diff --git a/.gitattributes b/.gitattributes
new file mode 100644
index 0000000000000000000000000000000000000000..28df5f900b358436f0267334b3e3e9af33f917ba
--- /dev/null
+++ b/.gitattributes
@@ -0,0 +1,55 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.lz4 filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+# Audio files - uncompressed
+*.pcm filter=lfs diff=lfs merge=lfs -text
+*.sam filter=lfs diff=lfs merge=lfs -text
+*.raw filter=lfs diff=lfs merge=lfs -text
+# Audio files - compressed
+*.aac filter=lfs diff=lfs merge=lfs -text
+*.flac filter=lfs diff=lfs merge=lfs -text
+*.mp3 filter=lfs diff=lfs merge=lfs -text
+*.ogg filter=lfs diff=lfs merge=lfs -text
+*.wav filter=lfs diff=lfs merge=lfs -text
+# Image files - uncompressed
+*.bmp filter=lfs diff=lfs merge=lfs -text
+*.gif filter=lfs diff=lfs merge=lfs -text
+*.png filter=lfs diff=lfs merge=lfs -text
+*.tiff filter=lfs diff=lfs merge=lfs -text
+# Image files - compressed
+*.jpg filter=lfs diff=lfs merge=lfs -text
+*.jpeg filter=lfs diff=lfs merge=lfs -text
+*.webp filter=lfs diff=lfs merge=lfs -text
diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..94e294542f49cf75a174f3e064ef9c1192e1972c
--- /dev/null
+++ b/README.md
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+---
+license: odc-by
+task_categories:
+ - text-generation
+language:
+ - en
+pretty_name: FineWeb
+size_categories:
+ - n>1T
+configs:
+ - config_name: default
+ data_files:
+ - split: train
+ path: data/*/*
+ - config_name: CC-MAIN-2024-10
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2024-10/*
+ - config_name: CC-MAIN-2023-50
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2023-50/*
+ - config_name: CC-MAIN-2023-40
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2023-40/*
+ - config_name: CC-MAIN-2023-23
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2023-23/*
+ - config_name: CC-MAIN-2023-14
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2023-14/*
+ - config_name: CC-MAIN-2023-06
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2023-06/*
+ - config_name: CC-MAIN-2022-49
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2022-49/*
+ - config_name: CC-MAIN-2022-40
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2022-40/*
+ - config_name: CC-MAIN-2022-33
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2022-33/*
+ - config_name: CC-MAIN-2022-27
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2022-27/*
+ - config_name: CC-MAIN-2022-21
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2022-21/*
+ - config_name: CC-MAIN-2022-05
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2022-05/*
+ - config_name: CC-MAIN-2021-49
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2021-49/*
+ - config_name: CC-MAIN-2021-43
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2021-43/*
+ - config_name: CC-MAIN-2021-39
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2021-39/*
+ - config_name: CC-MAIN-2021-31
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2021-31/*
+ - config_name: CC-MAIN-2021-25
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2021-25/*
+ - config_name: CC-MAIN-2021-21
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2021-21/*
+ - config_name: CC-MAIN-2021-17
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2021-17/*
+ - config_name: CC-MAIN-2021-10
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2021-10/*
+ - config_name: CC-MAIN-2021-04
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2021-04/*
+ - config_name: CC-MAIN-2020-50
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2020-50/*
+ - config_name: CC-MAIN-2020-45
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2020-45/*
+ - config_name: CC-MAIN-2020-40
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2020-40/*
+ - config_name: CC-MAIN-2020-34
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2020-34/*
+ - config_name: CC-MAIN-2020-29
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2020-29/*
+ - config_name: CC-MAIN-2020-24
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2020-24/*
+ - config_name: CC-MAIN-2020-16
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2020-16/*
+ - config_name: CC-MAIN-2020-10
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2020-10/*
+ - config_name: CC-MAIN-2020-05
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2020-05/*
+ - config_name: CC-MAIN-2019-51
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2019-51/*
+ - config_name: CC-MAIN-2019-47
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2019-47/*
+ - config_name: CC-MAIN-2019-43
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2019-43/*
+ - config_name: CC-MAIN-2019-39
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2019-39/*
+ - config_name: CC-MAIN-2019-35
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2019-35/*
