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Upload T5ForConditionalGeneration
Browse files- README.md +43 -0
- adapter_config.json +40 -0
- head_config.json +14 -0
- pytorch_adapter.bin +3 -0
- pytorch_model_head.bin +3 -0
README.md
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---
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tags:
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- adapter-transformers
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- t5
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datasets:
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- SALT-NLP/mnli_VALUE
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---
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# Adapter `liuyanchen1015/pfadapter-FLAN-T5-base-multi-task-VALUE` for liuyanchen1015/FLAN-T5_GLUE_finetuning_lr3e-4
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An [adapter](https://adapterhub.ml) for the `liuyanchen1015/FLAN-T5_GLUE_finetuning_lr3e-4` model that was trained on the [SALT-NLP/mnli_VALUE](https://huggingface.co/datasets/SALT-NLP/mnli_VALUE/) dataset.
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This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
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## Usage
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First, install `adapter-transformers`:
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```
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pip install -U adapter-transformers
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```
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_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
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Now, the adapter can be loaded and activated like this:
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```python
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from transformers import AutoAdapterModel
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model = AutoAdapterModel.from_pretrained("liuyanchen1015/FLAN-T5_GLUE_finetuning_lr3e-4")
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adapter_name = model.load_adapter("liuyanchen1015/pfadapter-FLAN-T5-base-multi-task-VALUE", source="hf", set_active=True)
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```
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## Architecture & Training
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<!-- Add some description here -->
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## Evaluation results
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<!-- Add some description here -->
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## Citation
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<!-- Add some description here -->
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adapter_config.json
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{
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"config": {
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"adapter_residual_before_ln": false,
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"cross_adapter": false,
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"factorized_phm_W": true,
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"factorized_phm_rule": false,
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"hypercomplex_nonlinearity": "glorot-uniform",
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"init_weights": "bert",
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"inv_adapter": null,
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"inv_adapter_reduction_factor": null,
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"is_parallel": false,
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"learn_phm": true,
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"leave_out": [],
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"ln_after": false,
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"ln_before": false,
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"mh_adapter": false,
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"non_linearity": "relu",
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"original_ln_after": true,
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"original_ln_before": true,
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"output_adapter": true,
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"phm_bias": true,
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"phm_c_init": "normal",
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"phm_dim": 4,
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"phm_init_range": 0.0001,
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"phm_layer": false,
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"phm_rank": 1,
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"reduction_factor": 16,
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"residual_before_ln": true,
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"scaling": 1.0,
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"shared_W_phm": false,
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"shared_phm_rule": true,
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"use_gating": false
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},
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"hidden_size": 768,
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"model_class": "T5ForConditionalGeneration",
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"model_name": "liuyanchen1015/FLAN-T5_GLUE_finetuning_lr3e-4",
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"model_type": "t5",
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"name": "multi-task VALUE",
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"version": "3.2.1"
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}
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head_config.json
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{
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"config": null,
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"hidden_size": 768,
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"label2id": {
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"LABEL_0": 0,
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"LABEL_1": 1
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},
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"model_class": "T5ForConditionalGeneration",
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"model_name": "liuyanchen1015/FLAN-T5_GLUE_finetuning_lr3e-4",
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"model_type": "t5",
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"name": null,
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"num_labels": 2,
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"version": "3.2.1"
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}
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pytorch_adapter.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:837a020657672d251d64b41a75f3506b5899f7d9cdc254d32e928d22a36d3ac8
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size 7190933
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pytorch_model_head.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:a89553035e8f5e97e4b863f8627f7538bb10d3a16a0c5c6fa773ce49ea5c3493
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size 98698060
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