--- license: odc-by task_categories: - text-generation language: - en - de - ja - fr - es - it - ru - pt - pl - nl - cs - zh - ro - sv - hu - sk - uk - th - da - id - el - fi - ca - tr - dag - hr - fa - bg - nb - kiu - ar - vi - sr - ko - sl - lt - hi - he - bs - ms - et - lv - bn - frp - is - glk - eu - gl - sq - mk - mr - ne - ka - la - pcm - mt - cy - vec - hy - nrm - wuu - anp - bcc - ur - af - az - ta - kk - nn pretty_name: FinePDFs-Edu size_categories: - n>1T configs: - config_name: eng_Latn default: true data_files: - split: train path: data/eng_Latn/train/* - config_name: deu_Latn data_files: - split: train path: data/deu_Latn/train/* - config_name: jpn_Jpan data_files: - split: train path: data/jpn_Jpan/train/* - config_name: fra_Latn data_files: - split: train path: data/fra_Latn/train/* - config_name: spa_Latn data_files: - split: train path: data/spa_Latn/train/* - config_name: ita_Latn data_files: - split: train path: data/ita_Latn/train/* - config_name: rus_Cyrl data_files: - split: train path: data/rus_Cyrl/train/* - config_name: por_Latn data_files: - split: train path: data/por_Latn/train/* - config_name: pol_Latn data_files: - split: train path: data/pol_Latn/train/* - config_name: nld_Latn data_files: - split: train path: data/nld_Latn/train/* - config_name: ces_Latn data_files: - split: train path: data/ces_Latn/train/* - config_name: cmn_Hani data_files: - split: train path: data/cmn_Hani/train/* - config_name: ron_Latn data_files: - split: train path: data/ron_Latn/train/* - config_name: swe_Latn data_files: - split: train path: data/swe_Latn/train/* - config_name: hun_Latn data_files: - split: train path: data/hun_Latn/train/* - config_name: slk_Latn data_files: - split: train path: data/slk_Latn/train/* - config_name: ukr_Cyrl data_files: - split: train path: data/ukr_Cyrl/train/* - config_name: tha_Thai data_files: - split: train path: data/tha_Thai/train/* - config_name: dan_Latn data_files: - split: train path: data/dan_Latn/train/* - config_name: ind_Latn data_files: - split: train path: data/ind_Latn/train/* - config_name: ell_Grek data_files: - split: train path: data/ell_Grek/train/* - config_name: fin_Latn data_files: - split: train path: data/fin_Latn/train/* - config_name: cat_Latn data_files: - split: train path: data/cat_Latn/train/* - config_name: tur_Latn data_files: - split: train path: data/tur_Latn/train/* - config_name: dag_Latn data_files: - split: train path: data/dag_Latn/train/* - config_name: hrv_Latn data_files: - split: train path: data/hrv_Latn/train/* - config_name: fas_Arab data_files: - split: train path: data/fas_Arab/train/* - config_name: bul_Cyrl data_files: - split: train path: data/bul_Cyrl/train/* - config_name: nob_Latn data_files: - split: train path: data/nob_Latn/train/* - config_name: kiu_Latn data_files: - split: train path: data/kiu_Latn/train/* - config_name: arb_Arab data_files: - split: train path: data/arb_Arab/train/* - config_name: vie_Latn data_files: - split: train path: data/vie_Latn/train/* - config_name: srp_Cyrl data_files: - split: train path: data/srp_Cyrl/train/* - config_name: kor_Hang data_files: - split: train path: data/kor_Hang/train/* - config_name: slv_Latn data_files: - split: train path: data/slv_Latn/train/* - config_name: lit_Latn data_files: - split: train path: data/lit_Latn/train/* - config_name: hin_Deva data_files: - split: train path: data/hin_Deva/train/* - config_name: heb_Hebr data_files: - split: train path: data/heb_Hebr/train/* - config_name: bos_Latn data_files: - split: train path: data/bos_Latn/train/* - config_name: zsm_Latn data_files: - split: train path: data/zsm_Latn/train/* - config_name: ekk_Latn data_files: - split: train path: data/ekk_Latn/train/* - config_name: lvs_Latn data_files: - split: train path: data/lvs_Latn/train/* - config_name: ben_Beng data_files: - split: train path: data/ben_Beng/train/* - config_name: frp_Latn data_files: - split: train path: data/frp_Latn/train/* - config_name: isl_Latn data_files: - split: train path: data/isl_Latn/train/* - config_name: glk_Arab data_files: - split: train path: data/glk_Arab/train/* - config_name: eus_Latn data_files: - split: train path: data/eus_Latn/train/* - config_name: glg_Latn data_files: - split: train path: data/glg_Latn/train/* - config_name: als_Latn data_files: - split: train path: data/als_Latn/train/* - config_name: mkd_Cyrl data_files: - split: train path: data/mkd_Cyrl/train/* - config_name: mar_Deva data_files: - split: train path: data/mar_Deva/train/* - config_name: npi_Deva data_files: - split: train path: data/npi_Deva/train/* - config_name: kat_Geor data_files: - split: train path: data/kat_Geor/train/* - config_name: lat_Latn data_files: - split: