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Fixes from Sofie

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Co-authored-by: Sofie Van Landeghem <svlandeg@users.noreply.github.com>
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Daniël de Kok 2024-04-11 15:27:32 +02:00 committed by GitHub
parent 10b935347a
commit 72c0f7c798
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2 changed files with 7 additions and 7 deletions
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8
spacy/cli/distill.py
View File

@ -64,7 +64,7 @@ def distill(
):
student_config_path = util.ensure_path(student_config_path)
output_path = util.ensure_path(output_path)
# Make sure all files and paths exists if they are needed
# Make sure all files and paths exist if they are needed
if not student_config_path or (
str(student_config_path) != "-" and not student_config_path.exists()
):
@ -82,12 +82,12 @@ def distill(
config = util.load_config(
student_config_path, overrides=overrides, interpolate=False
)
msg.divider("Initializing pipeline")
msg.divider("Initializing student pipeline")
with show_validation_error(student_config_path, hint_fill=False):
student = init_nlp_student(config, teacher, use_gpu=use_gpu)
msg.good("Initialized pipeline")
msg.divider("Distilling pipeline")
msg.good("Initialized student pipeline")
msg.divider("Distilling student pipeline from teacher")
distill_nlp(
teacher,
student,

6
website/docs/api/cli.mdx
View File

@ -1707,11 +1707,11 @@ $ python -m spacy project dvc [project_dir] [workflow] [--force] [--verbose] [--
Distill a _student_ pipeline from a _teacher_ pipeline. Distillation trains the
models in the student pipeline on the activations of the teacher's models. A
typical use case for distillation is to extract a smaller, more performant model
from large high-accuracy model. Since distillation uses the activations of the
teacher, distillation can be performed on a corpus without (gold standard)
from a larger high-accuracy model. Since distillation uses the activations of the
teacher, distillation can be performed on a corpus of raw text without (gold standard)
annotations.
`distill` will save out the best model from all epochs, as well as the final
`distill` will save out the best performing pipeline across all epochs, as well as the final
pipeline. The `--code` argument can be used to provide a Python file that's
imported before the training process starts. This lets you register
[custom functions](/usage/training#custom-functions) and architectures and refer