You are viewing a plain text version of this content. The canonical link for it is here.
Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/03/15 16:39:07 UTC
[GitHub] szha closed pull request #10074: Add vocabulary and embedding
szha closed pull request #10074: Add vocabulary and embedding
URL: https://github.com/apache/incubator-mxnet/pull/10074
This is a PR merged from a forked repository.
As GitHub hides the original diff on merge, it is displayed below for
the sake of provenance:
As this is a foreign pull request (from a fork), the diff is supplied
below (as it won't show otherwise due to GitHub magic):
diff --git a/docs/api/python/gluon/text.md b/docs/api/python/gluon/text.md
new file mode 100644
index 00000000000..f9fcf335356
--- /dev/null
+++ b/docs/api/python/gluon/text.md
@@ -0,0 +1,347 @@
+# Gluon Text API
+
+## Overview
+
+The `mxnet.gluon.text` APIs refer to classes and functions related to text data processing, such
+as bulding indices and loading pre-trained embedding vectors for text tokens and storing them in the
+`mxnet.ndarray.NDArray` format.
+
+This document lists the text APIs in `mxnet.gluon`:
+
+```eval_rst
+.. autosummary::
+ :nosignatures:
+
+ mxnet.gluon.text.embedding
+ mxnet.gluon.text.vocab
+ mxnet.gluon.text.utils
+```
+
+All the code demonstrated in this document assumes that the following modules or packages are
+imported.
+
+```python
+>>> from mxnet import gluon
+>>> from mxnet import nd
+>>> from mxnet.gluon import text
+>>> import collections
+
+```
+
+### Indexing words and using pre-trained word embeddings in `gluon`
+
+As a common use case, let us index words, attach pre-trained word embeddings for them, and use
+such embeddings in `gluon` in just a few lines of code.
+
+To begin with, suppose that we have a simple text data set in the string format. We can count word
+frequency in the data set.
+
+```python
+>>> text_data = " hello world \n hello nice world \n hi world \n"
+>>> counter = text.count_tokens_from_str(text_data)
+
+```
+
+The obtained `counter` has key-value pairs whose keys are words and values are word frequencies.
+This allows us to filter out infrequent words (See details at
+[Vocabulary API specifications](#mxnet.gluon.text.vocab.Vocabulary)).
+Suppose that we want to build indices for all the keys in `counter`. We need a Vocabulary instance
+with `counter` as its argument.
+
+```python
+>>> my_vocab = text.Vocabulary(counter)
+
+```
+
+To attach word embedding to indexed words in `my_vocab`, let us go on to create a fastText word
+embedding instance by specifying the embedding name `fasttext` and the pre-trained file name
+`wiki.simple.vec`.
+
+```python
+>>> fasttext = text.embedding.create('fasttext', file_name='wiki.simple.vec')
+
+```
+
+So we can attach word embedding `fasttext` to indexed words `my_vocab`.
+
+```python
+>>> my_vocab.set_embedding(fasttext)
+
+```
+
+Now we are ready to access the fastText word embedding vectors for indexed words, such as 'hello'
+and 'world'.
+
+```python
+>>> my_vocab.embedding[['hello', 'world']]
+
+[[ 3.95669997e-01 2.14540005e-01 -3.53889987e-02 -2.42990002e-01
+ ...
+ -7.54180014e-01 -3.14429998e-01 2.40180008e-02 -7.61009976e-02]
+ [ 1.04440004e-01 -1.08580001e-01 2.72119999e-01 1.32990003e-01
+ ...
+ -3.73499990e-01 5.67310005e-02 5.60180008e-01 2.90190000e-02]]
+<NDArray 2x300 @cpu(0)>
+
+```
+
+To demonstrate how to use pre-trained word embeddings in the `gluon` package, let us first obtain
+indices of the words 'hello' and 'world'.
+
+```python
+>>> my_vocab[['hello', 'world']]
+[2, 1]
+
+```
+
+We can obtain the vector representation for the words 'hello' and 'world' by specifying their
+indices (2 and 1) and the weight matrix `my_vocab.embedding.idx_to_vec` in
+`mxnet.gluon.nn.Embedding`.
+
+```python
+>>> input_dim, output_dim = my_vocab.embedding.idx_to_vec.shape
+>>> layer = gluon.nn.Embedding(input_dim, output_dim)
+>>> layer.initialize()
+>>> layer.weight.set_data(my_vocab.embedding.idx_to_vec)
+>>> layer(nd.array([2, 1]))
+
+[[ 3.95669997e-01 2.14540005e-01 -3.53889987e-02 -2.42990002e-01
+ ...
+ -7.54180014e-01 -3.14429998e-01 2.40180008e-02 -7.61009976e-02]
+ [ 1.04440004e-01 -1.08580001e-01 2.72119999e-01 1.32990003e-01
+ ...
+ -3.73499990e-01 5.67310005e-02 5.60180008e-01 2.90190000e-02]]
+<NDArray 2x300 @cpu(0)>
+
+```
+
+## Vocabulary
+
+The vocabulary builds indices for text tokens and can be attached with token embeddings. The input
+counter whose keys are candidate indices may be obtained via
+[`count_tokens_from_str`](#mxnet.gluon.text.utils.count_tokens_from_str).
+
+
+```eval_rst
+.. currentmodule:: mxnet.gluon.text.vocab
+.. autosummary::
+ :nosignatures:
+
+ Vocabulary
+```
+
+Suppose that we have a simple text data set in the string format. We can count word frequency in the
+data set.
+
+```python
+>>> text_data = " hello world \n hello nice world \n hi world \n"
+>>> counter = text.utils.count_tokens_from_str(text_data)
+
+```
+
+The obtained `counter` has key-value pairs whose keys are words and values are word frequencies.
+This allows us to filter out infrequent words. Suppose that we want to build indices for the 2 most
+frequent keys in `counter` with the unknown token representation '(unk)' and a reserved token
+'(pad)'.
+
+```python
+>>> my_vocab = text.Vocabulary(counter, max_size=2, unknown_token='(unk)',
+... reserved_tokens=['(pad)'])
+
+```
+
+We can access properties such as `token_to_idx` (mapping tokens to indices), `idx_to_token` (mapping
+indices to tokens), `unknown_token` (representation of any unknown token) and `reserved_tokens`
+(reserved tokens).
+
+
+```python
+>>> my_vocab.token_to_idx
+{'(unk)': 0, '(pad)': 1, 'world': 2, 'hello': 3}
+>>> my_vocab.idx_to_token
+['(unk)', '(pad)', 'world', 'hello']
+>>> my_vocab.unknown_token
+'(unk)'
+>>> my_vocab.reserved_tokens
+['(pad)']
+>>> len(my_vocab)
+4
+>>> my_vocab[['hello', 'world']]
+[3, 2]
+```
+
+Besides the specified unknown token '(unk)' and reserved_token '(pad)' are indexed, the 2 most
+frequent words 'world' and 'hello' are also indexed.
+
+
+### Attach token embedding to vocabulary
+
+A vocabulary instance can be attached with token embedding.
+
+To begin with, suppose that we have a simple text data set in the string format. We can count word
+frequency in the data set.
+
+```python
+>>> text_data = " hello world \n hello nice world \n hi world \n"
+>>> counter = text.count_tokens_from_str(text_data)
+
+```
+
+The obtained `counter` has key-value pairs whose keys are words and values are word frequencies.
+This allows us to filter out infrequent words.
+Suppose that we want to build indices for the most frequent 2 keys in `counter`.
+
+```python
+>>> my_vocab = text.Vocabulary(counter, max_size=2)
+
+```
+
+Let us define the fastText word embedding instance with the pre-trained file `wiki.simple.vec`.
+
+```python
+>>> fasttext = text.embedding.create('fasttext', file_name='wiki.simple.vec')
+
+```
+
+So we can attach word embedding `fasttext` to indexed words `my_vocab`.
+
+```python
+>>> my_vocab.set_embedding(fasttext)
+
+```
+
+Now we are ready to access the fastText word embedding vectors for the indexed words.
+
+```python
+>>> my_vocab.embedding[['hello', 'world']]
+
+[[ 3.95669997e-01 2.14540005e-01 -3.53889987e-02 -2.42990002e-01
+ ...
+ -7.54180014e-01 -3.14429998e-01 2.40180008e-02 -7.61009976e-02]
+ [ 1.04440004e-01 -1.08580001e-01 2.72119999e-01 1.32990003e-01
+ ...
+ -3.73499990e-01 5.67310005e-02 5.60180008e-01 2.90190000e-02]]
+<NDArray 2x300 @cpu(0)>
+
+```
+
+Let us define the GloVe word embedding with the pre-trained file `glove.6B.50d.txt`. Then,
+we can re-attach a GloVe text embedding instance to the vocabulary.
+
+```python
+>>> glove = text.embedding.create('glove', file_name='glove.6B.50d.txt')
+>>> my_vocab.set_embedding(glove)
+
+```
+
+Now we are ready to access the GloVe word embedding vectors for the indexed words.
+
+```python
+>>> my_vocab.embedding[['hello', 'world']]
+
+[[ -0.38497001 0.80092001
+ ...
+ 0.048833 0.67203999]
+ [ -0.41486001 0.71847999
+ ...
+ -0.37639001 -0.67541999]]
+<NDArray 2x50 @cpu(0)>
+
+```
+
+If a token is unknown to `my_vocab`, its embedding vector is initialized according to the default
+specification in `glove` (all elements are 0).
+
+```python
+
+>>> my_vocab.embedding['nice']
+
+[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
+ ...
+ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]
+<NDArray 50 @cpu(0)>
+
+```
+
+
+
+## Text token embedding
+
+To load token embeddings from an externally hosted pre-trained token embedding file, such as those
+of GloVe and FastText, use
+[`embedding.create(embedding_name, file_name)`](#mxnet.gluon.text.embedding.create).
+
+To get all the available `embedding_name` and `file_name`, use
+[`embedding.get_file_names()`](#mxnet.gluon.text.embedding.get_file_names).
+
+```python
+>>> text.embedding.get_file_names()
+{'glove': ['glove.42B.300d.txt', 'glove.6B.50d.txt', 'glove.6B.100d.txt', ...],
+'fasttext': ['wiki.en.vec', 'wiki.simple.vec', 'wiki.zh.vec', ...]}
+
+```
+
+Alternatively, to load embedding vectors from a custom pre-trained text token embedding file, use
+[`TokenEmbedding.from_file`](#mxnet.gluon.text.embedding.TokenEmbedding.from_file).
+
+
+```eval_rst
+.. currentmodule:: mxnet.gluon.text.embedding
+.. autosummary::
+ :nosignatures:
+
+ register
+ create
+ get_file_names
+ TokenEmbedding
+ GloVe
+ FastText
+```
+
+See [Assign token embedding to vocabulary](#assign-token-embedding-to-vocabulary) for how to attach
+token embeddings to vocabulary and use token embeddings.
+
+
+### Implement a new text token embedding
+
+For ``embedding``, create a subclass of `mxnet.gluon.text.embedding.TokenEmbedding`.
+Also add ``@mxnet.gluon.text.embedding.TokenEmbedding.register`` before this class. See
+[`embedding.py`](https://github.com/apache/incubator-mxnet/blob/master/python/mxnet/gluon/text/embedding.py)
+for examples.
+
+
+## Text utilities
+
+The following functions provide utilities for text data processing.
+
+```eval_rst
+.. currentmodule:: mxnet.gluon.text.utils
+.. autosummary::
+ :nosignatures:
+
+ count_tokens_from_str
+```
+
+
+## API Reference
+
+<script type="text/javascript" src='../../_static/js/auto_module_index.js'></script>
+
+```eval_rst
+
+.. automodule:: mxnet.gluon.text.embedding
+ :members: register, create, get_file_names
+.. autoclass:: mxnet.gluon.text.embedding.TokenEmbedding
+ :members: from_file
+.. autoclass:: mxnet.gluon.text.embedding.GloVe
+.. autoclass:: mxnet.gluon.text.embedding.FastText
+
+.. automodule:: mxnet.gluon.text.vocab
+.. autoclass:: mxnet.gluon.text.vocab.Vocabulary
+ :members: set_embedding, to_tokens
+
+.. automodule:: mxnet.gluon.text.utils
+ :members: count_tokens_from_str
+
+```
+<script>auto_index("api-reference");</script>
\ No newline at end of file
diff --git a/python/mxnet/gluon/text/__init__.py b/python/mxnet/gluon/text/__init__.py
new file mode 100644
index 00000000000..3c33272af40
--- /dev/null
+++ b/python/mxnet/gluon/text/__init__.py
@@ -0,0 +1,26 @@
+# 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.
+
+# coding: utf-8
+# pylint: disable=wildcard-import
+"""This module includes utilities for indexing and embedding text."""
+
+from .vocab import *
+
+from . import embedding
+
+from .utils import *
diff --git a/python/mxnet/gluon/text/_constants.py b/python/mxnet/gluon/text/_constants.py
new file mode 100644
index 00000000000..3457a294e5d
--- /dev/null
+++ b/python/mxnet/gluon/text/_constants.py
@@ -0,0 +1,704 @@
+# 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.
+
+# coding: utf-8
+
+"""Constants."""
+from __future__ import absolute_import
+from __future__ import print_function
+
+UNKNOWN_IDX = 0
+
+GLOVE_PRETRAINED_FILE_SHA1 = \
+ {'glove.42B.300d.zip': 'f8e722b39578f776927465b71b231bae2ae8776a',
+ 'glove.6B.zip': 'b64e54f1877d2f735bdd000c1d7d771e25c7dfdc',
+ 'glove.840B.300d.zip': '8084fbacc2dee3b1fd1ca4cc534cbfff3519ed0d',
+ 'glove.twitter.27B.zip': 'dce69c404025a8312c323197347695e81fd529fc'}
+
+GLOVE_PRETRAINED_ARCHIVE_SHA1 = \
+ {'glove.42B.300d.txt': '876767977d6bd4d947c0f84d44510677bc94612a',
+ 'glove.6B.50d.txt': '21bf566a9d27f84d253e0cd4d4be9dcc07976a6d',
+ 'glove.6B.100d.txt': '16b1dbfaf35476790bd9df40c83e2dfbd05312f1',
+ 'glove.6B.200d.txt': '17d0355ddaa253e298ede39877d1be70f99d9148',
+ 'glove.6B.300d.txt': '646443dd885090927f8215ecf7a677e9f703858d',
+ 'glove.840B.300d.txt': '294b9f37fa64cce31f9ebb409c266fc379527708',
+ 'glove.twitter.27B.25d.txt':
+ '767d80889d8c8a22ae7cd25e09d0650a6ff0a502',
+ 'glove.twitter.27B.50d.txt':
+ '9585f4be97e286339bf0112d0d3aa7c15a3e864d',
+ 'glove.twitter.27B.100d.txt':
+ '1bbeab8323c72332bd46ada0fc3c99f2faaa8ca8',
+ 'glove.twitter.27B.200d.txt':
+ '7921c77a53aa5977b1d9ce3a7c4430cbd9d1207a'}
+
+FAST_TEXT_ARCHIVE_SHA1 = \
+ {'crawl-300d-2M.zip': 'bb40313d15837ceecc1e879bc954e9be04b17c3c',
+ 'wiki.aa.zip': '0d85feb259e17d5258f38b2b615a2b87cd628427',
+ 'wiki.ab.zip': '7a8c555b9cf3837c9b31c901e9e0142209990365',