+ - config_name: CC-MAIN-2019-30
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2019-30/*
+ - config_name: CC-MAIN-2019-26
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2019-26/*
+ - config_name: CC-MAIN-2019-22
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2019-22/*
+ - config_name: CC-MAIN-2019-18
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2019-18/*
+ - config_name: CC-MAIN-2019-13
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2019-13/*
+ - config_name: CC-MAIN-2019-09
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2019-09/*
+ - config_name: CC-MAIN-2019-04
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2019-04/*
+ - config_name: CC-MAIN-2018-51
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2018-51/*
+ - config_name: CC-MAIN-2018-47
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2018-47/*
+ - config_name: CC-MAIN-2018-43
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2018-43/*
+ - config_name: CC-MAIN-2018-39
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2018-39/*
+ - config_name: CC-MAIN-2018-34
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2018-34/*
+ - config_name: CC-MAIN-2018-30
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2018-30/*
+ - config_name: CC-MAIN-2018-26
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2018-26/*
+ - config_name: CC-MAIN-2018-22
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2018-22/*
+ - config_name: CC-MAIN-2018-17
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2018-17/*
+ - config_name: CC-MAIN-2018-13
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2018-13/*
+ - config_name: CC-MAIN-2018-09
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2018-09/*
+ - config_name: CC-MAIN-2018-05
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2018-05/*
+ - config_name: CC-MAIN-2017-51
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2017-51/*
+ - config_name: CC-MAIN-2017-47
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2017-47/*
+ - config_name: CC-MAIN-2017-43
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2017-43/*
+ - config_name: CC-MAIN-2017-39
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2017-39/*
+ - config_name: CC-MAIN-2017-34
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2017-34/*
+ - config_name: CC-MAIN-2017-30
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2017-30/*
+ - config_name: CC-MAIN-2017-26
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2017-26/*
+ - config_name: CC-MAIN-2017-22
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2017-22/*
+ - config_name: CC-MAIN-2017-17
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2017-17/*
+ - config_name: CC-MAIN-2017-13
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2017-13/*
+ - config_name: CC-MAIN-2017-09
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2017-09/*
+ - config_name: CC-MAIN-2017-04
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2017-04/*
+ - config_name: CC-MAIN-2016-50
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2016-50/*
+ - config_name: CC-MAIN-2016-44
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2016-44/*
+ - config_name: CC-MAIN-2016-40
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2016-40/*
+ - config_name: CC-MAIN-2016-36
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2016-36/*
+ - config_name: CC-MAIN-2016-30
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2016-30/*
+ - config_name: CC-MAIN-2016-26
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2016-26/*
+ - config_name: CC-MAIN-2016-22
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2016-22/*
+ - config_name: CC-MAIN-2016-18
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2016-18/*
+ - config_name: CC-MAIN-2016-07
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2016-07/*
+ - config_name: CC-MAIN-2015-48
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2015-48/*
+ - config_name: CC-MAIN-2015-40
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2015-40/*
+ - config_name: CC-MAIN-2015-35
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2015-35/*
+ - config_name: CC-MAIN-2015-32
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2015-32/*
+ - config_name: CC-MAIN-2015-27
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2015-27/*
+ - config_name: CC-MAIN-2015-22
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2015-22/*
+ - config_name: CC-MAIN-2015-18
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2015-18/*
+ - config_name: CC-MAIN-2015-14
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2015-14/*
+ - config_name: CC-MAIN-2015-11
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2015-11/*
+ - config_name: CC-MAIN-2015-06
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2015-06/*
+ - config_name: CC-MAIN-2014-52
