Instructions to use ajrayman/FairnessReciprocity_fusion_longtext with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ajrayman/FairnessReciprocity_fusion_longtext with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ajrayman/FairnessReciprocity_fusion_longtext", device_map="auto") - Notebooks
- Google Colab
- Kaggle
FairnessReciprocity_fusion_longtext
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0228
- Rmse: 0.1558
- Mae: 0.1246
- Corr: 0.2167
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 | 314 | 0.0264 | 0.1648 | 0.1347 | 0.2290 |
| 0.0519 | 2.0 | 628 | 0.0232 | 0.1577 | 0.1238 | 0.2469 |
| 0.0519 | 3.0 | 942 | 0.0232 | 0.1581 | 0.1241 | 0.2522 |
| 0.0265 | 4.0 | 1256 | 0.0229 | 0.1548 | 0.1252 | 0.2605 |
| 0.0244 | 5.0 | 1570 | 0.0244 | 0.1593 | 0.1287 | 0.2349 |
| 0.0244 | 6.0 | 1884 | 0.0228 | 0.1558 | 0.1246 | 0.2167 |
Framework versions
- Transformers 4.44.1
- Pytorch 1.11.0
- Datasets 2.12.0
- Tokenizers 0.19.1
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