+ 'wiki.ace.zip': '51555fccbe53b726f6c86a84d704c026a78dd02f',
+ 'wiki.ady.zip': '725d2c30c03001c941ac4084549c55c7f8e1d766',
+ 'wiki.af.zip': '1a18d34e1b60433b837f5850750a44ca3845323d',
+ 'wiki.ak.zip': 'daecc2303cfd05bc6c33b24d78c14e0d7f33e3a7',
+ 'wiki.als.zip': '38851192e0b556e566be6c3c93370abf9867e525',
+ 'wiki.am.zip': '4576e0121448564b07f448e05e287236343f17c1',
+ 'wiki.ang.zip': '9c03da3b06d4becef5d387b9a61438b9362fc36a',
+ 'wiki.an.zip': '170f60bdd161cf8e4b5e018acd7d36e8bfc457a6',
+ 'wiki.arc.zip': 'c8dad8b00865bf736b087e7b323999ab404bda29',
+ 'wiki.ar.zip': '34e9869daa463fdc5609040ff33a03e67512e9fd',
+ 'wiki.arz.zip': '2d2790e11e401d46e1bce2970ee5264d5678a32b',
+ 'wiki.ast.zip': '1136515e2de556c077324bcd42ffe7f40c8d94c6',
+ 'wiki.as.zip': 'f9efde3e4ccda4a1e93fa275a3210f74036e9e46',
+ 'wiki.av.zip': '9f8568a3e094a48de4a3b6bea3bdb6fd7e875a08',
+ 'wiki.ay.zip': 'f09a422cedc6a0f15fbf30d290febe8057de83db',
+ 'wiki.azb.zip': 'd8895581050b9fdb5a10dfec3e27910a150b6faf',
+ 'wiki.az.zip': '2a34c2db872597ba3e345ce8b7db138241f9efbf',
+ 'wiki.bar.zip': 'd6e40135a6f4ba7a07fab11633034eccb1b05d0a',
+ 'wiki.bat_smg.zip': '5d08bd04f0515a36723776c0682b3de0f11d4264',
+ 'wiki.ba.zip': '412ac2f3bf9a605e56e2b0990bb0baed41ddf3b0',
+ 'wiki.bcl.zip': 'd3717cda357e08390cb57a64e07f5c7b7768d5be',
+ 'wiki.be.zip': 'b691e63b8080af23cc37f5f2b21b3154e464c425',
+ 'wiki.bg.zip': '08509a510a95e2a8905c19d83faf40d614d2268b',
+ 'wiki.bh.zip': 'a812600c6454b779d442b7680e3867e15d895095',
+ 'wiki.bi.zip': 'd0d4a3f57419424815f77b3951ef9c7336f6adf5',
+ 'wiki.bjn.zip': '0d81879ff7611380896eac6059bb677a5b3fe308',
+ 'wiki.bm.zip': 'f3a2a1a8dbc94973a74343c059595a310a66665b',
+ 'wiki.bn.zip': 'b3bc70520edf3963c2217873ff5c2537d3545650',
+ 'wiki.bo.zip': '2be9fe7701d6a8501461df7bd98fee26859cf83a',
+ 'wiki.bpy.zip': 'd44b9267bb4f86e3e43972a6a952cc0ccf90dd3c',
+ 'wiki.br.zip': '4bfa66f1ea5aa5cad736eccaa211f6025596bcd6',
+ 'wiki.bs.zip': '40c560c5994ab50485d08eeaffd88740f30236ab',
+ 'wiki.bug.zip': 'bc7cd87bb067ac477000259cd4f95f45bfb6e4df',
+ 'wiki.bxr.zip': '8396fd67ef53f3123540766788a0db54734c4f1a',
+ 'wiki.ca.zip': '8f5d3caf0f5d223b2771ec44f7e620e396974fb2',
+ 'wiki.cbk_zam.zip': '0af3be50823b564433455d10c8753df88461458f',
+ 'wiki.cdo.zip': '19024215aa0c13872c027fc6127b5d7506198b5f',
+ 'wiki.ceb.zip': '96374428bf36a43983ba4307d7f6fb5ab52a6c6a',
+ 'wiki.ce.zip': 'b27f1a8da448bc9315e15d4261519c64f00de8eb',
+ 'wiki.cho.zip': '20944e34c2b58f14adb849dd5a6f5168c7affdea',
+ 'wiki.chr.zip': 'b7f41ee3fa76e933e0b5ad6b793c507fc19afe98',
+ 'wiki.chy.zip': '4ef66004a609c724fd7d8aab2877f7634323d43f',
+ 'wiki.ch.zip': '7f73678b685c9b5f5d6eea9bc00322cfc18d40cb',
+ 'wiki.ckb.zip': 'b7db2805526ad8bed878af257b32ca9ba814855f',
+ 'wiki.co.zip': '1b9e19b11763cb87ca00520dbdd6ada565547c9c',
+ 'wiki.crh.zip': '792003bae25c4471d25721440002c983fa5af020',
+ 'wiki.cr.zip': '875e4aa0de8a829e57f6c8e13d43cac5103210de',
+ 'wiki.csb.zip': 'fa776014c4c83487d7cb2485bd08eaf6739d9dca',
+ 'wiki.cs.zip': 'dca18cb80460522cd281ccc3c9922cf2b3c08b81',
+ 'wiki.cu.zip': 'ed23b48ba3193181a358d7a73005afa7655a4fc3',
+ 'wiki.cv.zip': '27ccd50942c9c218e00365ee293fa0c3087a7646',
+ 'wiki.cy.zip': '78940d5be2969b82c99f785bda2ac5f4e18e149c',
+ 'wiki.da.zip': 'a45077d9d73328bd6a96efdba1b31ed9a3639dcd',
+ 'wiki.de.zip': '0d9e4bf80100b46237dcb73cfefe390103e7e827',
+ 'wiki.diq.zip': '0eef7d9e2f0ce3f100a22dc8fcede9449e466528',
+ 'wiki.dsb.zip': '903cd80550931effba1d4e52a19c22592837d11c',
+ 'wiki.dv.zip': '3fa06719641ff33ac8a5439d330a8108521da1e7',
+ 'wiki.dz.zip': '8bf3937971c3c996493c30b264cb8268627d7bd6',
+ 'wiki.ee.zip': 'e66bc50013d884fe69f4f67ba44af2e34fe97927',
+ 'wiki.el.zip': '3015f358036658fb126d42fa794d67a90c5b91ad',
+ 'wiki.eml.zip': '5be541be6115af5914ac2b8118a09232b771123b',
+ 'wiki.en.zip': '7f83d578a31a8168423c77ea25ad381494a5e920',
+ 'wiki.eo.zip': 'e7612df98c37cb872f0edc3c3e21dcd2f80a4d69',
+ 'wiki.es.zip': '1b7668b23db26810ea433173ce0c11281e801f74',
+ 'wiki.et.zip': 'aa31004e7b8ebf359e166b8ea6b8e6f77fac190f',
+ 'wiki.eu.zip': '8d7699451cbac4d69750caa8d58b4740cc72e0ca',
+ 'wiki.ext.zip': '3aeb4d77c48eb503b26ceb2a76a0a7d841124a71',
+ 'wiki.fa.zip': '08b6e805c8623fba526143d46f4685549c4380a6',
+ 'wiki.ff.zip': '64f690eda733a6fb4f794e42eb6ff05f09ec1d38',
+ 'wiki.fiu_vro.zip': '35c3fdcec0f0dc1ce303212967ea59936641daee',
+ 'wiki.fi.zip': '252299a2a59cc0ac07ba25f9458afc26bbac669f',
+ 'wiki.fj.zip': '004d1279c27324d02b961341cf0d6ee06dbe8966',
+ 'wiki.fo.zip': '12f1d6360d4867cdebcc93be87c024a4709d1af5',
+ 'wiki.frp.zip': '8a0f636b5440a9aab38014efada9edfdf94150d5',
+ 'wiki.frr.zip': '7c9e7b8109b98aa39b303dd77d837b37e96d4113',
+ 'wiki.fr.zip': 'd906e68760153d771e5982009b0150e913254b2d',
+ 'wiki.fur.zip': 'd5d2ae08696ed074a581eac563a60eb85467a792',
+ 'wiki.fy.zip': '342609d29882fae0a3b402d8ea1478606be0d93b',
+ 'wiki.gag.zip': 'f2b91f89dd9b9a1301727476f7823b7260b5f129',
+ 'wiki.gan.zip': 'd3ad3c1151555266e1feb9f98b066ee31ee5f410',
+ 'wiki.ga.zip': '798b0c26783c7af05d9c4f899ca9fddafeb1e0a1',
+ 'wiki.gd.zip': '49085fa182a528bdc51f10e99bef33c88c1e3112',
+ 'wiki.glk.zip': '9e16727ffcc691483b69ecbcd331b1df2efa4bcd',
+ 'wiki.gl.zip': 'c71c7e6601b2cbdc7930982fbeea636deddd107d',
+ 'wiki.gn.zip': '493ccb583211217ccd23e0a43f42ba773bd94f78',
+ 'wiki.gom.zip': '45bbd49750ddb7df5afe01fcfd5dda2958934dfa',
+ 'wiki.got.zip': '669d018f72827fb965e5ef37e224e21f4682b2e5',
+ 'wiki.gu.zip': '4afe874f7830f693e9f83508fc3fb444b33aebdf',
+ 'wiki.gv.zip': '9411197eebc07775949d9bb6e440780a68502a5c',
+ 'wiki.hak.zip': 'cd1e14bd5d50fa764883b148bda5b821375531e0',
+ 'wiki.haw.zip': 'cacd4eb4e476bdd842e8014764b8ae380b346ed2',
+ 'wiki.ha.zip': '14acc50950b451f40fe028fd08d042af44732398',
+ 'wiki.he.zip': 'a9e2cd13bc2e55d83820c529bac1f518a7198bc0',
+ 'wiki.hif.zip': 'dcdd488239deb0ede807cff263ddc972009c21f5',
+ 'wiki.hi.zip': '15899ec17985bc0e1db1df497e1b4a51bba1982b',
+ 'wiki.ho.zip': 'fde454bb4f3841ea5dde2bbf879138305a4d0b36',
+ 'wiki.hr.zip': 'f5d33ba967f7c56538fa9f5f0093f6d634e9db44',
+ 'wiki.hsb.zip': '64dc13c7645d2b65b8ba252bd8dfb1c616e8923a',
+ 'wiki.ht.zip': 'cf50a5cadcf91aba9ab58d095d65f348e2375d12',
+ 'wiki.hu.zip': 'b27f293caedf81a2d09204b11f52a7c8d7443643',
+ 'wiki.hy.zip': '641b8666bc2168998989fae1b20a09d3428766bb',
+ 'wiki.hz.zip': '1639f9f096de6fac84336a784a391ce73e523d62',
+ 'wiki.ia.zip': '37640aaf8a25c02883190951337b5a6f0157d781',
+ 'wiki.id.zip': '56ee0c7a38a6d232706932493eaa37b2a87667ee',
+ 'wiki.ie.zip': '7c3a5d7f96c801570e2305f45a40d401fcc038b9',
+ 'wiki.ig.zip': '405ebc2e8a959163c9f2f8dd015a0bcefd440111',
+ 'wiki.ii.zip': '1ec1c7d95d61eeca2dbbd8e432caf88524aaf28e',
+ 'wiki.ik.zip': 'e9d088c0d8d0ab420d6d0469c6a0fdb668f1833c',
+ 'wiki.ilo.zip': 'cbc9754978ce55e86da2eb3db20579f4a1f19947',
+ 'wiki.io.zip': '9e5ab1fd5c4f1094d111f501129e0eecccec69a0',
+ 'wiki.is.zip': '0744e63636cf794e0a406c922827628a3dd415b7',
+ 'wiki.it.zip': '29f4eb6a5d7dcf45b02b4d08a4a70dfae4c41200',
+ 'wiki.iu.zip': 'fb2e8de825d554257768d363a3a09f711afb001b',
+ 'wiki.jam.zip': '077cfb6de9d025aee4a5b2ea9ce15ada02f10a4f',
+ 'wiki.ja.zip': '7940f6c2bc490c04902f0faf0562b92cae7136bf',
+ 'wiki.jbo.zip': '3d086b6c9a369f197516cd0dc699a94612f45c6a',
+ 'wiki.jv.zip': '2f68cb3436b27a25ddfa40fab3e2cd44574b437e',
+ 'wiki.kaa.zip': '9fd5df362b7cb615f2267084d8b3fb8608be2693',
+ 'wiki.kab.zip': '96abf1440ad21de58d7274d3a16885ef4a2efda4',
+ 'wiki.ka.zip': '72ddb2382c87184fc05a93e89ed8aa4f54a62a0a',
+ 'wiki.kbd.zip': '81dfc3c6f8581c2aa15342c84688b4ba59b81cc6',
+ 'wiki.kg.zip': '4d07cabef6f804fc6432d3f630675ed4cbbdd49e',
+ 'wiki.ki.zip': '59b5c31df227ff9454ad8b3a1d16b065620dbddf',
+ 'wiki.kj.zip': '751b80c4a4d82dd217d3d2b3905eb39b349874d7',
+ 'wiki.kk.zip': '7fb733a2405f421a7c49b756381a52965a8af205',
+ 'wiki.kl.zip': '05a9d5c9bf12d8845356f88b546418d2e40f79c6',
+ 'wiki.km.zip': 'da0a67028fa0244a2e7257ae259c2f7a7544dc66',
+ 'wiki.kn.zip': '6cead946350b31fb2f353085fd00b8ea9c9ecc77',
+ 'wiki.koi.zip': '0c61f83434404267527eaf583e89b4d8bb3a6a65',
+ 'wiki.ko.zip': 'c0825282faf1e7af6820bd8b28d06c77760dcbe4',
+ 'wiki.krc.zip': '0df3c3f0f89521299dab741be3d698b2c94c194e',
+ 'wiki.kr.zip': '71651f046cef420fb28ca15e35720bb7747c4586',
+ 'wiki.ksh.zip': '8b9ab88baa49e72e40a5a80bef98f3ea2afbdd07',
+ 'wiki.ks.zip': '02af37f12753662c9e7bcac3b8786dfd2f298710',
+ 'wiki.ku.zip': 'ca1d370b327ceca025884bf83139456024a3a978',
+ 'wiki.kv.zip': '28b3617c5566f3182f14bf11a906456b227840ba',
+ 'wiki.kw.zip': '075a02e8eaae26897c23898fb4d36f4e41e4d1d0',
+ 'wiki.ky.zip': '771601a934cd4d0a98e5059f6389d2496e8dcf7c',
+ 'wiki.lad.zip': '2788ba3f275d72299e877c96cde106bd8590f405',
+ 'wiki.la.zip': '759f6365874442ab8e04d992b047f53ad74231a6',
+ 'wiki.lbe.zip': 'c8105f1cf8a3d46ccfacff1d40a581f442b3c4a1',
+ 'wiki.lb.zip': 'dac5af52364f2c0d3a0c794411465d1254f2fb48',
+ 'wiki.lez.zip': '17331cb779dee8cb60f2734213af80d57acfcfad',
+ 'wiki.lg.zip': 'fd4e2d67d1f098474053abc9a1984dfe4a2854b7',
+ 'wiki.lij.zip': 'c29157f5e4d2b37c01cf6e389f03ddafef6acdb2',
+ 'wiki.li.zip': '10490e49a12230af2127543da69c427f92c6508f',
+ 'wiki.lmo.zip': 'cc44163572deddd78af6b006394f623cb21934fc',
+ 'wiki.ln.zip': 'bf52699c5cbf79bedb2e2856d8a720189b6864f3',
+ 'wiki.lo.zip': '3fd8a70d8e26071a365f10016875a4a4f15ffcee',
+ 'wiki.lrc.zip': 'e262b4fcc55cba48d997cd06d006b82a5abe09a9',
+ 'wiki.ltg.zip': 'df6a83f2fab35f9a2f97fd8d857cb1cfa59f331f',
+ 'wiki.lt.zip': 'a738a3f29a6a5481082a7a9a41b2040b9cf537e4',
+ 'wiki.lv.zip': '8e328d99aacaa021fcc51425caebc063e22e6cf4',
+ 'wiki.mai.zip': 'e909de86c27eced2cb5f02f550da7fc2502b5eda',
+ 'wiki.map_bms.zip': '192bf6b88f955746abb398893868482730585e3a',
+ 'wiki.mdf.zip': '3d0d5da3c85bef8ae52f0fd17e314a1960a26d36',
+ 'wiki.mg.zip': 'fe66055b63ce8771bf43f8dd543bbd967f8ea8b3',
+ 'wiki.mhr.zip': '33514c98da3bd9602851db96fa3dd8192aac0674',
+ 'wiki.mh.zip': 'dc77309103c6cfed7ff095b3f9f158e1ae437e71',
+ 'wiki.min.zip': '8b925eea6df0411ee09baef5801d807cfec8cfa4',
+ 'wiki.mi.zip': 'd57831e8d7cb2ec260fc9d83d4281f0bacfb29a5',
+ 'wiki.mk.zip': 'b1fc2d85527e99530a93e3bbc5fa9fcde89910f3',
+ 'wiki.ml.zip': 'b9d53b8e76a05f5e959afd190da3015b36793297',
+ 'wiki.mn.zip': '715bf0ee67b48ec872659380fcf63ad006ddcc7e',
+ 'wiki.mo.zip': 'fb273fe373eb61310051d94ad6911320f573d0ec',
+ 'wiki.mrj.zip': 'b0d1e43e37e1718c8e05fd81a511095636def361',
+ 'wiki.mr.zip': '67e942a7742cc957298c8cd0cd0af0531dc936d7',
+ 'wiki.ms.zip': 'e218f113702b039fc8e80a77b894cd9fa4eff77d',
+ 'wiki.mt.zip': 'd68d5b636eac07b2e1307186c2c05b9a80e39658',
+ 'wiki.multi.ar.zip': '31c7b742c63c3367e9bce5c4dca37d5ceb33f1a6',
+ 'wiki.multi.bg.zip': '8991e8123bce7fd6c8e4510c71ede5715ae36f01',
+ 'wiki.multi.ca.zip': '0786e071438150485d394a4bf2e976d3a1b313ff',
+ 'wiki.multi.cs.zip': '7237f291146e69f0fc7002a0e175c7fd003d44e8',
+ 'wiki.multi.da.zip': '5591c20015191101aee190c02738c99073a8fe76',
+ 'wiki.multi.de.zip': '986160e51a08f4a93f1573d17352e375cbaedd6d',
+ 'wiki.multi.el.zip': '570eb12811ce61f6176f263eff3e945be69e7da0',
+ 'wiki.multi.en.zip': '2c3ef35d8338d4a905e7d10645572ab7a6730d44',
+ 'wiki.multi.es.zip': 'c1db7c7175665a7230f92ed038b78de780e060e9',
+ 'wiki.multi.et.zip': '54d0515865c754331b445dd9ba0ae7ed79b770aa',
+ 'wiki.multi.fi.zip': 'c94abc803a42b89cd75b278114b1f2cf4e2f3ecd',
+ 'wiki.multi.fr.zip': 'd4904b79eaf8ae386a7011ad84afc9b4238c9928',
+ 'wiki.multi.he.zip': '370ec2a379eecc2d2e984cde3e0f6d0a027eade7',
+ 'wiki.multi.hr.zip': 'd3f25ae76b040ffa09e964f6edc55488f6086394',
+ 'wiki.multi.hu.zip': '4b64bcdf0fc1f01bbd8427bd7bf6b46319308e7a',
+ 'wiki.multi.id.zip': '3ad5f590d5c847b35a334f1bdb48b9c466f5de68',
+ 'wiki.multi.it.zip': '18746450e665e96c33f2e2026986f643a27e0945',
+ 'wiki.multi.mk.zip': '1d899f1449d8729b7dbae226f05151a656694626',
+ 'wiki.multi.nl.zip': 'ff0a04dbb07c2cdbc61d5a241175e30ed46b48d4',
+ 'wiki.multi.no.zip': 'd1af729024181e64f58ae37ab233fc53811e2601',
+ 'wiki.multi.pl.zip': '91c3984c4f3158b1cb1ff11d8cc4f9240631266e',
+ 'wiki.multi.pt.zip': 'a1782c4fa4337008f82c0e2bf78e4323d145be29',
+ 'wiki.multi.ro.zip': 'b1a0840d084009ce00c47a3c24c984648dbe8785',
+ 'wiki.multi.ru.zip': '540607ba4334dab6089de463f974861aac8a35ae',
+ 'wiki.multi.sk.zip': '2a2bb39e011cf2bf6dcb8cb6c482b8eb9764eea3',
+ 'wiki.multi.sl.zip': '99442dab442dc196c107868db9174c78e270db1e',
+ 'wiki.multi.sv.zip': 'b40be83d2d7c27633c712aea62ceec0d409cc03a',
+ 'wiki.multi.tr.zip': 'e2bffab1616f54d180ba3d8bfe5e94ec9a489184',
+ 'wiki.multi.uk.zip': 'e97f64d9ba2b58a5e80c9b896b87340aba1e0eb0',
+ 'wiki.multi.vi.zip': '532fa24d8787a8906fb04a88e74a713b00cb33ec',
+ 'wiki.mus.zip': '1bb0cad10889b8a3bfa36c36c7da1f2fb2237bb8',
+ 'wiki.mwl.zip': 'e3d1fd1fa6290521d403e84eba577e552e330844',
+ 'wiki.myv.zip': '64a6505691441778766b7941b5e7f45a624a64a5',
+ 'wiki.my.zip': '491ce8dbf174d4abff758db4950f49eda90883d9',
+ 'wiki.mzn.zip': '76abf410749fd4516ead20ced891b54245fcd4a3',
+ 'wiki.nah.zip': '0496592cdd70eaf61b257fb5345843d38f425592',
+ 'wiki.nap.zip': 'f0df66cdbef5734f0afeb806cda631722fb426d8',
+ 'wiki.na.zip': '2456e4776b5e985cfaedfac244e0b40cff4e613c',
+ 'wiki.nds_nl.zip': 'ffd10e05b749281634eb7a758102d8d6ff42760e',