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2014-52/*
+ - config_name: CC-MAIN-2014-49
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2014-49/*
+ - config_name: CC-MAIN-2014-42
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2014-42/*
+ - config_name: CC-MAIN-2014-41
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2014-41/*
+ - config_name: CC-MAIN-2014-35
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2014-35/*
+ - config_name: CC-MAIN-2014-23
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2014-23/*
+ - config_name: CC-MAIN-2014-15
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2014-15/*
+ - config_name: CC-MAIN-2014-10
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2014-10/*
+ - config_name: CC-MAIN-2013-48
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2013-48/*
+ - config_name: CC-MAIN-2013-20
+ data_files:
+ - split: train
+ path: data/CC-MAIN-2013-20/*
+---
+# π· FineWeb
+
+ 
+
+
+> 15 trillion tokens of the finest data the π web has to offer
+
+# Table of Contents
+- [π· FineWeb](#-fineweb)
+ * [What is it?](#what-is-it)
+ * [What is being released?](#what-is-being-released)
+ * [How to download and use π· FineWeb](#how-to-download-and-use-π·-fineweb)
+ + [Using π `datatrove`](#using-datatrove)
+ + [Using `huggingface_hub`](#using-huggingface_hub)
+ + [Using `datasets`](#using-datasets)
+ * [Breakdown by dump/crawl](#breakdown-by-dumpcrawl)
+ * [Dataset performance evaluation and ablations](#dataset-performance-evaluation-and-ablations)
+ + [Hyper-parameters for ablation models](#hyper-parameters-for-ablation-models)
+ + [Ablation evaluation benchmarks](#ablation-evaluation-benchmarks)
+ + [Comparison with other datasets](#comparison-with-other-datasets)
+- [Dataset card for π· FineWeb](#dataset-card-for-π·-fineweb)
+ * [Dataset Summary](#dataset-summary)
+ * [Dataset Structure](#dataset-structure)
+ + [Data Instances](#data-instances)
+ + [Data Fields](#data-fields)
+ + [Data Splits](#data-splits)
+ * [Dataset Creation](#dataset-creation)
+ + [Curation Rationale](#curation-rationale)
+ + [Source Data](#source-data)
+ + [Data processing steps](#data-processing-steps)
+ + [Annotations](#annotations)
+ + [Personal and Sensitive Information](#personal-and-sensitive-information)
+ * [Considerations for Using the Data](#considerations-for-using-the-data)
+ + [Social Impact of Dataset](#social-impact-of-dataset)
+ + [Discussion of Biases](#discussion-of-biases)
+ + [Other Known Limitations](#other-known-limitations)
+ * [Additional Information](#additional-information)
+ + [Licensing Information](#licensing-information)
+ + [Future work](#future-work)
+ + [Citation Information](#citation-information)
+
+## What is it?
+
+The π· FineWeb dataset consists of more than **15T tokens** of cleaned and deduplicated english web data from CommonCrawl. The data processing pipeline is optimized for LLM performance and ran on the π [`datatrove`](https://github.com/huggingface/datatrove/) library, our large scale data processing library.
+
+π· FineWeb was originally meant to be a fully open replication of π¦
[RefinedWeb](https://huggingface.co/papers/2306.01116), with a release of the **full dataset** under the **ODC-By 1.0 license**. However, by carefully adding additional filtering steps, we managed to push the performance of π· FineWeb well above that of the original π¦
RefinedWeb, and models trained on our dataset also outperform models trained on other commonly used high quality web datasets (like C4, Dolma-v1.6, The Pile, SlimPajama, RedPajam2) on our aggregate group of [benchmark tasks](https://huggingface.co/datasets/HuggingFaceFW/fineweb/blob/main/lighteval_tasks.py).
+
+That said, we think there is still room for additional filtering and improvement and intend to continue exploring how to improve the dataset quality in coming versions of π· FineWeb.
+
+## What is being released?
+
+Along with the dataset, which includes all CommonCrawl dumps since 2013, we also share all the code needed to fully reproduce our processing setup using the π [`datatrove`](https://github.com/huggingface/datatrove/) library [here](https://github.com/huggingface/datatrove/blob/main/examples/fineweb.py). To enable full replication of our results, we have also published the small ablation models we have trained using [`nanotron`](https://github.com/huggingface/nanotron/) to validate the dataset and compare it with other reference datasets. You will find them [here](https://huggingface.co/collections/HuggingFaceFW/ablation-models-662457b0d213e8c14fe47f32), with checkpoints every 1000 steps. We have also published our evaluation results [here](https://huggingface.co/datasets/HuggingFaceFW/fineweb/blob/main/eval_results.csv). Our evaluation setup is available [here](https://huggingface.co/datasets/HuggingFaceFW/fineweb/blob/main/lighteval_tasks.py).
