update dataset

.gitignore

@@ -0,0 +1,2 @@
+data
+data.tar

frwiki_good_pages_el.py

@@ -17,7 +17,8 @@
 
 import pandas as pd
 import re
-
+import gzip
+import json
 import datasets
 from pathlib import Path
 
@@ -60,7 +61,7 @@ _LICENSE = ""
 # The HuggingFace dataset library don't host the datasets but only point to the original files
 # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
 _URLs = {
-    "frwiki": "",
+    "frwiki": "data.tar.gz",
 }
 
 _CLASS_LABELS = [
@@ -219,14 +220,14 @@ class FrWikiGoodPagesELDataset(datasets.GeneratorBasedBuilder):
         # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
         # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
         # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        # my_urls = _URLs[self.config.name]
-        # data_dir = dl_manager.download_and_extract(my_urls)
+        my_urls = _URLs[self.config.name]
+        data_dir = dl_manager.download_and_extract(my_urls)
         return [
             datasets.SplitGenerator(
                 name=datasets.Split.TRAIN,
                 # These kwargs will be passed to _generate_examples
                 gen_kwargs={
-                    "dataset_dir": Path(".", "data", "good-pages"),
+                    "data_dir": Path(data_dir, "data"),
                     "split": "train"
                 }
             )
@@ -234,36 +235,37 @@ class FrWikiGoodPagesELDataset(datasets.GeneratorBasedBuilder):
 
     def _generate_examples(
         # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-        self, dataset_dir, split
+        self, data_dir, split
     ):
         """ Yields examples as (key, example) tuples. """
         # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
         # The `key` is here for legacy reason (tfds) and is not important in itself.
 
-        with open(Path(dataset_dir, "list-good-pages.txt"), "rt", encoding="UTF-8") as f:
-            good_pages_list = f.read().split("\n")
-
-        wiki_df = pd.read_csv(Path(dataset_dir, "scrapped", "final-dataset.csv"),
-                              dtype=str, na_filter=False)
-
-        title2qid = dict(zip(wiki_df["title"], wiki_df["qid"]))
-        title2path = dict(zip(wiki_df["title"], wiki_df["path"]))
-        title2wikipedia = dict(
-            zip(wiki_df["title"], wiki_df["wikipedia_description"]))
-        title2wikidata = dict(
-            zip(wiki_df["title"], wiki_df["wikidata_description"]))
-
-        good_pages_list = [
-            gp.strip()
-            for gp in good_pages_list
-            if title2path[gp] != "" and gp.strip() != ""
-        ]
-
-        for id, title in enumerate(good_pages_list):
-            qid = title2qid[title]
-            path = title2path[title]
-            text = read_file(path)
-
-            features = text_to_el_features(
-                qid, title, text, title2qid, title2wikipedia, title2wikidata)
-            yield id, features
+        with open(Path(data_dir, "list-good-pages.txt"), "rt", encoding="UTF-8") as f:
+            good_pages_list = set(f.read().split("\n")).difference("")
+
+        entities_path = Path(data_dir, "entities.jsonl.gz")
+        corpus_path = Path(data_dir, "corpus.jsonl.gz")
+        title2wikipedia = {}
+        title2wikidata = {}
+        title2qid = {}
+        with gzip.open(entities_path, "rt", encoding="UTF-8") as ent_file:
+            for line in ent_file:
+                item = json.loads(line, parse_int=lambda x: x,
+                                  parse_float=lambda x: x, parse_constant=lambda x: x)
+                title = item["title"]
+                title2wikipedia[title] = item["wikipedia_description"]
+                title2wikidata[title] = item["wikidata_description"]
+                title2qid[title] = item["qid"]
+
+        with gzip.open(corpus_path, "rt", encoding="UTF-8") as crps_file:
+            for id, line in enumerate(crps_file):
+                item = json.loads(line, parse_int=lambda x: x,
+                                  parse_float=lambda x: x, parse_constant=lambda x: x)
+                qid = item["qid"]
+                title = item["title"]
+                text = item["text"]
+
+                features = text_to_el_features(
+                    qid, title, text, title2qid, title2wikipedia, title2wikidata)
+                yield id, features