Instructions to use ajrayman/Excitement_Seeking_fusion_longtext with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ajrayman/Excitement_Seeking_fusion_longtext with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ajrayman/Excitement_Seeking_fusion_longtext", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Excitement_Seeking_fusion_longtext
This model is a fine-tuned version of on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0430
- Rmse: 0.2129
- Mae: 0.1724
- Corr: 0.3729
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 | 0.0403 | 0.2052 | 0.1604 | 0.3639 |
| 0.0658 | 2.0 | 674 | 0.0417 | 0.2080 | 0.1621 | 0.3766 |
| 0.0417 | 3.0 | 1011 | 0.0430 | 0.2129 | 0.1724 | 0.3729 |
Framework versions
- Transformers 4.44.1
- Pytorch 1.11.0
- Datasets 2.12.0
- Tokenizers 0.19.1
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