Fix broken images
Marcus Blättermann
|
|
@ -167,7 +167,7 @@ validation error with more details.
|
|||
>
|
||||
> #### Example diff
|
||||
>
|
||||
> 
|
||||
> 
|
||||
|
||||
```cli
|
||||
$ python -m spacy init fill-config [base_path] [output_file] [--diff]
|
||||
|
|
@ -1449,7 +1449,7 @@ $ python -m spacy project document [project_dir] [--output] [--no-emoji]
|
|||
For more examples, see the templates in our
|
||||
[`projects`](https://github.com/explosion/projects) repo.
|
||||
|
||||
|
||||
|
||||
|
||||
</Accordion>
|
||||
|
||||
|
|
|
|||
|
|
@ -91,7 +91,7 @@ Main changes from spaCy v2 models:
|
|||
|
||||
### CNN/CPU pipeline design {#design-cnn}
|
||||
|
||||
|
||||
|
||||
|
||||
In the `sm`/`md`/`lg` models:
|
||||
|
||||
|
|
|
|||
|
|
@ -14,7 +14,7 @@ of the pipeline. The `Language` object coordinates these components. It takes
|
|||
raw text and sends it through the pipeline, returning an **annotated document**.
|
||||
It also orchestrates training and serialization.
|
||||
|
||||
|
||||
|
||||
|
||||
### Container objects {#architecture-containers}
|
||||
|
||||
|
|
@ -39,7 +39,7 @@ rule-based modifications to the `Doc`. spaCy provides a range of built-in
|
|||
components for different language processing tasks and also allows adding
|
||||
[custom components](/usage/processing-pipelines#custom-components).
|
||||
|
||||
|
||||
|
||||
|
||||
| Name | Description |
|
||||
| ----------------------------------------------- | ------------------------------------------------------------------------------------------- |
|
||||
|
|
|
|||
|
|
@ -5,7 +5,7 @@ referred to as the **processing pipeline**. The pipeline used by the
|
|||
and an entity recognizer. Each pipeline component returns the processed `Doc`,
|
||||
which is then passed on to the next component.
|
||||
|
||||
|
||||
|
||||
|
||||
> - **Name**: ID of the pipeline component.
|
||||
> - **Component:** spaCy's implementation of the component.
|
||||
|
|
|
|||
|
|
@ -41,7 +41,7 @@ marks.
|
|||
> - **Suffix:** Character(s) at the end, e.g. `km`, `)`, `”`, `!`.
|
||||
> - **Infix:** Character(s) in between, e.g. `-`, `--`, `/`, `…`.
|
||||
|
||||
|
||||
|
||||
|
||||
While punctuation rules are usually pretty general, tokenizer exceptions
|
||||
strongly depend on the specifics of the individual language. This is why each
|
||||
|
|
|
|||
|
|
@ -21,7 +21,7 @@ predictions become more similar to the reference labels over time.
|
|||
> Minimising the gradient of the weights should result in predictions that are
|
||||
> closer to the reference labels on the training data.
|
||||
|
||||
|
||||
|
||||
|
||||
When training a model, we don't just want it to memorize our examples – we want
|
||||
it to come up with a theory that can be **generalized across unseen data**.
|
||||
|
|
|
|||
|
|
@ -136,7 +136,7 @@ useful for your purpose. Here are some important considerations to keep in mind:
|
|||
|
||||
<Infobox title="Tip: Check out sense2vec" emoji="💡">
|
||||
|
||||
[](https://github.com/explosion/sense2vec)
|
||||
[](https://github.com/explosion/sense2vec)
|
||||
|
||||
[`sense2vec`](https://github.com/explosion/sense2vec) is a library developed by
|
||||
us that builds on top of spaCy and lets you train and query more interesting and
|
||||
|
|
|
|||
|
|
@ -85,7 +85,7 @@ difficult to swap components or retrain parts of the pipeline. Multi-task
|
|||
learning can affect your accuracy (either positively or negatively), and may
|
||||
require some retuning of your hyper-parameters.
