Super-squash branch 'main' using huggingface_hub

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README.md

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+---
+license: cc-by-4.0
+language:
+- en
+---
+# DCLM-Edu
+
+## Description
+This is a filtered version of [DCLM](https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0) dataset using FineWeb-Edu educational quality [classifier](https://huggingface.co/HuggingFaceFW/fineweb-edu-classifier). We annotate each web page based on the educational quality
+on a scale from 0 to 5 and only keep samples with a score higher than 2. This dataset is intended for language models training and was used to train [SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M) and [SmolLM2-360M](https://huggingface.co/HuggingFaceTB/SmolLM2-360M).
+
+**_Note:_** As show in teh performance section, we find that further filtering the dataset to only keep **samples with `edu_int_score>=3` yields even better downstream performance when training small laguage models**. We include score 2 samples to allow for rebalancing and added diversity, but you can filter the dataset with `datasets` or `datatrove` as shown below.
+
+## How to use
+### Using `datasets`
+
+```python
+from datasets import load_dataset
+
+fw = load_dataset("HuggingFaceTB/dclm-edu", split="train", streaming=True)
+```
+
+### Using 🏭 [`datatrove`](https://github.com/huggingface/datatrove/)
+
+```python
+from datatrove.pipeline.readers import ParquetReader
+
+# limit determines how many documents will be streamed (remove for all)
+data_reader = ParquetReader("hf://datasets/HuggingFaceTB/dclm-edu", glob_pattern="data/*.parquet", 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 ParquetWriter
+
+pipeline_exec = LocalPipelineExecutor(
+    pipeline=[
+        ParquetReader("hf://datasets/HuggingFaceTB/dclm-edu", limit=1000),
+        LambdaFilter(lambda doc: doc.metadata["edu_int_score"] >= 3),
+        ParquetWriter("some-output-path")
+    ],
+    tasks=10
+)
+pipeline_exec.run()
+```
+
+## Performance
+**Results of 360M ablation**
+We train a 360M model (using SmolLM2 setup) on 200B tokens from different datasets and evaluate on different benchmarks. DCLM-Edu denotes DCLM samples with an educational score higher than 3.
+
+<img src="https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/Nt4tu5s4iaoN7ZqmqndJK.png" width="600" alt="image">
+
+DCLM-Edu gives consistent gains. The plot below shows the per-benchmark performance:
+<img src="https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/L9sdCmfDVipTwDX5_dNcm.png" width="700" alt="image">
+
+We invite users to experiment with different data mixing depending on their model size.
+
+**Results of 1.7B ablation:**
+We also conducted some ablations at 1.7B scale but the gains for introducing the dataset mid-training durong SmolLM2 1.7B training weren't consistent with the ablation findings, so we only use the dataset for SmolLM2 135M and 360M. 
+We use an intermediate checkpoint of SmolLM2 1.7B (3T tokens) anddodoing a decay on different subsets of DCLM using the edu filtering:
+
+<img src="https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/ImwiEe712SN5TalxFOeeJ.png" width="700" alt="image">
+
+## License
+Following DCLM-Baseline, this dataset is licensed under CC-BY-4.0.
+
+## Citation
+```bash
+@misc{allal2025smollm2smolgoesbig,
+      title={SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model}, 
+      author={Loubna Ben Allal and Anton Lozhkov and Elie Bakouch and Gabriel Martín Blázquez and Guilherme Penedo and Lewis Tunstall and Andrés Marafioti and Hynek Kydlíček and Agustín Piqueres Lajarín and Vaibhav Srivastav and Joshua Lochner and Caleb Fahlgren and Xuan-Son Nguyen and Clémentine Fourrier and Ben Burtenshaw and Hugo Larcher and Haojun Zhao and Cyril Zakka and Mathieu Morlon and Colin Raffel and Leandro von Werra and Thomas Wolf},
+      year={2025},
+      eprint={2502.02737},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2502.02737}, 
+}
+```

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