Instructions to use DaInternet12/bert_affinity_davis with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DaInternet12/bert_affinity_davis with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("DaInternet12/bert_affinity_davis", device_map="auto") - Notebooks
- Google Colab
- Kaggle
results
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4819
- Mse: 0.4819
- Mae: 0.4031
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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Mse | Mae |
|---|---|---|---|---|---|
| 0.5617 | 1.1416 | 500 | 0.5514 | 0.5514 | 0.5555 |
| 0.5405 | 2.2831 | 1000 | 0.5391 | 0.5391 | 0.5233 |
| 0.822 | 3.4247 | 1500 | 0.5192 | 0.5192 | 0.4893 |
| 0.4446 | 4.5662 | 2000 | 0.5272 | 0.5272 | 0.4664 |
Framework versions
- Transformers 4.48.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
Inference Providers NEW
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