Datasets:

meg
/

dolma-v1_6-sample

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README.md ADDED Viewed

	@@ -0,0 +1,139 @@

+---
+license: odc-by
+viewer: false
+task_categories:
+- text-generation
+language:
+- en
+tags:
+- language-modeling
+- casual-lm
+- llm
+pretty_name: Dolma
+size_categories:
+- n>1T
+---
+# Dolma
+<img alt="Dolma's official logo. It's dolma written in yellow, round lowercase letters over a blue background." src="https://raw.githubusercontent.com/allenai/dolma/main/docs/assets/AI2_Blog_1400x685_2x.webp" width="100%">
+Dolma is a dataset of 3 trillion tokens from a diverse mix of web content, academic publications, code, books, and encyclopedic materials.
+More information:
+- Read Dolma **manuscript** and its **Data Sheet** [on ArXiv](https://arxiv.org/abs/2402.00159);
+- Explore the [**open source tools**](https://github.com/allenai/dolma) we created to curate Dolma.
+- Want to request removal of personal data? Use [this form](https://forms.gle/q4BNUUxUxKwKkfdT6) to notify us of documents containing PII about a specific user.
+To learn more about the toolkit used to create Dolma, including how to replicate this dataset, head over our [GitHub project page](https://github.com/allenai/dolma/tree/main/docs)!
+**2024-04-17: Dolma v1.7 Release.** We have released an updated version of Dolma that we used to train our latest [OLMo 7B-v1.7](https://huggingface.co/allenai/OLMo-7b-v1.7) model.
+**2024-04-15: License Change.** We have updated the license of Dolma to [ODC-BY](https://opendatacommons.org/licenses/by/1-0/). Please see this [blog post](https://blog.allenai.org/making-a-switch-dolma-moves-to-odc-by-8f0e73852f44) for more information.
+## Versions
+At the moment, there are six versions of Dolma available:
+| **Version** | **Default?** | **Release Date** | **Size** (gzip) | **Description** |
+|--|:--:|--|--|--|
+| `v1_7` | ✅ | 2024-04-15 | 4.5 TB | Used to train [OLMo-7B-v1.7](https://huggingface.co/allenai/OLMo-7b-v1.7). New sources, more quality filtering, fuzzy deduplication. |
+| `v1_6` |  | 2024-01-31 | 5.4 TB | An update to v1.5 with some deduplication of documents with too few tokens or too many repeated n-grams. |
+| `v1_6-sample` | | 2024-01-31 | 16.4 GB | A smaller sample of Dolma, with roughly 10 billion tokens. Useful for data exploration. |
+| `v1_5` | | 2023-10-31 | 6.4 TB | Used to train [OLMo-1B](https://huggingface.co/allenai/OLMo-1B). Roughly 3 trillion tokens. |
+| `v1_5-sample` | | 2023-10-31 | 2.9 TB | A sample of roughly 1.9 trillion tokens used to train [OLMo-7B](https://huggingface.co/allenai/OLMo-7B) |
+| `v1` | | 2023-08-18 | 6.0 TB | The first version of Dolma. |
+## Summary Statistics (v1.7)
+| **Source** | **Provenance** | **New?** | **Documents** (millions) |  **OLMo tokens** (billions) | **Sample Proportion** | **Cutoff Date** | **Processing**
+|--|--|--|--|--|--|--|--|
+| Dolma's CC | [Common Crawl](https://commoncrawl.org/) via Dolma v1.6 | Updated | 875.2 | 1,195.5 | 50% | Mar 2023 | Extracted using the Dolma pipeline; new quality filtering and deduplication steps. |
+| Refined Web | [Refined Web](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) | Yes | 664.0 | 456.4  | 100% | Feb 2023 | Filtered using the Dolma pipeline; new quality filtering and deduplication steps.  |
+| StarCoder | [StarCoder](https://huggingface.co/blog/starcoder) | Yes |  206.6 | 263.8  | 100% | May 2023 | No further processing. |
+| C4 | [C4](https://huggingface.co/datasets/c4) via Dolma v1.6 | Updated | 249.9 | 138.4 | 50% | Apr 2019 | Filtered using the Dolma pipeline; new quality filtering and deduplication steps. |
+| Reddit | [PushShift API](https://github.com/pushshift/api) | Updated | 377.4 | 79.9 | 100% | Mar 2023 | Extracted using the Dolma pipeline; new quality filtering and deduplication steps. |
+| Semantic Scholar ([S2ORC](https://aclanthology.org/2020.acl-main.447/) & [S2AG](https://www.semanticscholar.org/product/api)) | [peS2o](https://huggingface.co/datasets/allenai/peS2o) via Dolma v1.6 | No | 38.8  | 57.2 | 100% | Mar 2023 | Same as Dolma v1.6 |
+| arXiv | [RedPajama v1](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) | Yes | 1.5 | 28.0 | 100% | Mar 2023 | No further processing. |
+| StackExchange | [RedPajama v1](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-1T) | Yes | 29.3 | 19.6 | 100%  | Mar 2023  | No further processing.  |
