Instructions to use ajrayman/Friendliness_fusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ajrayman/Friendliness_fusion with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ajrayman/Friendliness_fusion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Friendliness_fusion
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0641
- Rmse: 0.2532
- Mae: 0.2020
- Corr: 0.2349
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 | 235 | 0.0676 | 0.2600 | 0.2001 | 0.2455 |
| No log | 2.0 | 470 | 0.0554 | 0.2354 | 0.1861 | 0.2737 |
| 0.0795 | 3.0 | 705 | 0.0552 | 0.2349 | 0.1937 | 0.2730 |
| 0.0795 | 4.0 | 940 | 0.0596 | 0.2441 | 0.1917 | 0.2672 |
| 0.0504 | 5.0 | 1175 | 0.0549 | 0.2344 | 0.1887 | 0.2591 |
| 0.0504 | 6.0 | 1410 | 0.0611 | 0.2472 | 0.2007 | 0.2432 |
| 0.0353 | 7.0 | 1645 | 0.0641 | 0.2532 | 0.2020 | 0.2349 |
Framework versions
- Transformers 4.44.1
- Pytorch 1.11.0
- Datasets 2.12.0
- Tokenizers 0.19.1
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