{UTDRM-RoBERTa}

This is the UTDRM-RoBERTa model from the paper UTDRM: Unsupervised Method for Training Debunked-narrative Retrieval Models. Please consider citing the following paper if you use this model.

@article{singh2023utdrm,
  title={UTDRM: unsupervised method for training debunked-narrative retrieval models},
  author={Singh, Iknoor and Scarton, Carolina and Bontcheva, Kalina},
  journal={EPJ Data Science},
  volume={12},
  number={1},
  pages={59},
  year={2023},
  publisher={Springer}
}

Usage (Sentence-Transformers)

Using this model becomes easy when you have sentence-transformers installed:

pip install -U sentence-transformers

Then you can use the model like this:

from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]

model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: MPNetModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
  (2): Normalize()
)