ruBert-large
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ruclip-vit-base-patch16-384
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View details ↗ruclip-vit-base-patch32-384
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View details ↗ruElectra-large
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View details ↗ruGPT-3.5-13B
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View details ↗rugpt3large_based_on_gpt2
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View details ↗rugpt3medium_based_on_gpt2
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View details ↗rugpt3small_based_on_gpt2
ai-foreverThe model architecture design, pretraining, and evaluation are documented in our preprint: **A Family of Pretrained Transformer Language Models for Russian**. The model was pretrained with sequence length 1024 using transformers by the SberDevices team on 80B tokens around 3 epochs. After that, the model was finetuned with the context size of 2048. + NLP core team RnD Telegram channel: + Dmitry Zmitrovich
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View details ↗ruRoberta-large
ai-foreverThe model architecture design, pretraining, and evaluation are documented in our preprint: **A Family of Pretrained Transformer Language Models for Russian**. The model is pretrained by the SberDevices team. * Task: `mask filling` * Type: `encoder` * Tokenizer: `BBPE` * Dict size: `50 257` * Num Parameters: `355 M` * Training Data Volume `250 GB`
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View details ↗ruSciBERT
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View details ↗ruT5-base
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View details ↗ruT5-large
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View details ↗sage-fredt5-distilled-95m
ai-foreverThe model corrects spelling and punctuation errors and typos by bringing all the words in the text to the norm of the Russian language. Corrector is a distilled version of the original model that had been trained based on the FRED-T5-1.7B architecture. An extensive dataset with “artificial” errors was taken as a training corpus: the corpus was assembled on the basis of the Russian-language Wikipedia and transcripts of Russian-language videos, then typos and spelling errors were automatically introduced into it using the library SAGE. Below are automatic metrics for determining the correctness of the spell checkers. We compare our solution with both open automatic spell checkers and the ChatGPT family of models on all four available datasets: - **RUSpellRU**: texts collected from (LiveJournal), with manually corrected typos and errors; - **MultidomainGold**: examples from 7 text sources, including the open web, news, social media, reviews, subtitles, policy documents and literary works; - **MedSpellChecker**: texts with errors from medical anamnesis; - **GitHubTypoCorpusRu**: spelling errors and typos in commits from GitHub; **RUSpellRU** | Model | Pr. (spell) | Rec. (spell) | F1 (spell) | Pr. (punc) | Rec. (punc) | F1 (punc) | Pr. (case) | Rec. (case) | F1 (case) | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | sage-fredt5-distilled-95m | 83.5 | 74.8 | 78.9 | 86.8 | 80.6 | 83.6 | 94.4 | 92.5 | 93.5 | | sage-ai-service | 90.3 | 86.3 | 88.2 | 90.3 | 86.6 | 88.4 | 95.2 | 95.9 | 95.6 | | gpt-3.5-turbo | 33.6 | 58.5 | 42.7 | 85.9 | 64.6 | 73.7 | 84.9 | 73.9 | 79.0 | | gpt-4 | 54.9 | 76.7 | 64.0 | 84.0 | 82.3 | 83.2 | 91.5 | 90.2 | 90.9 |
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View details ↗sage-fredt5-large
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View details ↗sbert_large_mt_nlu_ru
ai-foreverFor better quality, use mean token embeddings. You can use the model directly from the model repository to compute sentence embeddings: + SberDevices Team. + Aleksandr Abramov: HF profile, Github, Kaggle Competitions Master; + Denis Antykhov: Github;
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View details ↗sbert_large_nlu_ru
ai-foreverThe model is described in this article For better quality, use mean token embeddings. You can use the model directly from the model repository to compute sentence embeddings: + SberDevices Team. + Aleksandr Abramov: HF profile, Github, Kaggle Competitions Master; + Denis Antykhov: Github; + Ibragim Badertdinov: Github
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View details ↗T5-large-spell
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View details ↗Wan2.1-T2V-14B-NABLA-0.6-STA-11-3-3
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View details ↗Wan2.1-T2V-14B-NABLA-0.7
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View details ↗Zarya-0.6B
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View details ↗Zarya-4B
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View details ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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GLM 4.5V
智谱 AIGLM-4.5V is a vision-language foundation model for multimodal agent applications. Built on a Mixture-of-Experts (MoE) architecture with 106B parameters and 12B activated parameters, it achieves state-of-the-art results in video understanding,...
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$0.6 / M input tokensOpenRouter · z-ai/glm-4.5v
View details ↗GLM 4.6V
智谱 AIGLM-4.6V is a large multimodal model designed for high-fidelity visual understanding and long-context reasoning across images, documents, and mixed media. It supports up to 128K tokens, processes complex page layouts...
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$0.3 / M input tokensOpenRouter · z-ai/glm-4.6v
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