+
+You will find details on the different processing decisions we took and some interesting explorations of deduplication methods and differences between CommonCrawl dumps in our technical report to be published in the coming days.
+
+## How to download and use π· FineWeb
+
+You can load the full dataset or a specific crawl/dump (see table below). Dumps have the format `CC-MAIN-(year)-(week number)`.
+
+### Using π [`datatrove`](https://github.com/huggingface/datatrove/)
+
+```python
+from datatrove.pipeline.readers import ParquetReader
+
+# limit determines how many documents will be streamed (remove for all)
+# to fetch a specific dump: hf://datasets/HuggingFaceFW/fineweb/data/CC-MAIN-2024-10
+data_reader = ParquetReader("hf://datasets/HuggingFaceFW/fineweb/data", limit=1000)
+for document in data_reader():
+ # do something with document
+ print(document)
+
+###############################
+# OR for a processing pipeline:
+###############################
+
+from datatrove.executor import LocalPipelineExecutor
+from datatrove.pipeline.readers import ParquetReader
+from datatrove.pipeline.filters import LambdaFilter
+from datatrove.pipeline.writers import JsonlWriter
+
+pipeline_exec = LocalPipelineExecutor(
+ pipeline=[
+ ParquetReader("hf://datasets/HuggingFaceFW/fineweb/data/CC-MAIN-2024-10", limit=1000),
+ LambdaFilter(lambda doc: "hugging" in doc.text),
+ JsonlWriter("some-output-path")
+ ],
+ tasks=10
+)
+pipeline_exec.run()
+```
+
+### Using `huggingface_hub`
+
+```python
+from huggingface_hub import snapshot_download
+folder = snapshot_download(
+ "HuggingFaceFW/fineweb",
+ repo_type="dataset",
+ local_dir="./fineweb/",
+ allow_patterns="data/CC-MAIN-2023-50/*")
+```
+
+For faster downloads, make sure to install `pip install huggingface_hub[hf_transfer]` and set the environment variable `HF_HUB_ENABLE_HF_TRANSFER=1`.
+
+### Using `datasets`
+
+```python
+from datasets import load_dataset
+fw = load_dataset("HuggingFaceFW/fineweb", name="CC-MAIN-2024-10", split="train", streaming=True)
+```
+
+## Breakdown by dump/crawl
+
+| Dump | Time period | Disk size (GB) | gpt2 tokens (billions) |
+| --- | --- | --- | --- |
+| CC-MAIN-2024-10 | February/March 2024 | 432.0 | 157.2 |
+| CC-MAIN-2023-50 | November/December 2023 | 650.0 | 239.7 |
+| CC-MAIN-2023-40 | September/October 2023 | 578.6 | 213.0 |
+| CC-MAIN-2023-23 | May/June 2023 | 585.5 | 218.5 |
+| CC-MAIN-2023-14 | March/April 2023 | 543.3 | 202.4 |
+| CC-MAIN-2023-06 | January/February 2023 | 540.3 | 198.6 |
+| CC-MAIN-2022-49 | November/December 2022 | 557.9 | 205.6 |
+| CC-MAIN-2022-40 | September/October 2022 | 535.0 | 198.2 |
+| CC-MAIN-2022-33 | August 2022 | 381.4 | 141.1 |
+| CC-MAIN-2022-27 | June/July 2022 | 506.4 | 187.1 |
+| CC-MAIN-2022-21 | May 2022 | 573.7 | 211.7 |
+| CC-MAIN-2022-05 | January 2022 | 464.7 | 171.3 |
+| CC-MAIN-2021-49 | November/December 2021 | 368.2 | 135.8 |
+| CC-MAIN-2021-43 | October 2021 | 601.5 | 221.0 |
+| CC-MAIN-2021-39 | September 2021 | 518.9 | 190.6 |