+| Flan | [Flan Collection](https://arxiv.org/abs/2301.13688), reproduced following the [original code](https://github.com/google-research/FLAN/tree/main/flan/v2), as performed by [Dettmers et al., (2023)](https://openreview.net/forum?id=OUIFPHEgJU)   | Yes | 52.1 | 16.5 | 100%  | Feb 2023  | After reproducing Flan, sampled to balance different Flan subsets. Reformatted for pretraining with newlines separating instruction and demonstration. |
+| CC News | [Common Crawl](https://commoncrawl.org/blog/news-dataset-available) | Yes | 22.0 | 14.3  | 100%  | Mar 2023  | Extracted using the Dolma pipeline; new quality filtering and deduplication steps. |
+| OpenWebMath | [OpenWebMath](https://huggingface.co/datasets/open-web-math/open-web-math) via [Proof Pile II](https://huggingface.co/datasets/EleutherAI/proof-pile-2) | Yes | 2.9 |  12.6 | 100% | May 2023 | Training subset; no further processing. |
+| Algebraic Stack | [Proof Pile II](https://huggingface.co/datasets/EleutherAI/proof-pile-2) | Yes | 2.8 |  12.6 | 100% | Oct 2023 | Training subset; no further processing. |
+| Project Gutenberg | [Project Gutenberg](https://www.gutenberg.org) via Dolma v1.6 | No |  0.0556 | 5.3 | 100% | Mar 2023 | Same as Dolma v1.6 |
+| MegaWika | [MetaWika](https://huggingface.co/datasets/hltcoe/megawika) | Yes | 3.2 | 4.6 | 100%  | Jul 2023  | English web pages cited from Wikipedia; curated using the full Dolma pipeline.  |
+| Wikipedia & Wikibooks | [Wikimedia](https://dumps.wikimedia.org) via Dolma v1.6 | No |  6.2 |  3.7 | 200%  | Mar 2023 |  Same as Dolma v1.6 |
+| **Total** | | | **2532.0** | **2,308.5** | **1,715.1** | **Oct 2023** | |
+(A subset of total data was used for training of OLMo 7B-v1.7. The token counts are based on the full dataset, whereas taking into account sampling proportion gives the final actual token counts used for training --- 1.715 trillion tokens.)
+## Summary Statistics (v1.6)
+| **Source** | **Doc Type** | **UTF-8 bytes** (GB) | **Documents** (millions) | **Unicode words** (billions) | **Llama tokens** (billions) |
+|--|--|--|--|--|--|
+| Common Crawl | web pages | 9,022 | 3,370 | 1,775 | 2,281 |
+| The Stack | code| 1,043| 210 | 260| 411 |
+| C4 | web pages | 790 | 364 | 153| 198 |
+| Reddit| social media| 339 | 377| 72| 89 |
+| PeS2o | STEM papers| 268 | 38.8| 50| 70 |
+| Project Gutenberg | books | 20.4 | 0.056 | 4.0 | 6.0 |
+| Wikipedia, Wikibooks | encyclopedic | 16.2 | 6.2 | 3.7 | 4.3 |
+| **Total** | | **11,519** | **4,367** | **2,318** | **3,059** |
+## Download
+The fastest way to download Dolma is to clone this repository and use the files in the `url` directory.
+We recommend using wget in parallel mode to download the files. For example:
+```bash
+DATA_DIR="<path_to_your_data_directory>"
+PARALLEL_DOWNLOADS="<number_of_parallel_downloads>"
+DOLMA_VERSION="<version_of_dolma_to_download>"
+git clone https://huggingface.co/datasets/allenai/dolma
+mkdir -p "${DATA_DIR}"
+cat "dolma/urls/${DOLMA_VERSION}.txt" | xargs -n 1 -P "${PARALLEL_DOWNLOADS}" wget -q -P "$DATA_DIR"
+```
+Then, to load this data using HuggingFace's `datasets` library, you can use the following code:
+```python
+import os
+from datasets import load_dataset
+os.environ["DATA_DIR"] = "<path_to_your_data_directory>"
+dataset = load_dataset("allenai/dolma", split="train")
+```
+### Licensing Information
+We are releasing this dataset under the terms of [ODC-BY](https://opendatacommons.org/licenses/by/1-0/).
+By using this dataset, you are also bound any license agreements and terms of use of the original data sources.
+## Bibtex
+If you use our dataset or tooling, please cite us at:
+```bibtex
+@article{dolma,
+  title = {{Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research}},
+  author={
+    Luca Soldaini and Rodney Kinney and Akshita Bhagia and Dustin Schwenk and David Atkinson and
+    Russell Authur and Ben Bogin and Khyathi Chandu and Jennifer Dumas and Yanai Elazar and
+    Valentin Hofmann and Ananya Harsh Jha and Sachin Kumar and Li Lucy and Xinxi Lyu and
+    Nathan Lambert and Ian Magnusson and Jacob Morrison and Niklas Muennighoff and Aakanksha Naik and
+    Crystal Nam and Matthew E. Peters and Abhilasha Ravichander and Kyle Richardson and Zejiang Shen and
+    Emma Strubell and Nishant Subramani and Oyvind Tafjord and Pete Walsh and Luke Zettlemoyer and
+    Noah A. Smith and Hannaneh Hajishirzi and Iz Beltagy and Dirk Groeneveld and Jesse Dodge and Kyle Lo
+  },
+  year = {2024},
+  journal={arXiv preprint},
+}
+```

