language:
- ru
license: apache-2.0
tags:
- PyTorch
- Transformers
- bert
- exbert
pipeline_tag: fill-mask
thumbnail: https://github.com/sberbank-ai/model-zoo
Model Card for ruBert-large
Model Details
Model Description
- Developed by: Sberbank-ai
- Shared by [Optional]: Hugging Face
- Model type: Fill-Mask
- Language(s) (NLP): ru
- License: apache-2.0
- Related Models: exbert
- Parent Model: bert
- Resources for more information:
Uses
Direct Use
Fill-Mask
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 recomendations.
Training Details
Training Data
More information needed
Training Procedure
Preprocessing
More information needed
Speeds, Sizes, Times
- Task:
mask filling - Type:
encoder - Tokenizer:
bpe - Dict size:
120 138 - Num Parameters:
178 M - Training Data Volume
30 GB
Evaluation
Testing Data, Factors & Metrics
Testing Data
More information needed
Factors
More information needed
Metrics
More information needed
Results
| Model | Task | Type | Tokenizer | Dict size | Num Parameters | Training Data Volume |
|---|---|---|---|---|---|---|
| ruBERT-large | mask filling | encoder | bpe | 120 138 | 427 M | 30 GB |
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
More information needed
Compute Infrastructure
More information needed
Hardware
More information needed
Software
More information needed
Citation
BibTeX:
More information needed
APA:
More information needed
Glossary [optional]
More information needed
More Information [optional]
More information needed
Model Card Authors [optional]
Sberbank-ai in collaberation 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, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("asahi417/tner-xlm-roberta-base-ontonotes5")
model = AutoModelForTokenClassification.from_pretrained("asahi417/tner-xlm-roberta-base-ontonotes5")