Instructions to use ajrayman/Artistic_Interests_fusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ajrayman/Artistic_Interests_fusion with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ajrayman/Artistic_Interests_fusion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Artistic_Interests_fusion
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0544
- Rmse: 0.2333
- Mae: 0.1867
- Corr: 0.2900
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 | 235 | 0.0494 | 0.2223 | 0.1847 | 0.3472 |
| No log | 2.0 | 470 | 0.0513 | 0.2264 | 0.1824 | 0.3344 |
| 0.0754 | 3.0 | 705 | 0.0482 | 0.2196 | 0.1788 | 0.3414 |
| 0.0754 | 4.0 | 940 | 0.0559 | 0.2364 | 0.1871 | 0.3277 |
| 0.0461 | 5.0 | 1175 | 0.0544 | 0.2333 | 0.1867 | 0.2900 |
Framework versions
- Transformers 4.44.1
- Pytorch 1.11.0
- Datasets 2.12.0
- Tokenizers 0.19.1
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