Create README.md

README.md

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+---
+tags:
+- question-answering
+- bert
+- adapterhub:qa/squad1
+- adapter-transformers
+datasets:
+- squad
+language:
+- en
+---
+
+# Adapter `AdapterHub/bert-base-uncased-pf-squad` for bert-base-uncased
+
+An [adapter](https://adapterhub.ml) for the `bert-base-uncased` model that was trained on the [qa/squad1](https://adapterhub.ml/explore/qa/squad1/) dataset and includes a prediction head for question answering.
+
+This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
+
+## Usage
+
+First, install `adapter-transformers`:
+
+```
+pip install -U adapter-transformers
+```
+_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)_
+
+Now, the adapter can be loaded and activated like this:
+
+```python
+from transformers import AutoModelWithHeads
+
+model = AutoModelWithHeads.from_pretrained("bert-base-uncased")
+adapter_name = model.load_adapter("AdapterHub/bert-base-uncased-pf-squad", source="hf")
+model.active_adapters = adapter_name
+```
+
+## Architecture & Training
+
+The training code for this adapter is available at https://github.com/adapter-hub/efficient-task-transfer.
+In particular, training configurations for all tasks can be found [here](https://github.com/adapter-hub/efficient-task-transfer/tree/master/run_configs).
+
+
+## Evaluation results
+
+Refer to [the paper](https://arxiv.org/pdf/2104.08247) for more information on results.
+
+## Citation
+
+If you use this adapter, please cite our paper ["What to Pre-Train on? Efficient Intermediate Task Selection"](https://arxiv.org/pdf/2104.08247):
+
+```bibtex
+@inproceedings{poth-etal-2021-pre,
+    title = "{W}hat to Pre-Train on? {E}fficient Intermediate Task Selection",
+    author = {Poth, Clifton  and
+      Pfeiffer, Jonas  and
+      R{"u}ckl{'e}, Andreas  and
+      Gurevych, Iryna},
+    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
+    month = nov,
+    year = "2021",
+    address = "Online and Punta Cana, Dominican Republic",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.emnlp-main.827",
+    pages = "10585--10605",
+}
+```