add load dataset method

matrix.py

@@ -0,0 +1,121 @@
+# -*- coding: utf-8 -*-
+
+import glob
+import orjson
+import os
+
+import datasets
+from itertools import islice
+
+_DESCRIPTION = """
+An open-source pretraining dataset containing 4690 billion tokens,
+this bilingual dataset with both English and Chinese texts is used for training neo models.
+"""
+
+_CITATION = """
+@article{zhang2024mapneo,
+    title   = {MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series},
+    author  = {
+        Ge Zhang and
+        Scott Qu and
+        Jiaheng Liu and
+        Chenchen Zhang and
+        Chenghua Lin and
+        Chou Leuang Yu and
+        Danny Pan and
+        Esther Cheng and
+        Jie Liu and
+        Qunshu Lin and
+        Raven Yuan and
+        Tuney Zheng and
+        Wei Pang and
+        Xinrun Du and
+        Yiming Liang and
+        Yinghao Ma and
+        Yizhi Li and
+        Ziyang Ma and
+        Bill Lin and
+        Emmanouil Benetos and
+        Huan Yang and
+        Junting Zhou and
+        Kaijing Ma and
+        Minghao Liu and
+        Morry Niu and
+        Noah Wang and
+        Quehry Que and
+        Ruibo Liu and
+        Sine Liu and
+        Shawn Guo and
+        Soren Gao and
+        Wangchunshu Zhou and
+        Xinyue Zhang and
+        Yizhi Zhou and
+        Yubo Wang and
+        Yuelin Bai and
+        Yuhan Zhang and
+        Yuxiang Zhang and
+        Zenith Wang and
+        Zhenzhu Yang and
+        Zijian Zhao and
+        Jiajun Zhang and
+        Wanli Ouyang and
+        Wenhao Huang and
+        Wenhu Chen
+    },
+    year    = {2024},
+    journal = {arXiv preprint arXiv: 2405.19327}
+}
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/m-a-p/Matrix"
+
+
+class MatrixDataset(datasets.GeneratorBasedBuilder):
+    """Custom dataset for JSON files with filtering capabilities."""
+
+    VERSION = datasets.Version("1.0.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features({
+                "id": datasets.Value("string"),
+                "text": datasets.Value("string"),
+            }),
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        import random
+
+        data_files = glob.glob("*/*.jsonl")
+        data_shards = []
+        for filepath in data_files:
+            # max size of each shard is 1GB
+            num_shards = -os.path.getsize(filepath) // -1024**3
+            for i in range(num_shards):
+                data_shards.append((filepath, i, num_shards))
+        random.Random(42).shuffle(data_shards)
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "data_shards": data_shards,
+                },
+            ),
+        ]
+
+    def _generate_examples(self, data_shards):
+        for file, split, num_shards in data_shards:
+            with open(file, "r") as f:
+                for i, line in islice(enumerate(f), split, None, num_shards):
+                    data = orjson.loads(line)
+                    if 'id' not in data:
+                        data['id'] = f"{file}_{i}"
+                    if 'content' in data and 'text' not in data:
+                        data['text'] = data.pop('content')
+                    if data['text'] is not None:
+                        yield data["id"], data