+ 'wiki.nds.zip': '2455e9fa4294828b25b32bdad7307a105f9fbe1d',
+ 'wiki-news-300d-1M-subword.zip': '697f4c8f37443be3aee7b96abe28fd7ebec95ef3',
+ 'wiki-news-300d-1M.zip': '567ef9c2e207be25da23e61312e6ba620da30466',
+ 'wiki.new.zip': 'a781885678cc1079d4be221c414339eb9bee8d19',
+ 'wiki.ne.zip': '180b068343288cda40d012aaa99d29459d341eb4',
+ 'wiki.ng.zip': '6db8111ab700f7b0841af87f1f1453341048014e',
+ 'wiki.nl.zip': '582420f290947cf38503b7f4b8ea9bb21918005e',
+ 'wiki.nn.zip': '4a0e30376b361ee19800e6d897a865572e330f84',
+ 'wiki.nov.zip': 'ac98c0300302019ff855698561708abd81730db3',
+ 'wiki.no.zip': '6893a7912ab3756e31d09ef1f9023c27c0b047f8',
+ 'wiki.nrm.zip': 'bd27aadf25a165ebbac486437ea6a06b710fdda6',
+ 'wiki.nso.zip': 'c55dfebb83351c952831db34e779e0a380212f05',
+ 'wiki.nv.zip': 'cf122e5ee041287917c594a2cb6cd247978f1ec0',
+ 'wiki.ny.zip': '9086021a60babd7e87afa469dbadb004523f5fd2',
+ 'wiki.oc.zip': '15075544cf837135127d8688cd06fb8e4c8b7f3d',
+ 'wiki.olo.zip': '523628bb652e1563b4dd5a94b518addf10699f74',
+ 'wiki.om.zip': 'a29360ab3930d889c4eb5b385589f84c1ff9f06e',
+ 'wiki.or.zip': 'a782e649ae5307dece445b0c11b15ffb9ce88297',
+ 'wiki.os.zip': '0d76ca005afd48b87dea5c9784c4c48bb51d3e3e',
+ 'wiki.pag.zip': 'b046ef71badc9d7eec161e3aec2ffc3abb7bad20',
+ 'wiki.pam.zip': 'abed25ef407e05209f2653d571bba5bc7c66e7b3',
+ 'wiki.pap.zip': '5d099bfc65c85f824634a191ce33e8e42f947ded',
+ 'wiki.pa.zip': '2066ed0016720b9f8779f55f2cc2de08511025f6',
+ 'wiki.pcd.zip': '66914c99e5531c0484448b84568971362cdad0f6',
+ 'wiki.pdc.zip': '6ed181fa1f8782917ae7849490c0a5cb0b0b9b29',
+ 'wiki.pfl.zip': '8d271226af8509962b15a96c4d6e41d9aabd972c',
+ 'wiki.pih.zip': '365955dbecb17027435fe487ab92a7a267fa25bd',
+ 'wiki.pi.zip': 'eeb863545392c92cff0f3e3d9c3f61539d3fa1dd',
+ 'wiki.pl.zip': '2b0cae8af2637bc24b958e6757149d1b9f8c8fea',
+ 'wiki.pms.zip': '9eff2e96e1cb9bf02adf816c4feb5aa3cd1a384f',
+ 'wiki.pnb.zip': '23f77d1d9469f5b2c342984288cb3092d53d8dee',
+ 'wiki.pnt.zip': '84cc9532d2fd7b322bcba91e01ac36c9a719e23a',
+ 'wiki.ps.zip': '18c9ffb2a81cbc25299b26e35170a29b7de9309c',
+ 'wiki.pt.zip': '37752109a44829de5ea10b173d7c0cecc0b1a0d7',
+ 'wiki.qu.zip': '5582c07eeeaec10d9382b3ab90d2921fc97fa2e0',
+ 'wiki.rmy.zip': 'a106ab536001e92e7a9708417faee9418f4058d0',
+ 'wiki.rm.zip': '67a324941f2b895a418fbd89314a18bfda19b1de',
+ 'wiki.rn.zip': 'ce17294909c046e90bb0131632e1d795d1771816',
+ 'wiki.roa_rup.zip': 'a9a378e90cd46353283c92cfb7d34dd485a018d2',
+ 'wiki.roa_tara.zip': '953fe4cf1667cbb9b3b8e11666885bfedf74b411',
+ 'wiki.ro.zip': '6bbb0f9452398416d9183e00e6cd091a02fb351f',
+ 'wiki.rue.zip': 'e9f9b8ab63c7722b4b68e8c465b1c69436132553',
+ 'wiki.ru.zip': 'f8f68aa5792941d7750b545e56f1ff5127e88cc2',
+ 'wiki.rw.zip': '018b9fb76fca5ce7a3e1f266df33fcc1bbc50493',
+ 'wiki.sah.zip': 'f6c94dbd3b719b154217388310fab72e5a69f823',
+ 'wiki.sa.zip': '4dc78b48d651056546d14b659c6598770c6bce77',
+ 'wiki.scn.zip': '218ba35c042cb3e179988bac9acf51cccf37422b',
+ 'wiki.sco.zip': 'daa8cedbb223e87d48f720aed9ce63dd0c81c632',
+ 'wiki.sc.zip': '909cc5160cad60fda34ab89c2b87ae4229402eeb',
+ 'wiki.sd.zip': '5468ed141bf2f1d9b1f8d7b31fee926b496ea9db',
+ 'wiki.se.zip': '0eb962f8768d88ffcbde3aac833e134a263c2055',
+ 'wiki.sg.zip': '651035aa74dc2f515253444f48aa9911094f9d27',
+ 'wiki.sh.zip': 'cf3057b61bd5bca6f47640801681d451aee210cf',
+ 'wiki.simple.zip': '367737535e39defb0e713a7ff2374cb932c5a9bc',
+ 'wiki.si.zip': 'cebb2f4011b0d679fe856c5950076e3c48496ecc',
+ 'wiki.sk.zip': '6c43758d0c0f52351210c558cc33266a65709068',
+ 'wiki.sl.zip': 'd0239eefc830e5919bef8d9173a884e9e7371e7a',
+ 'wiki.sm.zip': '2e3cf33f17b449c8f81cc9ea4c84d542cfd23a14',
+ 'wiki.sn.zip': '4d3844ee350ee0065e5fe910a3f669ef863a2fc9',
+ 'wiki.so.zip': '9da45db9b21d1f27c4f73152539c1e4fc9b1c49c',
+ 'wiki.sq.zip': '0db976ec147df49e648cf8256562371d0ae6f2f0',
+ 'wiki.srn.zip': '120e229d522cc22008c50e0eb74b23d9f6eca51d',
+ 'wiki.sr.zip': '63b67391158bdd7a642f7d8412771c22e1041744',
+ 'wiki.ss.zip': '4368f7931f6730a6e8cb9b5794906f2d827582a8',
+ 'wiki.stq.zip': 'fb1ba577bf6fb7f7fcdc52bf392e63ed8492465d',
+ 'wiki.st.zip': 'b7e96392b3880c19e210fd42bc72e3f76c07a4c3',
+ 'wiki.su.zip': '4c4880cfca1ff954c88e44a32f201218eb2be146',
+ 'wiki.sv.zip': 'e2b10091585f795dd18289c4a65a1da591a78196',
+ 'wiki.sw.zip': '726631d8998ba1647d040e6b70f4bad7b8d8c367',
+ 'wiki.szl.zip': 'a70de974cff95cad0443f5faa6c8412c92998100',
+ 'wiki.ta.zip': '6bafd0bb523f654038393ba191012527745b940b',
+ 'wiki.tcy.zip': 'b4bd573eaf9fd87300a25648b38a053161d12c39',
+ 'wiki.tet.zip': '7e5608958977164e544850a5a169f5d55cd47a20',
+ 'wiki.te.zip': '948e5a6ec13ac95b595c3f52a6e7b9642a56c530',
+ 'wiki.tg.zip': '5b46429024d6819f6b511a4924b90c958615d40e',
+ 'wiki.th.zip': 'b8ee0878cec41b4ab1055a17d0ed669de1ed9afd',
+ 'wiki.ti.zip': 'd55abb74bb3ff195d2293ee9e77886111ee50e52',
+ 'wiki.tk.zip': '20263f39a31a1d55343f9dea7aecaa2860aefde8',
+ 'wiki.tl.zip': '2f2b809017249f8c4f8d5eb62979b58f16e8732b',
+ 'wiki.tn.zip': '0aa11b07b1ad6437bc1e9b6476d51ddd35dad994',
+ 'wiki.to.zip': '6b90b32ae258a56e67b42736675236b91163b3ad',
+ 'wiki.tpi.zip': 'ca9591e621ae667a1521d0bb5275435d45e974cc',
+ 'wiki.tr.zip': '3b6f86c2a115c7adec1b073b1f5624890e680148',
+ 'wiki.ts.zip': '8a00b16f2881977ad6f8c8665316c27fcab9b842',
+ 'wiki.tt.zip': '8d2f559bf1e09180d6dc4b127d61815a27670a20',
+ 'wiki.tum.zip': '5b3f6f3d8cae4d9534cd1fd3afc2f64ec8342b8d',
+ 'wiki.tw.zip': '7c189fabfcdb2973178c25d35fd10e46ee7148aa',
+ 'wiki.tyv.zip': '5e3811a19bbf961a5361ac37ff3502287c9ab022',
+ 'wiki.ty.zip': 'a7f31f8cabf4282533773aa7e63f294315cc85ea',
+ 'wiki.udm.zip': '643df5ab0914535e46e6839845d0ab585c81a119',
+ 'wiki.ug.zip': 'a5388269893ac4c7da28b2284f3536ca0f3c9341',
+ 'wiki.uk.zip': 'fdc9b0a0ab806e5845e9d89b8887ec9d555a0547',
+ 'wiki.ur.zip': '75579eb5609ea31d79bc2d1bd81d01f48e01bc7c',
+ 'wiki.uz.zip': 'aa149200f8c6e3e8bb5aa3c67112675d136900b8',
+ 'wiki.vec.zip': '58c4c9528154e256fbefeb97b8c1675356079f74',
+ 'wiki.vep.zip': '966b371afcc383058a5fbc6ee8f822620f03feac',
+ 'wiki.ve.zip': '6450e3ec2c78980c5a41d71ff159aa27918dda75',
+ 'wiki.vi.zip': 'bfa287fbb358a66b4f9576585df3e46607e1595c',
+ 'wiki.vls.zip': '7335bfda43890f42e045b8a5de25d1a8629fe012',
+ 'wiki.vo.zip': 'c2ca18bea165cb1253c1d88fa9958a25088fc84b',
+ 'wiki.war.zip': '5cda8fdd64e3acf5488ad361b68a63fb23747559',
+ 'wiki.wa.zip': '2e538c10a0e9f43ea5875c90a8ce01a07c4695a7',
+ 'wiki.wo.zip': 'f54c65ab63f98ffec7b3fb5bdd51a814034bd673',
+ 'wiki.wuu.zip': '68d9ad802836737392d62056231bf1b7a58594c9',
+ 'wiki.xal.zip': 'fb39fed41ccba2e4e58ab7714a53aae3695dbe04',
+ 'wiki.xh.zip': 'd37caa4d94e66588879231d0826798d8aa4b0a44',
+ 'wiki.xmf.zip': '956c43bca0d88e9348099cde43d58898e43d9f27',
+ 'wiki.yi.zip': '151c1670c48e976e4202272b066d7080a8c83615',
+ 'wiki.yo.zip': 'fdbd0fc6e35bb04c3aef1fa6f0262ba261b11199',
+ 'wiki.za.zip': '11f6a5dcb49c4d0571d5ac4fb3d7dda1d378fc06',
+ 'wiki.zea.zip': '22159a722c5c0390bad9206eb75e6e166efe38e9',
+ 'wiki.zh_classical.zip': 'c689d61d2254caf1ecec0909249523b09a737717',
+ 'wiki.zh_min_nan.zip': '0516a413565484d924a4c8b50c690d39344cdb64',
+ 'wiki.zh_yue.zip': '464f4c1c2039194cbae7502ed3a2eeff4df9e34f',
+ 'wiki.zh.zip': '2374ec566f6411b9bb570077636695fe9768a5ba',
+ 'wiki.zu.zip': 'a6d0325dab37cd551e6d7f6c783dd13f4c71db2f'}
+
+FAST_TEXT_FILE_SHA1 = \
+ {'crawl-300d-2M.vec': '9b556504d099a6c01f3dd76b88775d02cb2f1946',
+ 'wiki.aa.vec': '5cce30fc85471572c498f278bbe495184577363e',
+ 'wiki.ab.vec': '9d89a403a9a866d3da8dd8cfab849f59ee499343',
+ 'wiki.ace.vec': '85d00074f7a08626f39da6a0c8a5cfa250096ab9',
+ 'wiki.ady.vec': '9d17d74f0348224cdebf8a831e61af0825f8952d',
+ 'wiki.af.vec': '999e64bcd8dab8de42cb1feceeca360def35324d',
+ 'wiki.ak.vec': '6092b8af335c2dc93e8df2bbf1d715f01e637bb4',
+ 'wiki.als.vec': '96052e96870695cca50857b5fde5f9f42219139a',
+ 'wiki.am.vec': 'dff7fcdd8f5ba0638ab9e1758a89800766156d72',
+ 'wiki.ang.vec': 'a7c30e02422d97d23a0701279c5c1c03159130a5',
+ 'wiki.an.vec': '5b4c2b1de5c04e4e0be83841410ca84c47305d21',
+ 'wiki.arc.vec': 'fd3ad743103f80cde9cfc048d7ca509e50efb35a',
+ 'wiki.ar.vec': 'c46e2142f799cc385bd25f0c0a8943ca565505a4',
+ 'wiki.arz.vec': '5e904087043b91f4945dd708f4230fdf51360132',
+ 'wiki.ast.vec': '89a90357101953b7c292697fd050c00fe5c38ac5',
+ 'wiki.as.vec': 'cad5883b5147cbe6cdbf604f65cabdb675a59258',
+ 'wiki.av.vec': '99976a63ca8c4231f808fd4314f0433db35e290d',
+ 'wiki.ay.vec': 'be359dad25b2c742d3abfa94c5f5db13f86c730e',
+ 'wiki.azb.vec': 'e23af0a436b97434813c3cb14ed114cc5b352faa',
+ 'wiki.az.vec': '9581d55d9056ad398a153c37b502f3a07867d091',
+ 'wiki.bar.vec': '96130f1f2e5bffdd06c202ad4472e5234020980a',
+ 'wiki.bat_smg.vec': 'cb3aef58da2011183b39fca64cabf3d9d7a62f4b',
+ 'wiki.ba.vec': '22147ee16b2d163cc88d09a035264fd0c10dab68',
+ 'wiki.bcl.vec': 'd4117b5c443438ddfa608b10a5be2c2501817e7e',
+ 'wiki.be.vec': '6cf81322cd7b046a7f02ec4c4960ad27045383fa',
+ 'wiki.bg.vec': '7c1cc6d0c52b038e4b7173259b0c009f242cf486',
+ 'wiki.bh.vec': 'ab2d29017afa015c49566a6d9bf75393c23ac4c0',
+ 'wiki.bi.vec': '15785220cd6e6c86cc87e7d3f3322a5541a4fe5d',
+ 'wiki.bjn.vec': '5f134cf288e8042dcd048a3ee76159aab42c7288',
+ 'wiki.bm.vec': 'f36a19c95e90865f6518d4487e59f363b47bd865',
+ 'wiki.bn.vec': '6fc3bfd9af455719f55bee0bea31b11afc70cf06',
+ 'wiki.bo.vec': '2e9358e03dcfa09da23d2e1499d84b10348fd8a9',
+ 'wiki.bpy.vec': 'c2bb15487c4bdb8fa869772694300ae1fee73896',
+ 'wiki.br.vec': 'df44e16abd2017e2a1b6c6588ee02779b19907f6',
+ 'wiki.bs.vec': 'c4943a290819ceae1611dd11179b40aab0df0471',
+ 'wiki.bug.vec': '942d8f7dadde5faa33aa72862501434f48e29f60',
+ 'wiki.bxr.vec': 'eaf767690c6b194605ae778719212e3874873d4c',
+ 'wiki.ca.vec': 'f5971edee11c939f6a7accfd33a9a45caa54141a',
+ 'wiki.cbk_zam.vec': '6fef47b4559eec402ce371de20dfb018acd6347d',
+ 'wiki.cdo.vec': '95e8196bf76323dbabab1b8a49ba4d677af3ccea',
+ 'wiki.ceb.vec': 'b8516a55537b8f80c927d77d95cdf7e4ff849a05',
+ 'wiki.ce.vec': '1d94b0168a773895b23889f7f07d7cf56c11a360',
+ 'wiki.cho.vec': 'cec6778f025fa9ae4134046c6c3a6291bd9c63f9',
+ 'wiki.chr.vec': '8501bf86b41074ed6c8d15b9209ef7ce83122e70',
+ 'wiki.ch.vec': '46803f3a1734f6a7b0d8cb053bbb86a6915d02e9',
+ 'wiki.chy.vec': '26c87688551ffe3a0c7a5952e894306651e62131',
+ 'wiki.ckb.vec': 'adb2fef309f1d93f429442b9c16c1564192c58f3',
+ 'wiki.co.vec': 'af876a918594e5541207bc12f17bfc4268df7b93',
+ 'wiki.crh.vec': 'c0d2310a1207fcacc94b25b149420b33bf835015',
+ 'wiki.cr.vec': '61dd9f044b7dfa56dcf1c3c07c7504c569420528',
+ 'wiki.csb.vec': '649cb2692f08414987c875dc331022567d367497',
+ 'wiki.cs.vec': 'f3ec1502aeee6a550d8cf784273fa62f61419a4e',
+ 'wiki.cu.vec': 'ddadb14ea00ea1dda716ee33732497ec049b526f',
+ 'wiki.cv.vec': '9cdb0bee5a0fea030def85597dba7108f21b0424',
+ 'wiki.cy.vec': '32d976a9bfc4dd6e39328c906eead0f597bd9e25',
+ 'wiki.da.vec': '526947dab1ffbc1465c7a766f2bca4de50676b08',
+ 'wiki.de.vec': '2ed2696afe55f023b0040b238d9a47e5fedfe48b',
+ 'wiki.diq.vec': '77f3c370d1d77806fafe368cf788af550ff607dd',
+ 'wiki.dsb.vec': 'e49a647a441fbf011ac5411dd6005e8725b9a65d',
+ 'wiki.dv.vec': 'e135ba97c711a021bc3317db2b95db5212c17658',
+ 'wiki.dz.vec': '24888f0b2cd156360bfb5e9e905240163ba798d8',
+ 'wiki.ee.vec': 'afd1670655daa7ffba51187a415fdd0b43f1d487',
+ 'wiki.el.vec': '6f034271390feaa6f9d7d16f933ddef637755979',
+ 'wiki.eml.vec': 'de6be7a2ffdda226eec730dd54b4c614bd7f5dca',
+ 'wiki.en.vec': 'c1e418f144ceb332b4328d27addf508731fa87df',
+ 'wiki.eo.vec': 'b56998fd69f66755b722a9481a9bdaf10f62c9aa',
+ 'wiki.es.vec': '2f41401aa0925167176bcd7a6770423d891dfef5',
+ 'wiki.et.vec': '64d56b66c02d5e49b1b66a85854d67d2dd9ebd41',
+ 'wiki.eu.vec': '5e72f4ef93666971fea5d2180b354e0a0821ba91',
+ 'wiki.ext.vec': '456c5632b13a0f136cd180ebe2dda67b83f78397',
+ 'wiki.fa.vec': '09b6cc685c895c66b853af9617787d3ab0891e2c',
+ 'wiki.ff.vec': '12b09d695f5fb8de4b5da9d36a73eb178b293a04',
+ 'wiki.fiu_vro.vec': '168a71a2b1c478e6810fa5dce9612d8bf8a273dc',
+ 'wiki.fi.vec': '91d19baae994d7e556b5b5938be2dc6013f9c706',
+ 'wiki.fj.vec': '36d36dc14001a109926bfc633594f6a2f7401697',
+ 'wiki.fo.vec': 'eead8ddc7bb74b12b16784723abf802bb51f844d',
+ 'wiki.frp.vec': '0eb70a613ccf807c7308c1f62535f0606465029d',
+ 'wiki.frr.vec': 'cde62af939cb2de35e341cef2c74813802a58ed4',
+ 'wiki.fr.vec': 'b092229005a65d8683a4112852fe6eb8161a6917',
+ 'wiki.fur.vec': 'd4a595cffa1abcdcf4229ba15277179ce5d20bc6',
+ 'wiki.fy.vec': 'd4beef537b7ff142a3986513879ff51a9ec14a7b',
+ 'wiki.gag.vec': 'c82ec7a5d081f0673661824f4fc34345dee255f0',
+ 'wiki.gan.vec': '7e53a33b7bd5b0360ea4cb452145616c09445029',
+ 'wiki.ga.vec': 'caaa5b2167a499893313ac1aa38416a6a0fe9a24',
+ 'wiki.gd.vec': 'f4b513598a1bf0f0d5b6521ea8ce363e9596cb97',
+ 'wiki.glk.vec': '20a7759075916e10531f5b3577302353cef565cd',
+ 'wiki.gl.vec': '8888bb8f3d70b36729b9ae479fe3765e0c083862',
+ 'wiki.gn.vec': '98594af7897c5a1f35885ddecc77556a7e7ae981',
+ 'wiki.gom.vec': '5a1193d9e5d49d06354c14e2b7c01bea176e13f1',
+ 'wiki.got.vec': 'dfa06de83a0e3099027c57b84561d7d990ea8310',
+ 'wiki.gu.vec': 'f9e13452eb63d92bea44c7c3db8fba9945c7000e',
+ 'wiki.gv.vec': '993a7ee31bdacc91763dad656aa6c2947b873473',
+ 'wiki.hak.vec': '9e83512d34c7f81739492bf0abbb25ff1ef88573',
+ 'wiki.ha.vec': '677a24efeeb1bcb8c0a931407775f18b18e875ae',
