--- model-index: - name: ru-en-RoSBERTa results: - dataset: config: default name: CEDRClassification (rus-Cyrl) revision: c0ba03d058e3e1b2f3fd20518875a4563dd12db4 split: test type: ai-forever/cedr-classification metrics: - type: accuracy value: 44.68650371944739 - type: f1 value: 40.7601061886426 - type: lrap value: 70.69633368756747 - type: main_score value: 44.68650371944739 task: type: MultilabelClassification - dataset: config: default name: GeoreviewClassification (rus-Cyrl) revision: 3765c0d1de6b7d264bc459433c45e5a75513839c split: test type: ai-forever/georeview-classification metrics: - type: accuracy value: 49.697265625 - type: f1 value: 47.793186725286866 - type: f1_weighted value: 47.79131720298068 - type: main_score value: 49.697265625 task: type: Classification - dataset: config: default name: GeoreviewClusteringP2P (rus-Cyrl) revision: 97a313c8fc85b47f13f33e7e9a95c1ad888c7fec split: test type: ai-forever/georeview-clustering-p2p metrics: - type: main_score value: 65.42249614873316 - type: v_measure value: 65.42249614873316 - type: v_measure_std value: 0.8524815312312278 task: type: Clustering - dataset: config: default name: HeadlineClassification (rus-Cyrl) revision: 2fe05ee6b5832cda29f2ef7aaad7b7fe6a3609eb split: test type: ai-forever/headline-classification metrics: - type: accuracy value: 78.0029296875 - type: f1 value: 77.95151940601424 - type: f1_weighted value: 77.95054643947716 - type: main_score value: 78.0029296875 task: type: Classification - dataset: config: default name: InappropriatenessClassification (rus-Cyrl) revision: 601651fdc45ef243751676e62dd7a19f491c0285 split: test type: ai-forever/inappropriateness-classification metrics: - type: accuracy value: 61.32324218750001 - type: ap value: 57.11029460364367 - type: ap_weighted value: 57.11029460364367 - type: f1 value: 60.971337406307214 - type: f1_weighted value: 60.971337406307214 - type: main_score value: 61.32324218750001 task: type: Classification - dataset: config: default name: KinopoiskClassification (rus-Cyrl) revision: 5911f26666ac11af46cb9c6849d0dc80a378af24 split: test type: ai-forever/kinopoisk-sentiment-classification metrics: - 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type: accuracy value: 46.328125 - type: f1 value: 44.19158709013339 - type: f1_weighted value: 44.190957945676026 - type: main_score value: 46.328125 task: type: Classification - dataset: config: default name: RuSciBenchOECDClusteringP2P (rus-Cyrl) revision: 26c88e99dcaba32bb45d0e1bfc21902337f6d471 split: test type: ai-forever/ru-scibench-oecd-classification metrics: - type: main_score value: 47.28635342613908 - type: v_measure value: 47.28635342613908 - type: v_measure_std value: 0.7431017612993989 task: type: Clustering - dataset: config: ru name: MTEB STS22 (ru) revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3 split: test type: mteb/sts22-crosslingual-sts metrics: - type: cosine_pearson value: 63.10139371129796 - type: cosine_spearman value: 67.06445400504978 - type: euclidean_pearson value: 62.74563386470613 - type: euclidean_spearman value: 67.06445400504978 - type: main_score value: 67.06445400504978 - type: manhattan_pearson value: 62.540465664732395 - type: manhattan_spearman value: 66.65899492022648 - type: pearson value: 63.10139371129796 - type: spearman value: 67.06445400504978 task: type: STS - dataset: config: default name: SensitiveTopicsClassification (rus-Cyrl) revision: 416b34a802308eac30e4192afc0ff99bb8dcc7f2 split: test type: ai-forever/sensitive-topics-classification metrics: - type: accuracy value: 33.0712890625 - type: f1 value: 38.063573562290024 - type: lrap value: 49.586995442707696 - type: main_score value: 33.0712890625 task: type: MultilabelClassification - dataset: config: default name: TERRa (rus-Cyrl) revision: 7b58f24536063837d644aab9a023c62199b2a612 split: dev type: ai-forever/terra-pairclassification metrics: - type: cosine_accuracy value: 61.563517915309454 - type: cosine_accuracy_threshold value: 75.3734290599823 - type: cosine_ap value: 60.78861909325018 - type: cosine_f1 value: 67.25663716814158 - type: cosine_f1_threshold value: 54.05237674713135 - type: cosine_precision value: 50.836120401337794 - type: cosine_recall value: 99.34640522875817 - type: dot_accuracy value: 61.563517915309454 - type: dot_accuracy_threshold value: 75.37343502044678 - type: dot_ap value: 60.78861909325018 - type: dot_f1 value: 67.25663716814158 - type: dot_f1_threshold value: 54.05237674713135 - type: dot_precision value: 50.836120401337794 - type: dot_recall value: 99.34640522875817 - type: euclidean_accuracy value: 61.563517915309454 - type: euclidean_accuracy_threshold value: 70.18057107925415 - type: euclidean_ap value: 60.78861909325018 - type: euclidean_f1 value: 67.25663716814158 - type: euclidean_f1_threshold value: 95.86195945739746 - type: euclidean_precision value: 50.836120401337794 - type: euclidean_recall value: 99.34640522875817 - type: main_score value: 60.78861909325018 - type: manhattan_accuracy value: 60.91205211726385 - type: manhattan_accuracy_threshold value: 1813.1645202636719 - type: manhattan_ap value: 