Instructions to use ajrayman/Self_Efficacy_fusion_longtext with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ajrayman/Self_Efficacy_fusion_longtext with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ajrayman/Self_Efficacy_fusion_longtext", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Self_Efficacy_fusion_longtext
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0344
- Rmse: 0.1916
- Mae: 0.1467
- Corr: 0.3026
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 | 337 | 0.0342 | 0.1908 | 0.1454 | 0.2827 |
| 0.0775 | 2.0 | 674 | 0.0329 | 0.1866 | 0.1437 | 0.3131 |
| 0.0368 | 3.0 | 1011 | 0.0339 | 0.1894 | 0.1451 | 0.3174 |
| 0.0368 | 4.0 | 1348 | 0.0344 | 0.1916 | 0.1467 | 0.3026 |
Framework versions
- Transformers 4.44.1
- Pytorch 1.11.0
- Datasets 2.12.0
- Tokenizers 0.19.1
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