Instructions to use ajrayman/Cautiousness_fusion_longtext with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ajrayman/Cautiousness_fusion_longtext with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ajrayman/Cautiousness_fusion_longtext", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Cautiousness_fusion_longtext
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0610
- Rmse: 0.2527
- Mae: 0.2042
- Corr: 0.3362
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 | 0.0575 | 0.2451 | 0.1998 | 0.3533 |
| 0.0891 | 2.0 | 674 | 0.0566 | 0.2435 | 0.1991 | 0.3682 |
| 0.0607 | 3.0 | 1011 | 0.0640 | 0.2570 | 0.2093 | 0.3550 |
| 0.0607 | 4.0 | 1348 | 0.0610 | 0.2527 | 0.2042 | 0.3362 |
Framework versions
- Transformers 4.44.1
- Pytorch 1.11.0
- Datasets 2.12.0
- Tokenizers 0.19.1
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