Instructions to use morality-ai/deberta-emfd-loyalty with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use morality-ai/deberta-emfd-loyalty with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="morality-ai/deberta-emfd-loyalty")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("morality-ai/deberta-emfd-loyalty") model = AutoModelForSequenceClassification.from_pretrained("morality-ai/deberta-emfd-loyalty") - Notebooks
- Google Colab
- Kaggle
deberta-emfd-loyalty
This model is a fine-tuned version of microsoft/deberta-v3-small on an unknown dataset.
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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 147 | 0.4836 | 0.7679 |
Framework versions
- Transformers 4.57.0
- Pytorch 2.8.0+cu128
- Datasets 4.1.1
- Tokenizers 0.22.1
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Model tree for morality-ai/deberta-emfd-loyalty
Base model
microsoft/deberta-v3-small