Instructions to use ajrayman/Sympathy_fusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ajrayman/Sympathy_fusion with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ajrayman/Sympathy_fusion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Sympathy_fusion
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0370
- Rmse: 0.1924
- Mae: 0.1527
- Corr: 0.2937
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 | 0.0382 | 0.1956 | 0.1596 | 0.3166 |
| No log | 2.0 | 470 | 0.0328 | 0.1812 | 0.1450 | 0.3328 |
| 0.056 | 3.0 | 705 | 0.0346 | 0.1859 | 0.1455 | 0.3451 |
| 0.056 | 4.0 | 940 | 0.0326 | 0.1805 | 0.1437 | 0.3404 |
| 0.0322 | 5.0 | 1175 | 0.0335 | 0.1830 | 0.1458 | 0.3194 |
| 0.0322 | 6.0 | 1410 | 0.0370 | 0.1924 | 0.1527 | 0.2937 |
Framework versions
- Transformers 4.44.1
- Pytorch 1.11.0
- Datasets 2.12.0
- Tokenizers 0.19.1
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