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Upload T5ForConditionalGeneration

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README.md ADDED
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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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+ - minh21/cpgQA-v1.0-unique-context-test-10-percent-validation-10-percent
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+ ---
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+
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+ # Adapter `legacy107/flan-t5-large-bottleneck-ia3-union-cpgQA` for google/flan-t5-large
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+
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+ An [adapter](https://adapterhub.ml) for the `google/flan-t5-large` model that was trained on the [minh21/cpgQA-v1.0-unique-context-test-10-percent-validation-10-percent](https://huggingface.co/datasets/minh21/cpgQA-v1.0-unique-context-test-10-percent-validation-10-percent/) dataset.
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+
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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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+
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+ ## Usage
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+
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+ First, install `adapter-transformers`:
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+
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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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+
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+ Now, the adapter can be loaded and activated like this:
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+
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+ ```python
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+ from transformers import AutoAdapterModel
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+
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+ model = AutoAdapterModel.from_pretrained("google/flan-t5-large")
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+ adapter_name = model.load_adapter("legacy107/flan-t5-large-bottleneck-ia3-union-cpgQA", source="hf", set_active=True)
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+ ```
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+
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+ ## Architecture & Training
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+
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+ <!-- Add some description here -->
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+
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+ ## Evaluation results
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+
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+ <!-- Add some description here -->
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+
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+ ## Citation
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+
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+ <!-- Add some description here -->
adapter_config.json ADDED
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+ {
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+ "config": {
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+ "architecture": "union",
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+ "configs": [
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+ {
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+ "alpha": 1,
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+ "architecture": "lora",
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+ "attn_matrices": [
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+ "q",
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+ "v"
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+ ],
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+ "composition_mode": "scale",
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+ "dropout": 0.0,
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+ "init_weights": "ia3",
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+ "intermediate_lora": true,
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+ "output_lora": false,
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+ "r": 1,
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+ "selfattn_lora": true,
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+ "use_gating": false
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+ },
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+ {
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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": true,
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+ "non_linearity": "swish",
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+ "original_ln_after": true,
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+ "original_ln_before": false,
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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": 8,
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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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+ ]
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+ },
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+ "hidden_size": 1024,
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+ "model_class": "T5ForConditionalGeneration",
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+ "model_name": "google/flan-t5-large",
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+ "model_type": "t5",
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+ "name": "question_answering_union",
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+ "version": "3.2.1"
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+ }
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+ {
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+ "config": null,
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+ "hidden_size": 1024,
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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": "google/flan-t5-large",
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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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