|
||||
|
||||
|
||||
|
||||
|
||||
| Shared | Independent |
|
||||
| ------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------- |
|
||||
|
|
@ -99,7 +99,7 @@ components by adding a [`Transformer`](/api/transformer) or
|
|||
later in the pipeline can "connect" to it by including a **listener layer** like
|
||||
[Tok2VecListener](/api/architectures#Tok2VecListener) within their model.
|
||||
|
||||
|
||||
|
||||
|
||||
At the beginning of training, the [`Tok2Vec`](/api/tok2vec) component will grab
|
||||
a reference to the relevant listener layers in the rest of your pipeline. When
|
||||
|
|
@ -249,7 +249,7 @@ the standard way, like any other spaCy pipeline. Instead of using the
|
|||
transformers as subnetworks directly, you can also use them via the
|
||||
[`Transformer`](/api/transformer) pipeline component.
|
||||
|
||||
|
||||
|
||||
|
||||
The `Transformer` component sets the
|
||||
[`Doc._.trf_data`](/api/transformer#custom_attributes) extension attribute,
|
||||
|
|
|
|||
|
|
@ -111,7 +111,7 @@ If you're using a modern editor like Visual Studio Code, you can
|
|||
custom Thinc plugin and get live feedback about mismatched types as you write
|
||||
code.
|
||||
|
||||
[](https://thinc.ai/docs/usage-type-checking#linting)
|
||||
[](https://thinc.ai/docs/usage-type-checking#linting)
|
||||
|
||||
</Accordion>
|
||||
|
||||
|
|
@ -785,7 +785,7 @@ To use our new relation extraction model as part of a custom
|
|||
[trainable component](/usage/processing-pipelines#trainable-components), we
|
||||
create a subclass of [`TrainablePipe`](/api/pipe) that holds the model.
|
||||
|
||||
|
||||
|
||||
|
||||
```python
|
||||
### Pipeline component skeleton
|
||||
|
|
|
|||
|
|
@ -1154,7 +1154,7 @@ different signature from all the other components: it takes a text and returns a
|
|||
[`Doc`](/api/doc), whereas all other components expect to already receive a
|
||||
tokenized `Doc`.
|
||||
|
||||
|
||||
|
||||
|
||||
To overwrite the existing tokenizer, you need to replace `nlp.tokenizer` with a
|
||||
custom function that takes a text and returns a [`Doc`](/api/doc).
|
||||
|
|
|
|||
|
|
@ -1156,7 +1156,7 @@ pipeline is loaded. For more background on this, see the usage guides on the
|
|||
[config lifecycle](/usage/training#config-lifecycle) and
|
||||
[custom initialization](/usage/training#initialization).
|
||||
|
||||
|
||||
|
||||
|
||||
A component's `initialize` method needs to take at least **two named
|
||||
arguments**: a `get_examples` callback that gives it access to the training
|
||||
|
|
@ -1272,7 +1272,7 @@ trainable components that have their own model instance, make predictions over
|
|||
`Doc` objects and can be updated using [`spacy train`](/api/cli#train). This
|
||||
lets you plug fully custom machine learning components into your pipeline.
|
||||
|
||||
|
||||
|
||||
|
||||
You'll need the following:
|
||||
|
||||
|
|
|
|||
|
|
@ -27,7 +27,7 @@ and share your results with your team. spaCy projects can be used via the new
|
|||
[`spacy project`](/api/cli#project) command and we provide templates in our
|
||||
[`projects`](https://github.com/explosion/projects) repo.
|
||||
|
||||
|
||||
|
||||
|
||||
<Project id="pipelines/tagger_parser_ud">
|
||||
|
||||
|
|
@ -594,7 +594,7 @@ commands:
|
|||
> For more examples, see the [`projects`](https://github.com/explosion/projects)
|
||||
> repo.
|
||||
>
|
||||
> 
|
||||
> 
|
||||
|
||||
When your custom project is ready and you want to share it with others, you can
|
||||
use the [`spacy project document`](/api/cli#project-document) command to
|
||||
|
|
@ -887,7 +887,7 @@ commands:
|
|||
|
||||
> #### Example train curve output
|
||||
>
|
||||
> [](https://prodi.gy/docs/recipes#train-curve)
|
||||
> [](https://prodi.gy/docs/recipes#train-curve)
|
||||
|
||||
The [`train-curve`](https://prodi.gy/docs/recipes#train-curve) recipe is another
|
||||
cool workflow you can include in your project. It will run the training with
|
||||
|
|
@ -942,7 +942,7 @@ full embedded visualizer, as well as individual components.