dolma-v1_6-sample.py ADDED Viewed

	@@ -0,0 +1,140 @@

+# Copyright 2024 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 gzip
+import json
+import os
+from typing import List
+import datasets
+logger = datasets.logging.get_logger(__name__)
+_DESCRIPTION = """\
+Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research
+"""
+_URL_LISTS = {
+    "v1": "urls/v1.txt",
+    "v1_5": "urls/v1_5.txt",
+    "v1_5-sample": "urls/v1_5-sample.txt",
+    "v1_6": "urls/v1_6.txt",
+    "v1_6-sample": "urls/v1_6-sample.txt",
+    "v1_7": "urls/v1_7.txt",
+}
+_VERSIONS = {
+    "v1": "1.0.0",
+    "v1_5": "1.5.0",
+    "v1_5-sample": "1.5.0",
+    "v1_6": "1.6.0",
+    "v1_6-sample": "1.6.0",
+    "v1_7": "1.7.0",
+}
+_DATES = {
+    "v1": "(Aug 2023)",
+    "v1_5": "(Oct 2023)",
+    "v1_5-sample": "(Oct 2023)",
+    "v1_6": "(Jan 2024)",
+    "v1_6-sample": "(Jan 2024)",
+    "v1_7": "(Apr 2024)",
+}
+_BASE_URL = "https://olmo-data.org"
+_DATA_DIR = os.environ.get("DOLMA_DATA_DIR", None)
+_CITATION = """\
+@article{dolma,
+  title = {{Dolma: An Open Corpus of Three Trillion Tokens for Language Model Pretraining Research}},
+  author = {
+    Luca Soldaini and Rodney Kinney and Akshita Bhagia and Dustin Schwenk and David Atkinson and
+    Russell Authur and Ben Bogin and Khyathi Chandu and Jennifer Dumas and Yanai Elazar and
+    Valentin Hofmann and Ananya Harsh Jha and Sachin Kumar and Li Lucy and Xinxi Lyu and Ian Magnusson and
+    Jacob Morrison and Niklas Muennighoff and Aakanksha Naik and Crystal Nam and Matthew E. Peters and
+    Abhilasha Ravichander and Kyle Richardson and Zejiang Shen and Emma Strubell and Nishant Subramani and
+    Oyvind Tafjord and Evan Pete Walsh and Hannaneh Hajishirzi and Noah A. Smith and Luke Zettlemoyer and
+    Iz Beltagy and Dirk Groeneveld and Jesse Dodge and Kyle Lo
+},
+  year = {2024},
+  journal={arXiv preprint},
+}
+"""
+class Dolma(datasets.GeneratorBasedBuilder):
+    """Dolma: an Open Corpus of Three Trillion Tokens for Language Model Pretraining Research"""
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name=name,
+            version=_VERSIONS[name],
+            description=f"{_DESCRIPTION} {_DATES[name]}",
+        )
+        for name in _URL_LISTS.keys()
+    ]
+    DEFAULT_CONFIG_NAME = "v1_6-sample"
+    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"),
+                    "created": datasets.Value("string"),
+                    "source": datasets.Value("string"),
+                }
+            ),
+            supervised_keys=None,
+        )
+    def _split_generators(self, dl_manager: datasets.DownloadManager) -> List[datasets.SplitGenerator]:
+        path = dl_manager.download(_URL_LISTS[self.config.name])
+        with open(path, mode="rt", encoding="utf-8") as f:  # type: ignore[no-untyped-call]
+            subset_urls = f.read().splitlines()
+        if _DATA_DIR is not None:
+            subset_files = [os.path.join(_DATA_DIR, url.replace(_BASE_URL, "").lstrip("/")) for url in subset_urls]
+        else:
+            subset_files = dl_manager.download(subset_urls)
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,  # type: ignore[assignment]
+                gen_kwargs={"files": subset_files},
+            )
+        ]
+    def _generate_examples(self, files: List[str]):
+        """This function returns the examples in the raw (text) form."""
+        for fn in files:
+            logger.info("generating examples from = %s", fn)
+            with gzip.open(fn, mode="rt", encoding="utf-8") as f:
+                for line in f:
+                    row = json.loads(line)
+                    yield row["id"], {
+                        "id": row["id"],
+                        "text": row["text"],
+                        "added": row.get("added", ""),
+                        "created": row.get("created", ""),
+                        "source": row.get("source", ""),
+                    }

urls/v1.txt ADDED Viewed