Update comment syntax in MDX

website/docs/api/pipe.mdx

@@ -12,7 +12,7 @@ spaCy pipeline. See the docs on
 [writing trainable components](/usage/processing-pipelines#trainable-components)
 for how to use the `TrainablePipe` base class to implement custom components.
 
-<!-- TODO: Pipe vs TrainablePipe, check methods below (all renamed to TrainablePipe for now) -->
+{/* TODO: Pipe vs TrainablePipe, check methods below (all renamed to TrainablePipe for now) */}
 
 > #### Why is it implemented in Cython?
 >

website/docs/models/index.mdx

@@ -7,7 +7,7 @@ menu:
   - ['Pipeline Design', 'design']
 ---
 
-<!-- TODO: include interactive demo -->
+{/* TODO: include interactive demo */}
 
 ### Quickstart {hidden="true"}
 

website/docs/usage/embeddings-transformers.mdx

@@ -170,7 +170,7 @@ factory = "ner"
 @architectures = "spacy.MaxoutWindowEncoder.v2"
 ```
 
-<!-- TODO: Once rehearsal is tested, mention it here. -->
+{/* TODO: Once rehearsal is tested, mention it here. */}
 
 ## Using transformer models {id="transformers"}
 
@@ -309,14 +309,13 @@ of objects by referring to creation functions, including functions you register
 yourself. For details on how to get started with training your own model, check
 out the [training quickstart](/usage/training#quickstart).
 
-<!-- TODO: <Project id="pipelines/transformers">
+{/* TODO: <Project id="pipelines/transformers"> */}
 
-The easiest way to get started is to clone a transformers-based project
-template. Swap in your data, edit the settings and hyperparameters and train,
-evaluate, package and visualize your model.
+{/* The easiest way to get started is to clone a transformers-based project */}
+{/* template. Swap in your data, edit the settings and hyperparameters and train, */}
+{/* evaluate, package and visualize your model. */}
 
-</Project>
--->
+{/* </Project> */}
 
 The `[components]` section in the [`config.cfg`](/api/data-formats#config)
 describes the pipeline components and the settings used to construct them,

website/docs/usage/facts-figures.mdx

@@ -57,7 +57,7 @@ spaCy v3.0 introduces transformer-based pipelines that bring spaCy's accuracy
 right up to **current state-of-the-art**. You can also use a CPU-optimized
 pipeline, which is less accurate but much cheaper to run.
 
-<!-- TODO: update benchmarks and intro -->
+{/* TODO: update benchmarks and intro */}
 
 > #### Evaluation details
 >
@@ -117,6 +117,4 @@ comments.
 
 </figure>
 
-<!-- TODO: ## Citing spaCy {id="citation"}
-
--->
+{/* TODO: ## Citing spaCy {id="citation"} */}

website/docs/usage/projects.mdx

@@ -392,7 +392,7 @@ For example, a command for training a pipeline may depend on a
 it will export a directory `model-best`, which you can then re-use in other
 commands.
 
-<!-- prettier-ignore -->
+{/* prettier-ignore */}
 ```yaml
 ### project.yml
 commands:
@@ -445,7 +445,7 @@ directory:
 
 > #### project.yml
 >
-> <!-- prettier-ignore -->
+> {/* prettier-ignore */}
 > ```yaml
 > directories: ['assets', 'configs', 'corpus', 'metas', 'metrics', 'notebooks', 'packages', 'scripts', 'training']
 > ```
@@ -549,7 +549,7 @@ override settings on the command line – for example using `--vars.batch_size`.
 > everything with the same Python (not some other Python installed on your
 > system). It also normalizes references to `python3`, `pip3` and `pip`.
 
-<!-- prettier-ignore -->
+{/* prettier-ignore */}
 ```yaml
 ### project.yml
 vars:
@@ -618,7 +618,7 @@ up to date.
 
 Note that the contents of an existing file will be **replaced** if no existing
 auto-generated docs are found. If you want spaCy to ignore a file and not update
-it, you can add the comment marker `<!-- SPACY PROJECT: IGNORE -->` anywhere in
+it, you can add the comment marker `{/* SPACY PROJECT: IGNORE */}` anywhere in
 your markup.
 
 </Infobox>
@@ -691,9 +691,9 @@ according to a hash of the command string and the command's dependencies.
 Finally, within those directories are files, named according to an MD5 hash of
 their contents.
 
-<!-- TODO: update with actual real example? -->
+{/* TODO: update with actual real example? */}
 
-<!-- prettier-ignore -->
+{/* prettier-ignore */}
 ```yaml
 └── urlencoded_file_path            # Path of original file
     ├── some_command_hash           # Hash of command you ran
@@ -818,9 +818,7 @@ workflows, but only one can be tracked by DVC.
 
