Instructions to use ShengdingHu/compactor_roberta-base_rte with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ShengdingHu/compactor_roberta-base_rte with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ShengdingHu/compactor_roberta-base_rte", dtype="auto") - Notebooks
- Google Colab
- Kaggle
| { | |
| "backbone_checkpoint_name": "roberta-base", | |
| "backbone_class": "RobertaForSequenceClassification", | |
| "backbone_hash": "f0e286d3ae46ea2d1d6eb03e77fa0691", | |
| "bottleneck_dim": null, | |
| "common_structure": true, | |
| "delta_type": "compactor", | |
| "factorized_phm": true, | |
| "factorized_phm_rule": false, | |
| "hypercomplex_division": 4, | |
| "hypercomplex_nonlinearity": "glorot-uniform", | |
| "kronecker_prod": null, | |
| "learn_phm": true, | |
| "modified_modules": [ | |
| "attn", | |
| "ff" | |
| ], | |
| "non_linearity": "gelu_new", | |
| "opendelta_version": "0.0.1", | |
| "phm_c_init": "normal", | |
| "phm_init_range": 0.0001, | |
| "phm_rank": 1, | |
| "reduction_factor": 16, | |
| "sequential": null, | |
| "shared_W_phm": false, | |
| "shared_phm_rule": false, | |
| "transformers_version": "4.16.0.dev0" | |
| } | |