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deepvk
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roberta-base

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Transformers
PyTorch
Safetensors
Russian
English
roberta
text-embeddings-inference
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roberta-base
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---
license: apache-2.0
language:
- ru
- en
library_name: transformers
---

# RoBERTa-base from deepvk

<!-- Provide a quick summary of what the model is/does. -->

Pretrained bidirectional encoder for russian language. 

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->
Model was pretrained using standard MLM objective on a large text corpora including open social data, books, Wikipedia, webpages etc. 


- **Developed by:** VK Applied Research Team
- **Model type:** RoBERTa
- **Languages:** Mostly russian and small fraction of other languages
- **License:** Apache 2.0

## How to Get Started with the Model

```
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("deepvk/roberta-base")
model = AutoModel.from_pretrained("deepvk/roberta-base")

text = "Привет, мир!"

inputs = tokenizer(text, return_tensors='pt')
predictions = model(**inputs)
```


## Training Details

### Training Data

<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->

Mix of the following data:


### Training Procedure 

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

[More Information Needed]


#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

Standard RoBERTA-base size;

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

<!-- This should link to a Data Card if possible. -->

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

[More Information Needed]

### Results

[More Information Needed]

#### Summary


## Compute Infrastructure

Model was trained using 8xA100 for ~22 days.