Instructions to use ajrayman/Liberalism_fusion_longtext with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ajrayman/Liberalism_fusion_longtext with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ajrayman/Liberalism_fusion_longtext", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Liberalism_fusion_longtext
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0372
- Rmse: 0.1898
- Mae: 0.1502
- Corr: 0.6537
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 | 338 | 0.0405 | 0.2003 | 0.1632 | 0.5937 |
| 0.0571 | 2.0 | 676 | 0.0407 | 0.1997 | 0.1587 | 0.6404 |
| 0.0392 | 3.0 | 1014 | 0.0359 | 0.1871 | 0.1501 | 0.6509 |
| 0.0392 | 4.0 | 1352 | 0.0364 | 0.1883 | 0.1505 | 0.6512 |
| 0.0343 | 5.0 | 1690 | 0.0372 | 0.1898 | 0.1502 | 0.6537 |
Framework versions
- Transformers 4.44.1
- Pytorch 1.11.0
- Datasets 2.12.0
- Tokenizers 0.19.1
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