| --- |
| license: apache-2.0 |
| language: |
| - eng |
| - deu |
| - fra |
| - pol |
| - por |
| - spa |
| - ita |
| - cmn |
| - nld |
| - afr |
| - als |
| - amh |
| - arb |
| - ars |
| - ary |
| - arz |
| - asm |
| - azj |
| - bel |
| - ben |
| - bew |
| - bod |
| - bos |
| - bul |
| - cat |
| - ces |
| - ckb |
| - cym |
| - dan |
| - div |
| - ekk |
| - ell |
| - epo |
| - eus |
| - fas |
| - fil |
| - fin |
| - gle |
| - glg |
| - gmh |
| - guj |
| - heb |
| - hif |
| - hin |
| - hrv |
| - hun |
| - hye |
| - ind |
| - isl |
| - jpn |
| - kan |
| - kat |
| - kaz |
| - khk |
| - khm |
| - kir |
| - kmr |
| - kor |
| - lao |
| - lat |
| - lit |
| - ltz |
| - lvs |
| - mal |
| - mar |
| - mkd |
| - mlt |
| - mya |
| - nno |
| - nob |
| - npi |
| - nrm |
| - ory |
| - pan |
| - pbt |
| - plt |
| - ron |
| - rus |
| - sin |
| - slk |
| - slv |
| - snd |
| - som |
| - srp |
| - srp |
| - swe |
| - swh |
| - tam |
| - tat |
| - tel |
| - tgk |
| - tha |
| - tur |
| - uig |
| - ukr |
| - urd |
| - uzn |
| - uzn |
| - vie |
| - ydd |
| - zsm |
| --- |
| |
| # FineWeb2-HQ-plus-Classifier |
|
|
| This repository contains the model weights of the trained deep learning classifier used to identify high-quality text samples for the [FineWeb2-HQ-plus](https://huggingface.co/datasets/epfml/FineWeb2-HQ-plus) dataset. |
|
|
| FineWeb2-HQ-plus is a high-quality, model-filtered, multilingual pretraining dataset derived as a subset [FineWeb2](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2) and an improvement over [FineWeb2-HQ](https://huggingface.co/datasets/epfml/FineWeb2-HQ) covering 100 languages. The classifier uses [XLM-RoBERTa](https://huggingface.co/FacebookAI/xlm-roberta-base) embeddings to score the documents and supports English and additional 100 languages. |
|
|
| For more details, see our paper [Toward Cross-Lingual Quality Classifiers for Multilingual Pretraining Data Selection](https://arxiv.org/abs/2604.20549). |
|
|
| ## Quickstart |
|
|
| Classifier uses a simple architecture that takes mean-pooled XLM-RoBERTa embeddings as input and outputs the score logit. |
|
|
| ```python |
| import torch |
| import torch.nn.functional as F |
| from transformers import AutoModel, AutoTokenizer |
| import huggingface_hub |
| |
| |
| class BinaryClassifier(torch.nn.Module): |
| def __init__(self, embedding_dim=768, hidden_dim=256): |
| super(BinaryClassifier, self).__init__() |
| self.classifier = torch.nn.Sequential( |
| torch.nn.Linear(embedding_dim, hidden_dim), |
| torch.nn.ReLU(), |
| torch.nn.Dropout(0.2), |
| torch.nn.Linear(hidden_dim, 1), |
| ) |
| |
| def forward(self, X): |
| return self.classifier(X) |
| |
| def to_pt(self, file_name): |
| torch.save(self.state_dict(), file_name) |
| |
| @classmethod |
| def from_pt(cls, file_name, embedding_dim=768, hidden_dim=256): |
| state_dict = torch.load( |
| file_name, |
| weights_only=True, |
| map_location=torch.device("cpu"), |
| ) |
| classifier = BinaryClassifier( |
| embedding_dim=embedding_dim, |
| hidden_dim=hidden_dim, |
| ) |
| classifier.load_state_dict(state_dict) |
| classifier.eval() |
| return classifier |
| |
| |
| if __name__ == "__main__": |
| embedding_model_name = "FacebookAI/xlm-roberta-base" |
| tokenizer = AutoTokenizer.from_pretrained(embedding_model_name) |
| embedding_model = AutoModel.from_pretrained( |
| embedding_model_name, |
| dtype=torch.bfloat16, |
| ) |
| |
| classifiers_dir = huggingface_hub.snapshot_download("epfml/FineWeb2-HQ-plus-Classifier") |
| classifier_model = BinaryClassifier.from_pt(f"{classifiers_dir}/model.pt") |
| |
| def score_sample(text, classifier_model): |
| inputs = tokenizer([text], return_tensors="pt") |
| embeddings = embedding_model(**inputs).last_hidden_state.float().mean(1) |
| score = F.sigmoid(classifier_model(embeddings)).item() |
| return score |
| |
| text_en = "Question: How is bipolar disorder different from unipolar depression or 'regular' depression?\nAnswer: Both bipolar disorder and major depression are typically associated with depressive episodes. So both illnesses are accompanied by depressions. The difference is that in bipolar disorder people also have periods of elevation -- or severe irritability. We call these manic or hypomanic episodes." |
| score = score_sample(text_en, classifier_model) |
| print(f"{score:0.4f}") # 0.9952 |
| |
| text_en = "Custom Wedding Gifts\nPersonalized photo frames, albums & keepsakes. Heirloom quality!\nCustom Engraved Journals\nHandmade in Florence Italy. Dozens of sizes and paper styles!" |
| score = score_sample(text_en, classifier_model) |
| print(f"{score:0.4f}") # 0.0001 |
| ``` |
|
|
| ## Citation information |
| ``` |
| @misc{turki2026crosslingualqualityclassifiersmultilingual, |
| title={Toward Cross-Lingual Quality Classifiers for Multilingual Pretraining Data Selection}, |
| author={Yassine Turki and Vinko Sabolčec and Bettina Messmer and Martin Jaggi}, |
| year={2026}, |
| eprint={2604.20549}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL}, |
| url={https://arxiv.org/abs/2604.20549}, |
| } |
| ``` |
