Instructions to use ajrayman/Extra_fusion_longtext with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ajrayman/Extra_fusion_longtext with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ajrayman/Extra_fusion_longtext", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Extra_fusion_longtext
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0272
- Rmse: 0.1663
- Mae: 0.1303
- Corr: 0.3061
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 | 0.0247 | 0.1561 | 0.1242 | 0.3140 |
| 0.0501 | 2.0 | 674 | 0.0230 | 0.1524 | 0.1194 | 0.3215 |
| 0.0242 | 3.0 | 1011 | 0.0225 | 0.1508 | 0.1193 | 0.3408 |
| 0.0242 | 4.0 | 1348 | 0.0231 | 0.1526 | 0.1206 | 0.3192 |
| 0.0198 | 5.0 | 1685 | 0.0272 | 0.1663 | 0.1303 | 0.3061 |
Framework versions
- Transformers 4.44.1
- Pytorch 1.11.0
- Datasets 2.12.0
- Tokenizers 0.19.1
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