+| CC-MAIN-2021-31 | July/August 2021 | 593.9 | 217.7 |
+| CC-MAIN-2021-25 | June 2021 | 424.4 | 155.7 |
+| CC-MAIN-2021-21 | May 2021 | 455.9 | 167.4 |
+| CC-MAIN-2021-17 | April 2021 | 556.0 | 204.1 |
+| CC-MAIN-2021-10 | February/March 2021 | 463.2 | 169.6 |
+| CC-MAIN-2021-04 | January 2021 | 562.4 | 205.4 |
+| CC-MAIN-2020-50 | November/December 2020 | 422.8 | 154.3 |
+| CC-MAIN-2020-45 | October 2020 | 426.9 | 155.8 |
+| CC-MAIN-2020-40 | September 2020 | 555.5 | 202.4 |
+| CC-MAIN-2020-34 | August 2020 | 379.6 | 138.7 |
+| CC-MAIN-2020-29 | July 2020 | 489.6 | 178.7 |
+| CC-MAIN-2020-24 | May/June 2020 | 398.7 | 145.1 |
+| CC-MAIN-2020-16 | March/April 2020 | 454.0 | 165.6 |
+| CC-MAIN-2020-10 | February 2020 | 369.6 | 134.7 |
+| CC-MAIN-2020-05 | January 2020 | 483.3 | 176.4 |
+| CC-MAIN-2019-51 | December 2019 | 359.3 | 130.9 |
+| CC-MAIN-2019-47 | November 2019 | 395.4 | 144.0 |
+| CC-MAIN-2019-43 | October 2019 | 422.3 | 153.9 |
+| CC-MAIN-2019-39 | September 2019 | 394.4 | 143.7 |
+| CC-MAIN-2019-35 | August 2019 | 454.2 | 165.4 |
+| CC-MAIN-2019-30 | July 2019 | 416.6 | 151.5 |
+| CC-MAIN-2019-26 | June 2019 | 412.9 | 150.1 |
+| CC-MAIN-2019-22 | May 2019 | 432.8 | 157.4 |
+| CC-MAIN-2019-18 | April 2019 | 426.7 | 155.3 |
+| CC-MAIN-2019-13 | March 2019 | 417.8 | 152.1 |
+| CC-MAIN-2019-09 | February 2019 | 467.2 | 169.9 |
+| CC-MAIN-2019-04 | January 2019 | 438.1 | 158.7 |
+| CC-MAIN-2018-51 | December 2018 | 498.6 | 180.8 |
+| CC-MAIN-2018-47 | November 2018 | 437.7 | 158.9 |
+| CC-MAIN-2018-43 | October 2018 | 468.8 | 169.9 |
+| CC-MAIN-2018-39 | September 2018 | 429.2 | 155.2 |
+| CC-MAIN-2018-34 | August 2018 | 408.2 | 148.0 |
+| CC-MAIN-2018-30 | July 2018 | 501.5 | 181.4 |
+| CC-MAIN-2018-26 | June 2018 | 467.5 | 170.0 |
+| CC-MAIN-2018-22 | May 2018 | 398.6 | 144.2 |
+| CC-MAIN-2018-17 | April 2018 | 435.1 | 158.1 |
+| CC-MAIN-2018-13 | March 2018 | 471.5 | 171.5 |
+| CC-MAIN-2018-09 | February 2018 | 490.2 | 178.0 |
+| CC-MAIN-2018-05 | January 2018 | 493.5 | 180.7 |
+| CC-MAIN-2017-51 | December 2017 | 442.6 | 161.5 |
+| CC-MAIN-2017-47 | November 2017 | 457.9 | 167.1 |
+| CC-MAIN-2017-43 | October 2017 | 535.6 | 194.9 |
+| CC-MAIN-2017-39 | September 2017 | 444.5 | 162.3 |
+| CC-MAIN-2017-34 | August 2017 | 503.2 | 183.4 |
+| CC-MAIN-2017-30 | July 2017 | 439.2 | 161.2 |
+| CC-MAIN-2017-26 | June 2017 | 491.5 | 179.8 |
+| CC-MAIN-2017-22 | May 2017 | 441.0 | 161.5 |
+| CC-MAIN-2017-17 | April 2017 | 596.8 | 218.6 |
+| CC-MAIN-2017-13 | March 2017 | 579.8 | 212.1 |
+| CC-MAIN-2017-09 | February 2017 | 492.2 | 180.2 |
+| CC-MAIN-2017-04 | January 2017 | 474.3 | 174.4 |
+| CC-MAIN-2016-50 | December 2016 | 448.9 | 165.4 |
+| CC-MAIN-2016-44 | October 2016 | 467.8 | 172.0 |
+| CC-MAIN-2016-40 | September 2016 | 386.1 | 142.8 |
+| CC-MAIN-2016-36 | August 2016 | 339.6 | 126.3 |
+| CC-MAIN-2016-30 | July 2016 | 346.0 | 128.4 |
+| CC-MAIN-2016-26 | June 2016 | 256.5 | 95.5 |
+| CC-MAIN-2016-22 | May 2016 | 310.9 | 115.4 |
