add model

README.md

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+---
+tags:
+- adapter-transformers
+- xlm-roberta
+- adapterhub:quality_estimation/wmt21
+---
+
+# Adapter `Gregor/xlm-roberta-large-wmt21-qe` for xlm-roberta-large
+
+An [adapter](https://adapterhub.ml) for the xlm-roberta-large model that was trained on the [quality_estimation/wmt21](https://adapterhub.ml/explore/quality_estimation/wmt21/) dataset and includes a prediction head for classification.
+
+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("xlm-roberta-large")
+adapter_name = model.load_adapter("Gregor/xlm-roberta-large-wmt21-qe")
+model.active_adapters = adapter_name
+```
+
+## Architecture & Training
+
+<!-- Add some description here -->
+
+## Evaluation results
+
+<!-- Add some description here -->
+
+## Citation
+
+<!-- Add some description here -->

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": "gelu",
+    "original_ln_after": true,
+    "original_ln_before": true,
+    "output_adapter": true,
+    "reduction_factor": 8,
+    "residual_before_ln": true
+  },
+  "hidden_size": 1024,
+  "model_class": "XLMRobertaModelWithHeads",
+  "model_name": "xlm-roberta-large",
+  "model_type": "xlm-roberta",
+  "name": "qe_wmt21"
+}

head_config.json

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+{
+  "config": {
+    "activation_function": "tanh",
+    "bias": true,
+    "head_type": "classification",
+    "label2id": {
+      "LABEL_0": 0
+    },
+    "layers": 2,
+    "num_labels": 1,
+    "use_pooler": false
+  },
+  "hidden_size": 1024,
+  "model_class": "XLMRobertaModelWithHeads",
+  "model_name": "xlm-roberta-large",
+  "model_type": "xlm-roberta",
+  "name": "qe_wmt21"
+}

pytorch_adapter.bin

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+size 25311151

pytorch_model_head.bin

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+size 4204175