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release 0.9.2
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Summary: release 0.9.2

Reviewed By: EdouardGrave

Differential Revision: D21257005

fbshipit-source-id: 0d67dae2a196d36927f3ae7d7f5c47d40dc1519c
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Celebio authored and facebook-github-bot committed Apr 28, 2020
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6 changes: 3 additions & 3 deletions README.md
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Expand Up @@ -89,9 +89,9 @@ There is also the master branch that contains all of our most recent work, but c
### Building fastText using make (preferred)

```
$ wget https://github.com/facebookresearch/fastText/archive/v0.9.1.zip
$ unzip v0.9.1.zip
$ cd fastText-0.9.1
$ wget https://github.com/facebookresearch/fastText/archive/v0.9.2.zip
$ unzip v0.9.2.zip
$ cd fastText-0.9.2
$ make
```

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24 changes: 12 additions & 12 deletions docs/supervised-tutorial.md
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Expand Up @@ -18,14 +18,14 @@ The first step of this tutorial is to install and build fastText. It only requir
Let us start by downloading the [most recent release](https://github.com/facebookresearch/fastText/releases):

```bash
$ wget https://github.com/facebookresearch/fastText/archive/v0.9.1.zip
$ unzip v0.9.1.zip
$ wget https://github.com/facebookresearch/fastText/archive/v0.9.2.zip
$ unzip v0.9.2.zip
```

Move to the fastText directory and build it:

```bash
$ cd fastText-0.9.1
$ cd fastText-0.9.2
# for command line tool :
$ make
# for python bindings :
Expand Down Expand Up @@ -80,32 +80,32 @@ DESCRIPTION
FUNCTIONS
load_model(path)
Load a model given a filepath and return a model object.

read_args(arg_list, arg_dict, arg_names, default_values)

tokenize(text)
Given a string of text, tokenize it and return a list of tokens

train_supervised(*kargs, **kwargs)
Train a supervised model and return a model object.

input must be a filepath. The input text does not need to be tokenized
as per the tokenize function, but it must be preprocessed and encoded
as UTF-8. You might want to consult standard preprocessing scripts such
as tokenizer.perl mentioned here: http://www.statmt.org/wmt07/baseline.html

The input file must must contain at least one label per line. For an
example consult the example datasets which are part of the fastText
repository such as the dataset pulled by classification-example.sh.

train_unsupervised(*kargs, **kwargs)
Train an unsupervised model and return a model object.

input must be a filepath. The input text does not need to be tokenized
as per the tokenize function, but it must be preprocessed and encoded
as UTF-8. You might want to consult standard preprocessing scripts such
as tokenizer.perl mentioned here: http://www.statmt.org/wmt07/baseline.html

The input field must not contain any labels or use the specified label prefix
unless it is ok for those words to be ignored. For an example consult the
dataset pulled by the example script word-vector-example.sh, which is
Expand Down Expand Up @@ -366,7 +366,7 @@ This is much better! Another way to change the learning speed of our model is to
<!--DOCUSAURUS_CODE_TABS-->
<!--Command line-->
```bash
>> ./fasttext supervised -input cooking.train -output model_cooking -lr 1.0
>> ./fasttext supervised -input cooking.train -output model_cooking -lr 1.0
Read 0M words
Number of words: 9012
Number of labels: 734
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2 changes: 1 addition & 1 deletion setup.py
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Expand Up @@ -21,7 +21,7 @@
import platform
import io

__version__ = '0.9.1'
__version__ = '0.9.2'
FASTTEXT_SRC = "src"

# Based on https://github.com/pybind/python_example
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