readme: ---
  language: 
    - en
  pipeline_tag: sentence-similarity
  tags:
  - Pytorch
  - Sentence Transformers
  - Transformers
  license: "apache-2.0"
  ---
  
  # Twitter4SSE
  
  This model maps texts to 768 dimensional dense embeddings that encode semantic similarity. 
  It was trained with Multiple Negatives Ranking Loss (MNRL) on a Twitter dataset. 
  It was initialized from [BERTweet](https://huggingface.co/vinai/bertweet-base) and trained with [Sentence-transformers](https://www.sbert.net/). 
  
  ## Usage
  
  The model is easier to use with sentence-trainsformers library
  
  ```
  pip install -U sentence-transformers
  ```
  
  ```
  from sentence_transformers import SentenceTransformer
  sentences = ["This is the first tweet", "This is the second tweet"]
  
  model = SentenceTransformer('digio/Twitter4SSE')
  embeddings = model.encode(sentences)
  print(embeddings)
  ```
  
  
  Without sentence-transfomer library, please refer to [this repository](https://huggingface.co/sentence-transformers) for detailed instructions on how to use Sentence Transformers on Huggingface. 
  
  ## Citing & Authors
  
  The official paper [Exploiting Twitter as Source of Large Corpora of Weakly Similar Pairs for Semantic Sentence Embeddings](https://arxiv.org/abs/2110.02030) will be presented at EMNLP 2021. Further details will be available soon. 
  
  ```
  @inproceedings{di-giovanni-brambilla-2021-exploiting,
      title = "Exploiting {T}witter as Source of Large Corpora of Weakly Similar Pairs for Semantic Sentence Embeddings",
      author = "Di Giovanni, Marco  and
        Brambilla, Marco",
      booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
      month = nov,
      year = "2021",
      address = "Online and Punta Cana, Dominican Republic",
      publisher = "Association for Computational Linguistics",
      url = "https://aclanthology.org/2021.emnlp-main.780",
      pages = "9902--9910",
  }
  ```
  
  The official code is available on [GitHub](https://github.com/marco-digio/Twitter4SSE)
  
  
  