Duplicate from Zyphra/Zyda-2

Co-authored-by: Yury Tokpanov <yury-zyphra@users.noreply.huggingface.co>

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+*.pcm filter=lfs diff=lfs merge=lfs -text
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+# Audio files - compressed
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README.md

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+---
+license: odc-by
+pretty_name: Zyda-2
+task_categories:
+- text-generation
+language:
+- en
+size_categories:
+- n>1T
+configs:
+  - config_name: default
+    data_files:
+      - split: train
+        path: data/*/*/*
+  - config_name: sample-100BT
+    data_files:
+      - split: train
+        path: sample/100BT/*/*
+  - config_name: dclm_crossdeduped
+    data_files:
+      - split: train
+        path: data/dclm_crossdeduped/*/*
+  - config_name: zyda_crossdeduped-filtered 
+    data_files:
+      - split: train
+        path: data/zyda_crossdeduped-filtered /*/*
+  - config_name: dolma-cc_crossdeduped-filtered
+    data_files:
+      - split: train
+        path: data/dolma-cc_crossdeduped-filtered/*
+  - config_name: fwe3
+    data_files:
+      - split: train
+        path: data/fwe3/*/*
+---
+
+# Zyda-2
+
+<!-- Provide a quick summary of the dataset. -->
+
+Zyda-2 is a 5 trillion token language modeling dataset created by collecting open and high quality datasets and combining them and cross-deduplication and model-based quality filtering. Zyda-2 comprises diverse sources of web data, highly educational content, math, code, and scientific papers.
+
+To construct Zyda-2, we took the best open-source datasets available: [Zyda](https://huggingface.co/datasets/Zyphra/Zyda), [FineWeb](https://huggingface.co/datasets/HuggingFaceFW/fineweb), [DCLM](https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0), and [Dolma](https://huggingface.co/datasets/allenai/dolma). Models trained on Zyda-2 significantly outperform identical models trained on the Pile, RefinedWeb, FineWeb, FineWeb-Edu, and DCLM. Due to our post-processing deduplication, filtering, and weighting pipeline, Zyda-2 outperforms all its constituent datasets in resulting model quality.
+
+An early version of Zyda-2 was used as the primary dataset for phase 1 pretraining of our Zamba2 [series](https://huggingface.co/Zyphra/Zamba2-7B) [of](Zyphra/Zamba2-2.7B) [models](Zyphra/Zamba2-1.2B) which perform extremely strongly on a per-token basis and are often state-of-the-art for their size, testifying to the strength of Zyda-2 as a pretraining dataset.
+
+According to our evaluations, Zyda-2 is the most performant per-token open dataset available. Zyda-2 excels at educational and natural language reasoning content. For code performance, we recommend mixing it with a pure code dataset such as [Starcoder](https://huggingface.co/bigcode/starcoder). 
+
+
+<center>
+<img src="https://cdn-uploads.huggingface.co/production/uploads/65455aca468722e935103b17/-nxHBcU38QJ-MNdKXPiYS.png" width="600" alt="Zyda-2 evaluation scores">
+</center>
+
+
+For more information, please see our [technical blog](https://www.zyphra.com/post/building-zyda-2).
+
+## How to download
+We preserved the schemas of original component datasets, meaning that every component has its own schema. For that reason attempting to download the whole dataset using `datasets.load_dataset()` will fail during the stage of generating a split. If you attempt to stream the default config, it will also fail.
+
+To download the whole dataset we recommend to either clone the repository, or, if you must use the `datasets.load_dataset()`, download individual components separately.
+
+Only `nemo_id` and `text` are common columns between the components. Select those for every component first, and only then interleave the datasets with optimal weights (see example at the bottom of this section).
+
+Example command to clone the repository using huggingface-cli: `huggingface-cli download Zyphra/Zyda-2 --repo-type dataset`
+
+Commands to download individual components:
+ - DCLM:  `ds_dclm = datasets.load_dataset("Zyphra/Zyda-2", name="dclm_crossdeduped", split="train")`
+ - Zyda:  `ds_zyda = datasets.load_dataset("Zyphra/Zyda-2", name="zyda_crossdeduped-filtered", split="train")`
+ - Dolma-CC:  `ds_dolma = datasets.load_dataset("Zyphra/Zyda-2", name="dolma-cc_crossdeduped-filtered", split="train")`
+ - Fineweb-Edu:  `ds_fwe = datasets.load_dataset("Zyphra/Zyda-2", name="fwe3", split="train")`
+
+In this repository we provide raw results of cross deduplication and filtering. To achieve the best possible performance, one will need to use appropriate weights during training.
