Instructions to use ajrayman/PuritySanctity_fusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ajrayman/PuritySanctity_fusion with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ajrayman/PuritySanctity_fusion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
PuritySanctity_fusion
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0448
- Rmse: 0.2115
- Mae: 0.1685
- Corr: 0.3391
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 | 215 | 0.0448 | 0.2116 | 0.1750 | 0.3773 |
| No log | 2.0 | 430 | 0.0459 | 0.2142 | 0.1760 | 0.3805 |
| 0.0587 | 3.0 | 645 | 0.0429 | 0.2072 | 0.1677 | 0.3788 |
| 0.0587 | 4.0 | 860 | 0.0395 | 0.1987 | 0.1589 | 0.3757 |
| 0.031 | 5.0 | 1075 | 0.0437 | 0.2090 | 0.1645 | 0.3417 |
| 0.031 | 6.0 | 1290 | 0.0448 | 0.2115 | 0.1685 | 0.3391 |
Framework versions
- Transformers 4.44.1
- Pytorch 1.11.0
- Datasets 2.12.0
- Tokenizers 0.19.1
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