Instructions to use ajrayman/Trust_fusion_longtext with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ajrayman/Trust_fusion_longtext with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ajrayman/Trust_fusion_longtext", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Trust_fusion_longtext
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0520
- Rmse: 0.2309
- Mae: 0.1833
- Corr: 0.2894
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 | 336 | 0.0495 | 0.2266 | 0.1781 | 0.3076 |
| 0.0715 | 2.0 | 672 | 0.0457 | 0.2171 | 0.1733 | 0.3340 |
| 0.0475 | 3.0 | 1008 | 0.0472 | 0.2210 | 0.1738 | 0.3551 |
| 0.0475 | 4.0 | 1344 | 0.0456 | 0.2170 | 0.1741 | 0.3270 |
| 0.0393 | 5.0 | 1680 | 0.0545 | 0.2359 | 0.1864 | 0.2806 |
| 0.0279 | 6.0 | 2016 | 0.0520 | 0.2309 | 0.1833 | 0.2894 |
Framework versions
- Transformers 4.44.1
- Pytorch 1.11.0
- Datasets 2.12.0
- Tokenizers 0.19.1
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