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
File size: 418 Bytes
75bcf0a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | {
"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
}
|