add model
Browse files- README.md +44 -0
- adapter_config.json +23 -0
- head_config.json +28 -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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- adapterhub:named-entity-recognition/multiconer
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- xlm-roberta
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datasets:
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- multiconer
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---
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# Adapter `asahi417/tner-xlm-roberta-large-multiconer-mix-adapter` for xlm-roberta-large
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An [adapter](https://adapterhub.ml) for the `xlm-roberta-large` model that was trained on the [named-entity-recognition/multiconer](https://adapterhub.ml/explore/named-entity-recognition/multiconer/) dataset and includes a prediction head for tagging.
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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 AutoModelWithHeads
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model = AutoModelWithHeads.from_pretrained("xlm-roberta-large")
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adapter_name = model.load_adapter("asahi417/tner-xlm-roberta-large-multiconer-mix-adapter", 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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"inv_adapter": null,
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"inv_adapter_reduction_factor": null,
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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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"reduction_factor": 16,
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"residual_before_ln": true
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},
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"hidden_size": 1024,
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"model_class": "XLMRobertaModelWithHeads",
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"model_name": "xlm-roberta-large",
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"model_type": "xlm-roberta",
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"name": "ner"
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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": null,
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"head_type": "tagging",
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"label2id": {
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"B-corporation": 8,
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"B-group": 10,
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"B-location": 1,
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"B-person": 4,
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"B-product": 6,
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"B-work of art": 2,
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"I-corporation": 9,
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"I-group": 11,
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"I-location": 12,
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"I-person": 5,
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"I-product": 7,
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"I-work of art": 3,
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"O": 0
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},
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"layers": 1,
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"num_labels": 13
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},
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"hidden_size": 1024,
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"model_class": "XLMRobertaModelWithHeads",
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"model_name": "xlm-roberta-large",
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"model_type": "xlm-roberta",
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"name": "ner"
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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 sha256:0cb2749dc754177dd96f07d5e0669f91baaec714014be01d40b0fc87d5556188
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size 12718917
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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 sha256:435982a429bc1f69b179228bcf349816664ff2ee640ba461a588a6f0bd35b6f3
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size 54247
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