+ 'wiki.haw.vec': '58fea5aa1b37723797d26fb3d050ce6176757240',
+ 'wiki.he.vec': '55534560247394669e3f5c169136770c93bc2708',
+ 'wiki.hif.vec': '49697cf784814d3f1a47559724028e0fc0940d36',
+ 'wiki.hi.vec': '8049bb8604bc049d48bd934e27b0e184c480a413',
+ 'wiki.ho.vec': '9c75a09e099213aa8cd1f1020b223427537cbdd8',
+ 'wiki.hr.vec': '0c96f9af092cf8a84b03aec1426cd23921671489',
+ 'wiki.hsb.vec': '3dc7830544c58535bed308c552d609e13b973502',
+ 'wiki.ht.vec': '5039dfb58a074ac046813f2dae81159be8c5213f',
+ 'wiki.hu.vec': 'cd777e9efca3d4bd97c89f01690cfa4840d9c46f',
+ 'wiki.hy.vec': '21f9259d04cfd22db446a45d3622af225f00cf20',
+ 'wiki.hz.vec': '2a94b1390d68027748a05169fbc0c11a9a183456',
+ 'wiki.ia.vec': '2a348dc924638efc20c34785852b0837364aed76',
+ 'wiki.id.vec': 'c49d5c9bec89114599427f6c12a5bda2e5523dfd',
+ 'wiki.ie.vec': '01b0d11c0e7397418e73853d220e97bdcf7a8961',
+ 'wiki.ig.vec': 'd2d1643b4fb1a18a4d002cf2969073f7f201b3b2',
+ 'wiki.ii.vec': '41c6cd68b3ebe4ece2a06c37b06dca5d07c9fb3a',
+ 'wiki.ik.vec': 'af31cbec7b839f50fa70553ec63c58f7067d3ea8',
+ 'wiki.ilo.vec': 'c0e43835a3f4e0033ea5d7c6ff189982b2f26a05',
+ 'wiki.io.vec': 'af0c480c5872bff31d82e767c1116da2a6be0c00',
+ 'wiki.is.vec': 'ae0b018f92b3e218f2dacb2045a8f0a0446788a5',
+ 'wiki.it.vec': 'ac4a985e85ffae48047034e2603d804bf126caa9',
+ 'wiki.iu.vec': '5d51b2ba215005216ae003f4a6d6ef39fb30ca2e',
+ 'wiki.jam.vec': '6d51e384c56330097c2531fdbf4e74418909e388',
+ 'wiki.ja.vec': '7a2b1af1e46d795410692a002e40fa3085135f69',
+ 'wiki.jbo.vec': 'c90481946aa4b6b304528292612ae620f6549f3e',
+ 'wiki.jv.vec': '2ff7927d3ff04b8208133497b3778ede00ea463f',
+ 'wiki.kaa.vec': 'd990d3b9bd511d2d630f923099a6b9110231b2ed',
+ 'wiki.kab.vec': 'e3b73d41267d8d4cd42f6cc5a0c05dc4e021bf74',
+ 'wiki.ka.vec': '8b92b73f27f9b77818211e053a33985589de7c62',
+ 'wiki.kbd.vec': 'f5b8dbe47a7fae702232b5680b070ef6e865539e',
+ 'wiki.kg.vec': '1550647b6059e6eb649b100e31c53bd0661117b2',
+ 'wiki.ki.vec': 'c4e373e2ea13f7fa1e95b0733365e4b3fc8b2cc8',
+ 'wiki.kj.vec': 'c27e563683f9c96ff6f680a6d6bb9e9e2f9960d0',
+ 'wiki.kk.vec': '6343b2b31bad2e13d03a110b91c38fab4adc01cd',
+ 'wiki.kl.vec': 'e5def7fb1b56c5956b6e951e912d53ba0ff089f8',
+ 'wiki.km.vec': '64f7fff1df90b1f7241b232e901f76223a3719e0',
+ 'wiki.kn.vec': '32763f4f860f0d081f3aabf3e7d17b7858e7d877',
+ 'wiki.koi.vec': '4001f0617fe0fdd3b22116b304f497b7b16c6e4c',
+ 'wiki.ko.vec': '042c85a788c2778cca538cf716b8a78f0d7fa823',
+ 'wiki.krc.vec': '0c6ef043d51e5f337a309804f1db180fa0bb2cb8',
+ 'wiki.kr.vec': '25d5b4d5911a819c48328c48fb346417d07d4070',
+ 'wiki.ksh.vec': '4c3bb4f12073532b6fb7cc6c2be5e53319ef5b65',
+ 'wiki.ks.vec': '5056a87c4ee2d8bf0792436fc6b2b61648014de9',
+ 'wiki.ku.vec': '4d3a2401527dd9ba6be2b0cd31f6cd3edebadce9',
+ 'wiki.kv.vec': '164dc44d701b9d606a45f0b0446076adc3858dca',
+ 'wiki.kw.vec': 'f9eaa35a7e4f077f6de85c7801f74582f91b52c1',
+ 'wiki.ky.vec': '13b0ae3f23822317a0243bd9182105c631c834b3',
+ 'wiki.lad.vec': 'c510e520cde97050bf1cbeb36f2b90e6348ceed4',
+ 'wiki.la.vec': '9ea6286a0581084533db8d6ee96e0b7d15166543',
+ 'wiki.lbe.vec': '283619d93255571f14fd4545bb0577979171b990',
+ 'wiki.lb.vec': 'b146f23628c84e64314a35a5b6cc65a33777e22d',
+ 'wiki.lez.vec': '8e579b984a500ad89fc66767bfd7319766bd669b',
+ 'wiki.lg.vec': 'b096f5248dfbb343dc4696c97ea253510e1c4ef9',
+ 'wiki.lij.vec': '4ff5bb405c820e4119f0636efc301da15a08c00a',
+ 'wiki.li.vec': '0fb9ec4ac93676d8ef651692062bc3d7f6ae0843',
+ 'wiki.lmo.vec': 'a89414d9ceee4823622258f18936f67faf7e06e7',
+ 'wiki.ln.vec': '70b6a286b42958e25cb80824e0d8f1aee2de6dde',
+ 'wiki.lo.vec': '7c83f82b80c49b8eab21f62ecdb3681b8bda40a6',
+ 'wiki.lrc.vec': 'c1ae4fb79a19d44bfe8f601f0a30fbec841fa612',
+ 'wiki.ltg.vec': 'ec2f13d1290bd54afcaa74569e66e43e9bfef264',
+ 'wiki.lt.vec': '58d3ebef24e5e31be1a8318b45c08ebb16ad775a',
+ 'wiki.lv.vec': 'ef6b549f96e22718f513d47a611d3d6bc001a164',
+ 'wiki.mai.vec': '7f513ff36e485b19f91f83b30c32dd82e9e497f6',
+ 'wiki.map_bms.vec': 'e7deab5fdd38fa3331b1bcb4a16432b38c512e21',
+ 'wiki.mdf.vec': 'b16099ce0283a241339716eac41cfd99fdea7f36',
+ 'wiki.mg.vec': '0808252740909d6129f672584311263e7b2adadc',
+ 'wiki.mhr.vec': '39f62e292336cabc364f0d1913540b881b406393',
+ 'wiki.mh.vec': '7d2d8bff722fe0a5d869d9da11792a406aff3dc3',
+ 'wiki.min.vec': '3bb0fa596cf27a1d165c55684bebdc8d40cb8ad7',
+ 'wiki.mi.vec': 'e8acf9c7c2ab840a192c563aa776201a88e4ca89',
+ 'wiki.mk.vec': '85a3d3f13fa88ffde023d2326c65bdded4983dff',
+ 'wiki.ml.vec': '2b70fe76e8cf199a18551de782784a21e8db0b66',
+ 'wiki.mn.vec': '7cef7ecdf9d98484d9b598b25d0e717dba6acfd9',
+ 'wiki.mo.vec': 'cc54b661aefabdf516b49d24acb51273b3acf210',
+ 'wiki.mrj.vec': 'aa1c1ecba1ffd6b42c8d9659a8a04ab328ae1650',
+ 'wiki.mr.vec': '2cd6cf88bfdfb24850d345749ce0cfea8d65829e',
+ 'wiki.ms.vec': '458e1a079799a54cdc0a7b78c7fa1729d2683a6d',
+ 'wiki.mt.vec': '81f4c1d84dd4cc4276d59cb903fcc9aba46be981',
+ 'wiki.multi.ar.vec': 'f1f12cc9d629382af574a3db74fe49c2fd615c8f',
+ 'wiki.multi.bg.vec': '22470e664e4b35761a33c64433ea2f0c12140673',
+ 'wiki.multi.ca.vec': 'bc8d98b4d86d740d1985d73d211d887d561bcdd7',
+ 'wiki.multi.cs.vec': '17358b62e63f96b0479d6a70e9235a0421493884',
+ 'wiki.multi.da.vec': 'ebc75f428714d26fb1fa31accce49ad3b31e273b',
+ 'wiki.multi.de.vec': 'b9a63406aedf4446b467b94d12674bfe4723b52d',
+ 'wiki.multi.el.vec': '03d33db85bf83f35b943ce93b18c02fa98a0bc05',
+ 'wiki.multi.en.vec': '696719afdbe470ee4a2eb668229486dba1df19cc',
+ 'wiki.multi.es.vec': '98c9e35564ec57fee5dbc6155890150452f45d3f',
+ 'wiki.multi.et.vec': 'db10189093387e853f2fd3978770e1cc7bc07820',
+ 'wiki.multi.fi.vec': '746916885a1c7d4ec3f139a32cf267f9e15f5363',
+ 'wiki.multi.fr.vec': 'fe1535827b631d934beb02f8d36ba901b2c94a46',
+ 'wiki.multi.he.vec': '6dd112f018165317da22971a2b6fdb2a15dafa91',
+ 'wiki.multi.hr.vec': 'ff9f23cf595ec8dd93cd93c6b48049730c34253b',
+ 'wiki.multi.hu.vec': '6da405c9b048f3cbb990bfb29ef149f0430aa2e7',
+ 'wiki.multi.id.vec': '34edadab182682198c37ade8538530c545635742',
+ 'wiki.multi.it.vec': 'c55802bd73d46a6fc86771097670e02a70b5d46d',
+ 'wiki.multi.mk.vec': 'cec8550503ebca0bdc7ad11f2c15085b7072a990',
+ 'wiki.multi.nl.vec': 'c3f45a5fe8a8bc213cdf35dce51651b752ca60c4',
+ 'wiki.multi.no.vec': '105236df530c8fc2ce5b1e2550a2059bbc46fc28',
+ 'wiki.multi.pl.vec': '676eb5acb22982c0c9a7d6e4c90d26730c6d120e',
+ 'wiki.multi.pt.vec': '625b0a5384873c79a5dcfff5ee3fde49a3a65013',
+ 'wiki.multi.ro.vec': '82bd59674509b69f988f9870e3a291836ba43e84',
+ 'wiki.multi.ru.vec': 'a7d9c5f2ab2abb448a5111d352caa921adabe830',
+ 'wiki.multi.sk.vec': '98d849ee77f0320472cc5afa002bfde129be7089',
+ 'wiki.multi.sl.vec': 'fb5cfb8a9c44380d74fb21ddd204e820c4e05c31',
+ 'wiki.multi.sv.vec': '95d6cc3ba23dffff9be6adb467b617dd57780cb2',
+ 'wiki.multi.tr.vec': 'ecb0e353eaccba3fcacc6994d93065934ef429e9',
+ 'wiki.multi.uk.vec': '35f4f5a1ead8bd66bcaf865021fc3aae94456ab6',
+ 'wiki.multi.vi.vec': 'b1abe06360e1d65a0db65dd41ead7b2f9d651ea0',
+ 'wiki.mus.vec': 'fa1066f7bd09df4589993ca498c19aeb6cf986fd',
+ 'wiki.mwl.vec': '3d10a218242b94fcc3981aa3beb012b701827a55',
+ 'wiki.my.vec': 'e7c7989e32b23ca1a9caf534cc65ecaf9e1b9112',
+ 'wiki.myv.vec': '7de0927fd3d65677de7f770b3bd57c73b58df85d',
+ 'wiki.mzn.vec': 'aefad49237808acab99e1ca8eeaaf531666f261d',
+ 'wiki.nah.vec': 'c52e01cf4479fb7ec91ef39f298e8f97aeb6496e',
+ 'wiki.nap.vec': '6c9bd8ce1e85ee679b25189fd6f6d36afb119b6c',
+ 'wiki.na.vec': '8a592eb3dbe5693372714dff495d01cabc3ea215',
+ 'wiki.nds_nl.vec': '1cd96d12e78e5cd3f65ca2773a17696bda387b9f',
+ 'wiki.nds.vec': '7bf293149c08226e05bcf0442ac6e601162b9ffd',
+ 'wiki.ne.vec': '1045d7876f947cd4602d9ca79f7c4323a5d3a52d',
+ 'wiki-news-300d-1M-subword.vec': '717a3058e0ba5ef3cde52c3df0d4f0f60b0a113a',
+ 'wiki-news-300d-1M.vec': '11cac9efe6f599e659be182f5766d6fbd5b1cab9',
+ 'wiki.new.vec': '51f6c0b4ef1aee9fad4ab1cb69a7479db35e39a5',
+ 'wiki.ng.vec': 'c3016cc07d40bd43bea84b7c600244ff3d2a928e',
+ 'wiki.nl.vec': 'd796ee27e37b7d1d464e03c265c31ab62b52533e',
+ 'wiki.nn.vec': '35aeab89ffeca0377accbbd3bf18b81913c75448',
+ 'wiki.no.vec': 'd52e8019d7cc48569c8c3b514d2b1bd10261b5c0',
+ 'wiki.nov.vec': '5455c6e8463b1c43dd073e3e177702fb9a1dd834',
+ 'wiki.nrm.vec': 'b4cb941b126b26fa045c5fc75a490a31a969101c',
+ 'wiki.nso.vec': 'a906271509c2b343df35d1471509492bbfa883aa',
+ 'wiki.nv.vec': 'f5a6ea213bfe95c82cb22b53b4965df8b67ffeab',
+ 'wiki.ny.vec': '3aec3dcaea6c35f8254c407621644f87df37e411',
+ 'wiki.oc.vec': 'cc1833492899d75571148c2c305591f53d63f0b1',
+ 'wiki.olo.vec': 'cbadb4cada4dc579d0becdac93dfb479d76bf6c8',
+ 'wiki.om.vec': '91789a8d9f9284f7e71e4bb8d9a60eae4af4adca',
+ 'wiki.or.vec': 'a6b120fe536b6c0133b077dca0043c3bc97eef0b',
+ 'wiki.os.vec': '791b26cc300e9a1f0a08c7b2213a264e41ce30d6',
+ 'wiki.pag.vec': '03f71faf060c4eb33802275279967349c0337553',
+ 'wiki.pam.vec': '8fbd31e70d0ca0c61eb1a152efaa8ecb29180967',
+ 'wiki.pap.vec': '8cd98267cc55a4f9de80212e29651ddf7a9e83fd',
+ 'wiki.pa.vec': '4939d0db77a5b28d7d5aab0fab4f999d93b2053e',
+ 'wiki.pcd.vec': 'd2e8e7321b6f1bce94c563cb8ef8af2b45cc3e48',
+ 'wiki.pdc.vec': '401e24d0fb9b0ae9e06a5c700684361f58727fcf',
+ 'wiki.pfl.vec': '0ad9b7f3ae13f909f12835107432fee4c4ed3031',
+ 'wiki.pih.vec': '4ae6ef2a9c6c88e9322eda900e0f58be5592a29b',
+ 'wiki.pi.vec': 'd388db284357042f4260e1a567cb489b05bb8e0b',
+ 'wiki.pl.vec': 'd031adb6f83eda0364a861dcbf5ef779b5951c0b',
+ 'wiki.pms.vec': 'e30bda8d33d61db43243c157b9ac2feeaff316c8',
+ 'wiki.pnb.vec': '35f38862d3d83012d6db7baa8a4105e3e0a416e7',
+ 'wiki.pnt.vec': '38134772012d68f247e34daf220d9d4ed3e7f489',
+ 'wiki.ps.vec': '64f1bec5d5b937289199ceae2e1da6557ce48852',
+ 'wiki.pt.vec': '7f11ebdb0cbf5929b38319f1e977d2c13bcd741b',
+ 'wiki.qu.vec': '58de8c8290e8bc8f2a6a677312e28457113437b2',
+ 'wiki.rm.vec': '5d3144b47a0dd98648a6df0636384ab2a010ad7b',
+ 'wiki.rmy.vec': '3d36d3485961900c23355a0f7c2ba656a8558c29',
+ 'wiki.rn.vec': '80b6171b78dd932f59f70dbef074abb906af4eee',
+ 'wiki.roa_rup.vec': 'e31a44353cd84b976586c8df35a2ab58318120f0',
+ 'wiki.roa_tara.vec': 'b3fcb01ff0bac53a0ba08c5c0c411f26ee83a95a',
+ 'wiki.ro.vec': 'c088ea2752d5ec8b42e32410c191a14839ae8a1f',
+ 'wiki.rue.vec': 'fe539e0ea0bbbfd3ee06bd0c5521a035c7361ec5',
+ 'wiki.ru.vec': '7514a2c60ee4118abb451ed32a0d61cb52dec384',
+ 'wiki.rw.vec': 'af2ec410da6519a86ba21004c8b4c7fde768a91c',
+ 'wiki.sah.vec': '202470467194a1cbdcd571b14ef68371a29b38d9',
+ 'wiki.sa.vec': '7fed78d1d7674453b9876ee99aeeeba85ea46699',
+ 'wiki.scn.vec': 'bde043a235551e1643506774c5d9b61ecf2fc424',
+ 'wiki.sco.vec': '4625a5ad90a57f994be9b3aa4f8f3ecda941a821',
+ 'wiki.sc.vec': 'dba8dc7754ef04b1ba0cd702d94eea9575cde91c',
+ 'wiki.sd.vec': '36852d1253496e598fbd9b9009f07f454a6bea5b',
+ 'wiki.se.vec': 'f46b35ee6b893c2f12dd1b929bbc2b8120cbcd8d',
+ 'wiki.sg.vec': '90ece136bef7ad6e4e97776a1c7238499544405d',
+ 'wiki.sh.vec': '016691ecb26ace442731d92b1265e5c6c3d8ca5f',
+ 'wiki.simple.vec': '55267c50fbdf4e4ae0fbbda5c73830a379d68795',
+ 'wiki.si.vec': 'd05ed6a0bc1ee56e5d2e5f881d47372095f6eb0c',
+ 'wiki.sk.vec': '98759aacf7352d49a51390fae02030776510ae13',
+ 'wiki.sl.vec': 'b26997c0ed1de26a47b11efdc26ac1e7f189fa54',
+ 'wiki.sm.vec': '88c2c57ca483626b052403418cb4372d72352bc9',
+ 'wiki.sn.vec': '8dbb1019dcc8f842a8c0f550295ae697f8e1b7e0',
+ 'wiki.so.vec': '294756b60b03fe57cb08abd8d677d6a717b40bc8',
+ 'wiki.sq.vec': 'd07ffed553f5eb4756d0a1548a7ba9a51a52f7c6',
+ 'wiki.srn.vec': 'faee05e550f5b08809a9ae5586ac4b08c9a1c359',
+ 'wiki.sr.vec': '3cf09f476f55a92fdd2880f7ba336656ab232736',
+ 'wiki.ss.vec': '488546a3b2f88f549c50ae9f32f1997cc441b039',
+ 'wiki.stq.vec': '1bf88af29f1d86cac16042a5bea6b1651c96a8c1',
+ 'wiki.st.vec': '963646055d12873b1c83b0eef8649ecaf473d42e',
+ 'wiki.su.vec': '25e864495acb6d280bab0e62480f68550c9ceed4',
+ 'wiki.sv.vec': 'eab83ae36701139696477b91b6e8d292ef175053',
+ 'wiki.sw.vec': '8e70d207dbbd14e60a48e260a23fbf284a8e9f06',
+ 'wiki.szl.vec': '0573cf888ec70b459b0596d34814fe60fd69f190',
+ 'wiki.ta.vec': 'b66b5358527b1f3a6a421ab26464a3c1e75e18af',
+ 'wiki.tcy.vec': '388b1d89642fcc790b688e9643b3d19e14d66f40',
+ 'wiki.tet.vec': 'f38fe0e76b9b08ff652689eeee42c4fdadd9a47e',
+ 'wiki.te.vec': 'e71dcf3cc45da1bcdae5e431324025bd2026d0c8',
+ 'wiki.tg.vec': '6a5cd5bfe571ca0359b66d21bf6950553213f42d',
+ 'wiki.th.vec': '1d6e0d525392a1042d017534f6c320c5a0afd345',
+ 'wiki.ti.vec': 'c769fbc99bbb4138a40231e573685c7948d4a4c4',
+ 'wiki.tk.vec': '33ae577f77d339ab7a0dff88855b8d5c974d0aef',
+ 'wiki.tl.vec': 'd508e229ced7201510999e76d583de3ff2339d8b',
+ 'wiki.tn.vec': '39f45f3fa86645bb25c54150204abcd51cc1048c',
+ 'wiki.to.vec': '64d512665b55e9ef9a3915e8167347be79310fa0',
+ 'wiki.tpi.vec': '407b96d235f54f3e0be9dc23a3bab89c6593a621',
+ 'wiki.tr.vec': '13234aa1bf5f99e81d933482b3b83c3e4bf6c85e',
+ 'wiki.ts.vec': '00f8229e2f230afd388221c0f823a1de9fc0e443',
+ 'wiki.tt.vec': '913bb3a11da6f8142b3bbec3ef065162d9350f1d',
+ 'wiki.tum.vec': 'bfbe43364724af882a520d2edcc2ce049c7357cd',
+ 'wiki.tw.vec': 'f329b667d70d9f0b753e55e1b1579b5a5191d3bd',
+ 'wiki.ty.vec': 'b881f60b8c75a71864d9847a17961d368f3058fc',
+ 'wiki.tyv.vec': 'e8f9a36dc58e4108c553f96e247a877a099ab5ba',
+ 'wiki.udm.vec': '336a8526f22e177faac69573661dc9c3ce36591f',
+ 'wiki.ug.vec': '586d2febafaf17c9187c599ffd7b96e559103c34',