+We found the following optimal weights by number of tokens (in the sense of weights in the resultant dataset): DCLM - 4.0, FWE3 - 4.0, Zyda - 0.16, Dolma-CC - 0.24.
+
+Below you will find an example of how to get proper dataset object.
+It demonstrates how to select only `nemo_id` and `text` columns, and then interleave the datasets with probabilities computed from the weights above.
+One needs to be careful with weights normalization, as `interleave_datasets()` returns documents, while our weights are token-wise. We provide precomputed document-wise weights in the example below.
+To stream the dataset, add `streaming=True` to the `load_dataset()` commands.
+
+```
+common_columns = ["nemo_id", "text"]
+ds_dclm = ds_dclm.select_columns(common_columns)
+ds_zyda = ds_zyda.select_columns(common_columns)
+ds_dolma = ds_dolma.select_columns(common_columns)
+ds_fwe = ds_zyda.select_columns(common_columns)
+norm_weights = [0.4038, 0.0316, 0.0585, 0.5061]
+ds = datasets.interleave_datasets([ds_dclm, ds_zyda, ds_dolma, ds_fwe], probabilities=norm_weights, stopping_strategy="all_exhausted")
+```
+
+### (Smaller) sample version
+Along with the configs above, you can also download a smaller version of the dataset with the following config:
+- `sample-100BT`: a subset randomly sampled from the whole dataset of around 100B gpt-neox tokens (252GB, 91.2M documents).
+
+This sample only has common columns `nemo-id` and `text`. In addition, it was sampled according to optimal weights, so you can start using it directly.
+
+`ds_sample = datasets.load_dataset("Zyphra/Zyda-2", name="sample-100BT", split="train")`
+
+## Breakdown by component
+
+| Component                    | Download size (parquet, GBs) | Documents (millions) | gpt-neox tokens (billions) |
+| --- | --- | --- | --- |
+| dclm-crossdeduped              |   8,469.4                  |   2,590.5            |   3,348.942                |
+| zyda-crossdeduped-filtered     |   452.4                    |   247.7              |   163.6                    |
+| dolma_cc-crossdeduped-filtered |   668.2                    |   445.6              |   238.4                    |
+| fwe3                           |   3,490.5                  |   1,279.1            |   1,319.2                  |
+| Total                          |   13,080.5                 |   4,562.8            |   5,070.2                  |
+
+### Dataset Description
+
+<!-- Provide a longer summary of what this dataset is. -->
+
+- **Curated by:** Zyphra
+- **Language(s) (NLP):** Primarily English
+- **License:** Open Data Commons License
+
+
+## Dataset Structure
+
+<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
+
+Each component has their own individual schema. Please, consult with their respective sources for exact information.
+
+However, in all components the document text is in the `text` column, and the unique document id is in the `nemo_id` column.
+
+Our Zyda-1 and Dolma-CC versions also have two additional columns corresponding to prediction of Nvidia's quality model (https://huggingface.co/nvidia/quality-classifier-deberta): `quality_prob` and `quality_pred`. 
+
+### Source Data
+
+Zyda-2 is comprised of four high quality open-source datasets:
+
+Zyda-1: https://huggingface.co/datasets/Zyphra/Zyda
+
+Dolma-CC v1.7: https://huggingface.co/datasets/allenai/dolma
+
+DCLM-baseline: https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0
+
+FineWeb-Edu-score2: https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu-score-2
+
+<center>
+<img src="https://cdn-uploads.huggingface.co/production/uploads/65c05e75c084467acab2f84a/GQenkNxzyM65M4eR2YZcV.png" width="600" alt="Zyda-2 dataset composition">
+</center>
+
+#### Personal and Sensitive Information
+
+As a language modeling dataset, it likely contains PII which has not been filtered out of the component datasets and which may have been missed by our own filters.
+
+## Bias, Risks, and Limitations
+
+As a dataset comprised of open web scrapes, it is likely that it contains biased and toxic content. 
+
+## 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 by any license agreements and terms of use of the original data sources.
+
+## Citation
+
+If you use our dataset to train a model, please cite us at:
+
+```
+@misc{zyphra_nvidia_2024,
+    author = {Yury Tokpanov, Paolo Glorioso, Ayush Dattagupta, Vibhu Jawa, Ryan Wolf, Vikranth Jeyakumar, Arham Mehta, Quentin Anthony, Beren Millidge},
+    title = {Building {Zyda-2}, a 5 {Trillion} {Token} {High-Quality} {Dataset}, with {NVIDIA} {NeMo} {Curator}},
+    url = {https://www.zyphra.com/post/building-zyda-2},
+    publisher = {Zyphra},
+    year = {2024},
+    month = {October},
+    day = {15}
+}
+```
+

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