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hanhxiao
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# 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. | ||
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from typing import List | ||
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import numpy as np | ||
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from ..base import BaseTextEncoder | ||
from ...helper import batching, as_numpy_array | ||
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class CharEmbeddingEncoder(BaseTextEncoder): | ||
"""A random character embedding model. Only useful for testing""" | ||
is_trained = True | ||
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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]) | ||
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@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 |
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!DictIndexer | ||
gnes_config: | ||
name: my_fulltext_indexer # a customized name | ||
work_dir: $TEST_WORKDIR |
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!Chunk2DocTopkReducer | ||
parameters: | ||
reduce_op: avg |
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!PipelineEncoder | ||
components: | ||
- !PyTorchTransformers | ||
parameters: | ||
model_dir: $TORCH_TRANSFORMERS_MODEL | ||
model_name: bert-base-uncased | ||
- !PoolingEncoder | ||
parameters: | ||
pooling_strategy: REDUCE_MEAN | ||
backend: torch | ||
gnes_config: | ||
name: my_transformer # a customized name | ||
is_trained: true # indicate the model has been trained | ||
work_dir: $TEST_WORKDIR | ||
batch_size: 128 |
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!NumpyIndexer # just for testing | ||
gnes_config: | ||
name: my_faiss_indexer # a customized name | ||
work_dir: $TEST_WORKDIR | ||
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#!FaissIndexer | ||
#parameters: | ||
# num_dim: -1 # delay the spec on num_dim on first add | ||
# index_key: HNSW32 | ||
# data_path: /workspace/idx.faiss | ||
#gnes_config: | ||
# name: my_faiss_indexer # a customized name | ||
# work_dir: /workspace |