+ 'wiki.uk.vec': '77f7737b9f88eac2b3e130ea8abb8886336fd0c6',
+ 'wiki.ur.vec': 'cb8132102152a958df72bd3e25f1a72abb4c9c76',
+ 'wiki.uz.vec': '11c3a76dae12b454f693811e33ae2e60015743e2',
+ 'wiki.vec.vec': 'ae4b055fba21974e56beecab3a95f9dc24a62fd0',
+ 'wiki.vep.vec': 'a38a781fde24f4d7b52aa8bc450b9949dd4e1808',
+ 'wiki.ve.vec': 'b7d2947501de1c30a9f8496d5efae20c051104e1',
+ 'wiki.vi.vec': 'bc84245b52b2e212e28dc6856c0693ce9845a9c5',
+ 'wiki.vls.vec': '07e8636908c057b9870ce4b98c7130d460cf882a',
+ 'wiki.vo.vec': 'c830988b6965bfce2f932b1be193f7d1f755f411',
+ 'wiki.war.vec': '1f5d443d6f612b59a53820dd6f39fd886a6ad30f',
+ 'wiki.wa.vec': '18f9ca1a585e1d18c3630029141a2e19d7d34a8e',
+ 'wiki.wo.vec': '2ad96a7a9e640bc0dbcf316b1f414b92802dcb8e',
+ 'wiki.wuu.vec': 'e1cbae1d3ad52329d0f36ada764016fbacf07049',
+ 'wiki.xal.vec': 'b738222d84cb8c8fdb2b30a7219aa5d3bdc2f61c',
+ 'wiki.xh.vec': 'bf37f741b0b75953281d11df2b4d80100df9e666',
+ 'wiki.xmf.vec': 'dc1923cfd1a7002d5d60426b60e6756854ab4a14',
+ 'wiki.yi.vec': '299d61958b7dcc38774768f1489121384726d860',
+ 'wiki.yo.vec': 'e35c8aff2924ba07936be9d0d94bd298f09702a4',
+ 'wiki.za.vec': 'e3a0e58bd2e5b1891c71f1f7e37ff71997a20361',
+ 'wiki.zea.vec': 'ee12db26aab3f2b3b2745a298ef414e7aeb5a058',
+ 'wiki.zh_classical.vec': '840981c83dd8e5cb02d1cd695e2fe0870941316c',
+ 'wiki.zh_min_nan.vec': 'f91ccb013e200bb7ed560082ddf4bdd9c2f315bb',
+ 'wiki.zh.vec': '117ab34faa80e381641fbabf3a24bc8cfba44050',
+ 'wiki.zh_yue.vec': 'd2ac1ab9eb1a908797644f83f259c90cb3c1a350',
+ 'wiki.zu.vec': '4b244b9697a8280e6646842c5fc81bb3a6bc8ec7'}
diff --git a/python/mxnet/gluon/text/embedding.py b/python/mxnet/gluon/text/embedding.py
new file mode 100644
index 00000000000..1839212ee82
--- /dev/null
+++ b/python/mxnet/gluon/text/embedding.py
@@ -0,0 +1,581 @@
+# 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.
+
+# coding: utf-8
+# pylint: disable=consider-iterating-dictionary
+
+"""Text token embedding."""
+from __future__ import absolute_import
+from __future__ import print_function
+
+import io
+import logging
+import os
+import tarfile
+import warnings
+import zipfile
+
+from . import _constants as C
+from ... import nd
+from ... import registry
+from ..utils import check_sha1, download, _get_repo_file_url
+
+
+def register(embedding_cls):
+ """Registers a new token embedding.
+
+
+ Once an embedding is registered, we can create an instance of this embedding with
+ :func:`~mxnet.gluon.text.embedding.create`.
+
+
+ Examples
+ --------
+ >>> @mxnet.gluon.text.embedding.register
+ ... class MyTextEmbed(mxnet.gluon.text.embedding.TokenEmbedding):
+ ... def __init__(self, file_name='my_pretrain_file'):
+ ... pass
+ >>> embed = mxnet.gluon.text.embedding.create('MyTokenEmbed')
+ >>> print(type(embed))
+ <class '__main__.MyTokenEmbed'>
+ """
+
+ register_text_embedding = registry.get_register_func(TokenEmbedding, 'token embedding')
+ return register_text_embedding(embedding_cls)
+
+
+def create(embedding_name, **kwargs):
+ """Creates an instance of token embedding.
+
+
+ Creates a token embedding instance by loading embedding vectors from an externally hosted
+ pre-trained token embedding file, such as those of GloVe and FastText. To get all the valid
+ `embedding_name` and `file_name`, use `mxnet.gluon.text.embedding.get_file_names()`.
+
+
+ Parameters
+ ----------
+ embedding_name : str
+ The token embedding name (case-insensitive).
+
+
+ Returns
+ -------
+ An instance of `mxnet.gluon.text.embedding.TokenEmbedding`:
+ A token embedding instance that loads embedding vectors from an externally hosted
+ pre-trained token embedding file.
+ """
+
+ create_text_embedding = registry.get_create_func(TokenEmbedding, 'token embedding')
+ return create_text_embedding(embedding_name, **kwargs)
+
+
+def get_file_names(embedding_name=None):
+ """Get valid token embedding names and their pre-trained file names.
+
+
+ To load token embedding vectors from an externally hosted pre-trained token embedding file,
+ such as those of GloVe and FastText, one should use
+ `mxnet.gluon.text.embedding.create(embedding_name, file_name)`. This method returns all the
+ valid names of `file_name` for the specified `embedding_name`. If `embedding_name` is set to
+ None, this method returns all the valid names of `embedding_name` with their associated
+ `file_name`.
+
+
+ Parameters
+ ----------
+ embedding_name : str or None, default None
+ The pre-trained token embedding name.
+
+
+ Returns
+ -------
+ dict or list:
+ A list of all the valid pre-trained token embedding file names (`file_name`) for the
+ specified token embedding name (`embedding_name`). If the text embeding name is set to None,
+ returns a dict mapping each valid token embedding name to a list of valid pre-trained files
+ (`file_name`). They can be plugged into
+ `mxnet.gluon.text.embedding.create(embedding_name, file_name)`.
+ """
+
+ text_embedding_reg = registry.get_registry(TokenEmbedding)
+
+ if embedding_name is not None:
+ if embedding_name not in text_embedding_reg:
+ raise KeyError('Cannot find `embedding_name` %s. Use '
+ '`get_file_names(embedding_name=None).keys()` to get all the valid'
+ 'embedding names.' % embedding_name)
+ return list(text_embedding_reg[embedding_name].pretrained_file_name_sha1.keys())
+ else:
+ return {embedding_name: list(embedding_cls.pretrained_file_name_sha1.keys())
+ for embedding_name, embedding_cls in registry.get_registry(TokenEmbedding).items()}
+
+
+class TokenEmbedding(object):
+ """Token embedding base class.
+
+
+ To load token embedding from an externally hosted pre-trained token embedding file, such as
+ those of GloVe and FastText, use :func:`~mxnet.gluon.text.embedding.create(embedding_name,
+ file_name)`. To get all the available `embedding_name` and `file_name`, use
+ :func:`~mxnet.gluon.text.embedding.get_file_names()`.
+
+ Alternatively, to load embedding vectors from a custom pre-trained token embedding file, use
+ :func:`~mxnet.gluon.text.embedding.from_file()`.
+
+ For every unknown token, if its representation `self.unknown_token` is encountered in the
+ pre-trained token embedding file, index 0 of `self.idx_to_vec` maps to the pre-trained token
+ embedding vector loaded from the file; otherwise, index 0 of `self.idx_to_vec` maps to the
+ token embedding vector initialized by `init_unknown_vec`.
+
+ If a token is encountered multiple times in the pre-trained token embedding file, only the
+ first-encountered token embedding vector will be loaded and the rest will be skipped.
+
+
+ Parameters
+ ----------
+ unknown_token : hashable object, default '<unk>'
+ The representation for any unknown token. In other words, any unknown token will be indexed
+ as the same representation.
+
+
+ Properties
+ ----------
+ idx_to_vec : mxnet.ndarray.NDArray
+ For all the indexed tokens in this embedding, this NDArray maps each token's index to an
+ embedding vector.
+ unknown_token : hashable object
+ The representation for any unknown token. In other words, any unknown token will be indexed
+ as the same representation.
+ """
+
+ def __init__(self, unknown_token='<unk>'):
+ self._unknown_token = unknown_token
+ self._idx_to_token = [unknown_token]
+ self._token_to_idx = {token: idx for idx, token in enumerate(self._idx_to_token)}
+ self._idx_to_vec = None
+
+ @classmethod
+ def _get_download_file_name(cls, file_name):
+ return file_name
+
+ @classmethod
+ def _get_pretrained_file_url(cls, pretrained_file_name):
+ cls_name = cls.__name__.lower()
+
+ namespace = 'gluon/embeddings/{}'.format(cls_name)
+ return _get_repo_file_url(namespace, cls._get_download_file_name(pretrained_file_name))
+
+ @classmethod
+ def _get_pretrained_file(cls, embedding_root, pretrained_file_name):
+ cls_name = cls.__name__.lower()
+ embedding_root = os.path.expanduser(embedding_root)
+ url = cls._get_pretrained_file_url(pretrained_file_name)
+
+ embedding_dir = os.path.join(embedding_root, cls_name)
+ pretrained_file_path = os.path.join(embedding_dir, pretrained_file_name)
+ downloaded_file = os.path.basename(url)
+ downloaded_file_path = os.path.join(embedding_dir, downloaded_file)
+
+ expected_file_hash = cls.pretrained_file_name_sha1[pretrained_file_name]
+
+ if hasattr(cls, 'pretrained_archive_name_sha1'):
+ expected_downloaded_hash = \
+ cls.pretrained_archive_name_sha1[downloaded_file]
+ else:
+ expected_downloaded_hash = expected_file_hash
+
+ if not os.path.exists(pretrained_file_path) \
+ or not check_sha1(pretrained_file_path, expected_file_hash):
+ download(url, downloaded_file_path, sha1_hash=expected_downloaded_hash)
+
+ ext = os.path.splitext(downloaded_file)[1]
+ if ext == '.zip':
+ with zipfile.ZipFile(downloaded_file_path, 'r') as zf:
+ zf.extractall(embedding_dir)
+ elif ext == '.gz':
+ with tarfile.open(downloaded_file_path, 'r:gz') as tar:
+ tar.extractall(path=embedding_dir)
+ return pretrained_file_path
+
+ def _load_embedding(self, pretrained_file_path, elem_delim, init_unknown_vec, encoding='utf8'):
+ """Load embedding vectors from a pre-trained token embedding file.
+
+
+ For every unknown token, if its representation `self.unknown_token` is encountered in the
+ pre-trained token embedding file, index 0 of `self.idx_to_vec` maps to the pre-trained token
+ embedding vector loaded from the file; otherwise, index 0 of `self.idx_to_vec` maps to the
+ text embedding vector initialized by `init_unknown_vec`.
+
+ If a token is encountered multiple times in the pre-trained text embedding file, only the
+ first-encountered token embedding vector will be loaded and the rest will be skipped.
+ """
+
+ pretrained_file_path = os.path.expanduser(pretrained_file_path)
+
+ if not os.path.isfile(pretrained_file_path):
+ raise ValueError('`pretrained_file_path` must be a valid path to the pre-trained '
+ 'token embedding file.')
+
+ logging.info('Loading pre-trained token embedding vectors from %s', pretrained_file_path)
+ vec_len = None
+ all_elems = []
+ tokens = set()
+ loaded_unknown_vec = None
+ line_num = 0
+ with io.open(pretrained_file_path, 'r', encoding=encoding) as f:
+ for line in f:
+ line_num += 1
+ elems = line.rstrip().split(elem_delim)
+
+ assert len(elems) > 1, 'At line %d of the pre-trained token embedding file: the ' \
+ 'data format of the pre-trained token embedding file %s ' \
+ 'is unexpected.' % (line_num, pretrained_file_path)
+
+ token, elems = elems[0], [float(i) for i in elems[1:]]
+
+ if token == self.unknown_token and loaded_unknown_vec is None:
+ loaded_unknown_vec = elems
+ tokens.add(self.unknown_token)
+ elif token in tokens:
+ warnings.warn('At line %d of the pre-trained token embedding file: the '
+ 'embedding vector for token %s has been loaded and a duplicate '
+ 'embedding for the same token is seen and skipped.' %
+ (line_num, token))
+ elif len(elems) == 1:
+ warnings.warn('At line %d of the pre-trained token embedding file: token %s '
+ 'with 1-dimensional vector %s is likely a header and is '
+ 'skipped.' % (line_num, token, elems))
+ else:
+ if vec_len is None:
+ vec_len = len(elems)
+ # Reserve a vector slot for the unknown token at the very beggining because
+ # the unknown token index is 0.
+ all_elems.extend([0] * vec_len)
+ else:
+ assert len(elems) == vec_len, \
+ 'At line %d of the pre-trained token embedding file: the dimension ' \
+ 'of token %s is %d but the dimension of previous tokens is %d. ' \
+ 'Dimensions of all the tokens must be the same.' \
+ % (line_num, token, len(elems), vec_len)
+ all_elems.extend(elems)
+ self._idx_to_token.append(token)
+ self._token_to_idx[token] = len(self._idx_to_token) - 1
+ tokens.add(token)
+
+ self._idx_to_vec = nd.array(all_elems).reshape((-1, vec_len))
+
+ if loaded_unknown_vec is None:
+ self._idx_to_vec[C.UNKNOWN_IDX] = init_unknown_vec(shape=vec_len)
+ else:
+ self._idx_to_vec[C.UNKNOWN_IDX] = nd.array(loaded_unknown_vec)
+
+ @property
+ def idx_to_vec(self):
+ return self._idx_to_vec
+
+ @property
+ def unknown_token(self):
+ return self._unknown_token
+
+ def __contains__(self, x):
+ return x in self._token_to_idx
+
+ def __getitem__(self, tokens):
+ """Looks up embedding vectors of text tokens.
+
+
+ Parameters
+ ----------
+ tokens : str or list of strs
+ A token or a list of tokens.
+
+
+ Returns
+ -------
+ mxnet.ndarray.NDArray:
+ The embedding vector(s) of the token(s). According to numpy conventions, if `tokens` is
+ a string, returns a 1-D NDArray (vector); if `tokens` is a list of
+ strings, returns a 2-D NDArray (matrix) of shape=(len(tokens), vec_len).
+ """
+
+ to_reduce = not isinstance(tokens, (list, tuple))
+ if to_reduce:
+ tokens = [tokens]
+
+ indices = [self._token_to_idx.get(token, C.UNKNOWN_IDX) for token in tokens]
+
+ vecs = nd.Embedding(nd.array(indices), self.idx_to_vec, self.idx_to_vec.shape[0],
+ self.idx_to_vec.shape[1])
+
+ return vecs[0] if to_reduce else vecs
+
+ def __setitem__(self, tokens, new_vectors):
+ """Updates embedding vectors for tokens.
+
+
+ Parameters
+ ----------
+ tokens : str or a list of strs
+ A token or a list of tokens whose embedding vector are to be updated.
