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svlandeg 2020-12-29 14:59:39 +01:00
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@ -807,15 +807,16 @@ $ python -m spacy train [config_path] [--output] [--code] [--verbose] [--gpu-id]
## pretrain {#pretrain new="2.1" tag="command,experimental"}
Pretrain the "token to vector" ([`Tok2vec`](/api/tok2vec)) layer of pipeline
components on [raw text](/api/data-formats#pretrain), using an approximate
language-modeling objective. Specifically, we load pretrained vectors, and train
a component like a CNN, BiLSTM, etc to predict vectors which match the
pretrained ones. The weights are saved to a directory after each epoch. You can
then include a **path to one of these pretrained weights files** in your
components on raw text, using an approximate language-modeling objective.
Specifically, we load pretrained vectors, and train a component like a CNN,
BiLSTM, etc to predict vectors which match the pretrained ones. The weights are
saved to a directory after each epoch. You can then include a **path to one of
these pretrained weights files** in your
[training config](/usage/training#config) as the `init_tok2vec` setting when you
train your pipeline. This technique may be especially helpful if you have little
labelled data. See the usage docs on
[pretraining](/usage/embeddings-transformers#pretraining) for more info.
[pretraining](/usage/embeddings-transformers#pretraining) for more info. To read
the raw text, a [`JsonlCorpus`](/api/top-level#JsonlCorpus) is typically used.
<Infobox title="Changed in v3.0" variant="warning">
@ -835,7 +836,6 @@ auto-generated by setting `--pretraining` on
> $ python -m spacy pretrain config.cfg output_pretrain --paths.raw_text="data.jsonl"
> ```
```cli
$ python -m spacy pretrain [config_path] [output_dir] [--code] [--resume-path] [--epoch-resume] [--gpu-id] [overrides]
```