Instructions to use uhlenbeckmew/distilroberta-base-swift_shake with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use uhlenbeckmew/distilroberta-base-swift_shake with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="uhlenbeckmew/distilroberta-base-swift_shake")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("uhlenbeckmew/distilroberta-base-swift_shake") model = AutoModelForMaskedLM.from_pretrained("uhlenbeckmew/distilroberta-base-swift_shake", device_map="auto") - Notebooks
- Google Colab
- Kaggle
distilroberta-base-swift_shake
This model is a fine-tuned version of distilroberta-base on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.5309
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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 1.0 | 334 | 2.5817 |
| 2.7363 | 2.0 | 668 | 2.4499 |
| 2.4584 | 3.0 | 1002 | 2.5309 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
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