Instructions to use ajrayman/Morality_fusion_longtext with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ajrayman/Morality_fusion_longtext with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ajrayman/Morality_fusion_longtext", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Morality_fusion_longtext
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0407
- Rmse: 0.2073
- Mae: 0.1658
- Corr: 0.4574
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: 32
- seed: 1234
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 8
Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse | Mae | Corr |
|---|---|---|---|---|---|---|
| No log | 1.0 | 338 | 0.0381 | 0.2013 | 0.1606 | 0.4803 |
| 0.0673 | 2.0 | 676 | 0.0366 | 0.1990 | 0.1556 | 0.4933 |
| 0.0411 | 3.0 | 1014 | 0.0373 | 0.1988 | 0.1568 | 0.5052 |
| 0.0411 | 4.0 | 1352 | 0.0370 | 0.1997 | 0.1568 | 0.4819 |
| 0.0349 | 5.0 | 1690 | 0.0407 | 0.2073 | 0.1658 | 0.4574 |
Framework versions
- Transformers 4.44.1
- Pytorch 1.11.0
- Datasets 2.12.0
- Tokenizers 0.19.1
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