unify naming

README.md

@@ -1,6 +1,6 @@
 ---
 license: odc-by
-pretty_name: Zyda2-5T
+pretty_name: Zyda2
 task_categories:
 - text-generation
 language:
@@ -34,17 +34,17 @@ configs:
 
 <!-- Provide a quick summary of the dataset. -->
 
-Zynemo 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. Zynemo comprises diverse sources of web data, highly educational content, math, code, and scientific papers.
+Zyda2 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. Zyda2 comprises diverse sources of web data, highly educational content, math, code, and scientific papers.
 
-To construct Zynemo, we took the best open-source datasets available: Zyda, FineWeb, DCLM, Dolma. Models trained on Zynemo significantly outperform identical models trained on the Pile, RefinedWeb, FineWeb, FineWeb-Edu, and DCLM. Due to our post-processing deduplication, filtering, and weighting pipeline, Zynemo outperforms all its constituent datasets in resulting model quality.
+To construct Zyda2, we took the best open-source datasets available: Zyda, FineWeb, DCLM, Dolma. Models trained on Zyda2 significantly outperform identical models trained on the Pile, RefinedWeb, FineWeb, FineWeb-Edu, and DCLM. Due to our post-processing deduplication, filtering, and weighting pipeline, Zyda2 outperforms all its constituent datasets in resulting model quality.
 
-An early version of Zynemo was used as the primary dataset for phase 1 pretraining of our Zamba2 series [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 Zynemo as a pretraining dataset.
+An early version of Zyda2 was used as the primary dataset for phase 1 pretraining of our Zamba2 series [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 Zyda2 as a pretraining dataset.
 
-According to our evaluations, Zynemo is the most performant per-token open dataset available. Zynemo excels at educational and natural language reasoning content. For code performance, we reccomend mixing it with a pure code dataset such as [Starcoder](https://huggingface.co/bigcode/starcoder). 
+According to our evaluations, Zyda2 is the most performant per-token open dataset available. Zyda2 excels at educational and natural language reasoning content. For code performance, we reccomend 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/65c05e75c084467acab2f84a/YfOOh2JqRgkeHP1gHSSt9.png" width="600" alt="ZyNeMo evaluation scores">
+<img src="https://cdn-uploads.huggingface.co/production/uploads/65c05e75c084467acab2f84a/YfOOh2JqRgkeHP1gHSSt9.png" width="600" alt="Zyda2 evaluation scores">
 </center>
 
 
@@ -56,13 +56,13 @@ Since we preserved the schemas of original component datasets, attempting to dow
 
 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.
 
-Example command to clone the repository using huggingface-cli: `huggingface-cli download Zyphra/Zyda2-5T--repo-type dataset`
+Example command to clone the repository using huggingface-cli: `huggingface-cli download Zyphra/Zyda2--repo-type dataset`
 
 Commands to download individual components:
- - DCLM:  `ds = datasets.load_dataset("Zyphra/Zyda2-5T", name="dclm_crossdeduped", split="train")`
- - Zyda:  `ds = datasets.load_dataset("Zyphra/Zyda2-5T", name="zyda_crossdeduped-filtered", split="train")`
- - Dolma-CC:  `ds = datasets.load_dataset("Zyphra/Zyda2-5T", name="dolma-cc_crossdeduped-filtered", split="train")`
- - Fineweb-Edu:  `ds = datasets.load_dataset("Zyphra/Zyda2-5T", name="fwe3", split="train")`
+ - DCLM:  `ds = datasets.load_dataset("Zyphra/Zyda2", name="dclm_crossdeduped", split="train")`
+ - Zyda:  `ds = datasets.load_dataset("Zyphra/Zyda2", name="zyda_crossdeduped-filtered", split="train")`
+ - Dolma-CC:  `ds = datasets.load_dataset("Zyphra/Zyda2", name="dolma-cc_crossdeduped-filtered", split="train")`
+ - Fineweb-Edu:  `ds = datasets.load_dataset("Zyphra/Zyda2", 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 appropriate weights during training.
 We found the following optimal weights (in the sense of weights in the resultant dataset): DCLM - 4.0, FWE3 - 4.0, Zyda - 0.16, Dolma-CC - 0.24.
@@ -110,7 +110,7 @@ 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="ZyNeMo dataset composition">
+<img src="https://cdn-uploads.huggingface.co/production/uploads/65c05e75c084467acab2f84a/GQenkNxzyM65M4eR2YZcV.png" width="600" alt="Zyda2 dataset composition">
 </center>
 
 #### Personal and Sensitive Information