Instructions to use sgolkar/roberta_lorenz_xsmall with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sgolkar/roberta_lorenz_xsmall with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="sgolkar/roberta_lorenz_xsmall")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("sgolkar/roberta_lorenz_xsmall") model = AutoModelForMaskedLM.from_pretrained("sgolkar/roberta_lorenz_xsmall") - Notebooks
- Google Colab
- Kaggle
roberta_lorenz_xsmall
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- Loss: 3.0699
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: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
Training results
Framework versions
- Transformers 4.28.1
- Pytorch 1.13.1
- Datasets 2.11.0
- Tokenizers 0.11.0
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