train path: data/lat_Latn/train/* - config_name: pcm_Latn data_files: - split: train path: data/pcm_Latn/train/* - config_name: mlt_Latn data_files: - split: train path: data/mlt_Latn/train/* - config_name: cym_Latn data_files: - split: train path: data/cym_Latn/train/* - config_name: vec_Latn data_files: - split: train path: data/vec_Latn/train/* - config_name: hye_Armn data_files: - split: train path: data/hye_Armn/train/* - config_name: nrm_Latn data_files: - split: train path: data/nrm_Latn/train/* - config_name: wuu_Hani data_files: - split: train path: data/wuu_Hani/train/* - config_name: anp_Deva data_files: - split: train path: data/anp_Deva/train/* - config_name: bcc_Arab data_files: - split: train path: data/bcc_Arab/train/* - config_name: urd_Arab data_files: - split: train path: data/urd_Arab/train/* - config_name: afr_Latn data_files: - split: train path: data/afr_Latn/train/* - config_name: azj_Latn data_files: - split: train path: data/azj_Latn/train/* - config_name: tam_Taml data_files: - split: train path: data/tam_Taml/train/* - config_name: kaz_Cyrl data_files: - split: train path: data/kaz_Cyrl/train/* - config_name: nno_Latn data_files: - split: train path: data/nno_Latn/train/* --- # 📚 FinePDFs-Edu ![FinePDFs](https://cdn-uploads.huggingface.co/production/uploads/626ede24d2fa9e7d598c8709/dgGeCo6yfZvThn-Fc6Q8k.png) > 350B+ of highly educational tokens from PDFs 📄 ## What is it? 📚 FinePDFs-Edu dataset consists of **350B+ tokens** of educational PDFs filtered from 📄 [FinePDFs](https://huggingface.co/datasets/HuggingFaceFW/finepdfs) dataset covering 69 languages. FinePDFs was created using the formula inspired from [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu), we developed an [educational quality classifier](HuggingFaceFW/finepdfs_edu_classifier_eng_Latn) using annotations generated by Qwen3-235B-A22B-Instruct-2507 for each of 69 languages present in this dataset. We then used this classifier to retain only the most educational web pages. FinePDFs-Edu outperforms FinePDFs on popular benchmarks and shows the power of classifiers trained on synthetic data. The [Dataset Curation](https://huggingface.co/datasets/HuggingFaceFW/finepdfs_edu#dataset-curation) section details the process for creating the dataset. While it might seem that the dataset is an order of magnitude smaller than FineWeb-Edu, unlike its web ancestor, this dataset is globally deduplicated! ![datasets_comparison_edu](https://cdn-uploads.huggingface.co/production/uploads/626ede24d2fa9e7d598c8709/ivVKeFDP2J2MAyQL9s4xy.png) ## What is being released? Along with the dataset, which includes all filtered CommonCrawl dumps since `CC-MAIN-2013-20` to `CC-MAIN-2025-08`, we also release: - The [educational classifier](https://huggingface.co/HuggingFaceFW/finepdfs_edu_classifier_eng_Latn) used for the filtering (for each language) - The [dataset](https://huggingface.co/datasets/HuggingFaceFW/finepdfs_eng_Latn_labeled) with educational (and 3 other) labels by Qwen3-235B-A22B-Instruct-2507 for English. - The [dataset](HuggingFaceFW/finepdfs_fw_edu_labeled) with educational labels by Qwen3-235B-A22B-Instruct-2507 for 69 languages beyond English. - The [code](https://github.com/huggingface/finepdfs) for training it and running inference. ## How to download and use 📄 FinePDFs-Edu See the tables above for the `subset` of the language you want to download. We currently do not provide smaller `sample` versions, but by setting `limit` or using `streaming=True` you can easily fetch a sample of the data. If there is interest from the community we might upload smaller sampled versions later on. ### 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) # this will fetch the Portuguese filtered data data_reader = ParquetReader("hf://datasets/HuggingFaceFW/finepdfs-edu/data/por_Latn/train", 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 JsonlWriter pipeline_exec = LocalPipelineExecutor( pipeline=[ ParquetReader("hf://datasets/HuggingFaceFW/finepdfs-edu/data/por_Latn/train", limit=1000), LambdaFilter(lambda doc: "hugging" in doc.text), JsonlWriter("some-output-path") ], tasks=10 ) pipeline_exec.run() ``` ### Using `huggingface_hub` ```python from huggingface_hub import snapshot_download folder = snapshot_download( "HuggingFaceFW/finepdfs-edu", repo_type="dataset", local_dir="./finepdfs-edu/", # download the Czech filtered allow_patterns=["data/ces_Latn/train/*"]) ``` For faster downloads, make sure to install `pip install huggingface_hub[hf_transfer]` and set the environment variable `HF_HUB_ENABLE_HF_TRANSFER=1`. ### Using `datasets` ```python from datasets import load_dataset # get Croatian data fw = load_dataset("HuggingFaceFW/finepdfs-edu", name="hrv_Latn", split="train", streaming=True) ``` Similiar