|
|||
> $ pip install spacy-streamlit --pre
|
||||
> ```
|
||||
|
||||
|
||||
|
||||
|
||||
Using [`spacy-streamlit`](https://github.com/explosion/spacy-streamlit), your
|
||||
projects can easily define their own scripts that spin up an interactive
|
||||
|
|
@ -1102,9 +1102,9 @@ and you'll be able to see the impact it has on your results.
|
|||
> model_log_interval = 1000
|
||||
> ```
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
<Project id="integrations/wandb">
|
||||
|
||||
|
|
@ -1155,7 +1155,7 @@ After uploading, you will see the live URL of your pipeline packages, as well as
|
|||
the direct URL to the model wheel you can install via `pip install`. You'll also
|
||||
be able to test your pipeline interactively from your browser:
|
||||
|
||||
|
||||
|
||||
|
||||
In your `project.yml`, you can add a command that uploads your trained and
|
||||
packaged pipeline to the hub. You can either run this as a manual step, or
|
||||
|
|
|
|||
|
|
@ -208,7 +208,7 @@ you need to describe fields like this.
|
|||
|
||||
<Infobox title="Tip: Try the interactive matcher explorer">
|
||||
|
||||
[](https://explosion.ai/demos/matcher)
|
||||
[](https://explosion.ai/demos/matcher)
|
||||
|
||||
The [Matcher Explorer](https://explosion.ai/demos/matcher) lets you test the
|
||||
rule-based `Matcher` by creating token patterns interactively and running them
|
||||
|
|
@ -1211,7 +1211,7 @@ each new token needs to be linked to an existing token on its left. As for
|
|||
`founded` in this example, a token may be linked to more than one token on its
|
||||
right:
|
||||
|
||||
|
||||
|
||||
|
||||
The full pattern comes together as shown in the example below:
|
||||
|
||||
|
|
@ -1752,7 +1752,7 @@ print([(ent.text, ent.label_) for ent in doc.ents])
|
|||
> - `VBD`: Verb, past tense.
|
||||
> - `IN`: Conjunction, subordinating or preposition.
|
||||
|
||||
|
||||
|
||||
|
||||
In this example, "worked" is the root of the sentence and is a past tense verb.
|
||||
Its subject is "Alex Smith", the person who worked. "at Acme Corp Inc." is a
|
||||
|
|
@ -1835,7 +1835,7 @@ notice that our current logic fails and doesn't correctly detect the company as
|
|||
a past organization. That's because the root is a participle and the tense
|
||||
information is in the attached auxiliary "was":
|
||||
|
||||
|
||||
|
||||
|
||||
To solve this, we can adjust the rules to also check for the above construction:
|
||||
|
||||
|
|
|
|||
|
|
@ -30,7 +30,7 @@ quick introduction.
|
|||
|
||||
<Infobox title="Take the free interactive course">
|
||||
|
||||
[](https://course.spacy.io)
|
||||
[](https://course.spacy.io)
|
||||
|
||||
In this course you'll learn how to use spaCy to build advanced natural language
|
||||
understanding systems, using both rule-based and machine learning approaches. It
|
||||
|
|
@ -292,7 +292,7 @@ and part-of-speech tags like "VERB" are also encoded. Internally, spaCy only
|
|||
> - **StringStore**: The dictionary mapping hash values to strings, for example
|
||||
> `3197928453018144401` → "coffee".
|
||||
|
||||
|
||||
|
||||
|
||||
If you process lots of documents containing the word "coffee" in all kinds of
|
||||
different contexts, storing the exact string "coffee" every time would take up
|
||||
|
|
@ -437,7 +437,7 @@ source of truth", both at **training** and **runtime**.
