Instructions to use ajrayman/HarmCare_fusion_longtext with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ajrayman/HarmCare_fusion_longtext with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ajrayman/HarmCare_fusion_longtext", device_map="auto") - Notebooks
- Google Colab
- Kaggle
HarmCare_fusion_longtext
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0280
- Rmse: 0.1704
- Mae: 0.1357
- Corr: 0.2657
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 | 317 | 0.0273 | 0.1665 | 0.1283 | 0.3059 |
| 0.0619 | 2.0 | 634 | 0.0245 | 0.1583 | 0.1242 | 0.3036 |
| 0.0619 | 3.0 | 951 | 0.0245 | 0.1576 | 0.1267 | 0.3212 |
| 0.0276 | 4.0 | 1268 | 0.0241 | 0.1573 | 0.1239 | 0.3193 |
| 0.0249 | 5.0 | 1585 | 0.0246 | 0.1587 | 0.1251 | 0.3046 |
| 0.0249 | 6.0 | 1902 | 0.0280 | 0.1704 | 0.1357 | 0.2657 |
Framework versions
- Transformers 4.44.1
- Pytorch 1.11.0
- Datasets 2.12.0
- Tokenizers 0.19.1
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