 </Infobox>
 
-<!-- TODO: <Project id="integrations/dvc">
-
-</Project> -->
+{/* { TODO: <Project id="integrations/dvc"></Project>} */}
 
 ---
 
@@ -853,7 +851,7 @@ collected with Prodigy and training a spaCy pipeline:
 > $ python -m spacy project run all
 > ```
 
-<!-- prettier-ignore -->
+{/* prettier-ignore */}
 ```yaml
 ### project.yml
 vars:
@@ -895,7 +893,7 @@ different portions of the data, e.g. 25%, 50%, 75% and 100%. As a rule of thumb,
 if accuracy increases in the last segment, this could indicate that collecting
 more annotations of the same type might improve the model further.
 
-<!-- prettier-ignore -->
+{/* prettier-ignore */}
 ```yaml
 ### project.yml (excerpt)
 - name: "train_curve"
@@ -934,7 +932,7 @@ package helps you integrate spaCy visualizations into your Streamlit apps and
 quickly spin up demos to explore your pipelines interactively. It includes a
 full embedded visualizer, as well as individual components.
 
-<!-- TODO: update once version is stable -->
+{/* TODO: update once version is stable */}
 
 > #### Installation
 >
@@ -963,7 +961,7 @@ and explore your own custom trained pipelines.
 > $ python -m spacy project run visualize
 > ```
 
-<!-- prettier-ignore -->
+{/* prettier-ignore */}
 ```yaml
 ### project.yml
 commands:
@@ -1008,7 +1006,7 @@ query your API from Python and JavaScript (Vanilla JS and React).
 > $ python -m spacy project run serve
 > ```
 
-<!-- prettier-ignore -->
+{/* prettier-ignore */}
 ```yaml
 ### project.yml
   - name: "serve"
@@ -1114,7 +1112,7 @@ packaged pipeline to the hub. You can either run this as a manual step, or
 automatically as part of a workflow. Make sure to set `--build wheel` when
 running `spacy package` to build a wheel file for your pipeline package.
 
-<!-- prettier-ignore -->
+{/* prettier-ignore */}
 ```yaml
 ### project.yml
 - name: "push_to_hub"

website/docs/usage/rule-based-matching.mdx

@@ -1429,7 +1429,7 @@ rules included!
 
 ### Using a large number of phrase patterns {id="entityruler-large-phrase-patterns",version="2.2.4"}
 
-<!-- TODO: double-check that this still works if the ruler is added to the pipeline on creation, and include suggestion if needed -->
+{/* TODO: double-check that this still works if the ruler is added to the pipeline on creation, and include suggestion if needed */}
 
 When using a large amount of **phrase patterns** (roughly > 10000) it's useful
 to understand how the `add_patterns` function of the entity ruler works. For

website/docs/usage/saving-loading.mdx

@@ -292,7 +292,7 @@ custom components to spaCy automatically.
 
 </Infobox>
 
-<!-- ## Initializing components with data {id="initialization",version="3"} -->
+{/* ## Initializing components with data {id="initialization",version="3"} */}
 
 ## Using entry points {id="entry-points",version="2.1"}
 

website/docs/usage/training.mdx

@@ -1439,10 +1439,7 @@ def filter_batch(size: int) -> Callable[[Iterable[Example]], Iterator[List[Examp
     return create_filtered_batches
 ```
 
-<!-- TODO:
-* Custom corpus class
-* Minibatching
--->
+{/* TODO: Custom corpus class, Minibatching */}
 
 ### Data augmentation {id="data-augmentation"}
 
@@ -1483,7 +1480,6 @@ typically loaded from a JSON file. There are two types of orth variant rules:
 `"single"` for single tokens that should be replaced (e.g. hyphens) and
 `"paired"` for pairs of tokens (e.g. quotes).
 
-<!-- prettier-ignore -->
 ```json
 ### orth_variants.json
 {

website/docs/usage/v3-1.mdx

@@ -116,7 +116,7 @@ train_doc.spans["incorrect_spans"] = [
 ]
 ```
 
-<!-- TODO: more details and/or example project? -->
+{/* TODO: more details and/or example project? */}
 
 ### New pipeline packages for Catalan and Danish {id="pipeline-packages"}