update documentation

bin/opusfilter-autogen

@@ -53,7 +53,7 @@ parser.add_argument('--add-filter', nargs=2, action='append', default=[], metava
                           'filter identifier in order to allow multiple filters of the same class. Example: '
                           '--add-filter LanguageIDFilter.cld2 \'{"id_method": "cld2"}\''))
 parser.add_argument('--overwrite', action='store_true',
-                    help='Overwrite existing config file and intermediate files')
+                    help='Overwrite existing intermediate files')
 parser.add_argument('-o', '--output', type=argparse.FileType('w'),
                     default='-', metavar='CONFIGFILE', help='Output configuration file (default %(default)s)')
 args = parser.parse_args()

docs/automatic_configuration.md

@@ -58,7 +58,7 @@ options:
                         and a unique filter identifier in order to allow
                         multiple filters of the same class. Example: --add-
                         filter LanguageIDFilter.cld2 '{"id_method": "cld2"}'
-  --overwrite           Overwrite existing config file and intermediate files
+  --overwrite           Overwrite existing intermediate files
   -o CONFIGFILE, --output CONFIGFILE
                         Output configuration file (default -)
 ```
@@ -68,11 +68,11 @@ option `default` uses the default parameters defined in the filter
 classes. The option `percentiles` assumes that a proportion of the
 data (set by `--noisy-percentile`) is noisy, and sets the thresholds
 for each filter independently based on the percentile. The
-`clustering` option is likely the most useful of the three, and
-described in more detail below. However, it is applicable to a more
-limited set of filters.
+`clustering` option may be the most useful of the three, and described
+in more detail below. However, it is applicable to a more limited set
+of filters.
 
-## Unsupervised feature selection for filters
+## Unsupervised threshold selection for filters
 
 This implements the method introduced by {cite:t}`aulamo-etal-2023-unsupervised`.
 It takes a parallel corpus as an input and tries to separate the clean
@@ -95,3 +95,7 @@ the number of clusters (`--clusters`) may help.
 Figures from the clustering and score histograms are plotted given the
 `--plot` option. If you want also to save the intermediate files, make
 sure to use the `--inter-dir` argument.
+
+*Note: The method should be considered as experimental, and it is not
+expected to give good results on all corpora. If you try it, please
+consider giving feedback on the project issues page.*