Instructions to use Sandy1857/custom-dataset-deberta-xsmall-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sandy1857/custom-dataset-deberta-xsmall-1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="Sandy1857/custom-dataset-deberta-xsmall-1")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("Sandy1857/custom-dataset-deberta-xsmall-1") model = AutoModelForMaskedLM.from_pretrained("Sandy1857/custom-dataset-deberta-xsmall-1") - Notebooks
- Google Colab
- Kaggle
custom-dataset-deberta-xsmall-1
This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:
- Loss: 3.0808
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: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- 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 |
|---|---|---|---|
| 4.9858 | 1.0 | 5390 | 3.5954 |
| 3.6159 | 2.0 | 10780 | 3.1921 |
| 3.3492 | 3.0 | 16170 | 3.0936 |
Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
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