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@ -243,7 +243,14 @@ compound = 1.001
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### Using transformer models like BERT {#transformers}
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### Using transformer models like BERT {#transformers}
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<!-- TODO: document usage of spacy-transformers, refer to example config/project -->
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spaCy v3.0 lets you use almost any statistical model to power your pipeline. You
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can use models implemented in a variety of frameworks. A transformer model is
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just a statistical model, so the
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[`spacy-transformers`](https://github.com/explosion/spacy-transformers) package
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actually has very little work to do: it just has to provide a few functions that
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do the required plumbing. It also provides a pipeline component,
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[`Transformer`](/api/transformer), that lets you do multi-task learning and lets
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you save the transformer outputs for later use.
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<Project id="en_core_bert">
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<Project id="en_core_bert">
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@ -253,6 +260,10 @@ visualize your model.
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</Project>
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</Project>
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For more details on how to integrate transformer models into your training
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config and customize the implementations, see the usage guide on
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[training transformers](/usage/transformers#training).
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### Pretraining with spaCy {#pretraining}
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### Pretraining with spaCy {#pretraining}
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<!-- TODO: document spacy pretrain -->
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<!-- TODO: document spacy pretrain -->
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@ -18,8 +18,8 @@ frameworks to be wrapped with a common interface, using our machine learning
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library [Thinc](https://thinc.ai). A transformer model is just a statistical
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library [Thinc](https://thinc.ai). A transformer model is just a statistical
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model, so the
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model, so the
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[`spacy-transformers`](https://github.com/explosion/spacy-transformers) package
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[`spacy-transformers`](https://github.com/explosion/spacy-transformers) package
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actually has very little work to do: we just have to provide a few functions
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actually has very little work to do: it just has to provide a few functions that
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that do the required plumbing. We also provide a pipeline component,
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do the required plumbing. It also provides a pipeline component,
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[`Transformer`](/api/transformer), that lets you do multi-task learning and lets
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[`Transformer`](/api/transformer), that lets you do multi-task learning and lets
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you save the transformer outputs for later use.
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you save the transformer outputs for later use.
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@ -201,7 +201,8 @@ def configure_custom_sent_spans():
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To resolve the config during training, spaCy needs to know about your custom
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To resolve the config during training, spaCy needs to know about your custom
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function. You can make it available via the `--code` argument that can point to
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function. You can make it available via the `--code` argument that can point to
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a Python file:
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a Python file. For more details on training with custom code, see the
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[training documentation](/usage/training#custom-code).
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```bash
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```bash
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$ python -m spacy train ./train.spacy ./dev.spacy ./config.cfg --code ./code.py
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$ python -m spacy train ./train.spacy ./dev.spacy ./config.cfg --code ./code.py
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