|
|||
> initial_rate = 0.01
|
||||
> ```
|
||||
|
||||
|
||||
|
||||
|
||||
<Infobox title="Training configuration system" emoji="📖">
|
||||
|
||||
|
|
@ -466,7 +466,7 @@ configured via a single training config.
|
|||
> width = 128
|
||||
> ```
|
||||
|
||||
|
||||
|
||||
|
||||
<Infobox title="Custom trainable components" emoji="📖">
|
||||
|
||||
|
|
|
|||
|
|
@ -23,7 +23,7 @@ import Training101 from 'usage/101/_training.md'
|
|||
|
||||
<Infobox title="Tip: Try the Prodigy annotation tool">
|
||||
|
||||
[](https://prodi.gy)
|
||||
[](https://prodi.gy)
|
||||
|
||||
If you need to label a lot of data, check out [Prodigy](https://prodi.gy), a
|
||||
new, active learning-powered annotation tool we've developed. Prodigy is fast
|
||||
|
|
@ -222,7 +222,7 @@ config is available as [`nlp.config`](/api/language#config) and it includes all
|
|||
information about the pipeline, as well as the settings used to train and
|
||||
initialize it.
|
||||
|
||||
|
||||
|
||||
|
||||
At runtime spaCy will only use the `[nlp]` and `[components]` blocks of the
|
||||
config and load all data, including tokenization rules, model weights and other
|
||||
|
|
@ -1120,7 +1120,7 @@ because the component settings required for training (load data from an external
|
|||
file) wouldn't match the component settings required at runtime (load what's
|
||||
included with the saved `nlp` object and don't depend on external file).
|
||||
|
||||
|
||||
|
||||
|
||||
<Infobox title="How components save and load data" emoji="📖">
|
||||
|
||||
|
|
@ -1623,7 +1623,7 @@ each worker is given ownership of a subset of the parameter arrays. Every
|
|||
parameter array is owned by exactly one worker, and the workers are given a
|
||||
mapping so they know which worker owns which parameter.
|
||||
|
||||
|
||||
|
||||
|
||||
As training proceeds, every worker will be computing gradients for **all** of
|
||||
the model parameters. When they compute gradients for parameters they don't own,
|
||||
|
|
|
|||
|
|
@ -130,7 +130,7 @@ write any **attributes, properties and methods** to the `Doc`, `Token` and
|
|||
`Span`. You can add data, implement new features, integrate other libraries with
|
||||
spaCy or plug in your own machine learning models.
|
||||
|
||||
|
||||
|
||||
|
||||
<Infobox>
|
||||
|
||||
|
|
|
|||
|
|
@ -76,7 +76,7 @@ This project trains a span categorizer for Indonesian NER.
|
|||
|
||||
<Infobox title="Tip: Create data with Prodigy's new span annotation UI">
|
||||
|
||||
[](https://support.prodi.gy/t/3861)
|
||||
[](https://support.prodi.gy/t/3861)
|
||||
|
||||
The upcoming version of our annotation tool [Prodigy](https://prodi.gy)
|
||||
(currently available as a [pre-release](https://support.prodi.gy/t/3861) for all
|
||||
|
|
|
|||
|
|
@ -98,7 +98,7 @@ transformer support interoperates with [PyTorch](https://pytorch.org) and the
|
|||
[HuggingFace `transformers`](https://huggingface.co/transformers/) library,
|
||||
giving you access to thousands of pretrained models for your pipelines.
|
||||
|
||||
|
||||
|
||||
|
||||
import Benchmarks from 'usage/_benchmarks-models.md'
|
||||
|
||||
|
|
@ -170,7 +170,7 @@ your pipeline. Some settings can also be registered **functions** that you can
|
|||
swap out and customize, making it easy to implement your own custom models and
|
||||
architectures.
|
||||
|
||||
|
||||
|
||||
|
||||
<Infobox title="Details & Documentation" emoji="📖" list>
|
||||
|
||||
|
|
@ -210,7 +210,7 @@ follow the same unified [`Model`](https://thinc.ai/docs/api-model) API and each
|
|||
`Model` can also be used as a sublayer of a larger network, allowing you to
|
||||
freely combine implementations from different frameworks into a single model.