+| CC-MAIN-2016-18 | April 2016 | 298.1 | 110.8 |
+| CC-MAIN-2016-07 | February 2016 | 342.7 | 127.2 |
+| CC-MAIN-2015-48 | November 2015 | 353.9 | 131.3 |
+| CC-MAIN-2015-40 | September 2015 | 284.0 | 105.5 |
+| CC-MAIN-2015-35 | August 2015 | 359.4 | 133.2 |
+| CC-MAIN-2015-32 | July 2015 | 352.4 | 130.1 |
+| CC-MAIN-2015-27 | June 2015 | 335.5 | 124.0 |
+| CC-MAIN-2015-22 | May 2015 | 380.2 | 140.4 |
+| CC-MAIN-2015-18 | April 2015 | 389.0 | 143.8 |
+| CC-MAIN-2015-14 | March 2015 | 337.5 | 124.5 |
+| CC-MAIN-2015-11 | February 2015 | 361.4 | 133.3 |
+| CC-MAIN-2015-06 | January 2015 | 356.1 | 131.3 |
+| CC-MAIN-2014-52 | December 2014 | 388.5 | 143.3 |
+| CC-MAIN-2014-49 | November 2014 | 319.9 | 117.7 |
+| CC-MAIN-2014-42 | October 2014 | 371.1 | 136.4 |
+| CC-MAIN-2014-41 | September 2014 | 408.1 | 150.2 |
+| CC-MAIN-2014-35 | August 2014 | 395.7 | 145.6 |
+| CC-MAIN-2014-23 | July 2014 | 425.0 | 156.5 |
+| CC-MAIN-2014-15 | April 2014 | 369.1 | 135.7 |
+| CC-MAIN-2014-10 | March 2014 | 396.2 | 146.2 |
+| CC-MAIN-2013-48 | Winter 2013 | 396.8 | 145.9 |
+| CC-MAIN-2013-20 | Summer 2013 | 393.9 | 144.5 |
+| Total | | 41880.2 | 15352.9 |
+
+## Dataset performance evaluation and ablations
+
+We conducted our dataset performance ablations and evaluations by training a series of 1.8B parameters models on 27 billion tokens. To compare π· FineWeb with other datasets, we also trained one of these 1.8B models per target dataset, on 350 billion tokens sampled from it (or the entire dataset when its size was < 350 billion tokens).
+
+### Hyper-parameters for ablation models
+
+The detailed configurations for training the 1.8B parameters ablation model can be found here (link will be added soon).
+
+### Ablation evaluation benchmarks
+
+To conduct the ablations for each of our dataset filtering choices, we selected a set of benchmarks which we identified as βhigh-signalβ benchmarks. These benchmarks were selected according to the following criteria:
+
+- small variance between runs trained on different samplings of the same dataset
+- performance increasing monotically during training (or close)
+- separation between runs on datasets of known quality (C4, The Pile, RedPajama) higher than the variance between runs with various modeling/data seeds
+
+We used the following list of benchmark for our ablation runs:
+
+- commonsense_qa (acc_norm)
+- hellaswag (acc/acc_norm)
+- openbookqa (acc/acc_norm)
+- piqa (acc/acc_norm)
+- siqa (acc/acc_norm)
+- winogrande (acc/acc_norm)
+- sciq (acc/acc_norm)
+- arc (acc/acc_norm)
+- mmlu (acc/acc_norm)
+
+To compare runs we consider an aggregate score, the average of the scores for these tasks.
+
+The prompts for all these benchmarks are formatted in order to compute and compare the log-likelihood of the full answers for each multiple choice question. All the implementation details for the benchmarks are available in `lighteval` [here](https://huggingface.co/datasets/HuggingFaceFW/fineweb/blob/main/lighteval_tasks.py).
