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Merge branch 'website/curated-docs' of github.com:vin-ivar/spaCy into pr/vin-ivar/12677

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website/docs/api/curatedtransformer.mdx
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@ -58,8 +58,8 @@ The default config is defined by the pipeline component factory and describes
how the component should be configured. You can override its settings via the how the component should be configured. You can override its settings via the
`config` argument on [`nlp.add_pipe`](/api/language#add_pipe) or in your `config` argument on [`nlp.add_pipe`](/api/language#add_pipe) or in your
[`config.cfg` for training](/usage/training#config). See the [`config.cfg` for training](/usage/training#config). See the
[model architectures](/api/architectures#transformers) documentation for details [model architectures](/api/architectures#curated-trf) documentation for details
on the transformer architectures and their arguments and hyperparameters. on the curated transformer architectures and their arguments and hyperparameters.
Note that the default config does not include the mandatory `vocab_size` Note that the default config does not include the mandatory `vocab_size`
hyperparameter as this value can differ between different models. So, you will hyperparameter as this value can differ between different models. So, you will
@ -100,7 +100,7 @@ https://github.com/explosion/spacy-curated-transformers/blob/main/spacy_curated_
> "@architectures": "spacy-curated-transformers.XlmrTransformer.v1", > "@architectures": "spacy-curated-transformers.XlmrTransformer.v1",
> "vocab_size": 250002, > "vocab_size": 250002,
> "num_hidden_layers": 12, > "num_hidden_layers": 12,
> "hidden_width": 768 > "hidden_width": 768,
> "piece_encoder": { > "piece_encoder": {
> "@architectures": "spacy-curated-transformers.XlmrSentencepieceEncoder.v1" > "@architectures": "spacy-curated-transformers.XlmrSentencepieceEncoder.v1"
> } > }
@ -146,7 +146,7 @@ and all pipeline components are applied to the `Doc` in order. Both
> doc = nlp("This is a sentence.") > doc = nlp("This is a sentence.")
> trf = nlp.add_pipe("curated_transformer") > trf = nlp.add_pipe("curated_transformer")
> # This usually happens under the hood > # This usually happens under the hood
> processed = transformer(doc) > processed = trf(doc)
> ``` > ```
| Name | Description | | Name | Description |