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Merge branch 'explosion:master' into master
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@ -56,7 +56,7 @@ def build_tb_parser_model(
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non-linearity if use_upper=False.
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use_upper (bool): Whether to use an additional hidden layer after the state
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vector in order to predict the action scores. It is recommended to set
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this to False for large pretrained models such as transformers, and False
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this to False for large pretrained models such as transformers, and True
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for smaller networks. The upper layer is computed on CPU, which becomes
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a bottleneck on larger GPU-based models, where it's also less necessary.
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nO (int or None): The number of actions the model will predict between.
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@ -795,7 +795,7 @@ if there's no state to be passed through – spaCy can just take care of this fo
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you. The following two code examples are equivalent:
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```python
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# Statless component with @Language.factory
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# Stateless component with @Language.factory
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@Language.factory("my_component")
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def create_my_component():
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def my_component(doc):
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@ -3285,15 +3285,17 @@
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"slogan": "Forte is a toolkit for building Natural Language Processing pipelines, featuring cross-task interaction, adaptable data-model interfaces and composable pipelines.",
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"description": "Forte provides a platform to assemble state-of-the-art NLP and ML technologies in a highly-composable fashion, including a wide spectrum of tasks ranging from Information Retrieval, Natural Language Understanding to Natural Language Generation.",
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"github": "asyml/forte",
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"pip": "forte.spacy torch",
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"pip": "forte.spacy stave torch",
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"code_example": [
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"from forte.spacy import SpacyProcessor",
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"from fortex.spacy import SpacyProcessor",
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"from forte.processors.stave import StaveProcessor",
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"from forte import Pipeline",
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"from forte.data.readers import StringReader",
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"",
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"pipeline = Pipeline()",
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"pipeline.set_reader(StringReader())",
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"pipeline.add(SpacyProcessor())",
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"pipeline.add(StaveProcessor())",
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"pipeline.run('Running SpaCy with Forte!')"
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],
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"code_language": "python",
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