Update README.md

README.md

@@ -59,25 +59,41 @@ We preserved the schemas of original component datasets, meaning that every comp
 
 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 concatenate the datasets.
+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 = datasets.load_dataset("Zyphra/Zyda-2", name="dclm_crossdeduped", split="train")`
- - Zyda:  `ds = datasets.load_dataset("Zyphra/Zyda-2", name="zyda_crossdeduped-filtered", split="train")`
- - Dolma-CC:  `ds = datasets.load_dataset("Zyphra/Zyda-2", name="dolma-cc_crossdeduped-filtered", split="train")`
- - Fineweb-Edu:  `ds = datasets.load_dataset("Zyphra/Zyda-2", name="fwe3", split="train")`
+ - 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 (in the sense of weights in the resultant dataset): DCLM - 4.0, FWE3 - 4.0, Zyda - 0.16, Dolma-CC - 0.24.
+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 dataset 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 versions
 Along with the configs above dump, 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)
+- `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