language:
- ru
tags:
- PyTorch
- Transformers
thumbnail: https://github.com/sberbank-ai/model-zoo
Model Card for ruT5-base
Model Details
Model Description
More information needed
- Developed by: SberDevices team
- Shared by [Optional]: SberDevices team
- Model type: Text2text Generation
- Language(s) (NLP): Russian
- License: More information needed
- Parent Model: T5 base
- Resources for more information: More information neeeded
Uses
Direct Use
This model can be used for the task of text2text generation
Downstream Use [Optional]
More information needed.
Out-of-Scope Use
The model should not be used to intentionally create hostile or alienating environments for people.
Bias, Risks, and Limitations
Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
Training Details
Training Data
- Dict size:
32 101 - Training Data Volume
300 GB
Training Procedure
Preprocessing
More information needed
Speeds, Sizes, Times
- Type:
encoder-decoder - Tokenizer:
bpe - Num Parameters:
222 M
Evaluation
Testing Data, Factors & Metrics
Testing Data
More information needed
Factors
More information needed
Metrics
More information needed
Results
More information needed
Model Examination
More information needed
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: More information needed
- Hours used: More information needed
- Cloud Provider: More information needed
- Compute Region: More information needed
- Carbon Emitted: More information needed
Technical Specifications [optional]
Model Architecture and Objective
- Type:
encoder-decoder
Compute Infrastructure
More information needed
Hardware
More information needed
Software
More information needed.
Citation
More information needed
Glossary [optional]
More information needed
More Information [optional]
More information needed
Model Card Authors [optional]
SberDevices team in collaboration with Ezi Ozoani and the Hugging Face team
Model Card Contact
More information needed
How to Get Started with the Model
Use the code below to get started with the model.
Click to expand
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("sberbank-ai/ruT5-base")
model = AutoModelForSeq2SeqLM.from_pretrained("sberbank-ai/ruT5-base")