Update loader script for local files

README.md

@@ -25,6 +25,14 @@ while read line; do
 done < urls.txt
 ```
 
+After downloading the files, you can load the dataset from disk by setting the `RED_PAJAMA_DATA_DIR` environment variable to the directory containing the files:
+
+```python
+import os
+from datasets import load_dataset
+os.environ["RED_PAJAMA_DATA_DIR"] = "/path/to/download"
+ds = load_dataset("togethercomputer/RedPajama-Data-1T")
+```
 
 A smaller 1B-token sample of the dataset can be found [here](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T-Sample).
 

RedPajama-Data-1T.py

@@ -20,6 +20,7 @@ import json
 
 import datasets
 import traceback
+import os
 
 logger = datasets.logging.get_logger(__name__)
 
@@ -37,7 +38,9 @@ _URL_LISTS = {
     "stackexchange": "urls/stackexchange.txt",
     "wikipedia": "urls/wikipedia.txt",
 }
+_URL_BASE = 'https://data.together.xyz/redpajama-data-1T/v1.0.0'
 
+_DATA_DIR = os.environ.get('RED_PAJAMA_DATA_DIR', None)
 
 class RedPajama1TConfig(datasets.BuilderConfig):
     """BuilderConfig for RedPajama sample."""
@@ -48,7 +51,6 @@ class RedPajama1TConfig(datasets.BuilderConfig):
           **kwargs: keyword arguments forwarded to super.
         """
         super(RedPajama1TConfig, self).__init__(**kwargs)
-
         self.subsets = subsets
 
 
@@ -57,10 +59,59 @@ class RedPajama1T(datasets.GeneratorBasedBuilder):
 
     BUILDER_CONFIGS = [
         RedPajama1TConfig(
+            name = 'default',
             subsets = list(_URL_LISTS.keys()),
-            name="plain_text",
             version=datasets.Version("1.0.0", ""),
-            description="Plain text",
+            description="RedPajama1T",
+        ),
+
+        RedPajama1TConfig(
+            name = 'arxiv',
+            subsets = 'arxiv',
+            version=datasets.Version("1.0.0", ""),
+            description="RedPajama1T arxiv subset",
+        ),
+
+        RedPajama1TConfig(
+            name = 'book',
+            subsets = 'book',
+            version=datasets.Version("1.0.0", ""),
+            description="RedPajama1T book subset",
+        ),
+
+        RedPajama1TConfig(
+            name = 'c4',
+            subsets = 'c4',
+            version=datasets.Version("1.0.0", ""),
+            description="RedPajama1T c4 subset",
+        ),
+
+        RedPajama1TConfig(
+            name = 'common_crawl',
+            subsets = 'common_crawl',
+            version=datasets.Version("1.0.0", ""),
+            description="RedPajama1T common crawl subset",
+        ),
+
+        RedPajama1TConfig(
+            name = 'github',
+            subsets = 'github',
+            version=datasets.Version("1.0.0", ""),
+            description="RedPajama1T github subset",
+        ),
+
+        RedPajama1TConfig(
+            name = 'stackexchange',
+            subsets = 'stackexchange',
+            version=datasets.Version("1.0.0", ""),
+            description="RedPajama1T stackexchange subset",
+        ),
+
+        RedPajama1TConfig(
+            name = 'wikipedia',
+            subsets = 'wikipedia',
+            version=datasets.Version("1.0.0", ""),
+            description="RedPajama1T wikipedia subset",
         ),
     ]
 
@@ -86,9 +137,20 @@ class RedPajama1T(datasets.GeneratorBasedBuilder):
 
         for subset, url_list in url_lists.items():
             with open(url_list, encoding="utf-8") as f:
-                urls[subset] = [line.strip() for line in f][:1]
-
-        downloaded_files = dl_manager.download(urls)
+                urls[subset] = [line.strip() for line in f]
+
+        if _DATA_DIR is not None:
+            print(f'Reading data from {_DATA_DIR}')
+            url_prefix_slashes = len(_URL_BASE.split('/'))
+            downloaded_files = {
+                subset: [
+                    os.path.join(_DATA_DIR, *url.split('/')[url_prefix_slashes:])
+                    for url in url_list
+                ]
+                for subset, url_list in urls.items()
+            }
+        else:
+            downloaded_files = dl_manager.download(urls)
 
         return [
             datasets.SplitGenerator(