|
||||
|
||||
|
||||
|
||||
|
||||
<Infobox title="Details & Documentation" emoji="📖" list>
|
||||
|
||||
|
|
@ -246,7 +246,7 @@ project template, adjust it to fit your needs, load in your data, train a
|
|||
pipeline, export it as a Python package, upload your outputs to a remote storage
|
||||
and share your results with your team.
|
||||
|
||||
|
||||
|
||||
|
||||
spaCy projects also make it easy to **integrate with other tools** in the data
|
||||
science and machine learning ecosystem, including [DVC](/usage/projects#dvc) for
|
||||
|
|
@ -295,7 +295,7 @@ the [`ray`](/api/cli#ray) command to your spaCy CLI if it's installed in the
|
|||
same environment. You can then run [`spacy ray train`](/api/cli#ray-train) for
|
||||
parallel training.
|
||||
|
||||
|
||||
|
||||
|
||||
<Infobox title="Details & Documentation" emoji="📖" list>
|
||||
|
||||
|
|
@ -398,7 +398,7 @@ A pattern added to the dependency matcher consists of a **list of
|
|||
dictionaries**, with each dictionary describing a **token to match** and its
|
||||
**relation to an existing token** in the pattern.
|
||||
|
||||
|
||||
|
||||
|
||||
<Infobox title="Details & Documentation" emoji="📖" list>
|
||||
|
||||
|
|
@ -506,7 +506,7 @@ format for documenting argument and return types.
|
|||
|
||||
</div>
|
||||
|
||||
[](/api)
|
||||
[](/api)
|
||||
|
||||
</Grid>
|
||||
|
||||
|
|
|
|||
|
|
@ -44,7 +44,7 @@ doc = nlp("This is a sentence.")
|
|||
displacy.serve(doc, style="dep")
|
||||
```
|
||||
|
||||
|
||||
|
||||
|
||||
The argument `options` lets you specify a dictionary of settings to customize
|
||||
the layout, for example:
|
||||
|
|
@ -77,7 +77,7 @@ For a list of all available options, see the
|
|||
> displacy.serve(doc, style="dep", options=options)
|
||||
> ```
|
||||
|
||||
|
||||
|
||||
|
||||
### Visualizing long texts {#dep-long-text new="2.0.12"}
|
||||
|
||||
|
|
@ -267,7 +267,7 @@ rendering if auto-detection fails.
|
|||
|
||||
</Infobox>
|
||||
|
||||
|
||||
|
||||
|
||||
Internally, displaCy imports `display` and `HTML` from `IPython.core.display`
|
||||
and returns a Jupyter HTML object. If you were doing it manually, it'd look like
|
||||
|
|
@ -455,6 +455,6 @@ Alternatively, if you're using [Streamlit](https://streamlit.io), check out the
|
|||
helps you integrate spaCy visualizations into your apps. It includes a full
|
||||
embedded visualizer, as well as individual components.
|
||||
|
||||
|
||||
|
||||
|
||||
</Grid>
|
||||
|
|
|
|||
|
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|
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|
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|
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@ -22,10 +22,10 @@ import QuickstartTraining from '../widgets/quickstart-training'
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import Project from '../widgets/project'
|
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import Features from '../widgets/features'
|
||||
import Layout from '../components/layout'
|
||||
import courseImage from '../../docs/images/course.jpg'
|
||||
import prodigyImage from '../../docs/images/prodigy_overview.jpg'
|
||||
import projectsImage from '../../docs/images/projects.png'
|
||||
import tailoredPipelinesImage from '../../docs/images/spacy-tailored-pipelines_wide.png'
|
||||
import courseImage from '../../public/images/course.jpg'
|
||||
import prodigyImage from '../../public/images/prodigy_overview.jpg'
|
||||
import projectsImage from '../../public/images/projects.png'
|
||||
import tailoredPipelinesImage from '../../public/images/spacy-tailored-pipelines_wide.png'
|
||||
import { nightly, legacy } from '../../meta/dynamicMeta'
|
||||
|
||||
import Benchmarks from '../../docs/usage/_benchmarks-models.mdx'
|
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