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Ines Montani 2020-08-20 16:17:25 +02:00
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@ -18,8 +18,6 @@ website/.npm
website/logs
*.log
npm-debug.log*
website/www/
website/_deploy.sh
quickstart-training-generator.js
# Cython / C extensions

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website/docs/api/architectures.md
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@ -399,7 +399,7 @@ one component.
> subword_features = true
> ```
Build a transition-based parser model. Can apply to NER or dependency-parsing.
Build a transition-based parser model. Can apply to NER or dependency parsing.
Transition-based parsing is an approach to structured prediction where the task
of predicting the structure is mapped to a series of state transitions. You
might find [this tutorial](https://explosion.ai/blog/parsing-english-in-python)
@ -416,8 +416,6 @@ consists of either two or three subnetworks:
state representation. If not present, the output from the lower model is used
as action scores directly.
<!-- TODO: model return type -->
| Name | Description |
| ------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| `tok2vec` | Subnetwork to map tokens into vector representations. ~~Model[List[Doc], List[Floats2d]]~~ |
@ -426,7 +424,7 @@ consists of either two or three subnetworks:
| `maxout_pieces` | How many pieces to use in the state prediction layer. Recommended values are `1`, `2` or `3`. If `1`, the maxout non-linearity is replaced with a [`Relu`](https://thinc.ai/docs/api-layers#relu) non-linearity if `use_upper` is `True`, and no non-linearity if `False`. ~~int~~ |
| `use_upper` | Whether to use an additional hidden layer after the state vector in order to predict the action scores. It is recommended to set this to `False` for large pretrained models such as transformers, and `True` for smaller networks. The upper layer is computed on CPU, which becomes a bottleneck on larger GPU-based models, where it's also less necessary. ~~bool~~ |
| `nO` | The number of actions the model will predict between. Usually inferred from data at the beginning of training, or loaded from disk. ~~int~~ |
| **CREATES** | The model using the architecture. ~~Model~~ |
| **CREATES** | The model using the architecture. ~~Model[List[Docs], List[List[Floats2d]]]~~ |
### spacy.BILUOTagger.v1 {#BILUOTagger source="spacy/ml/models/simple_ner.py"}

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website/docs/usage/training.md
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@ -404,11 +404,15 @@ recipe once the dish has already been prepared. You have to make a new one.
spaCy includes a variety of built-in [architectures](/api/architectures) for
different tasks. For example:
<!-- TODO: select example architectures to showcase -->
<!-- TODO: model return types -->
| Architecture | Description |
| ----------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [HashEmbedCNN](/api/architectures#HashEmbedCNN) | Build spaCy’s “standard” embedding layer, which uses hash embedding with subword features and a CNN with layer-normalized maxout. ~~Model[List[Doc], List[Floats2d]]~~ |
| Architecture | Description |
| ----------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [HashEmbedCNN](/api/architectures#HashEmbedCNN) | Build spaCy’s "standard" embedding layer, which uses hash embedding with subword features and a CNN with layer-normalized maxout. ~~Model[List[Doc], List[Floats2d]]~~ |
| [TransitionBasedParser](/api/architectures#TransitionBasedParser) | Build a [transition-based parser](https://explosion.ai/blog/parsing-english-in-python) model used in the default [`EntityRecognizer`](/api/entityrecognizer) and [`DependencyParser`](/api/dependencyparser). ~~Model[List[Docs], List[List[Floats2d]]]~~ |
| [TextCatEnsemble](/api/architectures#TextCatEnsemble) | Stacked ensemble of a bag-of-words model and a neural network model with an internal CNN embedding layer. Used in the default [`TextCategorizer`](/api/textcategorizer). ~~Model~~ |
<!-- TODO: link to not yet existing usage page on custom architectures etc. -->
### Metrics, training output and weighted scores {#metrics}

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