Instructions to use ajrayman/Liberalism_fusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ajrayman/Liberalism_fusion with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ajrayman/Liberalism_fusion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Liberalism_fusion
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0369
- Rmse: 0.1921
- Mae: 0.1529
- Corr: 0.6296
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 | 0.0420 | 0.2049 | 0.1634 | 0.6003 |
| No log | 2.0 | 470 | 0.0372 | 0.1928 | 0.1561 | 0.6302 |
| 0.0535 | 3.0 | 705 | 0.0365 | 0.1911 | 0.1525 | 0.6442 |
| 0.0535 | 4.0 | 940 | 0.0383 | 0.1958 | 0.1543 | 0.6419 |
| 0.034 | 5.0 | 1175 | 0.0369 | 0.1921 | 0.1529 | 0.6296 |
Framework versions
- Transformers 4.44.1
- Pytorch 1.11.0
- Datasets 2.12.0
- Tokenizers 0.19.1
- Downloads last month
- 32
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support