Instructions to use ajrayman/IngroupLoyalty_fusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ajrayman/IngroupLoyalty_fusion with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ajrayman/IngroupLoyalty_fusion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
IngroupLoyalty_fusion
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0354
- Rmse: 0.1883
- Mae: 0.1515
- Corr: 0.3666
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 | 221 | 0.0321 | 0.1790 | 0.1446 | 0.4044 |
| No log | 2.0 | 442 | 0.0317 | 0.1782 | 0.1442 | 0.4247 |
| 0.0525 | 3.0 | 663 | 0.0320 | 0.1788 | 0.1444 | 0.4150 |
| 0.0525 | 4.0 | 884 | 0.0317 | 0.1780 | 0.1443 | 0.4129 |
| 0.0283 | 5.0 | 1105 | 0.0383 | 0.1956 | 0.1583 | 0.3827 |
| 0.0283 | 6.0 | 1326 | 0.0354 | 0.1883 | 0.1515 | 0.3666 |
Framework versions
- Transformers 4.44.1
- Pytorch 1.11.0
- Datasets 2.12.0
- Tokenizers 0.19.1
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