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--- |
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tags: |
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- bert |
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- adapterhub:ner/mit_movie_trivia |
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- adapter-transformers |
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language: |
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- en |
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--- |
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# Adapter `AdapterHub/bert-base-uncased-pf-mit_movie_trivia` for bert-base-uncased |
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An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [ner/mit_movie_trivia](https://adapterhub.ml/explore/ner/mit_movie_trivia/) 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("bert-base-uncased") |
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adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-mit_movie_trivia", source="hf") |
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model.active_adapters = adapter_name |
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``` |
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## Architecture & Training |
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The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer. |
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In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs). |
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## Evaluation results |
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Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results. |
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## Citation |
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If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247): |
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```bibtex |
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@inproceedings{poth-etal-2021-what-to-pre-train-on, |
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title={What to Pre-Train on? Efficient Intermediate Task Selection}, |
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author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych}, |
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booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)", |
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month = nov, |
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year = "2021", |
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address = "Online", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/2104.08247", |
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pages = "to appear", |
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} |
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``` |
