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Proofreading 

Finished with the API docs and started on the Usage, but Embedding & Transformers
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walterhenry 2020-09-29 12:39:10 +02:00
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website/docs/usage/embeddings-transformers.md
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@ -41,8 +41,8 @@ transformers is that word vectors model **lexical types**, rather than _tokens_.
If you have a list of terms with no context around them, a transformer model
like BERT can't really help you. BERT is designed to understand language **in
context**, which isn't what you have. A word vectors table will be a much better
fit for your task. However, if you do have words in context — whole sentences or
paragraphs of running text — word vectors will only provide a very rough
fit for your task. However, if you do have words in context – whole sentences or
paragraphs of running text – word vectors will only provide a very rough
approximation of what the text is about.
Word vectors are also very computationally efficient, as they map a word to a
@ -256,7 +256,7 @@ for doc in nlp.pipe(["some text", "some other text"]):
```
You can also customize how the [`Transformer`](/api/transformer) component sets
annotations onto the [`Doc`](/api/doc), by specifying a custom
annotations onto the [`Doc`](/api/doc) by specifying a custom
`set_extra_annotations` function. This callback will be called with the raw
input and output data for the whole batch, along with the batch of `Doc`
objects, allowing you to implement whatever you need. The annotation setter is
@ -675,7 +675,7 @@ given you a 10% error reduction, pretraining with spaCy might give you another
The [`spacy pretrain`](/api/cli#pretrain) command will take a **specific
subnetwork** within one of your components, and add additional layers to build a
network for a temporary task, that forces the model to learn something about
network for a temporary task that forces the model to learn something about
sentence structure and word cooccurrence statistics. Pretraining produces a
**binary weights file** that can be loaded back in at the start of training. The
weights file specifies an initial set of weights. Training then proceeds as