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Update docs [ci skip]

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This commit is contained in:
Ines Montani 2020-10-08 11:56:50 +02:00
parent 064575d79d
commit d1602e1ece
1 changed files with 4 additions and 4 deletions
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website/docs/usage/embeddings-transformers.md
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@ -235,7 +235,7 @@ The `Transformer` component sets the
which lets you access the transformers outputs at runtime. which lets you access the transformers outputs at runtime.
```cli ```cli
$ python -m spacy download en_core_trf_lg $ python -m spacy download en_core_web_trf
``` ```
```python ```python
@ -249,7 +249,7 @@ from thinc.api import use_pytorch_for_gpu_memory, require_gpu
use_pytorch_for_gpu_memory() use_pytorch_for_gpu_memory()
require_gpu(0) require_gpu(0)
nlp = spacy.load("en_core_trf_lg") nlp = spacy.load("en_core_web_trf")
for doc in nlp.pipe(["some text", "some other text"]): for doc in nlp.pipe(["some text", "some other text"]):
tokvecs = doc._.trf_data.tensors[-1] tokvecs = doc._.trf_data.tensors[-1]
``` ```
@ -269,7 +269,7 @@ def custom_annotation_setter(docs, trf_data):
for doc, data in zip(docs, doc_data): for doc, data in zip(docs, doc_data):
doc._.custom_attr = data doc._.custom_attr = data
nlp = spacy.load("en_core_trf_lg") nlp = spacy.load("en_core_web_trf")
nlp.get_pipe("transformer").set_extra_annotations = custom_annotation_setter nlp.get_pipe("transformer").set_extra_annotations = custom_annotation_setter
doc = nlp("This is a text") doc = nlp("This is a text")
assert isinstance(doc._.custom_attr, TransformerData) assert isinstance(doc._.custom_attr, TransformerData)
@ -286,7 +286,7 @@ of objects by referring to creation functions, including functions you register
yourself. For details on how to get started with training your own model, check yourself. For details on how to get started with training your own model, check
out the [training quickstart](/usage/training#quickstart). out the [training quickstart](/usage/training#quickstart).
<!-- TODO: <Project id="en_core_trf_lg"> <!-- TODO: <Project id="pipelines/transformers">
The easiest way to get started is to clone a transformers-based project The easiest way to get started is to clone a transformers-based project
template. Swap in your data, edit the settings and hyperparameters and train, template. Swap in your data, edit the settings and hyperparameters and train,