szha commented on a change in pull request #10074: Add vocabulary and embedding
URL: https://github.com/apache/incubator-mxnet/pull/10074#discussion_r174642802
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File path: docs/api/python/gluon/text.md
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+# 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
+
+```
+
+### Access pre-trained word embeddings for indexed words
+
+As a common use case, let us access pre-trained word embedding vectors for
indexed words in just a
+few lines of code.
+
+To begin with, let us 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')
+
+```
+
+Now, 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)
Review comment:
It doesn't seem necessary to create vocab just to access embedding vector.
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