Update README.md

README.md

@@ -973,6 +973,40 @@ For a detailed description of the dataset, please refer to https://hplt-project.
 This is the ```cleaned``` variant of the HPLT Datasets v2.0 converted to the Parquet format semi-automatically when being uploaded here. 
 The original JSONL files (which take ~4x fewer disk space than this HF version) and the larger non-cleaned version can be found at https://hplt-project.org/datasets/v2.0.
 
+**Dataset Performance**
+
+***External Evaluation***
+
+The HuggingFace team has [compared the utility of various multilingual corpora for training large language models in their FineWeb2 initiative](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2).  
+They found that the HPLT v2 datasets are next to their FineWeb 2, on par with the CulturaX dataset as shown in this figure produced by HuggingFace:
+
+<img src="https://huggingface.co/datasets/HuggingFaceFW/admin/resolve/main/multilingual_datasets_comparison.png" width="800" height="800" />
+
+This is a massive improvement compared to the HPLT v1 datasets, as can be seen on the plot above. 
+In fact, it’s even better: if one looks at the language-specific results, it becomes clear that on 
+Arabic, Hindi, Russian, Thai and Turkish (5 out of 9 languages HuggingFace evaluated on), [HPLT v2  is on par or better than FineWeb 2](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2#comparison-with-other-datasets). 
+The average score is lower mostly because of Chinese, so we have some work ahead for this language!
+Note that the source of the FineWeb 2 (and CulturaX) data is exclusively CommonCrawl, while the HPLT datasets are to a large extent composed of Internet Archive crawls. 
+Thus, **FineWeb 2 and HPLTv2 are complementary to each other and should be used together**.
+
+***Internal Evaluation***
+
+
+We also conducted FineWeb-style evaluations within the HPLT project, for now limited to English. 
+It confirmed the findings of HuggingFace in that HPLT v2 datasets are of much better quality than HPLT v1.2 data, which was released almost a year ago. 
+
+We replicated the FineWeb evaluation setting, training large language models with the same architecture and pretraining configuration 
+(e.g. 1.82B parameters, Llama architecture with a sequence length of 2048 tokens, GPT 2 tokenizer, and a global batch size of ~2 million tokens), with the only difference between the models being the training data. 
+We randomly sampled approximately 100B tokens from different versions of HPLT as well as FineWeb-data and trained a separate model on each of these datasets.
+
+Each model was trained with the GPT-NeoX framework on 8 nodes on the LUMI cluster, where each node has 4 MI250X GPUs. 
+For evaluation, we use the HuggingFace LightEval in a zero-shot setting with the tasks ARC (Easy and Challenge), Hellaswag, PICA, and OpenbookQA. 
+The figure shows the macro average of the acc_norm values for these evaluations.
+
+
+<img src="https://huggingface.co/datasets/HPLT/HPLT2.0_cleaned/resolve/3c6ded1865c1918b899ea8634897f4f6fc5a20b6/english-comparison-datasets-by-HPLT.png" width="800" height="800" />
+
+
 ***Languages***
 
 The ```cleaned``` version of HPLT Datasets v2.0 consists of subsets corresponding to 191 language codes.