Upload MBartForConditionalGeneration

README.md

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+---
+tags:
+- mbart
+- adapter-transformers
+datasets:
+- ETR-fr
+---
+
+# Adapter `koena/mbart-large-50-prefix-ETR-fr` for facebook/mbart-large-50
+
+An [adapter](https://adapterhub.ml) for the `facebook/mbart-large-50` model that was trained on the [ETR-fr](https://huggingface.co/datasets/ETR-fr/) dataset.
+
+This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
+
+## Usage
+
+First, install `adapters`:
+
+```
+pip install -U adapters
+```
+
+Now, the adapter can be loaded and activated like this:
+
+```python
+from adapters import AutoAdapterModel
+
+model = AutoAdapterModel.from_pretrained("facebook/mbart-large-50")
+adapter_name = model.load_adapter("koena/mbart-large-50-prefix-ETR-fr", set_active=True)
+```
+
+## 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": {
+    "architecture": "prefix_tuning",
+    "bottleneck_size": 512,
+    "cross_prefix": true,
+    "dropout": 0.0,
+    "encoder_prefix": true,
+    "flat": false,
+    "leave_out": [],
+    "non_linearity": "tanh",
+    "prefix_length": 150,
+    "shared_gating": true,
+    "use_gating": false
+  },
+  "hidden_size": 1024,
+  "model_class": "MBartForConditionalGeneration",
+  "model_name": "facebook/mbart-large-50",
+  "model_type": "mbart",
+  "name": "ETR-fr",
+  "version": "adapters.1.0.0"
+}

head_config.json

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+{
+  "config": null,
+  "hidden_size": 1024,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1,
+    "LABEL_2": 2
+  },
+  "model_class": "MBartForConditionalGeneration",
+  "model_name": "facebook/mbart-large-50",
+  "model_type": "mbart",
+  "name": null,
+  "num_labels": 3,
+  "version": "adapters.1.0.0"
+}

pytorch_adapter.bin

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+size 159536065

pytorch_model_head.bin

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