+ new_vectors : mxnet.ndarray.NDArray
+ An NDArray to be assigned to the embedding vectors of `tokens`. Its length must be equal
+ to the number of `tokens` and its width must be equal to the dimension of embedding of
+ the glossary. If `tokens` is a singleton, it must be 1-D or 2-D. If `tokens` is a list
+ of multiple strings, it must be 2-D.
+ """
+
+ assert self._idx_to_vec is not None, '`idx_to_vec` has not been initialized.'
+
+ if not isinstance(tokens, list) or len(tokens) == 1:
+ assert isinstance(new_vectors, nd.NDArray) and len(new_vectors.shape) in [1, 2], \
+ '`new_vectors` must be a 1-D or 2-D NDArray if `tokens` is a singleton.'
+ if not isinstance(tokens, list):
+ tokens = [tokens]
+ if len(new_vectors.shape) == 1:
+ new_vectors = new_vectors.expand_dims(0)
+
+ else:
+ assert isinstance(new_vectors, nd.NDArray) and len(new_vectors.shape) == 2, \
+ '`new_vectors` must be a 2-D NDArray if `tokens` is a list of multiple strings.'
+ assert new_vectors.shape == (len(tokens), self._idx_to_vec.shape[1]), \
+ 'The length of new_vectors must be equal to the number of tokens and the width of' \
+ 'new_vectors must be equal to the dimension of embedding of the glossary.'
+
+ indices = []
+ for token in tokens:
+ if token in self._token_to_idx:
+ indices.append(self._token_to_idx[token])
+ else:
+ raise ValueError('Token %s is unknown. To update the embedding vector for an '
+ 'unknown token, please specify it explicitly as the '
+ '`unknown_token` %s in `tokens`. This is to avoid unintended '
+ 'updates.' % (token, self._idx_to_token[C.UNKNOWN_IDX]))
+
+ self._idx_to_vec[nd.array(indices)] = new_vectors
+
+ @classmethod
+ def _check_pretrained_file_names(cls, file_name):
+ """Checks if a pre-trained token embedding file name is valid.
+
+
+ Parameters
+ ----------
+ file_name : str
+ The pre-trained token embedding file.
+ """
+
+ embedding_name = cls.__name__.lower()
+ if file_name not in cls.pretrained_file_name_sha1:
+ raise KeyError('Cannot find pre-trained file %s for token embedding %s. Valid '
+ 'pre-trained file names for embedding %s: %s' %
+ (file_name, embedding_name, embedding_name,
+ ', '.join(cls.pretrained_file_name_sha1.keys())))
+
+ @staticmethod
+ def from_file(file_path, elem_delim=' ', encoding='utf8', init_unknown_vec=nd.zeros, **kwargs):
+ """Creates a user-defined token embedding from a pre-trained embedding file.
+
+
+ This is to load embedding vectors from a user-defined pre-trained token embedding file.
+ For example, if `elem_delim` = ' ', the expected format of a custom pre-trained token
+ embedding file may look like:
+
+ 'hello 0.1 0.2 0.3 0.4 0.5\\\\nworld 1.1 1.2 1.3 1.4 1.5\\\\n'
+
+ where embedding vectors of words `hello` and `world` are [0.1, 0.2, 0.3, 0.4, 0.5] and
+ [1.1, 1.2, 1.3, 1.4, 1.5] respectively.
+
+
+ Parameters
+ ----------
+ file_path : str
+ The path to the user-defined pre-trained token embedding file.
+ elem_delim : str, default ' '
+ The delimiter for splitting a token and every embedding vector element value on the same
+ line of the custom pre-trained token embedding file.
+ encoding : str, default 'utf8'
+ The encoding scheme for reading the custom pre-trained token embedding file.
+ init_unknown_vec : callback
+ The callback used to initialize the embedding vector for the unknown token.
+
+
+ Returns
+ -------
+ instance of `~mxnet.gluon.text.embedding.TokenEmbedding`
+ The user-defined token embedding instance.
+ """
+ embedding = TokenEmbedding(**kwargs)
+ embedding._load_embedding(file_path, elem_delim, init_unknown_vec, encoding)
+
+ return embedding
+
+
+@register
+class GloVe(TokenEmbedding):
+ """The GloVe word embedding.
+
+
+ GloVe is an unsupervised learning algorithm for obtaining vector representations for words.
+ Training is performed on aggregated global word-word co-occurrence statistics from a corpus, and
+ the resulting representations showcase interesting linear substructures of the word vector
+ space. (Source from https://nlp.stanford.edu/projects/glove/)
+
+ Reference:
+
+ GloVe: Global Vectors for Word Representation.
+ Jeffrey Pennington, Richard Socher, and Christopher D. Manning.
+ https://nlp.stanford.edu/pubs/glove.pdf
+
+ Website:
+
+ https://nlp.stanford.edu/projects/glove/
+
+ To get the updated URLs to the externally hosted pre-trained token embedding
+ files, visit https://nlp.stanford.edu/projects/glove/
+
+ License for pre-trained embedding:
+
+ https://opendatacommons.org/licenses/pddl/
+
+
+ Parameters
+ ----------
+ file_name : str, default 'glove.6B.50d.txt'
+ The name of the pre-trained token embedding file.
+ embedding_root : str, default os.path.join('~', '.mxnet', 'embedding')
+ The root directory for storing embedding-related files.
+ init_unknown_vec : callback
+ The callback used to initialize the embedding vector for the unknown token.
+
+
+ Properties
+ ----------
+ idx_to_vec : mxnet.ndarray.NDArray
+ For all the indexed tokens in this embedding, this NDArray maps each token's index to an
+ embedding vector.
+ unknown_token : hashable object
+ The representation for any unknown token. In other words, any unknown token will be indexed
+ as the same representation.
+ """
+
+ # Map a pre-trained token embedding archive file and its SHA-1 hash.
+ pretrained_archive_name_sha1 = C.GLOVE_PRETRAINED_FILE_SHA1
+
+ # Map a pre-trained token embedding file and its SHA-1 hash.
+ pretrained_file_name_sha1 = C.GLOVE_PRETRAINED_ARCHIVE_SHA1
+
+ @classmethod
+ def _get_download_file_name(cls, file_name):
+ # Map a pre-trained embedding file to its archive to download.
+ src_archive = {archive.split('.')[1]: archive for archive in
+ GloVe.pretrained_archive_name_sha1.keys()}
+ archive = src_archive[file_name.split('.')[1]]
+ return archive
+
+ def __init__(self, file_name='glove.6B.50d.txt',
+ embedding_root=os.path.join('~', '.mxnet', 'embedding'),
+ init_unknown_vec=nd.zeros, **kwargs):
+ GloVe._check_pretrained_file_names(file_name)
+
+ super(GloVe, self).__init__(**kwargs)
+ pretrained_file_path = GloVe._get_pretrained_file(embedding_root, file_name)
+
+ self._load_embedding(pretrained_file_path, ' ', init_unknown_vec)
+
+
+@register
+class FastText(TokenEmbedding):
+ """The fastText word embedding.
+
+
+ FastText is an open-source, free, lightweight library that allows users to learn text
+ representations and text classifiers. It works on standard, generic hardware. Models can later
+ be reduced in size to even fit on mobile devices. (Source from https://fasttext.cc/)
+
+
+ References:
+
+ Enriching Word Vectors with Subword Information.
+ Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov.
+ https://arxiv.org/abs/1607.04606
+
+ Bag of Tricks for Efficient Text Classification.
+ Armand Joulin, Edouard Grave, Piotr Bojanowski, and Tomas Mikolov.
+ https://arxiv.org/abs/1607.01759
+
+ FastText.zip: Compressing text classification models.
+ Armand Joulin, Edouard Grave, Piotr Bojanowski, Matthijs Douze, Herve Jegou, and Tomas Mikolov.
+ https://arxiv.org/abs/1612.03651
+
+ For 'wiki.multi' embedding:
+ Word Translation Without Parallel Data
+ Alexis Conneau, Guillaume Lample, Marc'Aurelio Ranzato, Ludovic Denoyer, and Herve Jegou.
+ https://arxiv.org/abs/1710.04087
+
+ Website:
+
+ https://fasttext.cc/
+
+ To get the updated URLs to the externally hosted pre-trained token embedding files, visit
+ https://github.com/facebookresearch/fastText/blob/master/pretrained-vectors.md
+
+ License for pre-trained embedding:
+
+ https://creativecommons.org/licenses/by-sa/3.0/
+
+
+ Parameters
+ ----------
+ file_name : str, default 'glove.6B.50d.txt'
+ The name of the pre-trained token embedding file.
+ embedding_root : str, default os.path.join('~', '.mxnet', 'embedding')
+ The root directory for storing embedding-related files.
+ init_unknown_vec : callback
+ The callback used to initialize the embedding vector for the unknown token.
+
+
+ Properties
+ ----------
+ idx_to_vec : mxnet.ndarray.NDArray
+ For all the indexed tokens in this embedding, this NDArray maps each token's index to an
+ embedding vector.
+ unknown_token : hashable object
+ The representation for any unknown token. In other words, any unknown token will be indexed
+ as the same representation.
+ """
+
+ # Map a pre-trained token embedding archive file and its SHA-1 hash.
+ pretrained_archive_name_sha1 = C.FAST_TEXT_ARCHIVE_SHA1
+
+ # Map a pre-trained token embedding file and its SHA-1 hash.
+ pretrained_file_name_sha1 = C.FAST_TEXT_FILE_SHA1
+
+ @classmethod
+ def _get_download_file_name(cls, file_name):
+ # Map a pre-trained embedding file to its archive to download.
+ return '.'.join(file_name.split('.')[:-1]) + '.zip'
+
+ def __init__(self, file_name='wiki.simple.vec',
+ embedding_root=os.path.join('~', '.mxnet', 'embedding'),
+ init_unknown_vec=nd.zeros, **kwargs):
+ FastText._check_pretrained_file_names(file_name)
+
+ super(FastText, self).__init__(**kwargs)
+ pretrained_file_path = FastText._get_pretrained_file(embedding_root, file_name)
+
+ self._load_embedding(pretrained_file_path, ' ', init_unknown_vec)
diff --git a/python/mxnet/gluon/text/utils.py b/python/mxnet/gluon/text/utils.py
new file mode 100644
index 00000000000..a812be8c2df
--- /dev/null
+++ b/python/mxnet/gluon/text/utils.py
@@ -0,0 +1,77 @@
+# 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.
+
+# coding: utf-8
+
+"""Utilities for text data processing."""
+from __future__ import absolute_import
+from __future__ import print_function
+
+import collections
+import re
+
+
+def count_tokens_from_str(source_str, token_delim=' ', seq_delim='\n', to_lower=False,
+ counter_to_update=None):
+ """Counts tokens in the specified string.
+
+
+ For token_delim='(td)' and seq_delim='(sd)', a specified string of two sequences of tokens may
+ look like::
+
+ (td)token1(td)token2(td)token3(td)(sd)(td)token4(td)token5(td)(sd)
+
+
+ Parameters
+ ----------
+ source_str : str
+ A source string of tokens.
+ token_delim : str, default ' '
+ A token delimiter.
+ seq_delim : str, default '\\\\n'
+ A sequence delimiter.
+ to_lower : bool, default False
+ Whether to convert the source source_str to the lower case.
+ counter_to_update : collections.Counter or None, default None
+ The collections.Counter instance to be updated with the token counts of `source_str`. If
+ None, return a new collections.Counter instance counting tokens from `source_str`.
+
+
+ Returns
+ -------
+ collections.Counter
+ The `counter_to_update` collections.Counter instance after being updated with the token
+ counts of `source_str`. If `counter_to_update` is None, return a new collections.Counter
+ instance counting tokens from `source_str`.
+
+
+ Examples
+ --------
+ >>> source_str = ' Life is great ! \\n life is good . \\n'
+ >>> count_tokens_from_str(token_line, ' ', '\\n', True)
+ Counter({'!': 1, '.': 1, 'good': 1, 'great': 1, 'is': 2, 'life': 2})
+ """
+
+ source_str = filter(None, re.split(token_delim + '|' + seq_delim, source_str))
+ if to_lower:
+ source_str = [t.lower() for t in source_str]
+
+ if counter_to_update is None:
+ return collections.Counter(source_str)
+ else:
+ counter_to_update.update(source_str)
+ return counter_to_update
diff --git a/python/mxnet/gluon/text/vocab.py b/python/mxnet/gluon/text/vocab.py
new file mode 100644
index 00000000000..aa962af2428
--- /dev/null
+++ b/python/mxnet/gluon/text/vocab.py
@@ -0,0 +1,323 @@
+# 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.
+
+# coding: utf-8
+# pylint: disable=consider-iterating-dictionary
+
+"""Vocabulary."""
+from __future__ import absolute_import
+from __future__ import print_function
+
+import collections
+from ... import nd
+
+from . import _constants as C
+from . import embedding as ebd
+
+
+class Vocabulary(object):
+ """Indexing and embedding attachment for text tokens.
+
+
+ Parameters
+ ----------
+ counter : collections.Counter or None, default None
+ Counts text token frequencies in the text data. Its keys will be indexed according to
+ frequency thresholds such as `max_size` and `min_freq`. Keys of `counter`,
+ `unknown_token`, and values of `reserved_tokens` must be of the same hashable type.
+ Examples: str, int, and tuple.
+ max_size : None or int, default None
+ The maximum possible number of the most frequent tokens in the keys of `counter` that can be
+ indexed. Note that this argument does not count any token from `reserved_tokens`. Suppose
+ that there are different keys of `counter` whose frequency are the same, if indexing all of
+ them will exceed this argument value, such keys will be indexed one by one according to
+ their __cmp__() order until the frequency threshold is met. If this argument is None or
+ larger than its largest possible value restricted by `counter` and `reserved_tokens`, this
+ argument has no effect.
+ min_freq : int, default 1
+ The minimum frequency required for a token in the keys of `counter` to be indexed.
+ unknown_token : hashable object, default '<unk>'
+ The representation for any unknown token. In other words, any unknown token will be indexed
+ as the same representation. Keys of `counter`, `unknown_token`, and values of
+ `reserved_tokens` must be of the same hashable type. Examples: str, int, and tuple.
+ reserved_tokens : list of hashable objects or None, default None
+ A list of reserved tokens (excluding `unknown_token`) that will always be indexed, such as
+ special symbols representing padding, beginning of sentence, and end of sentence. It cannot
+ contain `unknown_token` or duplicate reserved tokens. Keys of `counter`, `unknown_token`,
+ and values of `reserved_tokens` must be of the same hashable type. Examples: str, int, and
+ tuple.
+
+
+ Properties
+ ----------
+ embedding : instance of :class:`~mxnet.gluon.text.embedding.TokenEmbedding`
+ The embedding of the indexed tokens.
+ idx_to_token : list of strs
+ A list of indexed tokens where the list indices and the token indices are aligned.
+ reserved_tokens : list of strs or None
+ A list of reserved tokens that will always be indexed.
+ token_to_idx : dict mapping str to int
+ A dict mapping each token to its index integer.
+ unknown_token : hashable object
+ The representation for any unknown token. In other words, any unknown token will be indexed
+ as the same representation.
+
+
+ Examples
+ --------
+
+ >>> text_data = " hello world \\\\n hello nice world \\\\n hi world \\\\n"
+ >>> counter = text.count_tokens_from_str(text_data)
+ >>> my_vocab = text.Vocabulary(counter)
+ >>> fasttext = text.embedding.create('fasttext', file_name='wiki.simple.vec')
+ >>> my_vocab.set_embedding(fasttext)
+ >>> my_vocab.embedding[['hello', 'world']]
+ [[ 3.95669997e-01 2.14540005e-01 -3.53889987e-02 -2.42990002e-01
+ ...
+ -7.54180014e-01 -3.14429998e-01 2.40180008e-02 -7.61009976e-02]
+ [ 1.04440004e-01 -1.08580001e-01 2.72119999e-01 1.32990003e-01
+ ...
+ -3.73499990e-01 5.67310005e-02 5.60180008e-01 2.90190000e-02]]
+ <NDArray 2x300 @cpu(0)>
+
+ >>> my_vocab[['hello', 'world']]
+ [2, 1]
+
+ >>> input_dim, output_dim = my_vocab.embedding.idx_to_vec.shape
+ >>> layer = gluon.nn.Embedding(input_dim, output_dim)
+ >>> layer.initialize()
+ >>> layer.weight.set_data(my_vocab.embedding.idx_to_vec)
+ >>> layer(nd.array([2, 1]))
+ [[ 3.95669997e-01 2.14540005e-01 -3.53889987e-02 -2.42990002e-01
+ ...
+ -7.54180014e-01 -3.14429998e-01 2.40180008e-02 -7.61009976e-02]
+ [ 1.04440004e-01 -1.08580001e-01 2.72119999e-01 1.32990003e-01
+ ...
+ -3.73499990e-01 5.67310005e-02 5.60180008e-01 2.90190000e-02]]
+ <NDArray 2x300 @cpu(0)>
+
+ >>> glove = text.embedding.create('glove', file_name='glove.6B.50d.txt')
+ >>> my_vocab.set_embedding(glove)
+ >>> my_vocab.embedding[['hello', 'world']]
+ [[ -0.38497001 0.80092001
+ ...
+ 0.048833 0.67203999]
+ [ -0.41486001 0.71847999
+ ...
+ -0.37639001 -0.67541999]]
+ <NDArray 2x50 @cpu(0)>
+
+ """
+
+ def __init__(self, counter=None, max_size=None, min_freq=1, unknown_token='<unk>',
+ reserved_tokens=None):
+
+ # Sanity checks.
+ assert min_freq > 0, '`min_freq` must be set to a positive value.'
+
+ if reserved_tokens is not None:
+ reserved_token_set = set(reserved_tokens)
+ assert unknown_token not in reserved_token_set, \
+ '`reserved_token` cannot contain `unknown_token`.'
+ assert len(reserved_token_set) == len(reserved_tokens), \
+ '`reserved_tokens` cannot contain duplicate reserved tokens.'
+
+ self._index_unknown_and_reserved_tokens(unknown_token, reserved_tokens)
+
+ if counter is not None:
+ self._index_counter_keys(counter, unknown_token, reserved_tokens, max_size, min_freq)
+
+ self._embedding = None
+
+ def _index_unknown_and_reserved_tokens(self, unknown_token, reserved_tokens):
+ """Indexes unknown and reserved tokens."""
+
+ self._unknown_token = unknown_token
+ self._idx_to_token = [unknown_token]
+
+ if reserved_tokens is None:
+ self._reserved_tokens = None
+ else:
+ self._reserved_tokens = reserved_tokens[:]
+ self._idx_to_token.extend(reserved_tokens)
+
+ self._token_to_idx = {token: idx for idx, token in enumerate(self._idx_to_token)}
+
+ def _index_counter_keys(self, counter, unknown_token, reserved_tokens, max_size,
+ min_freq):
+ """Indexes keys of `counter`.
+
+
+ Indexes keys of `counter` according to frequency thresholds such as `max_size` and
+ `min_freq`.
+ """
+
+ assert isinstance(counter, collections.Counter), \
+ '`counter` must be an instance of collections.Counter.'
