Feature Extraction
Transformers
PyTorch
Safetensors
Estonian
xlm-roberta
fill-mask
text-embeddings-inference
Instructions to use tartuNLP/EstRoBERTa with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tartuNLP/EstRoBERTa with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="tartuNLP/EstRoBERTa")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("tartuNLP/EstRoBERTa") model = AutoModelForMaskedLM.from_pretrained("tartuNLP/EstRoBERTa") - Notebooks
- Google Colab
- Kaggle
Model Card for EstRoBERTa
Model Details
Model Description
- Language(s) (NLP): Estonian
- License: cc-by-4.0
- Finetuned from model [optional]: XLM-RoBERTa base.
Uses
Direct Use
[More Information Needed]
Downstream Use [optional]
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Out-of-Scope Use
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Bias, Risks, and Limitations
[More Information Needed]
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.
How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
Training Details
Training Data
[More Information Needed]
Training Procedure
Training Hyperparameters
- Training regime: [More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
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Metrics
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Results
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Summary
Model Architecture and Objective
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Compute Infrastructure
[More Information Needed]
Hardware
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Software
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Citation
@misc{kairit_sirts_2025,
author = { Kairit Sirts },
title = { EstRoBERTa (Revision 5669b06) },
year = 2025,
url = { https://huggingface.co/tartuNLP/EstRoBERTa },
doi = { 10.57967/hf/7046 },
publisher = { Hugging Face }
}
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Base model
FacebookAI/xlm-roberta-base