+
+### Comparison with other datasets
+
+We compared π· FineWeb with the following datasets:
+
+- [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb)
+- [C4](https://huggingface.co/datasets/allenai/c4)
+- [Dolma v1.6](https://huggingface.co/datasets/allenai/dolma) (the CommonCrawl part)
+- [The Pile](https://huggingface.co/datasets/EleutherAI/pile)
+- [SlimPajama](https://huggingface.co/datasets/cerebras/SlimPajama-627B)
+- [RedPajama2](https://huggingface.co/datasets/togethercomputer/RedPajama-Data-V2) (deduplicated)
+
+You will find these models on [this collection](https://huggingface.co/collections/HuggingFaceFW/ablation-models-662457b0d213e8c14fe47f32). We have uploaded checkpoints at every 1000 training steps. You will also find our full [evaluation results here](https://huggingface.co/datasets/HuggingFaceFW/fineweb/blob/main/eval_results.csv).
+
+
+ 
+
+
+_Note:_ The plot is smoothed by averaging 5k steps in a rolling window.
+
+# Dataset card for π· FineWeb
+
+## Dataset Description
+
+- **Homepage and Repository:** [https://huggingface.co/datasets/HuggingFaceFW/fineweb](https://huggingface.co/datasets/HuggingFaceFW/fineweb)
+- **Point of Contact:** please create a discussion on the Community tab
+- **License:** Open Data Commons Attribution License (ODC-By) v1.0
+
+### Dataset Summary
+
+This dataset was created by processing 95 [CommonCrawl](https://commoncrawl.org/) dumps comprising web data crawled from the summer of 2013 to March of 2024. π· FineWeb includes a variety of domains and topics in English and is primarily intended to be used as a research artifact on public data in the context of pretraining dataset for large language models. The CommonCrawl data was carefully processed, filtered and deduplicated with the π [`datatrove`](https://github.com/huggingface/datatrove/) library, resulting in the largest publicly available clean LLM pretraining dataset, counting around 15 trillion tokens (gpt2 tokenizer).
+
+## Dataset Structure
+
+### Data Instances
+
+The following is an example sample from the dataset. It is part of the `CC-MAIN-2021-43` and was crawled on `2021-10-15T21:20:12Z`.
+
+```json
+{
+ "text": "This is basically a peanut flavoured cream thickened with egg yolks and then set into a ramekin on top of some jam. Tony, one of the Wedgwood chefs, suggested sprinkling on some toasted crushed peanuts at the end to create extra crunch, which I thought was a great idea. The result is excellent.",
+ "id": "",
+ "dump": "CC-MAIN-2021-43",
+ "url": "",
+ "date": "2021-10-15T21:20:12Z",
+ "file_path": "s3://commoncrawl/crawl-data/CC-MAIN-2021-43/segments/1634323583083.92/warc/CC-MAIN-20211015192439-20211015222439-00600.warc.gz",
+ "language": "en",
+ "language_score": 0.948729,
+ "token_count": 69
+}
+```
+
+### Data Fields
+
+- `text` (string): the main text content
+- `id` (string): original unique identifier for this sample from CommonCrawl
+- `dump` (string): the CommonCrawl dump this sample was a part of
+- `url` (string): url to the original page where `text` was present
+- `date` (string): crawl date (from CommonCrawl)
+- `file_path` (string): s3 path for the individual CommonCrawl warc file containing this sample
+- `language` (string): `en` for all the samples in this dataset
+- `language_score` (float): language prediction score (`0.01.0`) as reported by the [fastText language classifier](https://github.com/huggingface/datatrove/blob/main/src/datatrove/pipeline/filters/language_filter.py)
+- `token_count` (int): number of tokens when applying the `gpt2` tokenizer to this sample
+
+### Data Splits
+
+The `default` subset includes the entire dataset. If you would like to only use the data from a particular [CommonCrawl dump](https://commoncrawl.org/overview), you can use the dump name as a subset. You will find the full list of available dumps on the table above.
+From experiments we have run, not all dumps give the same performance. For relatively small trainings (<