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small fixes

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This commit is contained in:
svlandeg 2020-09-08 17:24:36 +02:00
parent 06ef66fd73
commit bd8f9b188b
3 changed files with 7 additions and 7 deletions
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4
spacy/ml/models/tok2vec.py
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@ -176,8 +176,8 @@ def CharacterEmbed(width: int, rows: int, nM: int, nC: int):
ensures that the final character is always in the last position, instead ensures that the final character is always in the last position, instead
of being in an arbitrary position depending on the word length. of being in an arbitrary position depending on the word length.
The characters are embedded in a embedding table with 256 rows, and the The characters are embedded in a embedding table with a given number of rows,
vectors concatenated. A hash-embedded vector of the NORM of the word is and the vectors concatenated. A hash-embedded vector of the NORM of the word is
also concatenated on, and the result is then passed through a feed-forward also concatenated on, and the result is then passed through a feed-forward
network to construct a single vector to represent the information. network to construct a single vector to represent the information.

8
website/docs/api/architectures.md
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@ -181,10 +181,10 @@ characters would be `"jumpping"`: 4 from the start, 4 from the end. This ensures
that the final character is always in the last position, instead of being in an that the final character is always in the last position, instead of being in an
arbitrary position depending on the word length. arbitrary position depending on the word length.
The characters are embedded in a embedding table with 256 rows, and the vectors The characters are embedded in a embedding table with a given number of rows,
concatenated. A hash-embedded vector of the `NORM` of the word is also and the vectors concatenated. A hash-embedded vector of the `NORM` of the word
concatenated on, and the result is then passed through a feed-forward network to is also concatenated on, and the result is then passed through a feed-forward
construct a single vector to represent the information. network to construct a single vector to represent the information.
| Name | Description | | Name | Description |
| ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------- | | ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------- |

2
website/docs/usage/training.md
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@ -683,7 +683,7 @@ You can also implement your own batch size schedule to use during training. The
import spacy import spacy
@spacy.registry.schedules("my_custom_schedule.v1") @spacy.registry.schedules("my_custom_schedule.v1")
def my_custom_schedule(start: int = 1, factor: int = 1.001): def my_custom_schedule(start: int = 1, factor: float = 1.001):
while True: while True:
yield start yield start
start = start * factor start = start * factor