Delete Matrix.py

Matrix.py

@@ -1,121 +0,0 @@
-# -*- 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