Create README.md
Browse files
README.md
ADDED
@@ -0,0 +1,36 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
pipeline_tag: feature-extraction
|
3 |
+
---
|
4 |
+
# Style Transformer for Authorship Representations - STAR
|
5 |
+
This is the repository for the [Style Transformer for Authorship Representations (STAR)](https://arxiv.org/abs/2310.11081) model. We present the weights of our model here.
|
6 |
+
|
7 |
+
Also check out our [github repo for STAR](https://github.com/jahuerta92/star) for replication.
|
8 |
+
|
9 |
+
## Feature extraction
|
10 |
+
```
|
11 |
+
tokenizer = AutoTokenizer.from_pretrained('roberta-large')
|
12 |
+
model = AutoModel.from_pretrained('AIDA-UPM/star')
|
13 |
+
|
14 |
+
examples = ['My text 1', 'This is another text']
|
15 |
+
|
16 |
+
def extract_embeddings(texts):
|
17 |
+
encoded_texts = tokenizer(texts)
|
18 |
+
with torch.no_grad():
|
19 |
+
style_embeddings = model(encoded_texts.input_ids, attention_mask=encoded_texts.attention_mask)
|
20 |
+
return style_embeddings
|
21 |
+
|
22 |
+
print(extract_embeddings(examples))
|
23 |
+
```
|
24 |
+
|
25 |
+
## Citation
|
26 |
+
```
|
27 |
+
@article{Huertas-Tato2023Oct,
|
28 |
+
author = {Huertas-Tato, Javier and Martin, Alejandro and Camacho, David},
|
29 |
+
title = {{Understanding writing style in social media with a supervised contrastively pre-trained transformer}},
|
30 |
+
journal = {arXiv},
|
31 |
+
year = {2023},
|
32 |
+
month = oct,
|
33 |
+
eprint = {2310.11081},
|
34 |
+
doi = {10.48550/arXiv.2310.11081}
|
35 |
+
}
|
36 |
+
```
|