Initial version.

README.md

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+---
+tags:
+- roberta
+- adapterhub:pos/ud_ewt
+- adapter-transformers
+datasets:
+- universal_dependencies
+language:
+- en
+---
+
+# Adapter `AdapterHub/roberta-base-pf-ud_pos` for roberta-base
+
+An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [pos/ud_ewt](https://adapterhub.ml/explore/pos/ud_ewt/) dataset and includes a prediction head for tagging.
+
+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("roberta-base")
+adapter_name = model.load_adapter("AdapterHub/roberta-base-pf-ud_pos", 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-what-to-pre-train-on,
+    title={What to Pre-Train on? Efficient Intermediate Task Selection},
+    author={Clifton Poth and Jonas Pfeiffer and Andreas Rücklé and Iryna Gurevych},
+    booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
+    month = nov,
+    year = "2021",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/2104.08247",
+    pages = "to appear",
+}
+```

adapter_config.json

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+{
+  "config": {
+    "adapter_residual_before_ln": false,
+    "cross_adapter": false,
+    "inv_adapter": null,
+    "inv_adapter_reduction_factor": null,
+    "leave_out": [],
+    "ln_after": false,
+    "ln_before": false,
+    "mh_adapter": false,
+    "non_linearity": "relu",
+    "original_ln_after": true,
+    "original_ln_before": true,
+    "output_adapter": true,
+    "reduction_factor": 16,
+    "residual_before_ln": true
+  },
+  "hidden_size": 768,
+  "model_class": "RobertaModelWithHeads",
+  "model_name": "roberta-base",
+  "model_type": "roberta",
+  "name": "universal_dependencies_en_ewt"
+}

head_config.json

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+{
+  "config": {
+    "activation_function": "tanh",
+    "head_type": "tagging",
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1,
+      "LABEL_10": 10,
+      "LABEL_11": 11,
+      "LABEL_12": 12,
+      "LABEL_13": 13,
+      "LABEL_14": 14,
+      "LABEL_15": 15,
+      "LABEL_16": 16,
+      "LABEL_17": 17,
+      "LABEL_2": 2,
+      "LABEL_3": 3,
+      "LABEL_4": 4,
+      "LABEL_5": 5,
+      "LABEL_6": 6,
+      "LABEL_7": 7,
+      "LABEL_8": 8,
+      "LABEL_9": 9
+    },
+    "layers": 1,
+    "num_labels": 18
+  },
+  "hidden_size": 768,
+  "model_class": "RobertaModelWithHeads",
+  "model_name": "roberta-base",
+  "model_type": "roberta",
+  "name": "universal_dependencies_en_ewt"
+}

pytorch_adapter.bin

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+size 3595887

pytorch_model_head.bin

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