Update README.md

README.md

@@ -653,10 +653,26 @@ Thus, by joining per-language datasets by meaning ids, one can obtain a bilingua
 
 The intended use of the dataset is to extract monolingual or bilingual dictionaries for the purposes of language learning by machines or humans.
 
-The code below illustrates how the dataset could be used to extract French definitions of Finnish words.
+The code below illustrates how the dataset could be used to extract all French definitions of Finnish words found on Panlex.
 
 ```Python
-TODO
+from datasets import load_dataset
+ds_fin_word = load_dataset('cointegrated/panlex-meanings', name='fin', split='train')
+ds_fra_def = load_dataset('cointegrated/panlex-definitions', name='fra', split='train')
+df_fin_word = ds_fin_word.to_pandas()
+df_fra_def = ds_fra_def.to_pandas()
+
+df_matched = df_fin_word.merge(df_fra_def, on='meaning', suffixes=['_wrd', '_def']).drop_duplicates(subset=['txt_wrd', 'txt_def'])
+print(df_matched.shape)
+# (11512, 13)
+
+print(df_matched.sample(5)[['txt_wrd', 'meaning', 'txt_def']])
+#                txt_wrd   meaning                                    txt_def
+# 101   kalsiumpitoisuus  30766618                          teneur en calcium
+# 7003              keho  28131094  Partie matérielle de tout être animé. (2)
+# 8180      mikä tahansa  27960302                    quel que soit celui qui
+# 4606            Safari   1689224                          Safari (logiciel)
+# 9833        suositella  28251812                                    à trier
 ```
 
 
@@ -665,28 +681,29 @@ TODO
 
 <!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
 
-The dataset is split by languages, denoted by their ISO 639 codes. Each language might contain multiple varieties; they are annotated within each per-language split.
+The dataset is split by languages of the definition, denoted by their ISO 639 codes. Each language might contain multiple varieties; they are annotated within each per-language split.
 
 To determine a code for your language, please consult the https://panlex.org webside. For additional information about a language, you may also want to consult https://glottolog.org/.
 
 Each split contains the following fields:
-- `id` (int): id of the expression
-- `langvar` (int): id of the language variaety
-- `txt` (str): the full text of the expression
-- `txt_degr` (str): degraded (i.e. simplified to facilitate lookup) text
-- `meaning` (int): id of the meaning. This is the column to join for obtaining synonyms (within a language) or translations (across languages)
-- `langvar_uid` (str): more human-readable id of the language (e.g. `eng-000` stands for generic English, `eng-001` for simple English, `eng-004` for American English). These ids could be looked up in the language dropdown at https://vocab.panlex.org/.
+- `id` (int): id of the definition
+- `meaning` (int): id of the meaning, joinable with [cointegrated/panlex-meanings](https://huggingface.co/datasets/cointegrated/panlex-meanings)
+- `langvar` (int): id of the language variety of the definition
+- `txt` (str): text of the definition
+- `langvar_uid` (str): more human-readable id of the definition language (e.g. `eng-000` stands for generic English, `eng-001` for simple English, `eng-004` for American English). These ids could be looked up in the language dropdown at https://vocab.panlex.org/.
+- `example` (str, optional): example of a word corresponding to the meaning of the definition (preferably, but not always, in the language of the definition)
+- `example_langvar` (int, optional): id of the language variety of the example
+- `example_langvar_uid` (str, optional): human-readable id of the language variety of the example
 
 
 ## Dataset Creation
 
-This dataset has been extracted from https://panlex.org (the `20240301` database dump) and automatically rearranged on the per-language basis.
+This dataset has been extracted from https://panlex.org (the `20250201` database dump) and automatically rearranged on the per-language basis.
 
 The rearrangement consisted of the following steps:
 1. Grouping together the language varieties from the `langvar` table with the same `lang_code`.
-2. For each language, selecting the corresponding subset from the `expr` table.
-3. Joining the selected set with the `denotation` table, to get the `meaning` id.
-  This increases the number of rows (for some languages, x5), because multiple meannings may be attached to the same expression.
+2. For each language, selecting the corresponding subset from the `definition` table.
+3. Joining the selected set with the `denotation` table, to match an example of expression id with the given meaning, and then with the `expr` table, to get the text and language of the expression.
 
 ## Bias, Risks, and Limitations
 
@@ -694,7 +711,6 @@ The rearrangement consisted of the following steps:
 
 As with any multilingual dataset, Panlex data may exhbit the problem of under- and mis-representation of some languages. 
 
-The dataset consists primarily of the standard written forms ("lemmas") of the expressions, so it may not well represent their usage within a language.
 
 ## Citation