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README.md

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----
-license: mit
----
+---
+language:
+- en
+license: apache-2.0
+---
+# UAR Play
+
+Literary Character Representations using [UAR Play](https://aclanthology.org/2024.findings-emnlp.744/)., trained on fictional character utterances.
+
+You can find the training and evaluation repository [here](https://github.com/deezer/character_embeddings_qa).
+
+This model is based on [LUAR implementation](https://aclanthology.org/2021.emnlp-main.70/). It uses `all-distillroberta-v1` as the base sentence encoder and was trained on the Play split of [DramaCV](https://huggingface.co/datasets/gasmichel/DramaCV), a dataset consisting of drama plays collected from Project Gutenberg.
+
+You can find the model trained on the Scene split at this [url](https://huggingface.co/gasmichel/UAR_scene).
+
+
+## Usage
+
+```python
+from transformers import AutoModel, AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained("gasmichel/UAR_Play")
+model = AutoModel.from_pretrained("gasmichel/UAR_Play")
+#`episodes` are embedded as colletions of documents presumed to come from an author
+# NOTE: make sure that `episode_length` consistent across `episode`
+batch_size = 3
+episode_length = 16
+text = [
+    ["Foo"] * episode_length,
+    ["Bar"] * episode_length,
+    ["Zoo"] * episode_length,
+]
+text = [j for i in text for j in i]
+tokenized_text = tokenizer(
+    text, 
+    max_length=32,
+    padding="max_length", 
+    truncation=True,
+    return_tensors="pt"
+)
+# inputs size: (batch_size, episode_length, max_token_length)
+tokenized_text["input_ids"] = tokenized_text["input_ids"].reshape(batch_size, episode_length, -1)
+tokenized_text["attention_mask"] = tokenized_text["attention_mask"].reshape(batch_size, episode_length, -1)
+print(tokenized_text["input_ids"].size())       # torch.Size([3, 16, 32])
+print(tokenized_text["attention_mask"].size())  # torch.Size([3, 16, 32])
+out = model(**tokenized_text)
+print(out.size())   # torch.Size([3, 512])
+# to get the Transformer attentions:
+out, attentions = model(**tokenized_text, output_attentions=True)
+print(attentions[0].size())     # torch.Size([48, 12, 32, 32])
+```
+
+## Citing & Authors
+
+If you find this model helpful, feel free to cite our [publication](https://aclanthology.org/2024.findings-emnlp.744/).
+
+```
+@inproceedings{michel-etal-2024-improving,
+    title = "Improving Quotation Attribution with Fictional Character Embeddings",
+    author = "Michel, Gaspard  and
+      Epure, Elena V.  and
+      Hennequin, Romain  and
+      Cerisara, Christophe",
+    editor = "Al-Onaizan, Yaser  and
+      Bansal, Mohit  and
+      Chen, Yun-Nung",
+    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
+    month = nov,
+    year = "2024",
+    address = "Miami, Florida, USA",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2024.findings-emnlp.744",
+    doi = "10.18653/v1/2024.findings-emnlp.744",
+    pages = "12723--12735",,
+}
+```
+
+## License
+
+UAR Scene is distributed under the terms of the Apache License (Version 2.0).
+
+All new contributions must be made under the Apache-2.0 licenses.