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Browse files- README.md +44 -0
- adapter_config.json +40 -0
- head_config.json +64 -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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- adapterhub:umls
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- adapter-transformers
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- bert
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datasets:
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- umls
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---
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# Adapter `reginaboateng/compacter_umls_relational_extraction_adapter_SciBERT` for allenai/scibert_scivocab_uncased
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An [adapter](https://adapterhub.ml) for the `allenai/scibert_scivocab_uncased` model that was trained on the [umls](https://adapterhub.ml/explore/umls/) dataset and includes a prediction head for classification.
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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("allenai/scibert_scivocab_uncased")
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adapter_name = model.load_adapter("reginaboateng/compacter_umls_relational_extraction_adapter_SciBERT", 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": true,
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"non_linearity": "gelu",
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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": true,
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"phm_rank": 1,
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"reduction_factor": 32,
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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": "BertModelWithHeads",
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"model_name": "allenai/scibert_scivocab_uncased",
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"model_type": "bert",
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"name": "umls_relation_extraction",
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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": {
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"activation_function": "tanh",
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"bias": true,
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"head_type": "classification",
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"label2id": {
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"LABEL_0": 0,
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"LABEL_1": 1,
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"LABEL_10": 10,
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"LABEL_11": 11,
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"LABEL_12": 12,
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"LABEL_13": 13,
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"LABEL_14": 14,
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"LABEL_15": 15,
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"LABEL_16": 16,
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"LABEL_17": 17,
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"LABEL_18": 18,
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"LABEL_19": 19,
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"LABEL_2": 2,
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"LABEL_3": 3,
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"LABEL_4": 4,
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"LABEL_40": 40,
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"LABEL_41": 41,
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"LABEL_42": 42,
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"LABEL_43": 43,
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"LABEL_44": 44,
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"LABEL_45": 45,
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"LABEL_5": 5,
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"LABEL_6": 6,
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"LABEL_7": 7,
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"LABEL_8": 8,
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"LABEL_9": 9
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},
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"layers": 2,
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"num_labels": 46,
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"use_pooler": false
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},
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"hidden_size": 768,
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"model_class": "BertModelWithHeads",
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"model_name": "allenai/scibert_scivocab_uncased",
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"model_type": "bert",
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"name": "umls_relation_extraction",
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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 sha256:33865d1c2ebf9d8c93412fedf12d4c375bad789b05d05b200eb7665f1c0a5cdd
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size 280753
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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 sha256:7f66ea4a9418a7ce53dc9c74d3ef3e10c2f7b46397e19366e510f7b7eec85fc6
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size 2505633
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