60.478709337038936 - type: manhattan_f1 value: 67.10816777041943 - type: manhattan_f1_threshold value: 2475.027275085449 - type: manhattan_precision value: 50.66666666666667 - type: manhattan_recall value: 99.34640522875817 - type: max_ap value: 60.78861909325018 - type: max_f1 value: 67.25663716814158 - type: max_precision value: 50.836120401337794 - type: max_recall value: 99.34640522875817 - type: similarity_accuracy value: 61.563517915309454 - type: similarity_accuracy_threshold value: 75.3734290599823 - type: similarity_ap value: 60.78861909325018 - type: similarity_f1 value: 67.25663716814158 - type: similarity_f1_threshold value: 54.05237674713135 - type: similarity_precision value: 50.836120401337794 - type: similarity_recall value: 99.34640522875817 task: type: PairClassification license: mit language: - ru - en tags: - mteb - transformers - sentence-transformers base_model: ai-forever/ruRoberta-large --- # Model Card for ru-en-RoSBERTa The ru-en-RoSBERTa is a general text embedding model for Russian. The model is based on [ruRoBERTa](https://huggingface.co./ai-forever/ruRoberta-large) and fine-tuned with ~4M pairs of supervised, synthetic and unsupervised data in Russian and English. Tokenizer supports some English tokens from [RoBERTa](https://huggingface.co./FacebookAI/roberta-large) tokenizer. For more model details please refer to our [article](https://arxiv.org/abs/2408.12503). ## Usage The model can be used as is with prefixes. It is recommended to use CLS pooling. The choice of prefix and pooling depends on the task. We use the following basic rules to choose a prefix: - `"search_query: "` and `"search_document: "` prefixes are for answer or relevant paragraph retrieval - `"classification: "` prefix is for symmetric paraphrasing related tasks (STS, NLI, Bitext Mining) - `"clustering: "` prefix is for any tasks that rely on thematic features (topic classification, title-body retrieval) To better tailor the model to your needs, you can fine-tune it with relevant high-quality Russian and English datasets. Below are examples of texts encoding using the Transformers and SentenceTransformers libraries. ### Transformers ```python import torch import torch.nn.functional as F from transformers import AutoTokenizer, AutoModel def pool(hidden_state, mask, pooling_method="cls"): if pooling_method == "mean": s = torch.sum(hidden_state * mask.unsqueeze(-1).float(), dim=1) d = mask.sum(axis=1, keepdim=True).float() return s / d elif pooling_method == "cls": return hidden_state[:, 0] inputs = [ # "classification: Он нам и не нужон ваш Интернет!", "clustering: В Ярославской области разрешили работу бань, но без посетителей", "search_query: Сколько программистов нужно, чтобы вкрутить лампочку?", # "classification: What a time to be alive!", "clustering: Ярославским баням разрешили работать без посетителей", "search_document: Чтобы вкрутить лампочку, требуется три программиста: один напишет программу извлечения лампочки, другой — вкручивания лампочки, а третий проведет тестирование.", ] tokenizer = AutoTokenizer.from_pretrained("ai-forever/ru-en-RoSBERTa") model = AutoModel.from_pretrained("ai-forever/ru-en-RoSBERTa") tokenized_inputs = tokenizer(inputs, max_length=512, padding=True, truncation=True, return_tensors="pt") with torch.no_grad(): outputs = model(**tokenized_inputs) embeddings = pool( outputs.last_hidden_state, tokenized_inputs["attention_mask"], pooling_method="cls" # or try "mean" ) embeddings = F.normalize(embeddings, p=2, dim=1) sim_scores = embeddings[:3] @ embeddings[3:].T print(sim_scores.diag().tolist()) # [0.4796873927116394, 0.9409002065658569, 0.7761015892028809] ``` ### SentenceTransformers ```python from sentence_transformers import SentenceTransformer inputs = [ # "classification: Он нам и не нужон ваш Интернет!", "clustering: В Ярославской области разрешили работу бань, но без посетителей", "search_query: Сколько программистов нужно, чтобы вкрутить лампочку?", # "classification: What a time to be alive!", "clustering: Ярославским баням разрешили работать без посетителей", "search_document: Чтобы вкрутить лампочку, требуется три программиста: один напишет программу извлечения лампочки, другой — вкручивания лампочки, а третий проведет тестирование.", ] # loads model with CLS pooling model = SentenceTransformer("ai-forever/ru-en-RoSBERTa") # embeddings are normalized by default embeddings = model.encode(inputs, convert_to_tensor=True) sim_scores = embeddings[:3] @ embeddings[3:].T print(sim_scores.diag().tolist()) # [0.47968706488609314, 0.940900444984436, 0.7761018872261047] ``` ## Citation ``` @misc{snegirev2024russianfocusedembeddersexplorationrumteb, title={The Russian-focused embedders' exploration: ruMTEB benchmark and Russian embedding model design}, author={Artem Snegirev and Maria Tikhonova and Anna Maksimova and Alena Fenogenova and Alexander Abramov}, year={2024}, eprint={2408.12503}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2408.12503}, } ``` ## Limitations The model is designed to process texts in Russian, the quality in English is unknown. Maximum input text length is limited to 512 tokens.