takes displacy_options for providing options for that visualizer.

New examples scripts:

05_visualize-spans.py

👥 Contributors

@pmbaumgartner, @svlandeg, @rmitsch

What's Changed

Fix title in example 03 by @svlandeg in https://github.com/explosion/spacy-streamlit/pull/35

visualize_spans function + manual argument + docstrings by @pmbaumgartner in https://github.com/explosion/spacy-streamlit/pull/37

Full Changelog: https://github.com/explosion/spacy-streamlit/compare/v1.0.3...v1.0.4
Source code(tar.gz)
Source code(zip)
v1.0.3(Mar 31, 2022)
✨ New features and improvements

Improvements to visualize_ner:

PR https://github.com/explosion/spacy-streamlit/pull/26: UX and layout improvements

PR https://github.com/explosion/spacy-streamlit/pull/27: Allow manual option in visualize_ner

PR https://github.com/explosion/spacy-streamlit/pull/31: Allow extra displacy_options to be passed to visualize_ner

PR https://github.com/explosion/spacy-streamlit/pull/25: Use the streamlit theming options directly in visualizer.py

PR https://github.com/explosion/spacy-streamlit/pull/29: Ensure the correct texts are passed onwards in visualize_similarity

New examples scripts:

03_visualize-ner-manual.py

04_visualize-ner-extra-options.py

👥 Contributors

@callistachang, @honnibal, @ines, @Jette16, @narayanacharya6, @svlandeg
Source code(tar.gz)
Source code(zip)
v1.0.2(Aug 25, 2021)
Remove deprecated beta_ prefixes for compatibility with streamlit v0.86.0+.

Source code(tar.gz)
Source code(zip)
v1.0.0(Mar 12, 2021)
Update for spaCy v3.0.

Source code(tar.gz)
Source code(zip)
v1.0.0rc1(Jan 27, 2021)
🌙 This release is a pre-release and requires spaCy v3 (nightly).

Upgrade to latest Streamlit and use columns and expander widgets for controls.

Move visualizer-specific settings into main content instead of sidebar.

Add show_config setting to show config.cfg of currently loaded model.

Add default_model setting to specify option to auto-select in dropdown.

Add show_pipeline_info setting to toggle pipeline description in sidebar.

Update default token attributes to include Token.morph and Token.is_sent_start.

Add get_default_text callback to generate default text based on nlp object (e.g. language).

Source code(tar.gz)
Source code(zip)
v1.0.0rc0(Oct 15, 2020)
🌙 This release is a pre-release and requires spaCy v3 (nightly).

Upgrade to latest Streamlit and use columns and expander widgets for controls.

Move visualizer-specific settings into main content instead of sidebar.

Add show_config setting to show config.cfg of currently loaded model.

Add default_model setting to specify option to auto-select in dropdown.

Add show_pipeline_info setting to toggle pipeline description in sidebar.

Update default token attributes to include Token.morph and Token.is_sent_start.

Source code(tar.gz)
Source code(zip)
v0.1.0(Sep 19, 2020)
Clean up custom CSS for color theme.

Support both lists of model names as well as a dict mapping models to descriptions to display in the dropdown.

Add show_visualizer_select option to allow user to toggle displayed visualizers (based on the specified options).

Use checkboxes instead of buttons for JSON Doc and model meta.

Source code(tar.gz)
Source code(zip)
v0.0.3(Sep 2, 2020)
Add key argument to widgets to prevent problem with duplicate widgets.

Add colors parameter to ner_visualizer.

Thanks to @Jcharis, @yagays, @pmbaumgartner, @andfanilo and @discdiver for the pull requests!
Source code(tar.gz)
Source code(zip)
v0.0.2(Jun 23, 2020)

Source code(tar.gz)
Source code(zip)
v0.0.1(Jun 23, 2020)

Source code(tar.gz)
Source code(zip)

Owner

Explosion

A software company specializing in developer tools for Artificial Intelligence and Natural Language Processing

GitHub https://share.streamlit.io/ines/spacy-streamlit-demo/master/app.py

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⚠️ Checkout develop branch to see what is coming in pyannote.audio 2.0: a much smaller and cleaner codebase Python-first API (the good old pyannote-au

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A Domain Specific Language (DSL) for building language patterns. These can be later compiled into spaCy patterns, pure regex, or any other format

RITA DSL This is a language, loosely based on language Apache UIMA RUTA, focused on writing manual language rules, which compiles into either spaCy co

60 Sep 26, 2022

👑 spaCy building blocks and visualizers for Streamlit apps

Related tags

Overview

spacy-streamlit: spaCy building blocks for Streamlit apps

🚀 Quickstart

📦 Example: 01_out-of-the-box.py

👑 Example: 02_custom.py

🎛 API

Visualizer components

function visualize

function visualize_parser

function visualize_ner

function visualize_textcat

visualize_similarity

function visualize_tokens

Cached helpers

function process_text

function load_model

Comments

My code

Errors I ran into

Unsuccessful attempts in order

What worked

Releases(v1.0.4)

v1.0.4(Jun 14, 2022)

✨ New features and improvements

👥 Contributors

What's Changed

v1.0.3(Mar 31, 2022)

✨ New features and improvements

👥 Contributors

v1.0.2(Aug 25, 2021)

v1.0.0(Mar 12, 2021)

v1.0.0rc1(Jan 27, 2021)

v1.0.0rc0(Oct 15, 2020)

v0.1.0(Sep 19, 2020)

v0.0.3(Sep 2, 2020)

v0.0.2(Jun 23, 2020)

v0.0.1(Jun 23, 2020)

Owner

Explosion

xFormers is a modular and field agnostic library to flexibly generate transformer architectures by interoperable and optimized building blocks.

Neural building blocks for speaker diarization: speech activity detection, speaker change detection, overlapped speech detection, speaker embedding

A Domain Specific Language (DSL) for building language patterns. These can be later compiled into spaCy patterns, pure regex, or any other format

A linter to manage all your python exceptions and try/except blocks (limited only for those who like dinosaurs).

NLP, before and after spaCy

NLP, before and after spaCy

🛸 Use pretrained transformers like BERT, XLNet and GPT-2 in spaCy

A full spaCy pipeline and models for scientific/biomedical documents.

NLP, before and after spaCy

🛸 Use pretrained transformers like BERT, XLNet and GPT-2 in spaCy

A full spaCy pipeline and models for scientific/biomedical documents.

🧪 Cutting-edge experimental spaCy components and features

✨Fast Coreference Resolution in spaCy with Neural Networks

spaCy plugin for Transformers , Udify, ELmo, etc.

✨Fast Coreference Resolution in spaCy with Neural Networks

spaCy plugin for Transformers , Udify, ELmo, etc.

DaCy: The State of the Art Danish NLP pipeline using SpaCy

SpikeX - SpaCy Pipes for Knowledge Extraction

Augmenty is an augmentation library based on spaCy for augmenting texts.

📦 Example: `01_out-of-the-box.py`

👑 Example: `02_custom.py`

`function` `visualize`

`function` `visualize_parser`

`function` `visualize_ner`

`function` `visualize_textcat`

`visualize_similarity`

`function` `visualize_tokens`

`function` `process_text`

`function` `load_model`