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
| { | |
| "activation_function": "Mish", | |
| "architectures": [ | |
| "SatisfactionModel" | |
| ], | |
| "hidden_size": 884, | |
| "loss_label_smoothing": 0.2, | |
| "model_type": "satisfaction_model", | |
| "num_classes": 3, | |
| "num_layers": 4, | |
| "original_model_name": "sentence-transformers/paraphrase-multilingual-mpnet-base-v2", | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.44.2", | |
| "use_bias": true, | |
| "use_weighted_loss": false | |
| } | |