Convert dataset to Parquet

README.md

@@ -1,8 +1,27 @@
 ---
+language:
+- ja
 license: apache-2.0
 task_categories:
 - text-retrieval
-language:
-- ja
 pretty_name: a
----
+dataset_info:
+  features:
+  - name: docid
+    dtype: string
+  - name: title
+    dtype: string
+  - name: text
+    dtype: string
+  splits:
+  - name: train
+    num_bytes: 82583007
+    num_examples: 129260
+  download_size: 44837491
+  dataset_size: 82583007
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+---

create_miracl_japanese_small.py

@@ -1,30 +0,0 @@
-import pandas as pd
-import json
-from tqdm import tqdm
-from datasets import load_dataset
-
-DEV_QREL_FILEPATH = "./qrels.miracl-v1.0-ja-dev.tsv"
-OUTPUT_FILEPATH = "./miracl-japanese-small-docs.jsonl"
-
-def extract_doc_ids(filepath):
-    dev_qrel = pd.read_csv(filepath, delimiter='\t',
-            names=['query_id', 'ph', 'doc_pas_id', 'rel'])
-    doc_ids = set([int(dp_id.split('#')[0]) for dp_id in dev_qrel.doc_pas_id])
-    return doc_ids
-
-
-if __name__ == '__main__':
-    dev_doc_ids = extract_doc_ids(DEV_QREL_FILEPATH)
-    doc_ids = dev_doc_ids
-    print("# of docids in dev", len(dev_doc_ids))
-
-    new_dataset = []
-    seen_doc_ids = set()
-    dataset = load_dataset("miracl/miracl-corpus", "ja")
-    for data in tqdm(dataset['train']):
-        docid = int(data["docid"].split("#")[0])
-        if docid in doc_ids:
-            new_dataset.append(data)
-    with open(OUTPUT_FILEPATH, 'w', encoding='utf-8') as f:
-        for data in new_dataset:
-            f.write(json.dumps(data) + '\n')

miracl-japanese-small-docs.jsonl.gz → data/train-00000-of-00001.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:53fd4b33ac6c1b1a7ab9d7af0c2da9f91aa7af0760da1724725184468bc49846
-size 32917772
+oid sha256:8b89f5a20e5fd08d4a181728f66eec6810b60387bc87c37b7aeab80b1d10e499
+size 44837491

miracl-japanese-small-corpus.py

@@ -1,75 +0,0 @@
-# coding=utf-8
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the 'License');
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an 'AS IS' BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import json
-import datasets
-
-_CITATION = '''
-'''
-
-_DESCRIPTION = 'dataset load script for MIRACL Japanese Small Corpus'
-
-_DATASET_URLS = {
-    'train': 'https://huggingface.co/datasets/mpkato/miracl-japanese-small-corpus/resolve/main/miracl-japanese-small-docs.jsonl.gz'
-}
-
-
-class MIRACLJapaneseSmallCorpus(datasets.GeneratorBasedBuilder):
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            version=datasets.Version('1.0.0'),
-            description=f'MIRACL Japanese Small dataset.'
-        )
-    ]
-
-    def _info(self):
-        features = datasets.Features({
-            'docid': datasets.Value('string'), 
-            'title': datasets.Value('string'),
-            'text': datasets.Value('string'),
-        })
-
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage='',
-            # License for the dataset if available
-            license='',
-            # Citation for the dataset
-            citation=_CITATION,
-        )
-
-    def _split_generators(self, dl_manager):
-        downloaded_files = dl_manager.download_and_extract(_DATASET_URLS)
-
-        splits = [
-            datasets.SplitGenerator(
-                name='train',
-                gen_kwargs={
-                    'filepath': downloaded_files['train'],
-                },
-            ),
-        ]
-        return splits
-
-    def _generate_examples(self, filepath):
-        with open(filepath, encoding="utf-8") as f:
-            for line in f:
-                data = json.loads(line)
-                yield data['docid'], data