+
+ unknown_and_reserved_tokens = set(reserved_tokens) if reserved_tokens is not None else set()
+ unknown_and_reserved_tokens.add(unknown_token)
+
+ token_freqs = sorted(counter.items(), key=lambda x: x[0])
+ token_freqs.sort(key=lambda x: x[1], reverse=True)
+
+ token_cap = len(unknown_and_reserved_tokens) + (
+ len(counter) if max_size is None else max_size)
+
+ for token, freq in token_freqs:
+ if freq < min_freq or len(self._idx_to_token) == token_cap:
+ break
+ if token not in unknown_and_reserved_tokens:
+ self._idx_to_token.append(token)
+ self._token_to_idx[token] = len(self._idx_to_token) - 1
+
+ @property
+ def embedding(self):
+ return self._embedding
+
+ @property
+ def idx_to_token(self):
+ return self._idx_to_token
+
+ @property
+ def reserved_tokens(self):
+ return self._reserved_tokens
+
+ @property
+ def token_to_idx(self):
+ return self._token_to_idx
+
+ @property
+ def unknown_token(self):
+ return self._unknown_token
+
+ def __contains__(self, token):
+ """Checks whether a text token exists in the vocabulary.
+
+
+ Parameters
+ ----------
+ token : str
+ A text token.
+
+
+ Returns
+ -------
+ bool
+ Whether the text token exists in the vocabulary (including `unknown_token`).
+ """
+
+ return token in self._token_to_idx
+
+ def __getitem__(self, tokens):
+ """Looks up indices of text tokens according to the vocabulary.
+
+
+ Parameters
+ ----------
+ tokens : str or list of strs
+ A source token or tokens to be converted.
+
+
+ Returns
+ -------
+ int or list of ints
+ A token index or a list of token indices according to the vocabulary.
+ """
+
+ if not isinstance(tokens, (list, tuple)):
+ return self._token_to_idx[tokens] if tokens in self._token_to_idx \
+ else C.UNKNOWN_IDX
+ else:
+ return [self._token_to_idx[token] if token in self._token_to_idx
+ else C.UNKNOWN_IDX for token in tokens]
+
+ def __len__(self):
+ return len(self._idx_to_token)
+
+ def set_embedding(self, *embeddings):
+ """Attaches embeddings to the indexed text tokens.
+
+
+ Parameters
+ ----------
+ embeddings : None or tuple of :class:`~mxnet.gluon.text.embedding.TokenEmbedding` instances
+ The embedding to be attached to the indexed tokens. If a tuple of multiple embeddings
+ are provided, their embedding vectors will be concatenated for the same token.
+ """
+
+ if len(embeddings) == 1 and embeddings[0] is None:
+ self._embedding = None
+ return
+
+ for embedding in embeddings:
+ assert isinstance(embedding, ebd.TokenEmbedding), \
+ 'The argument `embeddings` must be an instance or a list of instances of ' \
+ '`mxnet.gluon.text.embedding.TokenEmbedding`.'
+
+ new_embedding = ebd.TokenEmbedding(self.unknown_token)
+ new_embedding._token_to_idx = self.token_to_idx
+ new_embedding._idx_to_token = self.idx_to_token
+
+ new_vec_len = sum(embedding.idx_to_vec.shape[1] for embedding in embeddings
+ if embedding and embedding.idx_to_vec is not None)
+ new_idx_to_vec = nd.zeros(shape=(len(self), new_vec_len))
+
+ col_start = 0
+ # Concatenate all the embedding vectors in embedding.
+ for embedding in embeddings:
+ if embedding and embedding.idx_to_vec is not None:
+ col_end = col_start + embedding.idx_to_vec.shape[1]
+ # Cancatenate vectors of the unknown token.
+ new_idx_to_vec[0, col_start:col_end] = embedding[0]
+ new_idx_to_vec[1:, col_start:col_end] = embedding[self._idx_to_token[1:]]
+ col_start = col_end
+
+ new_embedding._idx_to_vec = new_idx_to_vec
+ self._embedding = new_embedding
+
+ def to_tokens(self, indices):
+ """Converts token indices to tokens according to the vocabulary.
+
+
+ Parameters
+ ----------
+ indices : int or list of ints
+ A source token index or token indices to be converted.
+
+
+ Returns
+ -------
+ str or list of strs
+ A token or a list of tokens according to the vocabulary.
+ """
+
+ to_reduce = False
+ if not isinstance(indices, (list, tuple)):
+ indices = [indices]
+ to_reduce = True
+
+ max_idx = len(self._idx_to_token) - 1
+
+ tokens = []
+ for idx in indices:
+ if not isinstance(idx, int) or idx > max_idx:
+ raise ValueError('Token index %d in the provided `indices` is invalid.' % idx)
+ else:
+ tokens.append(self._idx_to_token[idx])
+
+ return tokens[0] if to_reduce else tokens
diff --git a/tests/python/unittest/test_gluon_text.py b/tests/python/unittest/test_gluon_text.py
new file mode 100644
index 00000000000..ad4f4a036e0
--- /dev/null
+++ b/tests/python/unittest/test_gluon_text.py
@@ -0,0 +1,681 @@
+# 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.
+
+# coding: utf-8
+
+from __future__ import absolute_import
+from __future__ import print_function
+
+from collections import Counter
+
+from common import assertRaises
+from mxnet import ndarray as nd
+from mxnet.gluon import text
+from mxnet.test_utils import *
+
+
+def _get_test_str_of_tokens(token_delim, seq_delim):
+ seq1 = token_delim + token_delim.join(['Life', 'is', 'great', '!']) + token_delim + seq_delim
+ seq2 = token_delim + token_delim.join(['life', 'is', 'good', '.']) + token_delim + seq_delim
+ seq3 = token_delim + token_delim.join(['life', "isn't", 'bad', '.']) + token_delim + seq_delim
+ seqs = seq1 + seq2 + seq3
+ return seqs
+
+
+def _test_count_tokens_from_str_with_delims(token_delim, seq_delim):
+ source_str = _get_test_str_of_tokens(token_delim, seq_delim)
+
+ cnt1 = text.count_tokens_from_str(source_str, token_delim, seq_delim, to_lower=False)
+ assert cnt1 == Counter(
+ {'is': 2, 'life': 2, '.': 2, 'Life': 1, 'great': 1, '!': 1, 'good': 1, "isn't": 1,
+ 'bad': 1})
+
+ cnt2 = text.count_tokens_from_str(source_str, token_delim, seq_delim, to_lower=True)
+ assert cnt2 == Counter(
+ {'life': 3, 'is': 2, '.': 2, 'great': 1, '!': 1, 'good': 1, "isn't": 1, 'bad': 1})
+
+ counter_to_update = Counter({'life': 2})
+
+ cnt3 = text.utils.count_tokens_from_str(source_str, token_delim, seq_delim, to_lower=False,
+ counter_to_update=counter_to_update.copy())
+ assert cnt3 == Counter(
+ {'is': 2, 'life': 4, '.': 2, 'Life': 1, 'great': 1, '!': 1, 'good': 1, "isn't": 1,
+ 'bad': 1})
+
+ cnt4 = text.count_tokens_from_str(source_str, token_delim, seq_delim, to_lower=True,
+ counter_to_update=counter_to_update.copy())
+ assert cnt4 == Counter(
+ {'life': 5, 'is': 2, '.': 2, 'great': 1, '!': 1, 'good': 1, "isn't": 1, 'bad': 1})
+
+
+def test_count_tokens_from_str():
+ _test_count_tokens_from_str_with_delims(' ', '\n')
+ _test_count_tokens_from_str_with_delims('IS', 'LIFE')
+
+
+def test_vocabulary_getitem():
+ counter = Counter(['a', 'b', 'b', 'c', 'c', 'c', 'some_word$'])
+
+ vocab = text.Vocabulary(counter, max_size=None, min_freq=1, unknown_token='<unk>',
+ reserved_tokens=None)
+
+ i1 = vocab['c']
+ assert i1 == 1
+
+ i2 = vocab[['c']]
+ assert i2 == [1]
+
+ i3 = vocab[['<unk>', 'non-exist']]
+ assert i3 == [0, 0]
+
+ i4 = vocab[['a', 'non-exist', 'a', 'b']]
+ assert i4 == [3, 0, 3, 2]
+
+
+def test_vocabulary_to_tokens():
+ counter = Counter(['a', 'b', 'b', 'c', 'c', 'c', 'some_word$'])
+
+ vocab = text.Vocabulary(counter, max_size=None, min_freq=1,unknown_token='<unknown>',
+ reserved_tokens=None)
+ i1 = vocab.to_tokens(1)
+ assert i1 == 'c'
+
+ i2 = vocab.to_tokens([1])
+ assert i2 == ['c']
+
+ i3 = vocab.to_tokens([0, 0])
+ assert i3 == ['<unknown>', '<unknown>']
+
+ i4 = vocab.to_tokens([3, 0, 3, 2])
+ assert i4 == ['a', '<unknown>', 'a', 'b']
+
+ assertRaises(ValueError, vocab.to_tokens, 5)
+ assertRaises(ValueError, vocab.to_tokens, [5, 6])
+
+
+def test_vocabulary():
+ counter = Counter(['a', 'b', 'b', 'c', 'c', 'c', 'some_word$'])
+
+ v1 = text.Vocabulary(counter, max_size=None, min_freq=1, unknown_token='<unk>',
+ reserved_tokens=None)
+ assert len(v1) == 5
+ assert v1.token_to_idx == {'<unk>': 0, 'c': 1, 'b': 2, 'a': 3, 'some_word$': 4}
+ assert v1.idx_to_token[1] == 'c'
+ assert v1.unknown_token == '<unk>'
+ assert v1.reserved_tokens is None
+ assert v1.embedding is None
+ assert 'a' in v1
+ assert v1.unknown_token in v1
+
+ v2 = text.Vocabulary(counter, max_size=None, min_freq=2, unknown_token='<unk>',
+ reserved_tokens=None)
+ assert len(v2) == 3
+ assert v2.token_to_idx == {'<unk>': 0, 'c': 1, 'b': 2}
+ assert v2.idx_to_token[1] == 'c'
+ assert v2.unknown_token == '<unk>'
+ assert v2.reserved_tokens is None
+ assert v2.embedding is None
+ assert 'a' not in v2
+ assert v2.unknown_token in v2
+
+ v3 = text.Vocabulary(counter, max_size=None, min_freq=100, unknown_token='<unk>',
+ reserved_tokens=None)
+ assert len(v3) == 1
+ assert v3.token_to_idx == {'<unk>': 0}
+ assert v3.idx_to_token[0] == '<unk>'
+ assert v3.unknown_token == '<unk>'
+ assert v3.reserved_tokens is None
+ assert v3.embedding is None
+ assert 'a' not in v3
+
+ v4 = text.Vocabulary(counter, max_size=2, min_freq=1, unknown_token='<unk>',
+ reserved_tokens=None)
+ assert len(v4) == 3
+ assert v4.token_to_idx == {'<unk>': 0, 'c': 1, 'b': 2}
+ assert v4.idx_to_token[1] == 'c'
+ assert v4.unknown_token == '<unk>'
+ assert v4.reserved_tokens is None
+ assert v4.embedding is None
+ assert 'a' not in v4
+
+ v5 = text.Vocabulary(counter, max_size=3, min_freq=1, unknown_token='<unk>',
+ reserved_tokens=None)
+ assert len(v5) == 4
+ assert v5.token_to_idx == {'<unk>': 0, 'c': 1, 'b': 2, 'a': 3}
+ assert v5.idx_to_token[1] == 'c'
+ assert v5.unknown_token == '<unk>'
+ assert v5.reserved_tokens is None
+ assert v5.embedding is None
+ assert 'a' in v5
+
+ v6 = text.Vocabulary(counter, max_size=100, min_freq=1, unknown_token='<unk>',
+ reserved_tokens=None)
+ assert len(v6) == 5
+ assert v6.token_to_idx == {'<unk>': 0, 'c': 1, 'b': 2, 'a': 3,
+ 'some_word$': 4}
+ assert v6.idx_to_token[1] == 'c'
+ assert v6.unknown_token == '<unk>'
+ assert v6.reserved_tokens is None
+ assert v6.embedding is None
+ assert 'a' in v6
+
+ v7 = text.Vocabulary(counter, max_size=1, min_freq=2, unknown_token='<unk>',
+ reserved_tokens=None)
+ assert len(v7) == 2
+ assert v7.token_to_idx == {'<unk>': 0, 'c': 1}
+ assert v7.idx_to_token[1] == 'c'
+ assert v7.unknown_token == '<unk>'
+ assert v7.reserved_tokens is None
+ assert v7.embedding is None
+ assert 'a' not in v7
+
+ assertRaises(AssertionError, text.Vocabulary, counter, max_size=None,
+ min_freq=0, unknown_token='<unknown>', reserved_tokens=['b'])
+
+ assertRaises(AssertionError, text.Vocabulary, counter, max_size=None,
+ min_freq=1, unknown_token='<unknown>', reserved_tokens=['b', 'b'])
+
+ assertRaises(AssertionError, text.Vocabulary, counter, max_size=None,
+ min_freq=1, unknown_token='<unknown>', reserved_tokens=['b', '<unknown>'])
+
+ v8 = text.Vocabulary(counter, max_size=None, min_freq=1, unknown_token='<unknown>',
+ reserved_tokens=['b'])
+ assert len(v8) == 5
+ assert v8.token_to_idx == {'<unknown>': 0, 'b': 1, 'c': 2, 'a': 3, 'some_word$': 4}
+ assert v8.idx_to_token[1] == 'b'
+ assert v8.unknown_token == '<unknown>'
+ assert v8.reserved_tokens == ['b']
+ assert v8.embedding is None
+ assert 'a' in v8
+
+ v9 = text.Vocabulary(counter, max_size=None, min_freq=2, unknown_token='<unk>',
+ reserved_tokens=['b', 'a'])
+ assert len(v9) == 4
+ assert v9.token_to_idx == {'<unk>': 0, 'b': 1, 'a': 2, 'c': 3}
+ assert v9.idx_to_token[1] == 'b'
+ assert v9.unknown_token == '<unk>'
+ assert v9.reserved_tokens == ['b', 'a']
+ assert v9.embedding is None
+ assert 'a' in v9
+
+ v10 = text.Vocabulary(counter, max_size=None, min_freq=100, unknown_token='<unk>',
+ reserved_tokens=['b', 'c'])
+ assert len(v10) == 3
+ assert v10.token_to_idx == {'<unk>': 0, 'b': 1, 'c': 2}
+ assert v10.idx_to_token[1] == 'b'
+ assert v10.unknown_token == '<unk>'
+ assert v10.reserved_tokens == ['b', 'c']
+ assert v10.embedding is None
+ assert 'a' not in v10
+
+ v11 = text.Vocabulary(counter, max_size=1, min_freq=2, unknown_token='<unk>',
+ reserved_tokens=['<pad>', 'b'])
+ assert len(v11) == 4
+ assert v11.token_to_idx == {'<unk>': 0, '<pad>': 1, 'b': 2, 'c': 3}
+ assert v11.idx_to_token[1] == '<pad>'
+ assert v11.unknown_token == '<unk>'
+ assert v11.reserved_tokens == ['<pad>', 'b']
+ assert v11.embedding is None
+ assert 'a' not in v11
+
+ v12 = text.Vocabulary(counter, max_size=None, min_freq=2, unknown_token='b',
+ reserved_tokens=['<pad>'])
+ assert len(v12) == 3
+ assert v12.token_to_idx == {'b': 0, '<pad>': 1, 'c': 2}
+ assert v12.idx_to_token[1] == '<pad>'
+ assert v12.unknown_token == 'b'
+ assert v12.reserved_tokens == ['<pad>']
+ assert v12.embedding is None
+ assert 'a' not in v12
+
+ v13 = text.Vocabulary(counter, max_size=None, min_freq=2, unknown_token='a',
+ reserved_tokens=['<pad>'])
+ assert len(v13) == 4
+ assert v13.token_to_idx == {'a': 0, '<pad>': 1, 'c': 2, 'b': 3}
+ assert v13.idx_to_token[1] == '<pad>'
+ assert v13.unknown_token == 'a'
+ assert v13.reserved_tokens == ['<pad>']
+ assert v13.embedding is None
+ assert 'a' in v13
+
+ counter_tuple = Counter([('a', 'a'), ('b', 'b'), ('b', 'b'), ('c', 'c'), ('c', 'c'), ('c', 'c'),
+ ('some_word$', 'some_word$')])
+
+ v14 = text.Vocabulary(counter_tuple, max_size=None, min_freq=1,
+ unknown_token=('<unk>', '<unk>'), reserved_tokens=None)
+ assert len(v14) == 5
+ assert v14.token_to_idx == {('<unk>', '<unk>'): 0, ('c', 'c'): 1, ('b', 'b'): 2, ('a', 'a'): 3,
+ ('some_word$', 'some_word$'): 4}
+ assert v14.idx_to_token[1] == ('c', 'c')
+ assert v14.unknown_token == ('<unk>', '<unk>')
+ assert v14.reserved_tokens is None
+ assert v14.embedding is None
+ assert ('a', 'a') in v14
+ assert ('<unk>', '<unk>') in v14
+
+
+def _mk_my_pretrain_file(path, token_delim, pretrain_file):
+ path = os.path.expanduser(path)
+ if not os.path.exists(path):
+ os.makedirs(path)
+ seq1 = token_delim.join(['a', '0.1', '0.2', '0.3', '0.4', '0.5']) + '\n'
+ seq2 = token_delim.join(['b', '0.6', '0.7', '0.8', '0.9', '1.0']) + '\n'
+ seqs = seq1 + seq2
+ with open(os.path.join(path, pretrain_file), 'w') as fout:
+ fout.write(seqs)
+
+
+def _mk_my_pretrain_file2(path, token_delim, pretrain_file):
+ path = os.path.expanduser(path)
+ if not os.path.exists(path):
+ os.makedirs(path)
+ seq1 = token_delim.join(['a', '0.01', '0.02', '0.03', '0.04', '0.05']) + '\n'
+ seq2 = token_delim.join(['c', '0.06', '0.07', '0.08', '0.09', '0.1']) + '\n'
+ seqs = seq1 + seq2
+ with open(os.path.join(path, pretrain_file), 'w') as fout:
+ fout.write(seqs)
+
+
+def _mk_my_pretrain_file3(path, token_delim, pretrain_file):
+ path = os.path.expanduser(path)
+ if not os.path.exists(path):
+ os.makedirs(path)
+ seq1 = token_delim.join(['a', '0.1', '0.2', '0.3', '0.4', '0.5']) + '\n'
+ seq2 = token_delim.join(['b', '0.6', '0.7', '0.8', '0.9', '1.0']) + '\n'
+ seq3 = token_delim.join(['<unk1>', '1.1', '1.2', '1.3', '1.4',
+ '1.5']) + '\n'
+ seqs = seq1 + seq2 + seq3
+ with open(os.path.join(path, pretrain_file), 'w') as fout:
+ fout.write(seqs)
+
+
+def _mk_my_pretrain_file4(path, token_delim, pretrain_file):
+ path = os.path.expanduser(path)
+ if not os.path.exists(path):
+ os.makedirs(path)
+ seq1 = token_delim.join(['a', '0.01', '0.02', '0.03', '0.04', '0.05']) + '\n'
+ seq2 = token_delim.join(['c', '0.06', '0.07', '0.08', '0.09', '0.1']) + '\n'
+ seq3 = token_delim.join(['<unk2>', '0.11', '0.12', '0.13', '0.14', '0.15']) + '\n'
+ seqs = seq1 + seq2 + seq3
+ with open(os.path.join(path, pretrain_file), 'w') as fout:
+ fout.write(seqs)
+
+
+def _mk_my_invalid_pretrain_file(path, token_delim, pretrain_file):
+ path = os.path.expanduser(path)
+ if not os.path.exists(path):
+ os.makedirs(path)
+ seq1 = token_delim.join(['a', '0.1', '0.2', '0.3', '0.4', '0.5']) + '\n'
+ seq2 = token_delim.join(['b', '0.6', '0.7', '0.8', '0.9', '1.0']) + '\n'
+ seq3 = token_delim.join(['c']) + '\n'
+ seqs = seq1 + seq2 + seq3
+ with open(os.path.join(path, pretrain_file), 'w') as fout:
+ fout.write(seqs)
+
+
+def _mk_my_invalid_pretrain_file2(path, token_delim, pretrain_file):
+ path = os.path.expanduser(path)
+ if not os.path.exists(path):
+ os.makedirs(path)
+ seq1 = token_delim.join(['a', '0.1', '0.2', '0.3', '0.4', '0.5']) + '\n'
+ seq2 = token_delim.join(['b', '0.6', '0.7', '0.8', '0.9', '1.0']) + '\n'
+ seq3 = token_delim.join(['c', '0.6', '0.7', '0.8']) + '\n'
+ seqs = seq1 + seq2 + seq3
+ with open(os.path.join(path, pretrain_file), 'w') as fout:
+ fout.write(seqs)
+
+
+def test_token_embedding_from_file():
+ embed_root = 'embedding'
+ embed_name = 'my_embed'
+ elem_delim = '\t'
+ pretrain_file = 'my_pretrain_file.txt'
+
+ _mk_my_pretrain_file(os.path.join(embed_root, embed_name), elem_delim, pretrain_file)
+
+ pretrain_file_path = os.path.join(embed_root, embed_name, pretrain_file)
+
+ my_embed = text.embedding.TokenEmbedding.from_file(pretrain_file_path, elem_delim)
+
+ assert 'a' in my_embed
+ assert my_embed.unknown_token == '<unk>'
+ assert my_embed.unknown_token in my_embed
+
+ first_vec = my_embed.idx_to_vec[0]
+ assert_almost_equal(first_vec.asnumpy(), np.array([0, 0, 0, 0, 0]))
+
+ # Test __getitem__.
