Instructions to use ajrayman/HarmCare_fusion with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ajrayman/HarmCare_fusion with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ajrayman/HarmCare_fusion", device_map="auto") - Notebooks
- Google Colab
- Kaggle
HarmCare_fusion
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0267
- Rmse: 0.1634
- Mae: 0.1319
- Corr: 0.2955
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 | 221 | 0.0261 | 0.1617 | 0.1261 | 0.2911 |
| No log | 2.0 | 442 | 0.0249 | 0.1579 | 0.1241 | 0.2942 |
| 0.0556 | 3.0 | 663 | 0.0247 | 0.1571 | 0.1234 | 0.3099 |
| 0.0556 | 4.0 | 884 | 0.0252 | 0.1588 | 0.1266 | 0.3036 |
| 0.0261 | 5.0 | 1105 | 0.0267 | 0.1634 | 0.1319 | 0.2955 |
Framework versions
- Transformers 4.44.1
- Pytorch 1.11.0
- Datasets 2.12.0
- Tokenizers 0.19.1
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