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README.md

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+---
+tags:
+- t5
+- adapter-transformers
+datasets:
+- amazon_polarity
+---
+
+# Adapter `lenglaender/xlm-roberta-base-lora-lm-amazon-polarity` for google-t5/t5-base
+
+An [adapter](https://adapterhub.ml) for the `google-t5/t5-base` model that was trained on the [amazon_polarity](https://huggingface.co/datasets/amazon_polarity/) dataset and includes a prediction head for seq2seq lm.
+
+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("google-t5/t5-base")
+adapter_name = model.load_adapter("lenglaender/xlm-roberta-base-lora-lm-amazon-polarity", source="hf", set_active=True)
+```
+
+## Architecture & Training
+
+LoRA has r=8 and alpha=8
+
+## Evaluation results
+
+
+
+## Citation

adapter_config.json

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+{
+  "config": {
+    "alpha": 8,
+    "architecture": "lora",
+    "attn_matrices": [
+      "q",
+      "v"
+    ],
+    "composition_mode": "add",
+    "dropout": 0.0,
+    "init_weights": "lora",
+    "intermediate_lora": false,
+    "leave_out": [],
+    "output_lora": false,
+    "r": 8,
+    "selfattn_lora": true,
+    "use_gating": false
+  },
+  "hidden_size": 768,
+  "model_class": "T5AdapterModel",
+  "model_name": "google-t5/t5-base",
+  "model_type": "t5",
+  "name": "amazon_lm",
+  "version": "0.2.0"
+}

head_config.json

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+{
+  "config": {
+    "activation_function": null,
+    "bias": false,
+    "head_type": "seq2seq_lm",
+    "label2id": null,
+    "layer_norm": false,
+    "layers": 1,
+    "shift_labels": false,
+    "vocab_size": 32128
+  },
+  "hidden_size": 768,
+  "model_class": "T5AdapterModel",
+  "model_name": "google-t5/t5-base",
+  "model_type": "t5",
+  "name": "amazon_lm",
+  "version": "0.2.0"
+}

pytorch_adapter.bin

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+size 3590962

pytorch_model_head.bin

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