Instructions to use Daryaflp/roberta-retrained_ru_covid_papers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Daryaflp/roberta-retrained_ru_covid_papers with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Daryaflp/roberta-retrained_ru_covid_papers")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Daryaflp/roberta-retrained_ru_covid_papers") model = AutoModelForMaskedLM.from_pretrained("Daryaflp/roberta-retrained_ru_covid_papers") - Notebooks
- Google Colab
- Kaggle
roberta-retrained_ru_covid_papers
This model is a fine-tuned version of Daryaflp/roberta-retrained_ru_covid on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 1.9998
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
Training results
Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
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