Fill-Mask
Transformers
PyTorch
Safetensors
Russian
English
bert
pretraining
russian
embeddings
masked-lm
tiny
feature-extraction
sentence-similarity
Instructions to use cointegrated/rubert-tiny with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cointegrated/rubert-tiny with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="cointegrated/rubert-tiny")# Load model directly from transformers import AutoTokenizer, AutoModelForPreTraining tokenizer = AutoTokenizer.from_pretrained("cointegrated/rubert-tiny") model = AutoModelForPreTraining.from_pretrained("cointegrated/rubert-tiny") - Inference
- Notebooks
- Google Colab
- Kaggle
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This is a very small distilled version of the [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) model for Russian and English.
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This model is useful if you want to fine-tune it for a relatively simple Russian task (e.g. NER or sentiment classification), and you care more about speed and size than about accuracy. It is approximately x10 faster than [DeepPavlov/rubert-base-cased-sentence](https://huggingface.co/DeepPavlov/rubert-base-cased-sentence).
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It was trained on the [Yandex Translate corpus](https://translate.yandex.ru/corpus) using MLM loss (partially distilled from [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)) and translation ranking loss (partially distilled from [LaBSE](https://huggingface.co/sentence-transformers/LaBSE)).
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This is a very small distilled version of the [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) model for Russian and English.
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This model is useful if you want to fine-tune it for a relatively simple Russian task (e.g. NER or sentiment classification), and you care more about speed and size than about accuracy. It is approximately x10 smaller and faster than [DeepPavlov/rubert-base-cased-sentence](https://huggingface.co/DeepPavlov/rubert-base-cased-sentence).
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It was trained on the [Yandex Translate corpus](https://translate.yandex.ru/corpus) using MLM loss (partially distilled from [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased)) and translation ranking loss (partially distilled from [LaBSE](https://huggingface.co/sentence-transformers/LaBSE)).
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