to original FinePDFs, this dataset contains high amount of language switching samples, we thus recommend using the [filtering function](https://huggingface.co/datasets/HuggingFaceFW/finepdfs#code-switching) if this is not desired. ## Dataset curation We have used the same approach for FineWeb-Edu with minimal adjustments of the prompt. To scale to languages beyond English we decided to train separate classifier for each. ### Educational Scoring We used [Qwen3-235B-A22B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-235B-A22B-Instruct-2507) to score approximately 300,000 FinePDFs samples for educational quality on a 0–5 scale. The final prompt used for scoring is available [here](https://huggingface.co/HuggingFaceFW/finepdfs_edu_classifier_eng_Latn/blob/main/prompt.txt). After experimenting with several prompt variants, we found that the **FineWeb-Edu** prompt yielded the most consistent and reliable results. As in FineWeb-Edu, we observed that highly technical or graduate-level content did not correlate well with the benchmarks we track. However, unlike in FineWeb-Edu, the overall average score was noticeably lower—if we had used a fixed threshold of `score = 3`, only about 2% of samples would have been retained. To address this, we instead selected the **top 10%** of samples based on their education score. | Threshold | Drop Rate | | :-------: | :-------: | | 1 | 0.3028 | | 2 | 0.9451 | | 3 | 0.9802 | | 4 | 0.9906 | | 5 | 0.9987 | We also replaced the teacher model to improve multilingual coverage and take advantage of the better inference efficiency offered by Mixture-of-Experts (MoE) architectures. To identify a suitable model, we aimed for one that was most *“Claude-like”*, i.e., whose scoring behavior most closely matched **Claude Sonnet-4**. We compared models using mean squared error (MSE) on a 10k-sample development set and found that **Qwen3-235B-A22B-Instruct-2507** was both the most Claude-like and highly efficient—processing up to **14 chunks/sec on a single H100 GPU**. | Model | MSE (vs. Sonnet-4) | | :-------------------------------------------- | -----------------: | | Qwen_Qwen3-235B-A22B-Instruct-2507 | **0.398** | | Qwen_Qwen3-235B-A22B-Thinking-2507 | 0.812 | | Qwen_Qwen3-30B-A3B-Instruct-2507 | 0.364 | | Qwen_Qwen3-30B-A3B-Thinking-2507 | 0.925 | | google_gemma-3-27b-it | 2.727 | | meta-llama_Llama-3.3-70B-Instruct | 0.553 | | meta-llama_Llama-4-Maverick-17B-128E-Instruct | 0.707 | | meta-llama_Llama-4-Scout-17B-16E-Instruct | 1.177 | | mistralai_Magistral-Small-2507 | 0.717 | | zai-org_GLM-4.5-Air-FP8 | 0.510 | For long documents, we take the first 2,048 tokens from the top of the document. If the document exceeds 10,000 characters, we also take the last 2,048 tokens and compute the final score as `max(top_score, bottom_score)`. ### Classifier Training We fine-tuned a BERT-like regression model using these annotations, based on [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) for English and [jhu-clsp/mmBERT-base](https://huggingface.co/jhu-clsp/mmBERT-base) for other languages. Both models achieved the best F1 performance among the options we evaluated, while supporting FA2, which allowed us to label over 220 samples per second on an H100 GPU. For each model, we unfroze both the classifier head and the last four transformer layers. To address severe class imbalance, we rebalanced the training data. The resulting classifiers are available at: `https://huggingface.co/HuggingFaceFW/finepdfs_edu_classifier_{lang}` ### Filtering and results We then built 📚 FinePDFs-Edu by filtering out 90% of samples with lowest edu score for each language. Our ablation demonstrated that this refined dataset surpasses 📄 FinePDFs and all other open web datasets, with remarkable improvements on educational benchmarks such as MMLU and ARC. You will find all the ablation models and datasets in [this collection](https://huggingface.co/collections/HuggingFaceFW/finepdfs). ## Considerations for Using the Data See: [FinePDFs](https://huggingface.co/datasets/HuggingFaceFW/finepdfs). ## Additional Information ### Licensing Information The dataset is released under the **Open Data Commons Attribution License (ODC-By) v1.0** [license](https://opendatacommons.org/licenses/by/1-0/). The use of this dataset is also subject to [CommonCrawl's Terms of Use](https://commoncrawl.org/terms-of-use). ## Citation Information ``` @misc{kydlicek2025finepdfs, title={FinePDFs}, author={Hynek Kydl{\'\i}{\v{c}}ek and Guilherme Penedo and Leandro von Werra}, year={2025}, publisher = {Hugging Face}, journal = {Hugging Face repository}, howpublished = {\url{https://huggingface.co/datasets/HuggingFaceFW/finepdfs_edu}} } ```