+ unk_vec = my_embed['A']
+ assert_almost_equal(unk_vec.asnumpy(), np.array([0, 0, 0, 0, 0]))
+
+ a_vec = my_embed['a']
+ assert_almost_equal(a_vec.asnumpy(), np.array([0.1, 0.2, 0.3, 0.4, 0.5]))
+
+ # Test __setitem__.
+ my_embed['a'] = nd.array([1, 2, 3, 4, 5])
+ assert_almost_equal(my_embed['a'].asnumpy(), np.array([1, 2, 3, 4, 5]))
+ assertRaises(ValueError, my_embed.__setitem__, 'unknown$$$', nd.array([0, 0, 0, 0, 0]))
+
+ assertRaises(AssertionError, my_embed.__setitem__, '<unk>',
+ nd.array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]))
+
+ assertRaises(AssertionError, my_embed.__setitem__, '<unk>', nd.array([0]))
+
+ unk_vecs = my_embed['<un...@unk>', '<un...@unk>']
+ assert_almost_equal(unk_vecs.asnumpy(), np.array([[0, 0, 0, 0, 0], [0, 0, 0, 0, 0]]))
+
+ # Test loaded unknown vectors.
+ pretrain_file2 = 'my_pretrain_file2.txt'
+ _mk_my_pretrain_file3(os.path.join(embed_root, embed_name), elem_delim, pretrain_file2)
+ pretrain_file_path = os.path.join(embed_root, embed_name, pretrain_file2)
+ my_embed2 = text.embedding.TokenEmbedding.from_file(pretrain_file_path, elem_delim,
+ init_unknown_vec=nd.ones,
+ unknown_token='<unk>')
+ unk_vec2 = my_embed2['<unk>']
+ assert_almost_equal(unk_vec2.asnumpy(), np.array([1, 1, 1, 1, 1]))
+ unk_vec2 = my_embed2['<un...@unk>']
+ assert_almost_equal(unk_vec2.asnumpy(), np.array([1, 1, 1, 1, 1]))
+
+ my_embed3 = text.embedding.TokenEmbedding.from_file(pretrain_file_path, elem_delim,
+ init_unknown_vec=nd.ones,
+ unknown_token='<unk1>')
+ unk_vec3 = my_embed3['<unk1>']
+ assert_almost_equal(unk_vec3.asnumpy(), np.array([1.1, 1.2, 1.3, 1.4, 1.5]))
+ unk_vec3 = my_embed3['<un...@unk>']
+ assert_almost_equal(unk_vec3.asnumpy(), np.array([1.1, 1.2, 1.3, 1.4, 1.5]))
+
+ # Test error handling.
+ invalid_pretrain_file = 'invalid_pretrain_file.txt'
+ _mk_my_invalid_pretrain_file(os.path.join(embed_root, embed_name), elem_delim,
+ invalid_pretrain_file)
+ pretrain_file_path = os.path.join(embed_root, embed_name, invalid_pretrain_file)
+ assertRaises(AssertionError, text.embedding.TokenEmbedding.from_file, pretrain_file_path,
+ elem_delim)
+
+ invalid_pretrain_file2 = 'invalid_pretrain_file2.txt'
+ _mk_my_invalid_pretrain_file2(os.path.join(embed_root, embed_name), elem_delim,
+ invalid_pretrain_file2)
+ pretrain_file_path = os.path.join(embed_root, embed_name, invalid_pretrain_file2)
+ assertRaises(AssertionError, text.embedding.TokenEmbedding.from_file, pretrain_file_path,
+ elem_delim)
+
+
+def test_embedding_get_and_pretrain_file_names():
+ assert len(text.embedding.get_file_names(embedding_name='fasttext')) == 327
+ assert len(text.embedding.get_file_names(embedding_name='glove')) == 10
+
+ reg = text.embedding.get_file_names(embedding_name=None)
+
+ assert len(reg['glove']) == 10
+ assert len(reg['fasttext']) == 327
+
+ assertRaises(KeyError, text.embedding.get_file_names, 'unknown$$')
+
+
+def test_vocab_set_embedding_with_one_custom_embedding():
+ embed_root = 'embedding'
+ embed_name = 'my_embed'
+ elem_delim = '\t'
+ pretrain_file = 'my_pretrain_file1.txt'
+
+ _mk_my_pretrain_file(os.path.join(embed_root, embed_name), elem_delim, pretrain_file)
+
+ pretrain_file_path = os.path.join(embed_root, embed_name, pretrain_file)
+
+ counter = Counter(['a', 'b', 'b', 'c', 'c', 'c', 'some_word$'])
+
+ v1 = text.Vocabulary(counter, max_size=None, min_freq=1, unknown_token='<unk>',
+ reserved_tokens=['<pad>'])
+
+ e1 = text.embedding.TokenEmbedding.from_file(pretrain_file_path, elem_delim,
+ init_unknown_vec=nd.ones)
+
+ assert v1.embedding is None
+ v1.set_embedding(e1)
+ assert v1.embedding is not None
+
+ assert_almost_equal(v1.embedding.idx_to_vec.asnumpy(),
+ np.array([[1, 1, 1, 1, 1],
+ [1, 1, 1, 1, 1],
+ [1, 1, 1, 1, 1],
+ [0.6, 0.7, 0.8, 0.9, 1],
+ [0.1, 0.2, 0.3, 0.4, 0.5],
+ [1, 1, 1, 1, 1]])
+ )
+
+ assert_almost_equal(v1.embedding['c'].asnumpy(),
+ np.array([1, 1, 1, 1, 1])
+ )
+
+ assert_almost_equal(v1.embedding[['c']].asnumpy(),
+ np.array([[1, 1, 1, 1, 1]])
+ )
+
+ assert_almost_equal(v1.embedding[['a', 'not_exist']].asnumpy(),
+ np.array([[0.1, 0.2, 0.3, 0.4, 0.5],
+ [1, 1, 1, 1, 1]])
+ )
+
+ assert_almost_equal(v1.embedding[['a', 'b']].asnumpy(),
+ np.array([[0.1, 0.2, 0.3, 0.4, 0.5],
+ [0.6, 0.7, 0.8, 0.9, 1]])
+ )
+
+ assert_almost_equal(v1.embedding[['A', 'b']].asnumpy(),
+ np.array([[1, 1, 1, 1, 1],
+ [0.6, 0.7, 0.8, 0.9, 1]])
+ )
+
+ v1.embedding['a'] = nd.array([2, 2, 2, 2, 2])
+ v1.embedding['b'] = nd.array([3, 3, 3, 3, 3])
+
+ assert_almost_equal(v1.embedding.idx_to_vec.asnumpy(),
+ np.array([[1, 1, 1, 1, 1],
+ [1, 1, 1, 1, 1],
+ [1, 1, 1, 1, 1],
+ [3, 3, 3, 3, 3],
+ [2, 2, 2, 2, 2],
+ [1, 1, 1, 1, 1]])
+ )
+
+ v1.embedding['<unk>'] = nd.array([0, 0, 0, 0, 0])
+ assert_almost_equal(v1.embedding.idx_to_vec.asnumpy(),
+ np.array([[0, 0, 0, 0, 0],
+ [1, 1, 1, 1, 1],
+ [1, 1, 1, 1, 1],
+ [3, 3, 3, 3, 3],
+ [2, 2, 2, 2, 2],
+ [1, 1, 1, 1, 1]])
+ )
+ v1.embedding['<unk>'] = nd.array([10, 10, 10, 10, 10])
+ assert_almost_equal(v1.embedding.idx_to_vec.asnumpy(),
+ np.array([[10, 10, 10, 10, 10],
+ [1, 1, 1, 1, 1],
+ [1, 1, 1, 1, 1],
+ [3, 3, 3, 3, 3],
+ [2, 2, 2, 2, 2],
+ [1, 1, 1, 1, 1]])
+ )
+
+ v1.set_embedding(None)
+ assert v1.embedding is None
+
+
+def test_vocab_set_embedding_with_two_custom_embeddings():
+ embed_root = '.'
+ embed_name = 'my_embed'
+ elem_delim = '\t'
+ pretrain_file1 = 'my_pretrain_file1.txt'
+ pretrain_file2 = 'my_pretrain_file2.txt'
+
+ _mk_my_pretrain_file(os.path.join(embed_root, embed_name), elem_delim, pretrain_file1)
+ _mk_my_pretrain_file2(os.path.join(embed_root, embed_name), elem_delim, pretrain_file2)
+
+ pretrain_file_path1 = os.path.join(embed_root, embed_name, pretrain_file1)
+ pretrain_file_path2 = os.path.join(embed_root, embed_name, pretrain_file2)
+
+ my_embed1 = text.embedding.TokenEmbedding.from_file(pretrain_file_path1, elem_delim,
+ init_unknown_vec=nd.ones)
+ my_embed2 = text.embedding.TokenEmbedding.from_file(pretrain_file_path2, elem_delim)
+
+ counter = Counter(['a', 'b', 'b', 'c', 'c', 'c', 'some_word$'])
+
+ v1 = text.Vocabulary(counter, max_size=None, min_freq=1, unknown_token='<unk>',
+ reserved_tokens=None)
+ v1.set_embedding(my_embed1, my_embed2)
+ assert v1.embedding is not None
+
+ assertRaises(AssertionError, v1.set_embedding, my_embed1, None, my_embed2)
+
+ assert_almost_equal(v1.embedding.idx_to_vec.asnumpy(),
+ np.array([[1, 1, 1, 1, 1, 0, 0, 0, 0, 0],
+ [1, 1, 1, 1, 1, 0.06, 0.07, 0.08, 0.09, 0.1],
+ [0.6, 0.7, 0.8, 0.9, 1, 0, 0, 0, 0, 0],
+ [0.1, 0.2, 0.3, 0.4, 0.5,
+ 0.01, 0.02, 0.03, 0.04, 0.05],
+ [1, 1, 1, 1, 1, 0, 0, 0, 0, 0]])
+ )
+
+ assert_almost_equal(v1.embedding['c'].asnumpy(),
+ np.array([1, 1, 1, 1, 1, 0.06, 0.07, 0.08, 0.09, 0.1])
+ )
+
+ assert_almost_equal(v1.embedding[['b', 'not_exist']].asnumpy(),
+ np.array([[0.6, 0.7, 0.8, 0.9, 1, 0, 0, 0, 0, 0],
+ [1, 1, 1, 1, 1, 0, 0, 0, 0, 0]])
+ )
+
+ v1.embedding['a'] = nd.array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
+ v1.embedding['b'] = nd.array([3, 3, 3, 3, 3, 3, 3, 3, 3, 3])
+
+ assert_almost_equal(v1.embedding.idx_to_vec.asnumpy(),
+ np.array([[1, 1, 1, 1, 1, 0, 0, 0, 0, 0],
+ [1, 1, 1, 1, 1, 0.06, 0.07, 0.08, 0.09, 0.1],
+ [3, 3, 3, 3, 3, 3, 3, 3, 3, 3],
+ [2, 2, 2, 2, 2, 2, 2, 2, 2, 2],
+ [1, 1, 1, 1, 1, 0, 0, 0, 0, 0]])
+ )
+
+ # Test loaded unknown tokens
+ pretrain_file3 = 'my_pretrain_file3.txt'
+ pretrain_file4 = 'my_pretrain_file4.txt'
+
+ _mk_my_pretrain_file3(os.path.join(embed_root, embed_name), elem_delim, pretrain_file3)
+ _mk_my_pretrain_file4(os.path.join(embed_root, embed_name), elem_delim, pretrain_file4)
+
+ pretrain_file_path3 = os.path.join(embed_root, embed_name, pretrain_file3)
+ pretrain_file_path4 = os.path.join(embed_root, embed_name, pretrain_file4)
+
+ my_embed3 = text.embedding.TokenEmbedding.from_file(pretrain_file_path3, elem_delim,
+ init_unknown_vec=nd.ones, unknown_token='<unk1>')
+ my_embed4 = text.embedding.TokenEmbedding.from_file(pretrain_file_path4, elem_delim,
+ unknown_token='<unk2>')
+
+ v2 = text.Vocabulary(counter, max_size=None, min_freq=1, unknown_token='<unk>',
+ reserved_tokens=None)
+ v2.set_embedding(my_embed3, my_embed4)
+ assert_almost_equal(v2.embedding.idx_to_vec.asnumpy(),
+ np.array([[1.1, 1.2, 1.3, 1.4, 1.5,
+ 0.11, 0.12, 0.13, 0.14, 0.15],
+ [1.1, 1.2, 1.3, 1.4, 1.5,
+ 0.06, 0.07, 0.08, 0.09, 0.1],
+ [0.6, 0.7, 0.8, 0.9, 1,
+ 0.11, 0.12, 0.13, 0.14, 0.15],
+ [0.1, 0.2, 0.3, 0.4, 0.5,
+ 0.01, 0.02, 0.03, 0.04, 0.05],
+ [1.1, 1.2, 1.3, 1.4, 1.5,
+ 0.11, 0.12, 0.13, 0.14, 0.15]])
+ )
+
+ v3 = text.Vocabulary(counter, max_size=None, min_freq=1, unknown_token='<unk1>',
+ reserved_tokens=None)
+ v3.set_embedding(my_embed3, my_embed4)
+ assert_almost_equal(v3.embedding.idx_to_vec.asnumpy(),
+ np.array([[1.1, 1.2, 1.3, 1.4, 1.5,
+ 0.11, 0.12, 0.13, 0.14, 0.15],
+ [1.1, 1.2, 1.3, 1.4, 1.5,
+ 0.06, 0.07, 0.08, 0.09, 0.1],
+ [0.6, 0.7, 0.8, 0.9, 1,
+ 0.11, 0.12, 0.13, 0.14, 0.15],
+ [0.1, 0.2, 0.3, 0.4, 0.5,
+ 0.01, 0.02, 0.03, 0.04, 0.05],
+ [1.1, 1.2, 1.3, 1.4, 1.5,
+ 0.11, 0.12, 0.13, 0.14, 0.15]])
+ )
+
+ v4 = text.Vocabulary(counter, max_size=None, min_freq=1, unknown_token='<unk2>',
+ reserved_tokens=None)
+ v4.set_embedding(my_embed3, my_embed4)
+ assert_almost_equal(v4.embedding.idx_to_vec.asnumpy(),
+ np.array([[1.1, 1.2, 1.3, 1.4, 1.5,
+ 0.11, 0.12, 0.13, 0.14, 0.15],
+ [1.1, 1.2, 1.3, 1.4, 1.5,
+ 0.06, 0.07, 0.08, 0.09, 0.1],
+ [0.6, 0.7, 0.8, 0.9, 1,
+ 0.11, 0.12, 0.13, 0.14, 0.15],
+ [0.1, 0.2, 0.3, 0.4, 0.5,
+ 0.01, 0.02, 0.03, 0.04, 0.05],
+ [1.1, 1.2, 1.3, 1.4, 1.5,
+ 0.11, 0.12, 0.13, 0.14, 0.15]])
+ )
+
+ counter2 = Counter(['b', 'b', 'c', 'c', 'c', 'some_word$'])
+
+ v5 = text.Vocabulary(counter2, max_size=None, min_freq=1, unknown_token='a',
+ reserved_tokens=None)
+ v5.set_embedding(my_embed3, my_embed4)
+ assert v5.embedding._token_to_idx == {'a': 0, 'c': 1, 'b': 2, 'some_word$': 3}
+ assert v5.embedding._idx_to_token == ['a', 'c', 'b', 'some_word$']
+ assert_almost_equal(v5.embedding.idx_to_vec.asnumpy(),
+ np.array([[1.1, 1.2, 1.3, 1.4, 1.5,
+ 0.11, 0.12, 0.13, 0.14, 0.15],
+ [1.1, 1.2, 1.3, 1.4, 1.5,
+ 0.06, 0.07, 0.08, 0.09, 0.1],
+ [0.6, 0.7, 0.8, 0.9, 1,
+ 0.11, 0.12, 0.13, 0.14, 0.15],
+ [1.1, 1.2, 1.3, 1.4, 1.5,
+ 0.11, 0.12, 0.13, 0.14, 0.15]])
+ )
+
+
+def test_download_embed():
+ @text.embedding.register
+ class Test(text.embedding.TokenEmbedding):
+ # 33 bytes.
+ pretrained_file_name_sha1 = \
+ {'embedding_test.vec': '29b9a6511cf4b5aae293c44a9ec1365b74f2a2f8'}
+ namespace = 'test'
+
+ def __init__(self, embedding_root='embedding', init_unknown_vec=nd.zeros, **kwargs):
+ file_name = 'embedding_test.vec'
+ Test._check_pretrained_file_names(file_name)
+
+ super(Test, self).__init__(**kwargs)
+
+ file_path = Test._get_pretrained_file(embedding_root, file_name)
+
+ self._load_embedding(file_path, ' ', init_unknown_vec)
+
+ test_embed = text.embedding.create('test')
+ assert_almost_equal(test_embed['hello'].asnumpy(), (nd.arange(5) + 1).asnumpy())
+ assert_almost_equal(test_embed['world'].asnumpy(), (nd.arange(5) + 6).asnumpy())
+ assert_almost_equal(test_embed['<unk>'].asnumpy(), nd.zeros((5,)).asnumpy())
+
+
+if __name__ == '__main__':
+ import nose
+ nose.runmodule()
----------------------------------------------------------------
This is an automated message from the Apache Git Service.
To respond to the message, please log on GitHub and use the
URL above to go to the specific comment.
For queries about this service, please contact Infrastructure at:
users@infra.apache.org
With regards,
Apache Git Services