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https://issues.apache.org/jira/browse/TIKA-2720?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16601697#comment-16601697
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ASF GitHub Bot commented on TIKA-2720:
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chrismattmann commented on a change in pull request #248: Fix for TIKA-2720
[WIP]
URL: https://github.com/apache/tika/pull/248#discussion_r214555754
##########
File path:
tika-dl/src/test/resources/org/apache/tika/dl/text/sentencoder/tf-uni-sent-encoder-config.xml
##########
@@ -0,0 +1,28 @@
+<?xml version="1.0" encoding="UTF-8"?>
+
+<!--
+ ~ Licensed to the Apache Software Foundation (ASF) under one or more
+ ~ contributor license agreements. See the NOTICE file distributed with
+ ~ this work for additional information regarding copyright ownership.
+ ~ The ASF licenses this file to You 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.
+ -->
+<properties>
+ <parsers>
+ <parser class="org.apache.tika.dl.text.sentencoder.TFUniSentEncoder">
+ <mime>text/plain</mime>
+ <params>
+ <param name="modelURL"
type="string">https://www.dropbox.com/s/brls43rgzoqtee4/uni-sent-encoder.zip?dl=1</param>
Review comment:
any reason this is a dropbox URL. Did you train this one yourself?
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> A parser to output universal sentence encodings to text
> -------------------------------------------------------
>
> Key: TIKA-2720
> URL: https://issues.apache.org/jira/browse/TIKA-2720
> Project: Tika
> Issue Type: New Feature
> Components: tika-dl
> Reporter: Thejan Wijesinghe
> Priority: Major
> Fix For: 2.0
>
>
> This parser encodes a text into high dimensional vectors that can be used for
> text classification, semantic similarity, clustering and other natural
> language tasks. The model is trained and optimized for greater-than-word
> length text, such as sentences, phrases or short paragraphs. It is trained on
> a variety of data sources and a variety of tasks with the aim of dynamically
> accommodating a wide variety of natural language understanding tasks. The
> input is variable length English text and the output is a 512 dimensional
> vector.
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