Instructions to use acmc/satisfaction_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use acmc/satisfaction_model with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("acmc/satisfaction_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| library_name: transformers | |
| tags: | |
| - generated_from_trainer | |
| metrics: | |
| - accuracy | |
| model-index: | |
| - name: satisfaction_model | |
| results: [] | |
| <!-- This model card has been generated automatically according to the information the Trainer had access to. You | |
| should probably proofread and complete it, then remove this comment. --> | |
| # satisfaction_model | |
| This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset. | |
| It achieves the following results on the evaluation set: | |
| - Loss: 0.7983 | |
| - Accuracy: 0.82 | |
| ## 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: 0.00046878097862004293 | |
| - train_batch_size: 1 | |
| - eval_batch_size: 8 | |
| - seed: 42 | |
| - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
| - lr_scheduler_type: linear | |
| - num_epochs: 10 | |
| ### Training results | |
| ### Framework versions | |
| - Transformers 4.44.2 | |
| - Pytorch 2.3.1+cu121 | |
| - Datasets 2.21.0 | |
| - Tokenizers 0.19.1 | |