Instructions to use Dakie/mongolian-roberta-base-mn with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Dakie/mongolian-roberta-base-mn with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="Dakie/mongolian-roberta-base-mn")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("Dakie/mongolian-roberta-base-mn") model = AutoModelForTokenClassification.from_pretrained("Dakie/mongolian-roberta-base-mn") - Notebooks
- Google Colab
- Kaggle
mongolian-roberta-base-mn
This model is a fine-tuned version of bayartsogt/mongolian-roberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0728
- Precision: 0.9189
- Recall: 0.9301
- F1: 0.9245
- Accuracy: 0.9803
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
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