Instructions to use AIDA-UPM/star with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AIDA-UPM/star with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="AIDA-UPM/star")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("AIDA-UPM/star") model = AutoModel.from_pretrained("AIDA-UPM/star", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
| pipeline_tag: feature-extraction | |
| # Style Transformer for Authorship Representations - STAR | |
| 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. | |
| Also check out our [github repo for STAR](https://github.com/jahuerta92/star) for replication. | |
| ## Feature extraction | |
| ```python | |
| tokenizer = AutoTokenizer.from_pretrained('roberta-large') | |
| model = AutoModel.from_pretrained('AIDA-UPM/star') | |
| examples = ['My text 1', 'This is another text'] | |
| def extract_embeddings(texts): | |
| encoded_texts = tokenizer(texts) | |
| with torch.no_grad(): | |
| style_embeddings = model(encoded_texts.input_ids, | |
| attention_mask=encoded_texts.attention_mask).pooler_output | |
| return style_embeddings | |
| print(extract_embeddings(examples)) | |
| ``` | |
| ## Citation | |
| ``` | |
| @article{Huertas-Tato2023Oct, | |
| author = {Huertas-Tato, Javier and Martin, Alejandro and Camacho, David}, | |
| title = {{Understanding writing style in social media with a supervised contrastively pre-trained transformer}}, | |
| journal = {arXiv}, | |
| year = {2023}, | |
| month = oct, | |
| eprint = {2310.11081}, | |
| doi = {10.48550/arXiv.2310.11081} | |
| } | |
| ``` |