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---
language:
- en
license: apache-2.0
---
# UAR Scene
Literary Character Representations using [UAR Scene](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 Scene 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 Play split at this [url](https://huggingface.co/gasmichel/UAR_Play).
## Usage
```python
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("gasmichel/UAR_scene")
model = AutoModel.from_pretrained("gasmichel/UAR_scene")
#`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. |