Squashing commit

Co-authored-by: Dan Fu <Dan Fu@users.noreply.huggingface.co>
Co-authored-by: danfu09 <danfu09@users.noreply.huggingface.co>
Co-authored-by: csris <csris@users.noreply.huggingface.co>
Co-authored-by: zhangce <zhangce@users.noreply.huggingface.co>

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README.md

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+---
+task_categories:
+- text-generation
+language:
+- en
+pretty_name: Red Pajama 1T Sample
+---
+# Dataset Card for Dataset Name
+
+### Dataset Summary
+
+RedPajama is a clean-room, fully open-source implementation of the LLaMa dataset.
+This HuggingFace repo contains a 1B-token sample of the RedPajama dataset.
+The full dataset has the following token counts and is available for [download]( https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T):
+
+| Dataset       | Token Count |
+|---------------|-------------|
+| Commoncrawl   | 878 Billion        |
+| C4            | 175 Billion        |
+| GitHub        | 59 Billion         |
+| Books         | 26 Billion         |
+| ArXiv         | 28 Billion         |
+| Wikipedia     | 24 Billion         |
+| StackExchange | 20 Billion         |
+| Total         | 1.2 Trillion      |
+
+A full set of scripts to recreate the dataset from scratch can be found [here](https://github.com/togethercomputer/RedPajama-Data).
+
+### Languages
+
+Primarily English, though the Wikipedia slice contains multiple languages.
+
+## Dataset Structure
+
+The dataset structure is as follows:
+
+```
+{
+    "text": ...,
+    "meta": {"url": "...", "timestamp": "...", "source": "...", "language": "...", ...}
+}
+```
+
+## Dataset Creation
+
+This dataset was created to follow the LLaMa paper as closely as possible to try to reproduce its recipe.
+
+### Source Data
+
+#### Commoncrawl
+
+We download 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.
+
+#### C4
+
+C4 is downloaded from Huggingface. The only preprocessing step is to bring the data into our own format.
+
+#### GitHub
+
+The raw GitHub data is downloaded from Google BigQuery. We deduplicate on the file level and filter out low quality 
+files and only keep projects that are distributed under the MIT, BSD, or Apache license.
+
+#### Wikipedia
+We use the Wikipedia dataset available on Huggingface, which is based on the Wikipedia dump from 2023-03-20 and contains
+text in 20 different languages. The dataset comes in preprocessed format, so that hyperlinks, comments and other 
+formatting boilerplate has been removed.
+
+#### Gutenberg and Books3
+The PG19 subset of the Gutenberg Project and Books3 datasets are downloaded from Huggingface. After downloading, we use 
+simhash to remove near duplicates.
+
+#### ArXiv
+ArXiv data is downloaded from Amazon S3 in the `arxiv` requester pays bucket. We only keep latex source files and 
+remove preambles, comments, macros and bibliographies.
+
+#### Stackexchange
+The Stack Exchange split of the dataset is download from the 
+[Internet Archive](https://archive.org/download/stackexchange). Here we only keep the posts from the 28 largest sites,
+remove html tags, group the posts into question-answer pairs, and order answers by their score.
+
+<!--
+### Annotations
+
+#### Annotation process
+
+[More Information Needed]
+
+#### Who are the annotators?
+
+[More Information Needed]
+
+### Personal and Sensitive Information
+
+[More Information Needed]
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+[More Information Needed]
+
+### Discussion of Biases
+
+[More Information Needed]
+
+### Other Known Limitations
+
+[More Information Needed]
+
+## Additional Information
+
+### Dataset Curators
+
+[More Information Needed]
+
+### Licensing Information
+
+[More Information Needed]
+
+### Citation Information
+
+[More Information Needed]
+
+### Contributions
+
+[More Information Needed]
+-->

RedPajama-Data-1T-Sample.py

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+# 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

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