Update README.md

.gitattributes

@@ -52,9 +52,12 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+
+
 arxiv_sample.jsonl filter=lfs diff=lfs merge=lfs -text
 book_sample.jsonl filter=lfs diff=lfs merge=lfs -text
 c4_sample.jsonl filter=lfs diff=lfs merge=lfs -text
+cc_2023-05_sample.jsonl filter=lfs diff=lfs merge=lfs -text
 stackexchange_sample.jsonl filter=lfs diff=lfs merge=lfs -text
 wikipedia_sample.jsonl filter=lfs diff=lfs merge=lfs -text
 cc_2019-30_sample.jsonl filter=lfs diff=lfs merge=lfs -text
@@ -62,4 +65,3 @@ cc_2020-05_sample.jsonl filter=lfs diff=lfs merge=lfs -text
 cc_2021-04_sample.jsonl filter=lfs diff=lfs merge=lfs -text
 cc_2022-05_sample.jsonl filter=lfs diff=lfs merge=lfs -text
 github_sample.jsonl filter=lfs diff=lfs merge=lfs -text
-cc_2023-06_sample.jsonl filter=lfs diff=lfs merge=lfs -text

README.md

@@ -49,7 +49,7 @@ This dataset was created to follow the LLaMa paper as closely as possible to try
 
 #### Commoncrawl
 
-We download five dumps from Commoncrawl, and run the dumps through the official `cc_net` pipeline.
+We downlaod five dumps from Commoncrawl, and run the dumps through the official `cc_net` pipeline.
 We then deduplicate on the paragraph level, and filter out low quality text using a linear classifier trained to 
 classify paragraphs as Wikipedia references or random Commoncrawl samples.
 

RedPajama-Data-1T-Sample.py

@@ -1,107 +0,0 @@
-# Copyright 2023 Together Computer
-#
-# 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
-"""RedPajama: An Open-Source, Clean-Room 1.2 Trillion Token Dataset."""
-
-
-import json
-
-import datasets
-
-
-logger = datasets.logging.get_logger(__name__)
-
-
-_DESCRIPTION = """\
-RedPajama is a clean-room, fully open-source implementation of the LLaMa dataset. This is a 1B-token sample of the full dataset.
-"""
-
-_URLS = [
-    "arxiv_sample.jsonl",
-    "book_sample.jsonl",
-    "c4_sample.jsonl",
-    "cc_2019-30_sample.jsonl",
-    "cc_2020-05_sample.jsonl",
-    "cc_2021-04_sample.jsonl",
-    "cc_2022-05_sample.jsonl",
-    "cc_2023-06_sample.jsonl",
-    "github_sample.jsonl",
-    "stackexchange_sample.jsonl",
-    "wikipedia_sample.jsonl",
-]
-
-
-class RedPajama1TSampleConfig(datasets.BuilderConfig):
-    """BuilderConfig for RedPajama sample."""
-
-    def __init__(self, **kwargs):
-        """BuilderConfig for RedPajama sample.
-        Args:
-          **kwargs: keyword arguments forwarded to super.
-        """
-        super(RedPajama1TSampleConfig, self).__init__(**kwargs)
-
-
-class RedPajama1TSample(datasets.GeneratorBasedBuilder):
-    """RedPajama 1T Sample: version 1.0.0."""
-
-    BUILDER_CONFIGS = [
-        RedPajama1TSampleConfig(
-            name="plain_text",
-            version=datasets.Version("1.0.0", ""),
-            description="Plain text",
-        ),
-    ]
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "text": datasets.Value("string"),
-                    "meta": datasets.Value("string"),
-                }
-            ),
-            supervised_keys=None,
-        )
-
-    def _split_generators(self, dl_manager):
-        downloaded_files = dl_manager.download_and_extract(_URLS)
-
-        return [
-            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepaths": downloaded_files})
-        ]
-
-    def _generate_examples(self, filepaths):
-        """This function returns the examples in the raw (text) form."""
-        logger.info("generating examples from = %s", filepaths)
-        key = 0
-        for filepath in filepaths:
-            with open(filepath, encoding="utf-8") as f:
-                for row in f:
-                    data = json.loads(row)
-                    if "meta" not in data:
-                        text = data["text"]
-                        del data["text"]
-                        yield key, {
-                            "text": text,
-                            "meta": json.dumps(data),
-                        }
-                    else:
-                        yield key, {
-                            "text": data["text"],
-                            "meta": data["meta"],
-                        }
-                    key += 1