data

dolma.py

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+# Copyright 2023 Allen Institute for AI
+#
+# 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.
+
+# Lint as: python3
+"""Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research"""
+
+
+import json
+from pathlib import Path
+from typing import Dict, List
+
+import datasets
+import traceback
+import os
+
+logger = datasets.logging.get_logger(__name__)
+
+_CURRENT_DIR = Path(__file__).resolve().parent
+
+_DESCRIPTION = """\
+Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research
+"""
+
+_SUBSET_URLS = {
+    "v1": _CURRENT_DIR / "urls/dolma-v1.txt",
+    "v1_5r1": _CURRENT_DIR / "urls/dolma-v1_5r1.txt",
+    "v1_5r1-sample": _CURRENT_DIR / "urls/dolma-v1_5r1-sample.txt",
+    "v1_5r2": _CURRENT_DIR / "urls/dolma-v1_5r2.txt",
+}
+_SUBSET_VERSION = {
+    "v1": "1.0.0",
+    "v1_5r1": "1.5.0",
+    "v1_5r1-sample": "1.5.0",
+    "v1_5r2": "1.5.0",
+}
+_SUBSET_NAME = {
+    "v1": "Dolma v1 (Aug 2023)",
+    "v1_5r1": "Dolma v1.5r1 (Oct 2023)",
+    "v1_5r1-sample": "Dolma v1.5r1, 2T sample (Oct 2023)",
+    "v1_5r2": "Dolma v1.5r2 (Dec 2023)",
+}
+
+_DATA_DIR = os.environ.get("DOLMA_DATA_DIR", None)
+
+
+class RedPajama1TConfig(datasets.BuilderConfig):
+    """BuilderConfig for RedPajama sample."""
+
+    def __init__(self, *args, subsets: List[str], url_file: Path, **kwargs):
+        """BuilderConfig for RedPajama.
+        Args:
+          **kwargs: keyword arguments forwarded to super.
+        """
+        super(RedPajama1TConfig, self).__init__(*args, **kwargs)
+        self.subsets = subsets
+        self.url_file = url_file
+
+
+class RedPajama1T(datasets.GeneratorBasedBuilder):
+    """Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research"""
+
+    config: RedPajama1TConfig
+
+    BUILDER_CONFIGS = [
+        RedPajama1TConfig(
+            name=subset,
+            subsets=[subset],
+            url_file=_SUBSET_URLS[subset],
+            version=datasets.Version(_SUBSET_VERSION[subset], _SUBSET_NAME[subset]),
+            description=_DESCRIPTION,
+        )
+        for subset in _SUBSET_URLS
+    ]
+
+    DEFAULT_CONFIG_NAME = "v1_5r2"
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "text": datasets.Value("string"),
+                    "metadata": datasets.Value("string"),
+                    "added": datasets.Value("string"),
+                    # "metadata": datasets.Value("")
+                }
+            ),
+            supervised_keys=None,
+        )
+
+    def _split_generators(self, dl_manager):
+        with open(self.config.url_file, encoding="utf-8") as f:
+            subset_urls: Dict[str, List[str]] = json.load(f)
+
+        breakpoint()
+
+        url_lists: Dict[str, List[str]] = {}
+        for subset in self.config.subsets:
+            url_lists[subset] = dl_manager.download(subset_urls[subset])
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN._name,
+                gen_kwargs={"files": {subset: url_lists[subset] for subset in self.config.subsets}},
+            )
+        ]
+
+    def _generate_examples(self, files):
+        """This function returns the examples in the raw (text) form."""
+        breakpoint()