This repository has been archived by the owner on Feb 22, 2020. It is now read-only.
-
Notifications
You must be signed in to change notification settings - Fork 209
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
Merge pull request #311 from gnes-ai/feat-flow
feat(flow): first version of gnes flow
- Loading branch information
Showing
18 changed files
with
807 additions
and
36 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,3 @@ | ||
# COMPOSER WILL BE RETIRED IN THE FUTURE VERSION!!! | ||
# COMPOSER WILL BE RETIRED IN THE FUTURE VERSION!!! | ||
# COMPOSER WILL BE RETIRED IN THE FUTURE VERSION!!! |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,45 @@ | ||
# Tencent is pleased to support the open source community by making GNES available. | ||
# | ||
# Copyright (C) 2019 THL A29 Limited, a Tencent company. All rights reserved. | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
|
||
|
||
from typing import List | ||
|
||
import numpy as np | ||
|
||
from ..base import BaseTextEncoder | ||
from ...helper import batching, as_numpy_array | ||
|
||
|
||
class CharEmbeddingEncoder(BaseTextEncoder): | ||
"""A random character embedding model. Only useful for testing""" | ||
is_trained = True | ||
|
||
def __init__(self, dim: int = 128, *args, **kwargs): | ||
super().__init__(*args, **kwargs) | ||
self.dim = dim | ||
self.offset = 32 | ||
self.unknown_idx = 96 | ||
# in total 96 printable chars and 2 special chars = 98 | ||
self._char_embedding = np.random.random([97, dim]) | ||
|
||
@batching | ||
@as_numpy_array | ||
def encode(self, text: List[str], *args, **kwargs) -> List[np.ndarray]: | ||
# tokenize text | ||
sent_embed = [] | ||
for sent in text: | ||
ids = [ord(c) - 32 if 32 <= ord(c) <= 127 else self.unknown_idx for c in sent] | ||
sent_embed.append(np.mean(self._char_embedding[ids], axis=0)) | ||
return sent_embed |
Oops, something went wrong.