Instructions to use ShengdingHu/compacter_t5-base_mrpc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ShengdingHu/compacter_t5-base_mrpc with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ShengdingHu/compacter_t5-base_mrpc", dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 886 Bytes
56f0118 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | {
"backbone_checkpoint_name": "t5-base",
"backbone_class": "T5ForConditionalGeneration",
"backbone_hash": "8440e52442de128c282504938981a46c",
"bottleneck_dim": null,
"common_structure": true,
"delta_type": "compacter",
"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": "relu",
"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",
"unfrozen_modules": [
"deltas",
"layer_norm",
"final_layer_norm"
],
"use_bias_down_sampler": true,
"use_bias_up_sampler": true
}
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